This PDF file contains the front matter associated with SPIE Proceedings Volume 8531, including the Title Page, Copyright Information, Table of Contents, and the Conference Committee listing.

We present results of a remote sensing based modelling approach to simulate the 1D water transport in the vadose zone of unsaturated soils on a daily basis, which can be used for regional to global applications. To calculate the hydraulic conductivity our model is driven by van Genuchten parameters, which we calculated for Bavaria (South-East-Germany), which we choose as area of investigation, using the ISRIC-WISE Harmonized Global Soil Profile Dataset Ver. 3.1 and the Rosetta programme. Soil depth and layering of up to six layers were defined independently for each soil. Interception by vegetation is also considered by using Leaf Area Index (LAI) time series from SPOT-VEGETATION. Precipitation is based on daily time series from the European Centre for Medium-Range Weather Forecasts (ECMWF). The model was applied to the Biosphere Energy Transfer Hydrology (BETHY/DLR) vegetation model, driven at the German Aerospace Center (DLR), to discuss the possibility of regionalization of a global model concept, regarding the soil water budged. Furthermore we compare our results with ECMWF data and discuss the results for the state of Bavaria. We found a good agreement for the general characteristics of our results with this dataset, especially for soils which are close to the standard characteristics of the ECMWF. Disagreements were found for shallow soils and soils under stagnant moisture, which are not considered in the ECMWF modelling scheme, but are distinguished in our approach.

Leaf Area Index (LAI) is a relevant input parameter for flux modeling of energy and matter in the biosphere. However, in a landscape such as the European alpine upland with small-scale land use patterns and high vegetation heterogeneity, existing global products are less suited and a high spatial resolution is required. Within this study two methods are compared to derive the LAI for grassland in the prealpine River Ammer catchment from high spatial resolution RapidEye data: the empirical approach based on regression functions, and the physical approach of inverted radiation transfer modeling (RTM). Established vegetation indices (VIs) as well as new ones incorporating RapidEye’s red edge band are calculated for four dates of the vegetation period 2011 and correlated with in situ LAI data. The statistical regressions between VIs and LAI of the different time steps show high correlations (R² of 0.57 up to 0.85). However, the established regressions are scene specific and the method requires excessive field work. In the physical approach the RapidEye reflectances are used as input data to an inverted RTM (PROSAIL), which is parameterized with leaf and canopy properties collected in the field. The LAI derived by the RTM have a RMSE between 2.02 and 2.28 for the different dates. Both methods capture the general LAI pattern. However, due to the broad parameterization of the RTM used to cover the heterogeneous grassland conditions, resulting LAI values are generally higher than the statistically derived LAI values.

This paper is concerned with the identification of grassland management using both optical and radar data. In that context, grazing, mowing and a mix of these two managements are commonly used by the farmers on grassland fields. These practices and their intensity of use have different environmental impact. Thus, the objectives of this study are, firstly, to identify grassland management practices using a time series of optical and radar imagery at high spatial resolution and, secondly, to evaluate the contribution of radar data to improve identification of farming practices on grasslands. Because of cloud coverage and revisit frequency of satellite, the number of available optical data is limited during the vegetation period. Thus, radar data can be considered as an ideal complement. The present study is based on the use of SPOT, Landsat and RADARSAT-2 data, acquired in 2010 during the growing period. After a pre-processing step, several vegetation indices, biophysical variables, backscattering coefficients and polarimetric discriminators were computed on the data set. Then, with the help of some statistics, the most discriminating variables have been identified and used to classify grassland fields. In addition, to take into account the temporal variation of variables, dedicated indexes as first and second order derivatives were used. Classification process was based on training samples resulting from field campaigns and computed according six methods: Decision Trees, K-Nearest Neighbor, Neural Networks, Support Vector Machines, the Naive Bayes Classifier and Linear Discriminant Analysis. Results show that combined use of optical and radar remote sensing data is not more efficient for grassland management identification.

The developing of satellite-based precipitation retrieval systems, presents great potentialities for several ap­ plications ranging from weather and meteorological applications to hydrological modelling. Evaluating perfor­ mances for these estimates is essential in order to understand their real capabilities and suitability related to each application. In this study an evaluation analysis of satellite precipitation retrieval systems has been carried out for the area of Sicily (Italy). Sicily is an island in the Mediterranean sea with a particular climatology and morphology, which is considered as an interesting test site for satellite precipitation products on the European mid-latitude area. A high density rain-gauges network has been used to evaluate selected satellite precipitation products. Sicily has an area of 26,000 km2 and the gauge density of the network considered in this study is about 250 km2 /gauge. Four satellite products (CMORPH, PERSIANN, TMPA-RT, PERSIANN-CCS) along with two adjusted products (TMPA and PERSIANN Adjusted) have been selected for the evaluation. Evalua­ tion and comparisons among selected products is performed with reference to the data provided by the gauge network of Sicily and using statistical and visualization tools. Results show that bias is relevant for all satellite products and climatic considerations are reported to address this issue. Moreover bias errors are observed for the adjusted products even though they are reduced respect to only-satellite products. In order to analyze this result, the ground-based precipitation dataset used by adjusted products (GPCC dataset), has been examined and weaknesses arising from spatial sampling of precipitation process have been identified for the study area. Therefore possible issues deriving from using global ground-based datasets for local scales are pointed out from this application.

Jakarta is the capital of Indonesia and is home to approximately 10 million people on the coast of the Java Sea. The subsidence due to groundwater extraction, increased development, natural consolidation of soil and tectonics in Jakarta has been known since the early part of the 20^th century. Evidence of land subsidence exists through monitoring with GPS, level surveys and preliminary InSAR investigations [1].

World Bank studies conservatively estimate land subsidence in Jakarta occurring at an average rate of 5 cm per year, and in some areas, over 1 meter was already observed. Recent studies of land subsidence found that while typical subsidence rates were 7.5-10 cm a year, in localized areas of North Jakarta subsidence in the range 15-25 cm a year was occurring, which if sustained, would result in them sinking to 4 to 5 meters below sea level by 2025. Land subsidence will require major interventions, including increased pumping, dikes and most likely introducing major infrastructure investment for sea defence [1].

With the increasing prevalence of Earth Observation (EO), the World Bank and the European Space Agency (ESA) have set up a partnership that aims at highlighting the potential of EO information to support the monitoring and management of World Bank projects. It in this framework that was defined the EOWorld projects [2]. Altamira Information, company specialized in ground motion monitoring, has managed one of those projects, focusing on the assessment of land subsidence in Jakarta.

Monitoring and mapping of alluvial surfaces and distal fluvial system has an important role for studying the depositional basin and river behaviour at its terminus. Experiences show rivers in semi-arid areas get smaller through their terminus and create a complex pattern at downstream parts. Remote sensing images can be used to monitor distal fluvial system and reveal changes of channels activity. In this study, we examine the feasibility of mapping and identifying the changes over time of a semi-arid area by Landsat ETM+ images. The study area is at the terminus of a fluvial system in Bolivia. Change detection techniques were applied to emerge the temporal and spatial changes. We used precipitation data of the area for better interpreting the images in different dates. The ETM+ image analysis results show changes in river morphology. It was also observed by the visible bands that the reflectance of abandoned channels increased after several consecutive weeks of high precipitation. The changes in dry seasons are more observable by the infrared bands. The study shows that Landsat ETM+ images in combination with field work data have a good potential to identify temporal and spatial changes at river morphology in a qualitative manner.

Continuous monitoring of river basins has become a significant requirement of our times. Due to increasing water scarcity and unprecedented flood calamities, assessing existing water resources and gathering timely information on water increase are nowadays essential to develop suitable strategies in water resources management. Hydrological models are being studied to increase hydrological process understanding and to support decision making in this field. River basin management models typically operate on wide territories and, given the complexity of most river basins, they are based on semi-empirical lumped parameterizations of hydrological processes. To overcome the uncertainties inherent in such models and achieve acceptable model performance, calibration techniques are indispensable. Remote sensing and satellite-based data with high temporal resolution have the potential to fill such critical information gaps. With its nine spectral bands and very high resolutions (spectral and radiometric) WorldView-2 satellite sensor (WV-2) can provide new insights in the on-going debate comparing object-oriented and spectral-based classifications for the highest accuracy. This paper proposes an efficient object-based method for land cover mapping from Worldview-2 imagery in order to assess its potentiality in acquiring detailed basic information on an ephemeral river area (Lama di Castellaneta, Taranto, Italy), to support further studies in the field of hydrological processes modeling. The approach suggested was evaluated by estimating classification accuracy.

In irrigation management the estimation of the radiometric surface temperature is of fundamental importance in evaluating the spatial distribution of land surface evapotranspiration. However, obtaining both high spatial and temporal resolutions data is impossible for any real sensor. In this paper we propose and investigate the use of sequential Bayesian techniques for integrating heterogeneous data with complementary features. A validation is performed by means of images acquired from SEVIRI and MODIS sensors in the thermal channels IR 10:8 and 31, respectively.

The orchards are developing very fast in the northern China in recent years with the increasing demands on fruits in China. In most parts of the northern China, the risk of frost damage to fruit tree in early spring is potentially high under the background of global warming. The growing season comes earlier than it does in normal year due to the warm weather in earlier spring and the risk will be higher in this case. According to the reports, frost event in spring happens almost every year in Ningxia Region, China. In bad cases, late frosts in spring can be devastating all fruit. So lots of attention has been given to the study in monitoring, evaluating, preventing and mitigating frost. Two orchards in Ningxia, Taole and Jiaozishan orchards were selected as the study areas. MODIS data were used to monitor frost events in combination with minimum air temperature recorded at weather station. The paper presents the findings. The very good correlation was found between MODIS LST and minimum air temperature in Ningxia. Light, middle and severe frosts were captured in the study area by MODIS LST. The MODIS LST shows the spatial differences of temperature in the orchards. 10 frost events in April from 2000 to 2010 were captured by the satellite data. The monitoring information may be hours ahead circulated to the fruit farmers to prevent the damage and loss of fruit trees.

Coupling remote sensing data with crop model has been shown to improve accuracy of the model yield estimation. MOSICAS model simulates sugarcane yield in controlled conditions plot, based on different variables, including the interception efficiency index (_i). In this paper, we assessed the use of remote sensing data to sugarcane growth modeling by 1) comparing the sugarcane yield simulated with and without satellite data integration in the model, and 2) comparing two approaches of satellite data forcing. The forcing variable is the interception efficiency index (Ε_i). The yield simulations are evaluated on a data set of cane biomass measured on four on-farm fields, over three years, in Reunion Island. Satellite data are derived from a SPOT 10 m resolution time series acquired during the same period. Three types of simulations have been made: a raw simulation (where the only input data are daily precipitations, daily temperatures and daily global radiations), a partial forcing coupling method (where MOSICAS computed values of Ε_i have been replaced by NDVI computed Ε_i for each available satellite image), and complete forcing method (where all MOSICAS simulated Ε_i have been replaced by NDVI computed Ε_i). Results showed significant improvements of the yield's estimation with complete forcing approach (with an estimation of the yield 8.3 % superior to the observed yield), but minimal differences between the yields computed with raw simulations and those computed with partial forcing approach (with a mean overestimation of respectively 34.7 and 35.4 %). Several enhancements can be made, especially by optimizing MOSICAS parameters, or by using other remote sensing index, like NDWI.

The municipality of Petrolina, located in the semi-arid region of Brazil, is highlighted as an important agricultural growing region, however the irrigated areas have cleared natural vegetation inducing a loss of biodiversity. To analyze the contrast between these two ecosystems the large scale values of biomass production (BIO), evapotranspiration (ET) and water productivity (WP) were quantified. Monteith´s equation was applied for estimating the absorbed photosynthetically active radiation (APAR), while the new SAFER (Simple Algorithm For Evapotranspiration Retrieving) algorithm was used to retrieve ET. The water productivity (WP) was analysed by the ratio of BIO by ET at monthly time scale with four bands of MODIS satellite images together with agrometeorological data for the year of 2011. The period with the highest water productivity values were from March to April in the rainy period for both irrigated and not irrigated conditions. However the largest ET rates were in November for irrigated crops and April for natural vegetation. More uniformity of the vegetation and water variables occurs in natural vegetation, evidenced by the lower values of standard deviation when comparing to irrigated crops, due to the different crop stages, cultural and irrigation managements. The models applied with MODIS satellite images on a large scale are considered to be suitable for water productivity assessments and for quantifying the effects of increasing irrigated areas over natural vegetation on regional water consumption in situations of quick changing land use pattern.

Monitoring of vegetation dynamics in extensive irrigated croplands is essential for improving land and water management, especially to understand the reaction of the system to water scarcity and degradation processes. This study focuses on the assessment of irrigated cropland dynamics in the western part of the Aral Sea Basin in Central Asia during the past decade. Extend of cropland and spatio-temporal cropping patters are analyzed based on phenological profiles extracted from 16day MODIS vegetation index time series at a spatial resolution of 250m. Knowledge-based classifications which needed to be adjusted for every single year were applied to distinguish between cropland and other major land cover types, the desert or sparsely vegetated steppes, settled areas, and water bodies. Interannual variability of the time series in the maximum cropland extend recorded between 2001 and 2010 was assessed by using Pearson’s cross correlation (PCC) coefficient. Shifts of maximum one month (+/-) were tested and the highest PCC coefficient was selected. Accuracy assessment using a multi-annual MODIS classification conducted for a representative irrigation system between 2004 and 2007 returned acceptable results for the cropland mask (<90%). Comparing the inter-annual cropland dynamics revealed using PCC with both, the MODIS classifications 2004-2007 and pure pixels of aggregated ASTER based maps showed that the PCC only permits differentiation between different modalities in the time series, i.e. years of a varying number of intra-annual crop cycles. However, simply overlaying the cropland extends 2001-2010 already exhibits areas of unreliable water supply. In this light, integration of both, PCC analysis of MODIS time series and annual maps of the cropland extent can be concluded as valuable next steps for better understanding the dynamics of the irrigated cropland at regional scale not only in the Aral Sea Basin of Central Asia, but also in other arid environments, where irrigation agriculture is essential for rural income generation and food security.

Monitoring agriculture at regional to global scales with remote sensing requires the use of sensors that can provide information over large geographic extends with a high revisit frequency. Current sensors satisfying these criteria have, at best, a spatial resolution of the same order of magnitude as the field sizes in most agricultural landscapes. Research has demonstrated that crop specific monitoring is possible with medium spatial resolution instruments (such as with MODIS, 250 m at nadir) if a selection of purer time series is isolated. To do so, a mask of the target crop is necessary at fine spatial resolution in order to calculate the crop specific pixel purity at the coarser scale. Pixel purity represents the relative contribution of the surface of interest to the signal detected by the remote sensing instrument. A straightforward way to compute pixel purity is to calculate the area of the target crop that falls in the coarse spatial resolution grid. However, the observation footprint is generally larger than the squared projection of the pixel, especially when the observation is taken with high scan angles like MODIS does most of the time. Furthermore, the relative contribution within this footprint is not homogeneous: it depends on the spatial response of the sensor. This study analyses the error committed when crop specific pixel purity is calculated using the straightforward method instead of integrating the spatial response and taking into account gridding artefacts and other MODIS particularities such as the bow-tie effect. Differences caused by the orbit, i.e. whether MODIS is on a descending orbit for Terra or an ascending one for Aqua, are also explored. Finally, the consequence of overestimating the spatial response when calculating pixel purity is illustrated by analysing the effect on different agricultural landscapes.

At a time of rising global concern about environmental issues remote sensing techniques acquire increasing importance in vegetation state assessment and health diagnostics. Multispectral optical data have proved abilities in vegetation monitoring. The visible and near infrared region reveals significant sensitivity to plant biophysical variables and pigment content. The spectral signatures of leaves in this wavelength range are mostly defined by the composition of photosynthetic pigments and their stress-induced changes. As such, plant spectral response provides valuable information about the physiological status of plants. As far as chlorophyll content is a most important bioindicator of plant condition being responsible for light absorption and the photosynthetic process, techniques for its non-destructive assessment are of prime interest. In our study, multispectral data of reflected, transmitted and emitted by plants radiation have been used to reveal the performance of different spectral signatures in chlorophyll estimation. Vegetation indices, red edge shift, spectral transmittance, fluorescence parameters, and chromaticity features, have been related in a statistical manner to plant chlorophyll in order to examine the statistical significance of plant spectral response changes to chlorophyll variations. High correlations have been found permitting quantitative dependences to be established between chlorophyll in plants and their spectral properties. Empirical relationships have been derived that allow plant condition and stress assessment (in terms of chlorophyll inhibition) to be performed by using different spectral indicators.

Evapotranspiration (ET) is a critical variable in hydrological processes and an accurate estimation of the rate of evapotranspiration is required if we wish to apply integrated management procedures to water resources. This study offers new insights into remote sensing-based models that estimate ET at basin scale, evaluating the combination of a surface energy balance based on thermal remote sensing and the use of the crop coefficient (K_c), a simple operational method that is widely used in irrigated agriculture. The study area is the Guadalfeo river basin in southern Spain, a large watershed with major topographical and landscape contrasts. Reference evapotranspiration (ET_o) surfaces were generated by applying the FAO56-PM ^[1] equation, and real ET surfaces were estimated following a two-source energy balance model ^{[2] [3]}. Crop and vegetation coefficients were obtained as the ratio between ET and ET_o. K_c maps were analysed in terms of vegetation type and development. The resulting coefficients generally ranged between 0.1 and 1.5, and could be directly related to vegetation ground cover for the main vegetation types, including natural vegetation and crops, with the determination coefficient (r²) lying between 0.77 and 0.97 in both humid and dry seasons. Relationships based on these coefficients are proposed as a simple proxy to monitor the water use of the basin on a regular basis by means of optical remote sensors alone, providing data with higher frequency and spatial resolution than can be obtained by thermal measurements; data that could complement thermal sensors whenever these were available.

A two-source energy balance model that separates surface fluxes of the soil and canopy was applied to a drip-irrigated vineyard in central Spain, using a series of nine Landsat-5 images acquired during the summer of 2007. The model partitions the available energy, using surface radiometric temperatures to constrain the sensible heat flux, and computing ET as a residual of the energy balance. Flux estimations from the model are compared with half-hourly and daily values obtained by an eddy covariance flux tower installed on the site during the experiment. The performance of the twosource model to estimate ET under the low vegetation cover and semiarid conditions of the experiment, with RMSD between observed and model data equal to 49 W m^-2 for half-hourly estimations and RMSD=0.5 mm day^-1 at daily scale, is regarded as acceptable for irrigation management purposes. Model results in the separation of the beneficial (transpiration) and non-beneficial (evaporation from the soil) fractions, which is key information for the quest to improve water productivity, are also reported. However, the lack of measures of these components makes it difficult to draw conclusions about the final use of the water.

In the last years, modeling of surface processes - such as water, energy and carbon budgets, as well as vegetation growth- seems to be focused on integrated approaches that combine aspects of hydrology, biology and meteorology into unified analyses. In this context, remotely sensed data often have a core role due to the cross-cutting impact of this novel source of spatially distributed information on all these research areas. However, several applications - such as drought monitoring, yield forecasting and crop management - require spatially detailed products at sub-field scales, which can be obtained only with support of adequately fine resolution remote sensing data (< 100 m). In particular, observations in the visible to the near infrared (VIS/NIR) spectral region can be used to derive biophysical and biochemical properties of the vegetation (i.e., leaf area index and leaf chlorophyll). Complementarily, the thermal infrared (TIR) signal provides valuable information about land surface temperature, which in turn represents an accurate proxy indicator of the subsurface moisture status by means of surface energy budget analysis. Additionally, the strong link between crop water stress and stomatal closure allows inference of crop carbon assimilation using the same tools. In this work, an integrated approach is proposed to model both carbon and water budgets at field scale by means of a joint use of a thermal-based Two Source Energy Budget (TSEB) model and an analytical, Light-Use-Efficiency (LUE) based model of canopy resistance. This suite of models allows integration of information retrieved by both fine and coarse resolution satellites by means of a data fusion procedure. A set of Landsat and MODIS images are used to investigate the suitability of this approach, and the modeled fluxes are compared with observations made by several flux towers in terms of both water and carbon fluxes.

In the Nilo Coelho irrigation scheme, Brazil, the natural vegetation has been replaced by irrigated agriculture, bringing importance for the quantification of the effects on the energy exchanges between the mixed vegetated surfaces and the low atmosphere. Landsat satellite images and agro-meteorological stations from 1992 to 2011 were used together, for modelling these exchanges. Surface albedo (α₀), NDVI and surface temperature (T₀) were the basic remote sensing parameters necessary to calculate the latent heat flux (λE) and the surface resistance to evapotranspiration (r_s) at the large scale. The daily net radiation (R_n) was retrieved from α₀, air temperature (T_a) and transmissivity (τ_sw) throughout the slob equation, allowing the quantification of the daily sensible heat flux (H) by residual in the energy balance equation. With a threshold value for r_s, it was possible to separate the energy fluxes from crops and natural vegetation. The averaged fractions of R_n partitioned as H and λE, were in average 39 and 67%, respectively. It was observed an increase of the energy used in the evapotranspiration process inside irrigated areas from 51% in 1992 to 80% in 2011, with the ratio λE/R_n presenting an increase of 3 % per year. The tools and models applied in the current research, can subsidize the monitoring of the coupled climate and land use changes effects in irrigation perimeters, being valuable when aiming the sustainability of the irrigated agriculture in the future, avoiding conflicts among different water users.

Estimation of evapotranspiration (ET) in Sicilian vineyard is an emerging issue since these agricultural systems are more and more converted from rainfed to irrigated conditions, with significant impacts on the management of the scarce water resources of the region. The choice of the most appropriate methodology for assessing water use in these systems is still an issue of debating, due to the complexity of canopy and root systems and for their high spatial fragmentation. In vineyards, quality and quantity of the final product are dependent on the controlled stress conditions to be set trough irrigation. This paper reports an application of the well-known Penman-Monteith approach, applied in a distributed way, using high resolution remote sensing data to map the potential evapotranspiration (ET_p). In 2008 a series of airborne multispectral images were acquired on "Tenute Rapitalà", a wine farm located in the northwest of Sicily. Five airborne remote sensing scenes were collected using a SKY ARROW 351 650 TC/TCNS aircraft, at a height of about 1000 m a.g.l.. The acquisitions were performed encompassing a whole phenological period, period between June and September 2008 (approximately every three weeks). The platform had on board a multi-spectral camera with 3 spectral bands at green (G, 530-570 nm), red (R, 650-690 nm) and near infrared (NIR, 767-832 nm) wavelengths, and a thermal camera with a broad band in the range 7.5-13 µm. The nominal pixel resolution was approximately 0.7 m for VIS/NIR acquisitions, and 1.7 m for the thermal-IR data. Field data were acquired simultaneously to airborne acquisitions. These data include spectral reflectances in VIS-NIR-SWIR (shortwave infrared), leaf area index (LAI), soil moisture at different depths (both in row and below plants). Moreover, meteo variables were measured by a standard weather station whereas fluxes were measured by means of an Eddy correlation tower located within the field. The VIS-NIR bands were atmospherically corrected and calibrated in order to calculate albedo, NDVI and LAI, which represented the distributed inputs of the Penman-Monteith algorithm. Moreover a sensitivity analysis has been carried out on input parameters (such as albedo). A sensitivity analysis was carried out to highlight the variability of outputs (such as ET_p) on the accuracy in the parameters assessment obtainable using high spatial resolution airborne images. Scale effects have been also investigated by means of an artificial degradation of images spatial resolution. Finally the relationship between stress factor evaluated as the ratio between actual and reference ET and pre-dawn leaf water potential has been also investigated.

This study strives to highlight the potential of flood inundation monitoring and mapping in a catchment area in Cyprus (Yialias river) with the use of radar satellite images. Due to the lack of satellite data acquired during dates flood inundation events took place, the research team selected specific images acquired during dates that severe precipitation events were recorded from the rain gauge station network of Cyprus Meteorological Service in the specific study area. The relationship between soil moisture and precipitation was thoroughly studied and linear regression models were developed to predict future flood inundation events. Specifically, the application of fully polarimetric (ALOS PALSAR) and data acquired over different dates for soil moisture mapping is presented. The PALSAR (Phased Array type L-band Synthetic Aperture Radar) sensor carried by the ALOS (Advanced Land Observing Satellite) have quadruple polarizations (HH, VV, HV, VH). The amount of returned radiation (as backscatter echoes) that dictates the brightness of the image depends on factors such as the roughness, size of the target relative to the signal’s wavelength, volumetric and diffused scattering. The variation in soil moisture pattern during different precipitation events is presented through soil moisture maps obtained from ALOS PALSAR and data acquired during different dates with different precipitation rates. Soil moisture variation is clearly seen through soil moisture maps and the developed regression models are used to simulate future inundation events. The results indicated the considerable potential of radar satellite images in soil moisture and flood mapping in catchments areas of Mediterranean region.

The paper reports a critical analysis of the thermal inertia approach to map surface soil water content on bare and sparsely vegetated soils by means of remotely sensed data. The study area is an experimental area located in Barrax (Spain). Field data were acquired within the Barrax 2011 research project. AHS airborne images including VIS/NIR and TIR bands were acquired both day and night time by the INTA (Instituto Nacional de Técnica Aeroespacial) between the 11^th and 13^rd of June 2011. Images cover a corn pivot surrounded by bare soil, where a set of in situ data have been collected previously and simultaneously to overpasses. To validate remotely sensed estimations, a preliminary proximity sensing set up has been arranged, measuring spectra and surface temperatures on transects by means of ASD hand-held spectroradiometer and an Everest Interscience radiometric thermometer respectively. These data were collected on two transects: the first one on bare soil and the second from bare to sparsely vegetated soil; soil water content in both transects ranged approximately between field and saturation values. Furthermore thermal inertia was measured using a KD2Pro probe, and surface water content of soil was measured using FDR and TDR probes. This ground dataset was used: 1) to verify if the thermal inertia method can be applied to map water content also on soil covered by sparse vegetation, and 2) to quantify a correction factor of the downwelling shortwave radiation taking into account sky cloudiness effects on thermal inertia assessment. The experiment tests both Xue and Cracknell approximation to retrieve the thermal inertia from a dumped value of the phase difference and the three-temperature approach of Sobrino to estimate the phase difference spatial distribution. Both methods were then applied on the remotely sensed airborne images collected during the following days, in order to obtain the spatial distribution of the surface soil moisture on bare soils and sparse vegetation coverage. Results verify that the thermal inertia method can be applied on sparsely vegetated soil characterized by fractional cover up to ~0.25 (maximum value within this experiment); a lumped value of the phase difference allows a good estimate of the thermal inertia, whereas the comparison with the three-temperature approach did not give conclusive responses because ground radiometric temperatures were not acquired in optimal conditions. Results also show that clear sky only at the time of the remote sensing acquisitions is not a sufficient condition to apply the thermal inertia method. A corrective coefficient taking into account the actual sky cloudiness throughout the day allows accurate estimates of the spatial distribution of the thermal inertia (r² ~ 0.9) and soil water content (r² ~ 0.7).

This paper describes a fully automatic processing chain that makes use of SAR images for retrieving water stage information to be assimilated into a hydraulic forecasting model. This chain is composed of three steps: flood extent delineation, water stage retrieval and data assimilation of stage information into a hydraulic model. The flood-mapping step is addressed with a fully automatic algorithm, based on image statistics and applicable to all existing SAR datasets. Uncertainty on the flood extent map is represented with an ensemble of flood extent maps, obtained following a bootstrap methodology. Water stage observations are then retrieved by intersecting the flood shoreline with the floodplain topography. The ensemble of flood extent maps allows extracting multiple water levels at any river cross section of the hydraulic model, thereby taking into account the uncertainty associated with the floodmapping step. Finally, data assimilation consists in integrating uncertain observations, i.e. SAR-derived water stages, with uncertain hydraulic model simulations. The proposed processing chain was applied to two case studies. For the test case of June 2008 on the Po River (Italy), only low resolution but freely available satellite data were used. For the January 2011 flood on the Sure River (Luxembourg), higher resolution data were used and obtained at a cost. The results show that with the assimilation of SAR-derived water stages significant improvements can be achieved in the forecasting performance of the hydraulic model.

The saturation problem associated with the use of NDVI for biomass estimation in high canopy density vegetation is a well-known phenomenon. Recent field spectroscopy experiments have shown that narrow band vegetation indices computed from the red edge and the NIR shoulder can improve the estimation of biomass in such situations. However, the wide scale unavailability of high spectral resolution satellite sensors with red edge bands has not seen the up-scaling of these techniques to spaceborne remote sensing of high density biomass. This paper explored the possibility of estimating biomass in a densely vegetated wetland area using indices computed from Worldview-2 imagery, which contains a red edge band centred at 725 nm. Indices derived from the red edge band and the NIR shoulder yielded higher accuracies (R2 = 0.71) for biomass estimation as compared to indices computed from other portions of the electromagnetic spectrum. Predicting biomass on an independent test data set using the Random forest algorithm and 3 NDVIs computed from the red edge and NIR bands yielded a root mean square error of prediction (RMSEP) of 441g/m2 ( 13 % of observed mean biomass) as compared to the traditional spectral bands. The results demonstrate the utility of Worldview-2 imagery in estimating and ultimately mapping vegetation biomass at high density - a previously challenging task with broad band satellite sensors.

Net primary production (NPP) is the production of organic compounds from atmospheric or aquatic carbon dioxide, principally through the process of photosynthesis. Climate changes of this magnitude are expected to affect the NPP of the world’s land ecosystems. In this study, we used a light-use efficiency model and linear regression model to describe and analyze the spatial and temporal patterns of terrestrial net primary productivity (NPP) in China during 2002-2010. First, we used the reconstructed 16-day 0.05°MODIS NDVI product (MOD13C1), 0.05°gridded GLDAS (Global Land Data Assimilation System) meteorological data and land use map to estimate the NPP in China. The spatial variability of NPP was analyzed during all periods, growing seasons and different seasons, respectively. Based on regression analysis method, we quantified the trend of NPP change in China during 2002-2010.

The vegetation’s variation is very sensitive to climatic change and Human activates in Northwestern China. The vegetation distribution in Northwest of China since 1981 to 2003 is assessed with Normalized Difference Vegetation Index (NDVI) data. The vegetation season change is in accord with change of surface energy. And most of vegetation in Northwest China is related to rainfall. The value of drought index is in accord with the change of NDVI in Northwest China. More rainfall in spring or summer resulted in the increase of NDVI values in Shanxi, Inner Mongolia, Gansu, Ningxia, and Qinghai. But the NDVI in arid region, such as Xinjiang, has been little influenced by rainfall. The conclusion is that precipitation, melted snow or ice water, and irrigation are all the factors affecting vegetation growth.

In Mediterranean regions, where the water shortage is a serious and recurrent problem, it is essential to know the behaviour and evolution of the snow. Satellite remote sensing is widely used to find out the evolution of the snow cover extension at medium-large scales. But these techniques pose some constraints if snow is heterogeneously distributed, as they do not correctly represent the physical processes that occur on a smaller scale than the satellite image. In such cases, terrestrial photographs, whose resolution can be more easily adapted to the required resolution for these study cases, are an economic and also efficient alternative. This work presents a methodology for the georeferencing and automatic detection of snow in terrestrial photography, as an alternative to the use of satellite images for the study of the snow cover evolution in small areas. This methodology has been evaluated during the snowmelt period in the spring of 2009 at a study site in Sierra Nevada Natural Park (Southern Spain). The resulting snow maps have been compared with the results available for that period obtained from the analyses of four Landsat images. The results show that the use of Landsat generally overestimated the extension of the snow cover in the study area.

Characterization of the snow-pack is fundamental for several applications in hydrology, such as modelling and forecasting of snow melt runoff, water resource management and risk analysis. Thanks to its night/day capabilities and weather conditions independence, Synthetic Aperture Radar (SAR) represents a valuable tool for snow monitoring, especially in mountain areas often covered by clouds. The goal of the research project presented in this communication is to investigate the sensitivity of fully polarimetric Cband satellite SAR data to different conditions of the snow-pack. The work is based on the use of RADARSAT-2 C-band SAR data and collocated in-situ measurements acquired during two ground campaigns over an area located in the Catalan Pyrenees, that took place between February to October 2011. The main outcome of this study is the definition of two new polarimetric parameters sensitive to the snow presence, able to distinguish between dry-snow and non snow cover, allowing a qualitative remote sensing with C-band polarimetric space-borne SAR data. The importance of developing an application based on remote sensed data will be discussed. Results of the activity scheduled during the first year of the project will be highlighted. Observed deviations between SAR measurements and in situ measurement shall be analyzed and discussed.

High resolution airborne multispectral and thermal infrared imagery (1-meter pixel resolution) was acquired over several hydrothermal areas in Yellowstone National Park both in September of 2011 and in early March, during the winter of 2012, when snow cover was still present in most of the Park. The multi-temporal imagery was used to identify the extent of the geothermal areas, as snow accumulation is absent in hydrothermal areas. The presence or absence of snow depends on the heat flow generated at the surface as well as antecedent snow precipitation and temperature conditions. The paper describes the image processing and analysis methodology and examines temperature thresholds and conditions that result in the presence or absence of snow cover.

Accurate estimation of snow properties is important for effective water resources management especially in mountainous areas. In this work, we develop a snow data assimilation scheme based on ensemble Kalman filter (EnKF), which can assimilate remotely sensed snow observations into the Common Land Model (CoLM) to produce spatially continuous and temporally consistent snow variables. The snow cover fraction (SCF) product (MOD10C1) from the moderate resolution imaging spectroradiometer (MODIS) aboard the NASA Terra satellite was used to update CoLM snow properties. The assimilation experiment is conducted during 2003-2004, in Xingjiang province, west China. The preliminary results are very promising and show that distributions of snow variables (such as SCF, snow depth, and SWE) are more reasonable and reliable after assimilating MODIS SCF data. The results also indicate that EnKF is an effective and operationally feasible solution for improve snow properties prediction.

This paper reports the first results of the Project SESAMO - SistEma informativo integrato per l’acquisizione, geStione e condivisione di dati AMbientali per il supportO alle decisioni (Integrated Information System for the acquisition, management and sharing of environmental data aimed to decision making). The main aim of the project is to design and develop an integrated environmental information platform able to provide monitoring services for decision support, integrating data from different environmental monitoring systems (including WSN). This ICT platform, based on a service-oriented architecture (SOA), will be developed to coordinate a wide variety of data acquisition systems, based on heterogeneous technologies and communication protocols, providing different sort of environmental monitoring services. The implementation and validation of the SESAMO platform and its services will involve three specific environmental domains: 1) Urban water losses; 2) Early warning system for rainfall-induced landslides; 3) Precision irrigation planning. Services in the first domain are enabled by a low cost sensors network collecting and transmitting data, in order to allow the pipeline network managers to analyze pressure, velocity and discharge data for reducing water losses in an urban contest. This paper outlines the SESAMO functional and technological structure and then gives a concise description of the service design and development process for the second and third domain. Services in the second domain are enabled by a prototypal early warning system able to identify in near-real time high-risk zones of rainfall-induced landslides. Services in the third domain are aimed to optimize irrigation planning of vineyards depending on plant water stress.

Arctic tundra vegetation is characterized by an extreme heterogeneity at a small spatial scale. Optimizing the parameterization of tundra ecosystems in climate models requires detailed knowledge and understanding of soil vegetation- atmosphere feedback mechanisms at different spatial scales. We used a mobile multi-sensor platform for observing variable spectral and thermal responses of different representative vegetation communities within two 50 m long transects in August 2010. The observations sites are located on the North Slope of Alaska. The sensor platform was attached to a cable set up at a height of ~1 m above ground. The data were aggregated to distance increments of 45 cm along the transects and standardized (mean-centered) to account for observation date-specific offsets in measurements that were related to specific light and weather conditions but not to the local vegetation surface. A relative increase in the albedo of 0.01 (1%) was related to an increase in radiometric surface temperatures of 0.1 to 1 K, which is the inverse of the generally accepted surface temperature-albedo relationship observed at larger spatial scales. We explain this finding with cooling effects of the albedo-influencing surface wetness which primarily results from moss and soil evaporation. This cooling effect dominates over other more general heating effects that can be expected over surfaces with lower albedo under absence or near-absence of evaporation. Our findings are also supported by NDVI measurements. These locally inverted temperature-albedo feedbacks need to be considered in climate models that resolve Arctic environments with a high abundance of moss covers. Our results show that frequent observations of different tundra ecosystems using mobile multi-sensor platforms can provide data critical for understanding the land-atmosphere-interactions for the Arctic and the global system.

This study develops a new algorithm for estimating bare surface soil moisture using combined active / passive microwave remote sensing on the basis of TRMM (Tropical Rainfall Measuring Mission). Tropical Rainfall Measurement Mission was jointly launched by NASA and NASDA in 1997, whose main task was to observe the precipitation of the area in 40 ° N-40 ° S. It was equipped with active microwave radar sensors (PR) and passive sensor microwave imager (TMI). To accurately estimate bare surface soil moisture, precipitation radar (PR) and microwave imager (TMI) are simultaneously used for observation. According to the frequency and incident angle setting of PR and TMI, we first need to establish a database which includes a large range of surface conditions; and then we use Advanced Integral Equation Model (AIEM) to calculate the backscattering coefficient and emissivity. Meanwhile, under the accuracy of resolution, we use a simplified theoretical model (GO model) and the semi-empirical physical model (Qp Model) to redescribe the process of scattering and radiation. There are quite a lot of parameters effecting backscattering coefficient and emissivity, including soil moisture, surface root mean square height, correlation length, and the correlation function etc. Radar backscattering is strongly affected by the surface roughness, which includes the surface root mean square roughness height, surface correlation length and the correlation function we use. And emissivity is differently affected by the root mean square slope under different polarizations. In general, emissivity decreases with the root mean square slope increases in V polarization, and increases with the root mean square slope increases in H polarization. For the GO model, we found that the backscattering coefficient is only related to the root mean square slope and soil moisture when the incident angle is fixed. And for Qp Model, through the analysis, we found that there is a quite good relationship between Qpparameter and root mean square slope. So here, root mean square slope is a parameter that both models shared. Because of its big influence to backscattering and emissivity, we need to throw it out during the process of the combination of GO model and Qp model. The result we obtain from the combined model is the Fresnel reflection coefficient in the normal direction gama(0). It has a good relationship with the soil dielectric constant. In Dobson Model, there is a detailed description about Fresnel reflection coefficient and soil moisture. With the help of Dobson model and gama(0) that we have obtained, we can get the soil moisture that we want. The backscattering coefficient and emissivity data used in combined model is from TRMM/PR, TMI; with this data, we can obtain gama(0); further, we get the soil moisture by the relationship of the two parameters-- gama(0) and soil moisture. To validate the accuracy of the retrieval soil moisture, there is an experiment conducted in Tibet. The soil moisture data which is used to validate the retrieval algorithm is from GAME-Tibet IOP98 Soil Moisture and Temperature Measuring System (SMTMS). There are 9 observing sites in SMTMS to validate soil moisture. Meanwhile, we use the SMTMS soil moisture data obtained by Time Domain Reflectometer (TDR) to do the validation. And the result shows the comparison of retrieval and measured results is very good. Through the analysis, we can see that the retrieval and measured results in D66 is nearly close; and in MS3608, the measured result is a little higher than retrieval result; in MS3637, the retrieval result is a little higher than measured result. According to the analysis of the simulation results, we found that this combined active and passive approach to retrieve the soil moisture improves the retrieval accuracy.

Crop residue (or plant litter) on the soil surface can decrease soil erosion and runoff and improve soil quality. Quantification of crop residue cover is required to evaluate the effectiveness of conservation tillage practices as well as the extent of biofuel harvesting. Remote sensing techniques can provide reliable assessment od crop residue cover over large fields. With Landsat Thematic Mapper bands, crop residues can be brighter or darker than soils depending on soil type, crop type, moisture content, and residue age. With hyperspectral reflectance data, relatively narrow absorption features, centered near 2100 and 2300 nm, can be detected that are associated with cellulose and lignin concentrations. These features are evident in reflectance spectra of crop residues, but not in reflectance spectra of soils. Our objectives were to: (1) estimate crop residue cover using remotely sensed data over an agricultural site in central Iowa, and (2) evaluate alternative, less labor-intensive sampling schemes for acquiring crop residue cover surface reference data. We acquired EO-1 Hyperion imaging spectrometer data over agricultural fields in central Iowa shortly after planting in May 2004 and 2005. Crop residue cover was also measured in corn and soybean fields using line-point transects. The cellulose absorption index (CAI), which measured the relative intensity of the absorption feature near 2100 nm, was calculated using three relatively narrow bands centered at 2030, 2100, and 2210 nm. Results showed that crop residue cover was linearly related to CAI. Changes in the slopes of the regression line from year to year were related to scene moisture conditions. Tillage intensity classes corresponding to conventional tillage (≤ 30% cover) and conservation tillage (> 30% cover) were correctly identified in 75-82% of the fields. In addition, by combining information from previous season’s crop classification with crop residue cover after planting, an inventory of soil tillage intensity by previous crop was generated for the whole Hyperion scene for each year. Inventories and maps of tillage intensity are required for field- and watershed scale models to evaluate management practices that maximize production and minimize environmental impact.

Monitoring spatial and temporal variability of Vegetation Indices (VIs) is important to manage land and water resources, with significant impact on the sustainability of modern agriculture Although algorithms based on optical data give accurate products, cloud cover dramatically reduces the temporal resolution of these outputs. The launch of new Synthetic Aperture Radar (SAR) constellations such as COSMO-Skymed opened new opportunities to develop agro-hydrological applications. Indeed, these satellites may represent a suitable source of data for operational applications due to their high spatial and temporal resolutions (10 m in StripMap PingPong acquisition mode, best revisit time with 4 satellites: 4 images per day at equator; every 7 hours on average at 40° latitude). Although X band is not optimal for agricultural and hydrological applications, reliable continuous assessment of the VIs can be achieved combing optical and SAR images. To this aim, an algorithm was implemented and validated coupling a VI derived from optical DEIMOS images (VI_opt) and the crossed HV backscattering σ°_HV (PingPong in HV polarization). A correlation analysis has been performed between σ° and VIs measurements taken simultaneously to Cosmo-SkyMed acquisitions in several plots. The correlation analysis was based on: incidence angle, spatial resolution and polarization mode. Results have shown that temporal changes of σ°_HV (Δσ°_HV), acquired with high angles (off nadir angle, θ> 40°) is characterized by the best correlation with variations of VI (ΔVI). The correlation between ΔVI and Δσ°_HV is shown to be temporally robust. Based on this experimental evidence a model to infer VI_SAR at the time t_i+1 once known the VI_opt at a reference time, t_i and Δσ°_HV between times t_i+1 and t_i, was implemented and verified. The study is carried out over the SELE plain (Campania, Italy) mainly characterized by herbaceous crops. Five couples of COSMO-Skymed and optical DEIMOS images have been acquired between August and September 2011. Data have been collected within the COSMOLAND project (Use of COSMO-SkyMed SAR data for LAND cover classification and surface parameters retrieval over agricultural sites) funded by the Italian Space Agency (ASI). Results confirm that VI_SAR obtained using the combined model is a satisfactory surrogate of the VI_opt.

As illustrated by several literature case studies spread around different geographic locations, satellite precipitation estimates, obtained by means of consolidated algorithms, often result being considerably biased. Moreover observed bias is related to geographic location since particular features such as latitude, presence of coastal areas and climatological and weather regime, affect performances of satellite estimates. Bias adjusted products that make use of global ground-based precipitation estimates, are available as well but still these datasets may show a relevant bias level. In this study a procedure to bias-adjust satellite precipitation estimates has been developed for the local area of Sicily (Italy) based on rain-gauge network data. Considering that the latency time of satellite precipitation estimates is nowadays very short and close to that of satellite data availability, it has been investigated the possibility of designing a procedure that able to apply the bias reduction to satellite estimates without timely corresponding rain-gauge network data. Therefore, in order to obtain a tool that make available a first precipitation map estimate, the emphasis has been put on data readiness instead of achieving the best correction result. The developed procedure demonstrates to be able to improve the overall bias performances of examined satellite precipitation data. It is expected that such an approach increases its suitability as the developing of satellite estimates algorithms leads to better a description of rainfall dynamics.

This work shows the sensitivity of NDVI as an indicator of the global hydrological regime of the year. The annual water balance in the area was simulated through a physically-based distributed hydrological model previously calibrated and validated in the area from 2001 till 2010. NDVI was obtained from Landsat TM at the end of the dry season in 1000 points randomly distributed over a pine cover in a mountainous Mediterranean area. The influence of different hydrological processes related to the water balance in the soil on the NDVI values was analyzed through Pearson correlation matrices and Principal Components Analyses. Results showed that the NDVI was particularly sensitive to the regime of annual variables related to the snow layer dynamics, especially to snowmelt. These relationships were quantified, with the best fit being obtained between NDVI and the dimensionless index snowmelt divided by precipitation (R² around 0.7). The adjustments obtained could, in the future, constitute a tool for the estimation of hydrological variables from satellite data in data-poor situations conditioned by the commonly steep slopes and difficult access in mountainous areas.

Photosynthetic light-use efficiency (LUE) is an important indicator of plant photosynthesis, but assessment by remote sensing needs to be further explored. In principle, chlorophyll fluorescence combined with heat dissipation is an expression of the balance between light harvesting (absorption) and light utilization in the photosynthetic process. The aim of the present study was to examine the above principles using solar-induced chlorophyll fluorescence (ChlF) and photochemical reflectance index (PRI), which is sensitive to dynamic changes in the xanthophyll cycle. LUE-ChlF models were developed for ChlF at 688 nm (R² = 0.72) and 760 nm (R² = 0.59) based on the experiment data for winter wheat, which were also validated by three independent experiments, and the validation results showed that the LUEChlF relation was weakened, possibly by different species, canopy density and environmental conditions. Furthermore, the significant negative relation between non-photochemical quenching (NPQ) and PRI was confirmed. However, the PRI-LUE relation was evidently weakened by the canopy and environmental conditions. The PRI difference (ΔPRI) from the minimum reference PRI around noontime could greatly eliminate the interference factors. The LUE-ΔPRI model was developed based on the experiment data for winter wheat (with an R² value of 0.85), and validated by other three independent experiments.

Recent developments in environmental studies are greatly related to worldwide ecological problems associated with anthropogenic impacts on the biosphere and first of all on vegetation. Modern remote sensing technologies are involved in numerous ecology-related investigations dealing with problems of global importance, such as ecosystems preservation and biodiversity conservation. Agricultural lands are subjected to enormous pressure and their monitoring and assessment have become an important ecological issue. In agriculture, remote sensing is widely used for assessing plant growth, health condition, and detection of stress situations. Heavy metals constitute a group of environmentally hazardous substances whose deposition in soils and uptake by species affect soil fertility, plant development and productivity. This paper is devoted to the study of the impact of heavy metal contamination on the performance of agricultural species. The ability of different spectral indicators to detect heavy metal-induced stress in plants is examined and illustrated. Empirical relationships have been established between the pollutant concentration and plant growth variables and spectral response. This allows not only detection but quantification of the stress impact on plant performance.

Rice farming, one of the most important agricultural activities in the world producing staple food for nearly one-fifth of the global population, covers 153 MHa every year corresponding to a production of more than 670 Mton. Retrieve updated information on actual rice cultivated areas and on key phenological stages occurrence is fundamental to support policy makers, rice farmers and consumers providing the necessary information to increase food security and control market prices. In particular, remote sensing is very important to retrieve spatial distributed information on large scale fundamental to set up operational agro-ecosystem monitoring tool. The present work wants to assess the reliability of automatic image processing algorithm for the identification of rice cultivated areas. A method, originally tested for Asian tropical rice areas, was applied on temperate European Mediterranean environment. Modifications of the method have been evaluated to adapt the original algorithm to the different experimental conditions. Finally, a novel approach based on phenological detection analysis has been tested on Northern Italy rice district. Rice detection was conducted using times series of Vegetation Indices derived by MODIS MOD09A1 products for the year 2006 and the accuracy of the maps was assessed using available thematic cartography. Error matrix analysis shows that the new proposed method, applied in a fully automatic way, is comparable to the results of the original approach when it is customized and adapted for the specific study area. The new algorithm minimizes the use of external data and provides also spatial distributed information on crop phenological stages.

CwRS (Control with Remote Sensing) is a control method foreseen by the CAP (Common Agricultural Policy) of the European Union (EU) which is used to check if agriculture area-based subsidies are correctly granted to EU farmers. A series of Very High Resolution (VHR) and High Resolution (HR) satellite sensors participate in the acquisition program. Imagery is collected in specific multi-temporal, short time-windows and used for parcel area determination, for crop identification and for control of Good Agricultural and Environmental Conditions (GAECs).

In the 2003 Campaign 37 VHR zones with an overall area of 12.500 km² were checked with the CwRS technique; in the 2011 Campaign 426 VHR control zones were acquired covering an overall area of 242.000 km², with a total expenditure of 7.1 M euro. This is an enormous increase due to the success of the methodology which needs pointing out. Of interest is also the increasing requirements put on the imagery quality (higher elevation angle, better resolution and better radiometry.).

One of the crucial features requested by EU Member States (MS) is window length, for VHR this is usually quite short (6-8 weeks). A feasibility analysis for all zones is therefore done before each VHR Campaign starts to ensure a maximal statistical success rate. This paper describes the complexity of the technical and competitive feasibility assessment taking into account parameters such as satellite characteristics (revisit capacity, number of passes), zone size, shape and latitude; elevation angle, acquisition window length, programming priority level, weather forecast and competitive conflicting tasking.

To increase the efficiency of the image acquisition a real local tasking with the use of a Direct Access Facility (DAF) can be compared to a tasking performed through an Imaging and Processing Facility (IPF). Both approaches allow the integration of last minute information into the collection plan and yield for instance better chances of avoiding cloud cover. Illustrative examples are presented.

Accurate information about land use patterns is crucial for a sustainable and economical use of water in agricultural systems. Water demand estimation, yield modeling and agrarian policy are only a few applications addressed by land use classifications based on remote sensing imagery. In Central Asia, where fields are traditionally large and state order crops dominate the area, small units of fields are often separated for the additional cultivation of income crops for the farmers. Traditional object based land use classifications on multi-temporal satellite imagery using field boundaries show low classification accuracies on these separated fields, expressed by a high uncertainty of the final class labels. Although segmentation of smaller subfields was shown to be suitable for improving the classification result, the extraction of subfields is still a time-consuming and error-prone process. In this study, energy based Graph-Cut segmentation technique is used to enhance the segmentation process and finally to improve the classification result. The interactive segmentation technique was successfully adopted from bio-medical image analysis to fit remote sensing imagery in the spatial and in the temporal domain. A set of rules was developed to perform the image segmentation procedure on pixels of single satellite datasets and on objects representing time series of a vegetation index. An ensemble classifier based on Random Forest and Support Vector Machines was used to receive information about classification uncertainty before and after applying the segmentation. It is demonstrated that subfield extraction based on Graph Cuts outperforms traditional image segmentation approaches in simplicity and reduces the risk of under- and over-segmentation significantly. Classification uncertainty decreased using the derived subfields as object boundaries instead of original field boundaries. The segmentation technique performs well on several multi-temporal satellite images without changing parameters and may be used to refine object based land use classifications to subfield level.

The objectives of this study were to reveal the presence of viral infections in two varieties of tobacco plants (Nicotiana tabacum L.) as well as to discriminate the levels of the disease using hyperspectral leaf reflectance. Data sets were collected from two tobacco cultivars, Xanthi and Rustica, known as most widespread in Bulgaria. Experimental plants were grown in a greenhouse under controlled conditions. At growth stage 4-6 expanded leaf plants of cultivar Xanthi were inoculated with Potato virus Y (PVY) while the Rustica plants were inoculated with Tomato spotted wilt virus (TSWV). These two viruses are worldwide distributed and cause significant yield losses in many economically important crops. In the course of time after inoculation the concentration of the viruses in plant leaves was assessed by erological analysis via DAS-ELISA and RT-PCR techniques. Hyperspectral reflectance data were collected by a portable fibreoptics spectrometer in the visible and near-infrared spectral ranges (450-850 nm). As control plants healthy untreated tobacco plants were used. The significance of the differences between reflectance spectra of control and infected leaves was analyzed by means of Student’s t-criterion at p<0.05. The analyses were performed at ten wavebands selected to cover the green (520-580 nm), red (640-680 nm), red edge (690-720 nm) and near infrared (720-780 nm) spectral ranges. Changes in SRC were found for both viral treatments and comparative analysis showed that the influence of PVY was stronger. The discrimination of disease intensity was achieved by derivative analysis of the red edge position.

This work has as aim to quantify the energy changes between atmosphere and surface by modeling both net radiation and soil heat flux related to land use and cover. The methodology took into account modeling and mapping of physical and biophysical parameters using MODIS images and SEBAL algorithm in an area of native vegetation and irrigated crops. The results showed that there are variations in the values of the estimated parameters for different land cover types and mainly in caatinga cover. The dense caatinga presents mean values of soil heat flux (Go) of 124.9 Wm^-2 while sparse caatinga with incidence of erosion, present average value of 132.6 Wm^-2. For irrigated plots cultivated with banana, coconut, and papaya the mean Go values were 103.8, 98.6, 113.9 Wm^-2, respectively. With regard to the instantaneous net radiation (Rn), dense caatinga presented mean value of 626.1 Wm^-2, while sparse caatinga a mean value of 575.2 Wm^-2. Irrigated areas cultivated with banana, coconut, and papaya presented Rn of 658.1, 647.4 and 617.9 W m^-2 respectively. Applying daily mean net radiation (Rn_DAve) it was found that dense caatinga had a mean value of 417.1 W m^-2, while sparse caatinga had a mean value of 379.9 W m^-2. For the irrigated crops of banana, coconut and papaya the Rn_DAve values were 430.9, 431.3 and 411.6 W m^-2, respectively. Sinusoidal model can be applied to determine the maximum and Rn_DAve considering the diverse classes of LULC; however, there is a need to compare the results with field data for validation of this model.

Soil moisture is a primary product of Advanced Microwave Scanning Radiometer for EOS (AMSR-E) which onboard Aqua satellite. AMSR-E soil moisture product provides us global soil moisture dataset from 2002 to present. It is known to all that validation is a big problem in the use of satellite remote sensing soil moisture product. The instantaneous field of view (IFOV) of different channel of AMSR-E ranges from 10 km to 50 km. For the lower frequency which is more sensitive to soil moisture, IFOV is larger. This means that a single point of AMSR-E soil moisture product contain average soil moisture of hundreds of km2 land surface. The traditional soil moisture measurement only gives us a single point soil moisture measurement result which is not equal to large area soil moisture. In this paper, we present an experiment to validate the soil moisture product of AMSR-E. The experiment was carried out from July 15, 2008 to September 30, 2008. 9. The location of the experiment is in Xilinhot grassland, Neimeng province, China. During the former period, we put 9 soil moisture and temperature measurement systems (ECH2O) in an area of about 3kmx3km. We selected this area because it’s a very typical area in the Xilinhot grassland, which contain most land cover types and terrain types in the grassland. For each point, 5 layers of soil moisture and temperature were measured by ECH2O. The 5 layers are 2cm, 5cm, 10cm, 20cm and 50cm respectively. Considering the penetration depth of AMSR-E, 2cm measurements were used to do the validation. The location of the 9 ECH2O is very dispersive and the elevations of them are very different. Measurement result shows that the main factor that affects the soil moisture distribution is elevation. In grassland, soil moisture in higher place is always smaller than that of lower place. Which means a single soil moisture measurement is not suit for the soil moisture used purpose. Precipitation measurement was also done in this area during the experiment period. Result shows that the precipitation is more effective to the top layers soil moisture than the bottom layers. In our sites, only 2cm and 5cm layers are affected by the precipitation. Other layer didn’t change much during the whole period. The average soil moisture measurement result in this area during the experiment is 11.8cm3/cm3, while the soil moisture product of AMSR-E in this area shows that the average soil moisture during this period is 13.3cm3/cm3. The RMSE of them is 3.7cm3/cm3. Result show that AMSR-E soil moisture product has a good accuracy in the grassland of north China.

Drought occurs everywhere in the world and is one of the costliest natural hazards. The Group on Earth Observations (GEO) has advocated implementing a Global Drought Early Warning System (GDEWS) since 2007. Various indices have been developed and used to depict drought. According to the survey, various drought monitoring system with remote sensing at regional, national or local level are existing, but the integration with the drought system based on the weather station data, in particular at the global level is still weak. However, the GEO Global Agricultural Monitoring Initiative was recognized by the G20 agricultural ministers and will enhance the linkage between GEO-GLAM and GDEWS. The capability for a component of drought monitoring with remote sensing is there in place. MODIS data have been used to globally track the distribution of crop failures due to droughts. In China, the Chinese meteorological satellite, FY is also ready to monitoring drought globally. MERSI onboard FY-3 is similar with MODIS and helpful to monitor the occurrence, development of drought at different scales. JRC MARS issues periodical bulletin on agricultural conditions. Agricultural Division of Statistics, Canada issues weekly crop condition reports. In India, the biweekly drought bulletin and monthly reports is issued under National Agricultural Drought Assessment and Monitoring System (NADAMS). Similar program is followed in many countries world-wide. The informed information of drought is helpful for governmental officials and formers to in advance prepare for coping with the likely coming drought. The global efforts should be in place to promote the global drought information system with a remote sensing drought component.

Soil moisture (SM) is of fundamental importance to many agricultural, hydrological and climate studies. In this paper, a simple approach for mapping near-surface SM from Envisat ASAR data was developed. Four high-resolution images covering a semiarid region in Algeria were acquired with the same sensor configuration. We performed the pretreatment using the Basic Envisat SAR Toolbox of the European Space Agency. Then, we extracted the backscattering coefficient σ⁰ (dB) from the filtered and calibrated images. On the other hand, five training sites with different soil physical properties and vegetation cover were selected for monitoring SM. The field campaigns were conducted concurrent to satellite image acquisitions to measure soil water content in the top five centimeters using the gravimetric method. The study of linear regressions associated to the change detection approach allowed the expression of the backscattering coefficient as a function of volumetric soil moisture (σ⁰ = a*θ + b). The coefficients “a” and “b” of the equation slightly differ from one site to another and also from one season to the next. This difference is mainly due to the effects of surface roughness and vegetation biomass variations. Our study confirms a good agreement between the volumetric nearsurface SM and the radar backscattering coefficient for all the test fields. The comparison between measured and estimated SM proves the accuracy of the inversion models used here with a mean average error of less than 5%. At the end, high resolution maps of soil moisture distribution were obtained from the acquired radar images.

Drought monitoring models and products assist decision makers in drought planning, preparation, and mitigation, all of which can play a role in reducing drought impacts. In this study, the performance of two newly developed remote sensing-based drought indices, the perpendicular drought index (PDI) and modified perpendicular drought index (MPDI), are further explored for regional drought monitoring in agricultural regions located in central and south western Asia. The study area covers regions from moderate and wet climatological zones with dense vegetation coverage to semi-arid and arid climatological conditions with moderate to poor vegetation coverage. The spatio-temporal patterns of surface drought derived by PDI and MPDI from 250m MODerate Resolution Imaging Spectroradiometer (MODIS) data in 8-day time steps are compared against two other drought indices: the Standardized Precipitation Index (SPI) as a meteorological drought index and the potential evapotranspiration (ET0) as an agro-meteorological drought index, which both were calculated based on field-measured precipitation and regional meteorological parameters. In addition, 8-day MODIS Normalized Difference Vegetation Index (NDVI) was calculated and its performance to detect drought occurrence and measuring of drought severity compared with the two perpendicular drought indices. Significant correlations were found between the PDI, the MPDI and precipitation and other applied meteorological and agrometeorological drought indices. The results confirm previous studies which has been analyzing the PDI and the MPDI over some study points in Iran. In this research, however, implementation of higher resolution data (MOD09Q1) in both spatial (250 m) and temporal (8-days) dimensions revealed a greater agreement between the drought information extracted by the MPDI, PDI and field meteorological measurements. It could be concluded that the applied perpendicular indices could be used as a drought early warning system over case study region and other regions with similar arid and semi-arid climatological conditions.

Circular or elliptical vegetation community patches resulted from seismic exploration of Shengli Oil Field occurs widely across the Yellow River Delta in China. In order to facilitate the monitoring of vegetation extension and quantify the mechanism of vegetation patch succession, there is a clear need for accurate and economical of detecting the different structures of vegetation patches. This paper compares the efficacy of SPOT 5 and ALOS data in detecting vegetation community patches at Gudong oil field, China. As a result of shape differences between vegetation community patches and background in the image provided by each sensor, canny edge detector and mathematical morphological methods were employed. SPOT 5 data (2.5 m Ground Spatial Distance or GSD, detection accuracy, 91.2%) proved more effective in vegetation community patch delineation than ALOS data (2.5 m GSD, detection accuracy, 89.3%).

The singular characteristics of the Canarian archipelago (Spain) and, in particular, of the Gran Canaria island have allowed the development of a unique biological richness. Almost half of its territory is protected to preserve the natural environment and, in consequence, the monitoring of vegetated regions plays an important role for regional administrations which aim to develop the corresponding policies for the conservation of such ecosystems. The Normalized Difference Vegetation Index (NDVI) is a common index applied for vegetation studies. It is important to emphasize that NDVI is sensor-dependent, and changes are affected by soil background, irradiance, solar position, atmospheric attenuation, season, hydric situation and climate of the area. So, a fixed threshold cannot be set, even for the same sensor or season, to properly segment vegetated areas. In this context, a robust methodology has been applied to ensure a reliable estimation of changes using the same sensor in multiple dates or different sensors. To that respect, a supervised procedure is presented consisting on the selection of different regions within each image to precisely map each cover with its associated NDVI values and, in consequence, obtain for each individual image the optimal threshold to properly segment vegetation without the need to perform the complex preprocessing required to estimate the ground reflectivity. On the other hand, fires are an important aspect of an ecosystem and their study, a fundamental task to perform a complete assessment of the environmental and economic damage. In our work we have also analyzed in detail the fire occurring during 2007 and precisely assessed the results.

Forest systems are all sensitive to climatic factors and extreme events and are likely to have different vulnerability thresholds according to the species, the amplitude, and the rate of climatic stressors. As a result of global climate change, there is growing evidence that some of the most severe weather events could become more frequent in Romania over the next 50 to 100 years. Effects of climate extremes on forests can have both short-term and long-term implications for standing biomass, tree health and species composition. Multispectral, multiresolution and multitemporal satellite imagery is used to classify and map various forest and spatio-temporal land-cover changes. In mountain forests, the more frequent occurrence of climatic changes may accelerate the replacement of sensitive tree species and reduce carbonstocks, and the projected slight increase in the frequency of extreme storms by the end of the century could increase the risk of windthrow. Mountain forest landscape pattern and the biogeophysical variables (NDVI, EVI) controlling observed patterns can be addressed using time series remote sensing satellite imagery. The specific aim of this paper was to: 1) quantify the changes and rates of change between 1990 and 2011 in vegetative composition across a forest landscape in Romanian Carpathians on Prahova Valley using Landsat TM/ETM, IKONOS, MODIS images; 2) examine the changes in landscape structure in relation with climatic changes and extreme events; 3) assess the climate risks and their potential impact on Romanian mountain forests; 4) analysis of mountain forest spatio-temporal land cover composition, pattern and structure.

Laser-induced fluorescence (LIF) spectra of a bush and numerous branches of Chinese Longjing tea were investigated remotely with lidar techniques. The intensity ratio between the far red fluorescence (FRF) and red fluorescence (RF) due to the chlorophyll content of the tea branches were analyzed to study the growth conditions in different villages around Hangzhou, China. Dried Longjing tea leaves were also measured by LIF techniques in the laboratory. A chemometric method based on singular value decomposition (SVD) and linear discriminant analysis (LDA) was used to evaluate the tea qualities of the dried tea leaves.

Satellite imagery offers great potential for marine coastal areas mapping and spatio-temporal dynamics assessment. Due to climate and anthropogenic-induced changes on coastal zone morphology as well as on stocks of plankton and fish, climate–sea interactions and their response on marine ecosystems have recently been focus of considerable attention. Fuzzy theory applied for the analysis of coastal areas changes, for which each pixel on the map is assigned a membership grade in all classes, represent reasonable alternatives to traditional fixed classifications. Fuzzy classifications and mixture/subpixel models provide information on variations in spectral signature between pixels and not on differences in the on-the-ground content of mapped land cover. The coastal zone units are recognized on a ground truth map of an area using Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM) as well as IKONOS and Quickbird imagery for North-Western Black Sea coastal zone, Romania, over 1990-2011 period. The application of fuzzy logic for quantifying magnitudes of land cover changes is highly appealing because of its capability to deal with uncertainties such as in the case when one cannot accurately identify a threshold value to separate areas of change from areas of no-change. Anthropogenic eutrophication and coastal erosion affects the Romanian North-Western Black Sea to various extents.

Planktothrix rubescens is sadly famous for producing microcystins (MCs), which are powerful hepatotoxins. During the winter 2005/06, P. rubescens has been found in the Pozzillo, Nicoletti, Prizzi and Garcia reservoirs, Sicily; in 2008 it was also detected in SS. Trinità di Delia and Castello reservoirs. Indeed, during periods of low shortwave irradiance such as winter, when light weakly penetrates water column and the water cools, P. rubescens filaments float up to the surface, forming red-colored blooms. Ancillary meteorological measurements highlighted low air temperatures between two frosts in December 2007 and February 2008, with a simultaneous reduction in the incoming total solar radiation, which probably triggered P. rubescens floating up to water surface. The manuscript reports empirical relationships to calculate the cell density of P. rubescens. A basic cell density relationship was set up using in situ data and satellite images (MODIS Terra) collected simultaneously. In particular, water samples were collected for cells counting and MCs detection, whereas Red and Near-Infrared bands of MODIS at 250 m provided a good dataset for modeling an empirical relationship, given also their high temporal resolution. Spectroradiometric campaigns were also carried out in February/March 2008 to characterize the spectral reflectance of water with and without P. rubescens bloom. A further cross calibration, using MODIS as reference data, allowed to setup the empirical relationships to be applied to other sensors such as Landsat ETM+, MERIS Envisat and Sentinel-2. Among those, Landsat ETM+ have an appropriate spatial resolution for monitoring Sicilian reservoirs (characterized by an average extension of few kilometers) although images are currently acquired in SLC-off mode, thus compromising the suitability of such data for an operational monitoring. MERIS had a sufficient spatial resolution (300 m) but it was lost with Envisat (on May the 9th, 2012). Sentinel-2, once operational, will provide a suitable dataset. Generally, the retrieved empirical equations tend to overestimate the actual values for low (or absent) surface filaments.

Land surface temperature (LST) is an important parameter that modulates land surface process. The combination of infrared temperature and microwave temperature is a trend in the research of LST. Thermal infrared temperature and microwave temperature have different physical significances and values. However, they are always treated as the same temperature nowadays in the research on the combination of infrared temperature and microwave temperature. In this study, the homogeneous canopy is the leaf-dominated crown layer ignoring the effect of branches. Two layers with different temperature, the canopy layer and the soil layer, are considered. MESCAM model based on matrix doubling method has been modified by getting rid of the effects of the main and secondary stems. The effect of multiple scattering at L and C band has been studied by comparing the results of taoomiga model with that of the modified MESCAM model. Tao-omiga model was adopted to compute the canopy brightness temperature at L band and a simple geometric-optical model basing on gap probabilities was used to compute the canopy brightness temperature at thermal infrared band in the same scene. The relationship and the difference between thermal infrared canopy surface physical temperature and L band canopy effective physical temperature with different soil moisture have been analyzed in three different situations of T_C (the temperature of the foliage component) and T_S (the temperature of the soil component). It is a base of further exploring the cooperative inversion combining thermal infrared remote sensing with passive microwave remote sensing.

The main environmental issues affecting the broad acceptability of NPP (Nuclear Power Plant) are the emission of radioactive materials, the generation of radioactive and heat waste, and the potential for nuclear accidents. Satellite remote sensing is an important tool for spatio-temporal analysis and surveillance of environment, thermal heat waste of waters being a major concern in many coastal ecosystems involving nuclear power plants, as sharp changes in water temperature can significantly affect the distribution and physiology of aquatic biota and contribute to global warming. The thermal plume signature in the NPP hydrological system in TIR (Thermal Infrared) spectral bands of Landsat TM and ETM TIR band 6, ASTER, and MODIS TIR bands time series satellite have been used for WST (Water Surface Temperature) detection, mapping and monitoring. As a test case the methodology was applied for NPP Cernavoda, Romania during period of 1990-2011 years. Thermal discharge from two nuclear reactors cooling is dissipated as waste heat in Danube-Black -Sea Channel and Danube River. If during the winter thermal plume is localized to an area of a few km of NPP, the temperature difference between the plume and non-plume areas being about 1.5 ^oC, during summer and fall, is a larger thermal plume up to 5- 6 km far along Danube Black Sea Channel, the temperature change being of about 1.0 ^oC.

Fraction of Absorbed Photosynthetically Active Radiation (FPAR) characterizes vegetation canopy functioning and its energy absorption capacity. In this paper, we focus on climatic driving forces in inter-annual variation of global FPAR from 1982 to 2006 by Global Historical Climatology Network (GHCN-Monthly) data. Using FPAR-Simple Ratio Vegetation Index (SR) relationship, Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI) was used to estimate FPAR at the global scale. The correlation between inter-annual variation of FPAR and temperature, precipitation derived from GHCN-Monthly was examined, during the periods of March-May (MAM), June-August (JJA), September-November (SON), and December–February (DJF) over from 1982 to 2006. The analysis of climatic influence on global FPAR revealed the significant correlation with temperature and precipitation in some meteorological stations area, and a more significant correlation with precipitation was found than which with temperature. Some stations in the regions between 30° N and 60° N and around 30° S in South America, where the annual FPAR variation showed a significant positive correlation with temperature (P < 0.01 or P < 0.05) during MAM, SON, and DJF, as well as in Europe during MAM and SON period. A negative correlation for more stations was observed during JJA. For precipitation, there were many stations showed a significant positive correlation with inter-annual variation of global FPAR (P < 0.01 or P < 0.05), especially for the tropical rainfall forest of Africa and Amazon during the dry season of JJA and SON.

Solar irradiation is a way to characterize the climate of particular region, and used in tourism and agriculture. The Heliosat-Ⅱ method of deriving solar irradiation from FY-2C geostationary satellite images was evaluated for China. The results clear show that the Heliosat-Ⅱ method is feasible to mapping surface solar irradiation over China using FY-2 images, especially for the regions where the measured solar irradiation is not available.

Despite the growing use of Remote Sensing (RS) data in epidemiological studies, several diseases, including asthma, have not been studied yet using RS potentialities. Asthma is a chronic inflammatory disorder of the airway that affects people of all ages throughout the world. The expression of this disease can be influenced by some environmental factors such as allergens, air pollution or climate conditions. In this study, we modeled the distribution of asthma in each season, using Maximum entropy (Maxent) model and presence data obtained from a national database with asthma public hospitals admissions in Mainland Portugal, with discharges between years 2003 and 2008. We considered data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to retrieve estimates of near-surface air temperature and relative humidity. Land-use regression (LUR) models were developed to produce estimates of three pollutants: PM₁₀, NO₂, and CO. Moreover, MODIS Normalized Difference Vegetation Index (NDVI) was also used in the construction of Maxent models. All Maxent models predicted similar suitable areas and obtained acceptable area under the curve (AUC) values (~0.75) of the ROC plot. Our results show a strong relationship between asthma presence and NO₂, suggesting that asthmatic people living in urban areas with high traffic volume have an increased risk of suffering asthma attacks. Furthermore, there is evidence of the effect of PM₁₀, CO, and RH (during the Summer) in asthma expression. RS data have a great potential but also presents limitations that should be addressed to allow studying more complex diseases.

The native Eurasian red squirrel is considered endangered in the UK and under strict legal protection. Long term habitat management is a key goal of the UK conservation strategy. The importance of forest structural parameters for red squirrels habitat mapping was previously demonstrated: a General Linear Model (GLM) was used to relate the number of cones stripped by squirrels to mean canopy closure, mean tree height and total number of trees at the plot level, all significant predictors and explaining 43% of the variance in the number of stripped cones. The main aim of this study is to implement the GLM using LiDAR derived explanatory variables and to assess habitat suitability at Abernethy Forest, one of the proposed red squirrel strongholds in the UK. LiDAR-based GLM performance was explored by assessing the correlation between field-predicted and LiDAR-predicted number of stripped cones (Spearman rank correlation coefficient = 0.59; n=32, P< 0.00). Finally, habitat suitability maps were generated. Results suggest that when forest structure is considered, only 27% of the total forest area at Abernethy is suitable for red squirrel.

Reflectance measurements have been convex-hull-normalized to derive individual absorption features and the continuous spectra were used to calculate color parameters according to the Commission Internationale de l'Eclairage (CIE) color scheme. Subsequently, derived parameters of the convex hull normalized iron absorption band in the near infrared around 0.9 μm and the CIE-chromaticity coordinates were tested for their significance to predict the total iron content. Accordingly, a method for spectral detection of total iron content was generated based on statistical analysis which allows the prediction of the soils total iron content of the investigated soils with a cross-validated r² above 0.8. Since C.I.E. color coordinates were found to be well suitable parameters for predicting total iron content of soils under laboratory conditions, the reflectance values of the Landsat-TM bands were transformed into C.I.E. color coordinates. Subsequently, the C.I.E. based model approach was adopted to a Landsat image with low vegetation cover from July 1998 to predict spatial distribution of the soils total iron content. The transfer of the regression model to the satellite image allowed for prediction of the total iron content. Concentrations obtained from the satellite image are in accordance with the concentration range of the chemical analysis. The predicted total iron concentrations reflect the geographic conditions and show a dependence on the annual rainfall amount. A general trend to decreasing concentrations of total iron can be stated with increasing aridity. Furthermore, local conditions are well reflected by the predicted concentrations.

Soil salinity is a huge problem negatively affecting physiological and metabolic processes in plant life, ultimately diminishing growth and yield. An area with more than 70,000 ha sugarcane farming and its by-products are the major agricultural activities in the Khuzestan province, in the southwest of Iran. Therefore, mapping and identification of soil salinity is the most important issue to improve management of large scale crop production in this area. Besides labour intensive fieldwork, remote sensing is the most suitable technique to assess soil salinity for large areas. This study was carried out to investigate the capability of Hyperion spaceborne hyperspecteral data for mapping the salinity stress in the sugarcane fields and determine the best method to classify soil salinity into 3 classes (low, moderate and high salinity). For this purpose the capability of different classification methods like support Vector Machine (SVM), Spectral Angle Mapper (SAM), Minimum Distance (MD) and Maximum Likelihood (ML) in conjunction with different band combinations (all bands, principle component analysis (PCA), Vegetation Indices) as an input data was performed. Results indicated that best method for classification is SVM classifier when we use all bands or PCA(1-5) as an input data for classification with an overall accuracy and kappa coefficient of 78.7% and 0.68 respectively. Therefore, salinity stress can be classified in agricultural fields using Hyperion satellite imagery with good accuracy and salinity map can be very useful for management of agricultural activity and increase the crop production.

Detecting soil organic carbon (SOC) changes is important for both the estimation of carbon sequestration in soils and the development of soil quality. During a field campaign in May 2011 soil samples were collected from two agricultural fields northwest of Koethen (Saxony-Anhalt, Germany) and the SOC content of the samples was determined in the laboratory afterwards. At the same time image data of the test site was acquired by the hyperspectral airborne scanner AISA-DUAL (450-2500 nm). The image data was corrected for atmospheric and geometric effects and a spectral binning has been performed to improve the signal-to-noise ratio (SNR). For parameter prediction, an empirical model based on partial least squares regression (PLSR) was developed from AISA-DUAL image spectra extracted at the geographic location of the soil samples and analytical laboratory results. The obtained SOC concentrations from the AISA-DUAL data are in accordance with the concentration range of the chemical analysis. For this reason, the PLSR-model has been applied to the AISA-DUAL image data. The predicted SOC concentrations reflect the spatial conditions of the two investigated fields. The results indicate the potential of the used method as a quick screening tool for the spatial assessment of SOC, and therefore an appropriate alternative to time- and cost-intensive chemical analysis in the laboratory.

Large wildfires in forests of southern European countries such as Portugal, Spain, Greece, France and Italy are one key ecological disturbance of the Mediterranean environment. Optical data have been largely used for burned area mapping and literature provides an extensive reference for the typical spectral signal of burns and the methodologies applied to extract burn perimeters. However, optical remote sensing techniques have the major limitation of a reduced frequency of clear images due to cloud cover; moreover, for the specific application of burned area mapping, unburned targets such as shadows, can be spectrally confused and misclassified as burns. For this reason radar images could be integrated as an additional source of information. We developed an approach for mapping burned areas in Mediterranean regions based on Landsat TM/ETM+ data and vegetation indices that provided satisfactory results. However, we are currently working for further improving our approach by exploiting the synergy between optical and radar data. In this paper we present the first results of the analysis of the SAR backscatter over burned areas for future integration into the formal framework previously developed. Although results are preliminary, they encourage us to test the approach over different regions of the Mediterranean environment to evaluate its robustness.