The unprecedented snowfall during early February 2010 in the Baltimore/Washington area provided a unique opportunity to map, monitor and measure snowfall, snow cover extent, snow water equivalent (SWE), and snow melt using a suite of remote sensing instruments. Because snow cover in the Middle Atlantic area of the United States is in most years patchy and a true multi-layered snow pack is rarely established, utilizing a remote sensing approach to observe snow parameters is more challenging than in regions where falling snow and snow packs are more reliable. The Advanced Microwave Scanning Radiometer for EOS (AMSR-E) was used to assess SWE and the onset of melt. Although the passive microwave signatures illustrated in this study are clearly related to snow, it is not straightforward whether or not the signatures are due to variations in SWE or to snowpack metamorphism or to a combination of both. This study shows that the SWE algorithm was affected by the high variability of snowfall intensity and accumulation as well as by the complex surface features in the Baltimore/Washington area. On the two days when intense snowfalls occurred, February 6 and 10, 2010, retrievals of SWE were compromised. This was likely a result of thermal emission from water droplets in low-level clouds within portions of the storm, which acted to increase AMSR-E Tbs, thereby rendering minimal or zero values for SWE. The presence of such clouds strongly impacts the sensitivity of estimating SWE using radiometric measurements near 19 and 37 GHz. Glaze or icy layers within and on the surface of the snowpack served to increase scattering, thus lowering Tb and boosting the retrieved SWE values, resulting in an overestimation of SWE, first in southern portions of the study area and then farther north as the month of February progressed.

Sierra Nevada Mountains are the highest continental altitude in Spain. Located in the South, facing the Mediterranean Sea in a distance of less than 40 km, the high level of solar energy income throughout the year, together with the extremely variable character of climate in such latitudes, make it necessary to use energy balance approaches to characterize the snow cover evolution. Wind and relative humidity become decisive factors in the evolution of the snow cover due to the high evaporation rates that can arise under favourable meteorological conditions. This work presents the enhanced capability of the combination of Landsat TM data with the simulation of an energy balance model to produce sequences of hourly high resolution maps of snow cover and depth distribution under variable meteorological conditions such as those found in Mediterranean mountainous watersheds. Despite the good agreement found between observed and predicted snow pixels, different examples of disagreement arose in the boundaries, most of them related to the temperature and wind speed spatial pattern simulation together with the discrimination between rain and snowfall occurrence.

Characterization of snow pack evolution is a key parameter for regions where water supply is mainly due to snow melt and runoff. Main goal of the project "AGORA" is to study the impact of assimilating earth observed data in a water prediction numerical model. The site chosen for the study cover the Pyrenees area in Catalonia. As first step, an observation of the water balance terms, such as snow cover, snow water equivalent, changes in soil water content, have been done through extensive in situ campaigns in the area of study. Collocated earth observed data from both passive, e.g. MERIS, MODIS, and active, e.g. ENVISAT-ASAR, ENVISATRA, sensors have been collected during the surveys. These measurements will be used to develop and validate algorithms for the characterization of the snow pack appropriately tuned for the area of interest. The final phase of the project will evaluate the impact of assimilating remote sensing data into a hydrological model specifically developed to cope with the significant weather changes in time and space characteristic of the area of study. Preliminary results of the activity scheduled during the first year of the project will be highlighted. The importance of developing application based on both remote sensed and in situ data will be discussed.

Dams are common structures in order to guarantee water supply and control flash floods in Mediterranean mountainous watersheds. Even though they are known to modify in space and time the natural regimen of natural flows, little has been said about local effects on the ecosystem along the river banks upstream the dam. In 2002, Rules dam (southern Spain) started to function. This work presents the effects of the dam filling on the water balance in flood plains. The influence of the enhanced soil moisture in the surroundings of the free surface of the reservoir on the vegetation cover status was analyzed and related to meteorological agents and topographic features, before and after the construction of the dam. Meteorological, topographic, soil and land use data were analyzed in the contributing area of the dam, together with Landsat TM images during the period 1984-2010 to derive NDVI values. Results showed higher NDVI values (close to 20-30%) once the dam was filled and NDVI values in very dry years similar to the ones obtained in medium-wet years prior to the construction. Besides, NDVI values after the filling of the dam proved to be highly related to meteorological variables. Principal Component Analysis (PCA) was carried out in order to identify individual and combined interactions of meteorological and dam-derived effects. 85% of the total variance can be explained with the combination of three Principal Components (PC) in which the first one includes the combination of NDVI, meteorological (rainfall) and hydrological variables (interception, infiltration, evapotranspiration from the soil), whilst the second and third PC mainly include topographic features. These results quantify the dam influence along the river banks and the superficial recharge effects in dry years.

This paper addresses the issue of a detailed representation of an urban catchment in terms of hydraulic and hydrologic attributes. Modelling of urban flooding requires a detailed knowledge of urban surface characteristics. The advancement in spatial data acquisition technology such as airborne LiDAR (Light Detection and Ranging) has greatly facilitated the collection of high-resolution topographic information. While the use of the LiDAR-derived Digital Surface Model (DSM) has gained popularity over the last few years as input data for a flood simulation model, the use of LiDAR intensity data has remained largely unexplored in this regard. LiDAR intensity data are acquired along with elevation data during the data collection mission by an aircraft. The practice of using of just aerial images with RGB (Red, Green and Blue) wavebands is often incapable of identifying types of surface under the shadow. On the other hand, LiDAR intensity data can provide surface information independent of sunlight conditions. The focus of this study is the use of intensity data in combination with aerial images to accurately map pervious and impervious urban areas. This study presents an Object-Based Image Analysis (OBIA) framework for detecting urban land cover types, mainly pervious and impervious surfaces in order to improve the rainfall-runoff modelling. Finally, this study shows the application of highresolution DSM and land cover maps to flood simulation software in order to visualize the depth and extent of urban flooding phenomena.

The main objective of this study is to contribute to the development and the improvement of flood forecasting systems. Since hydrometric stations are often poorly distributed for monitoring the propagation of extreme flood waves, the study aims at evaluating the hydrometric value of the Global Navigation Satellite System (GNSS). Integrated with satellite telecommunication systems, drifting or anchored floaters equipped with navigation systems such as GPS and Galileo, enable the quasi-continuous measurement and near real-time transmission of water level and flow velocity data, from virtually any point in the world. The presented study investigates the effect of assimilating GNSS-derived water level and flow velocity measurements into hydraulic models in order to reduce the associated predictive uncertainty.

River discharge is an important quantity of the hydrologic cycle because it is essential for both scientific and operational applications related to water resources management and flood risk prevention. Streamflow measurements are sparse and for few sites along natural channels and, hence, they are not able to detect adequately the complexity of variation in surface water systems. Therefore, in recent years, the possibility to obtain river discharge estimates through remote sensing monitoring has received a great interest. In this context, the capability of the MODerate resolution Imaging Spectroradiometer (MODIS) for river discharge estimation is investigated here. Thanks to a very short revisiting time interval and a moderate spatial resolution (up to 250 m), MODIS has a significant potential for mapping flooded area extent and flow dynamics. Specifically, for the estimation of river discharge, the ratio of the MODIS channel 2 reflectance values between two pixels located within and outside the river is used. Time series of daily discharge between 2006 and 2010 measured at two gauging stations located along the Upper Tiber River basin (central Italy) are employed to test the procedure. The agreement between MODIS-derived and in situ discharge time series is found to be fairly good with correlation coefficient values close to 0.8.

Loss of soil has become a problem worldwide, and as concerns about the environment grow, active research has begun regarding soil erosion and soil-preservation polices. This study analyzed the trend of soil loss in South Korea over the past 30-year and predicted future soil loss in 2020 using the revised universal soil loss equation. In the period 1975-2005, soil loss showed an increasing trend, the 2005 value represents a 0.59 Mg/ha (2.58%) increase. Scenario 1 assumes that urban areas have a similar trend to that between 1975 and 2005 and that precipitation amount follows scenario A1B of the IPCC. The soil loss amount for 2020 land-cover map that account for the ECVAM should increase by 25.0~26.3% compared to 1975. In the case where the ECVAM is not considered, soil loss should increase by 27.7~31.8%. In Scenario 2, in which the urban area and precipitation follow the same trend as between 1975 and 2005, soil loss for 2020 land-cover map that consider the ECVAM will increase by 6.8%~7.9% compared to 1975. When the ECVAM is not considered, soil loss will increase by 9.1~12.6%. The environmental and legislative value of preservation should be considered to minimize erosion and allow for more sustainable development.

Soil moisture is a key state variable in hydrology, it controls the proportion of rainfall that infiltrates, runoff and evaporates from the land. For hydrological applications, soil moisture monitoring at catchment scale is required and, for that, microwave remote sensing sensors might be used. However, due to their coarse-spatial resolution, the skepticism on the suitability to retrieve the soil moisture at catchment scale takes still place. This work attempts to bring out if coarse resolution sensors for soil moisture monitoring can have some perspectives for hydrological applications. Two soil moisture products derived from the Advanced SCATterometer (ASCAT) and the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) are used for this purpose. The analysis is addressed by investigating: (i) the reliability of product data in the estimation of the wetness conditions of a catchment antecedent to rainfall events, and (ii) the benefit on runoff prediction if data are assimilated into a rainfall-runoff model. Rainfall-runoff observations are taken from several catchments in Italy and Luxembourg for testing. Results reveal that ASCAT and AMSR-E soil moisture products can be conveniently used to improve runoff prediction thus opening new important challenges and opportunities for the use of this new sources of data in the operational hydrology.

Rainfall interception by the vegetation may constitute a significant fraction in the water budget at local and watershed scales, especially in Mediterranean areas. Different approaches can be found to model locally the interception fraction, but a distributed analysis requires time series of vegetation along the watershed for the study period, which includes both type of vegetation and ground cover fraction. In heterogeneous watersheds, remote sensing is usually the only viable alternative to characterize medium to large size areas, but the high number of scenes necessary to capture the temporal variability during long periods, together with the sometimes extreme scarcity of data during the wet season, make it necessary to deal with a limited number of images and interpolate vegetation maps between consecutive dates. This work presents an interception model for heterogeneous watersheds which combines an interception continuous simulation derived from Gash model and their derivations, and a time series of vegetation cover fraction and type from Landsat TM data and vegetation inventories. A mountainous watershed in Southern Spain where a physical hydrological modelling had been previously calibrated was selected for this study. The dominant species distribution and their relevant characteristics regarding the interception process were analyzed from literature and digital cartography; the evolution of the vegetation cover fraction along the watershed during the study period (2002-2005) was produced by the application of a NDVI analysis on the available scenes of Landsat TM images. This model was further calibrated by field data collected in selected areas in the watershed.

As shown in the past years by researchers at the University of Florence, rainfall rate can be estimated in real time by means of tomographic processing applied to power attenuation measurements made simultaneously along microwave links. In this paper, we focus on the possibility to exploit the 'opportunity signals' provided by radio-base station networks for mobile communication systems. We describe a new tomographic algorithm that has been specifically developed for such kind of networks in urban areas, where a high number of microwave connections is typically possible. We describe the performance of the algorithm after having tested it on a 2 hours simulated rainfall event based on a sequence of real weather radar observations and on three kind of link networks, symmetric with lower link density and asymmetric with lower and higher density. We considered 12 GHz for the test carrier frequency of the radio-base network.

Southeastern Mexico, particularly Tabasco's flatlands which experienced a severe flood in 2007, was used as a case study for testing a methodology for the estimation of direct damage looses on agricultural crops by flooding. We proposed an accurate delineation of agricultural lands of multispectral images (SPOT-5) which consist on ensemble classifiers trough a majority voting, that combine spatial and spectral information. Finally in order to evaluate the impact of floodwater, a radar data (RADARSAT-1), were used for both, delineating the flood extent and estimating water depth. These layers were overlaid on the agricultural crop classification layer, and crop yield damage was estimated using a depth damage function. The results of this research quantified and evaluated the overall economic loss (tangible damage) from the impact of floodwater on agricultural crops.

In the context of defining a procedure for near real time land use/land cover (LULC) mapping with seasonal updated products, this research examines the use of time-series and phenological indicators from MODIS NDVI. 16-day NDVI composites from MODIS (MOD13Q1) covering the period from 2001 to the present were acquired for three test sites located in different parts of Europe. The newly proposed Whittaker smoother was used for filtering purposes. Metrics of vegetation dynamics (such as minimum, maximum and amplitude, etc.) were extracted from the filtered time-series. Subsequently, the capability of three data sets (raw, filtered data and phenological indicators) was evaluated to separate between different LULC classes by calculating the overall classification accuracy for the years 2002 and 2009. Ground truth data for model calibration and testing set was derived combining existing land cover products (GLC2000 and GlobCover 2009). Based on these results, the benefits of using phenological indicators and cleaned data for land cover classification are discussed.

In this study satellite data from five different multispectral sensors were used in a change detection study of vegetation disturbance on an Irish active raised bog. Radiometric normalisation was performed using Temporally Invariant Clusters (TIC) and cross calibration applied using linear regression of radiometrically stable ground-based targets. Erdas Imagine's Spatial Modeller was used to create a change detection model using pixel-to-pixel based subtraction with a Standard Deviation (SD) threshold. The effectiveness of the cross calibration process was shown with the aid of Kolmogorov Smirnov sample tests which showed a reduced D value between master and slave cumulative distribution curves after cross calibration. The spatial accuracy of various SD threshold levels was assessed, with 1.5 SD producing 0.19% error when compared to actual ground truth boundary data of change. An error matrix of change/ no change verified 1.5 SD as the optimum threshold for change detection, with user, producer, overall and kappa values all above 95%. Vegetation disturbance in the study was predominantly attributed to turf cutting on the boundaries of the bog. However in May 2008 a large burn event occurred on the northeastern side of the bog which removed all surface vegetation, equating to an area of 36ha (or 7.85% of total area).

Peatlands contain large amounts of soil organic carbon. In a pristine state they sequester atmospheric carbon dioxide (CO₂), however, when they are disturbed they emit it. In Ireland peatlands are extensive and cover 20% of the national land area. They contain between 53% and 62% of the total national soil organic carbon. However, large areas of Irish peatlands have been disturbed by anthropogenic activity. This activity includes drainage, mechanical extraction and burning. These activities lead to the reduction of the resilience of the peatland to climate and environmental change and can lead to the increased vulnerability of the peatland carbon stock. In this research an object oriented approach is used to examine high resolution imagery of a raised bog in Ireland and to extract a map of disturbed peatlands. The object oriented approach is implemented in ArcGIS with high resolution Geoeye-1 satellite imagery. The main disturbance classes identified were rough grazing, pasture, molinia, coniferous forest, high bog, drained bog and scrub. The users accuracy for each individual class ranged from 66% to 92% and the overall accuracy assessment for the disturbance map 85%.

VTT Technical Research Centre of Finland has developed a Fabry-Perot Interferometer (FPI) based hyperspectral imager compatible with the light weight UAV platforms. The concept of the hyperspectral imager has been published in the SPIE Proc. 7474 and 7668. In forest and agriculture applications the recording of multispectral images at a few wavelength bands is in most cases adequate. The possibility to calculate a digital elevation model of the forest area and crop fields provides means to estimate the biomass and perform forest inventory. The full UAS multispectral imaging system will consist of a high resolution false color imager and a FPI based hyperspectral imager which can be used at resolutions from VGA (480 x 640 pixels) up to 5 Mpix at wavelength range 500 - 900 nm at user selectable spectral resolutions in the range 10...40 nm @ FWHM. The resolution is determined by the order at which the Fabry- Perot interferometer is used. The overlap between successive images of the false color camera is 70...80% which makes it possible to calculate the digital elevation model of the target area. The field of view of the false color camera is typically 80 degrees and the ground pixel size at 150 m flying altitude is around 5 cm. The field of view of the hyperspectral imager is presently is 26 x 36 degrees and ground pixel size at 150 m flying altitude is around 3.5 cm. The UAS system has been tried in summer 2011 in Southern Finland for the forest and agricultural areas. During the first test campaigns the false color camera and hyperspectral imager were flown over the target areas at separate flights. The design and calibration of the hyperspectral imager will be shortly explained. The test flight campaigns on forest and crop fields and their preliminary results are also presented in this paper.

Automated object detection in remote-sensing as well as the statistical analysis of morphometric object parameters are valuable tools in the field of (remote-sensing) geomorphology. Both help to identify processes that are directly related to climatic boundary conditions. The automated detection of primary climate-related landforms and the assessment of their change during three decades of remote sensing observations allow us to quantify and possibly find predictors for the consequences of climate variability on a regional. Our goal is to create approaches and concepts for a catalog-based object detection of landforms within a commercial GIS suite. Our intention is to transfer the workflow onto different climate-tracer landforms to enable operational use of the workflow beyond the conceptual level.

We present a method based on passive spectroscopy with aim to remotely study flying birds. A compact spectrometer is continuously recording spectra of a small section of the sky, waiting for birds to obscure part of the field-of-view when they pass the field in flight. In such situations the total light intensity received through the telescope, looking straight up, will change very rapidly as compared to the otherwise slowly varying sky light. On passage of a bird, both the total intensity and the spectral shape of the captured light changes notably. A camera aimed in the same direction as the telescope, although with a wider field-of-view, is triggered by the sudden intensity changes in the spectrometer to record additional information, which may be used for studies of migration and orientation. Example results from a trial are presented and discussed. The study is meant to explore the information that could be gathered and extracted with the help of a spectrometer connected to a telescope. Information regarding the color, size and height of flying birds is discussed. Specifically, an application for passive distance determination utilizing the atmospheric oxygen A-band absorption at around 760 nm is discussed.

The FIS project - FIS stands for Fernerkundung in Schulen (Remote Sensing in Schools) - aims at a better integration of the topic "satellite remote sensing" in school lessons. According to this, the overarching objective is to teach pupils basic knowledge and fields of application of remote sensing. Despite the growing significance of digital geomedia, the topic "remote sensing" is not broadly supported in schools. Often, the topic is reduced to a short reflection on satellite images and used only for additional illustration of issues relevant for the curriculum. Without addressing the issue of image data, this can hardly contribute to the improvement of the pupils' methodical competences. Because remote sensing covers more than simple, visual interpretation of satellite images, it is necessary to integrate remote sensing methods like preprocessing, classification and change detection. Dealing with these topics often fails because of confusing background information and the lack of easy-to-use software. Based on these insights, the FIS project created different simple analysis tools for remote sensing in school lessons, which enable teachers as well as pupils to be introduced to the topic in a structured way. This functionality as well as the fields of application of these analysis tools will be presented in detail with the help of three different classification tools for satellite image classification.

Vegetation and soil temperatures have long been recognized as an indicator of water availability. When plotting the twodimensional distribution of temperature and vegetation index (T-VI) corresponding to an area with well distributed vegetation cover and moisture content, one gets a triangular or trapezoidal shape. Iso-moisture lines range between two edges of the distribution: the dry edge and the wet edge. This description is based on a simplified representation of the thermal and radiative properties of the soil/vegetation structure and of the heat and mass transfer. A large number of parameters, in addition to soil moisture, are actually influencing the apparent soil/canopy temperature. Stretching the temperature distribution along only one dimension like the VI is indeed not enough for allowing an unambiguous determination of soil moisture. We propose to improve the identifiability of soil moisture by introducing an additional observable parameter: albedo or CAI (Cellulose Absorption Index). Albedo was chosen to separate areas according to the absorbed solar radiation whereas CAI was chosen to separate areas according to the fraction of senescent vegetation. The aim of this study is to analyze the benefit of adding a third index to the classical T-VI empirical method for soil moisture mapping. The proposed procedures were applied on remote sensing data obtained during two airborne campaigns: HyEurope 2007 and AgriSAR 2006. In the first case, when applying the 2D trapezoïd method, the coefficient of determination between the inferred moisture index and the gravimetric moisture content reaches 0.61. It slightly diminishes to 0.59 when adding albedo whereas it increases to 0.69 when adding CAI instead. Therefore adding albedo doesn't seem to provide an improvement, at least for the considered cropland. The introduction of the Cellulose Absorption Index seems to be more promising. The crops during AgriSAR 2006 campaign presented in July a too high LAI whereas the soil surface was generally very dry. These conditions prevented to draw any significant conclusion. Better results are expected to be met for areas presenting lower vegetation cover as for example semi-arid regions.

The integrated water resource management required to face the water scarcity situation in semiarid regions relies on the ability to obtain accurate information about the use of water by crops and natural vegetation. Thermal remote sensing provides key data about the vegetation water status. The integration of this remotely sensed data into water and energy balance models help to better estimate evapotranspiration under heterogeneous cropping and natural vegetation patterns, extending the field of application of these models from point to basin and regional scales. In this work, we present an approach to estimate spatially distributed surface energy fluxes using a series of Landsat TM satellite images combined with simulation modeling and ground-based measurements. A physically-based method for the energy budget partitioning following the Two Source Model [1, 2] has been applied over an heterogeneous agricultural area located in southern Spain. The study was performed during 2009 crop growing season and the results were validated with field data collected with an eddy covariance system installed over a corn field during the season. The instantaneous and daily estimations were compared to the measured data, obtaining a general good adjustment at both scales and setting the basis for a larger scale application that may assist a decision - making tool for water resources planning in the region.

The Moderate Imaging Spectroradiometer (MODIS) provides operational products of the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the fraction of photosynthetic active radiation (fPAR). FPAR can be used in productivity models, but agricultural applications depend on sub-pixel heterogeneity. Examples for heterogeneous areas are the irrigation systems of the inner Aral Sea Basin, where the 1 km fPAR product proved less suited. An alternative can be to upscale fPAR to the 250 m scale, but there are few studies evaluating this approach. In this study, the use of MODIS 250 m NDVI and EVI for this approach was investigated in an irrigation system in western Uzbekistan. The analysis was based on high resolution fPAR maps and a crop map for the growing season 2009, derived from ground measurements and multitemporal RapidEye data. The data was used to explore statistical relationships between RapidEye fPAR and MODIS NDVI/EVI with respect to spatial heterogeneity. The correlations varied between products (daily NDVI, 8-day NDVI, 16-day NDVI/EVI), with results suggesting that 8-day NDVI performed best. The analyses and the compiled fPAR maps show that, compared to 1 km MODIS fPAR, the 250 m scale is more homogeneous, allows for crop-specific analyses, and better captures the spatial patterns in the study region.

Agricultural water requirement in arid and semi-arid environments represents an important fraction of the total water consumption, suggesting the need of appropriate water management practices to sparingly use the resource. Furthermore the quality and quantity of some crops products, such as grape, is improved under a controlled amount of water stress. The latter is related, on a side to actual evapotranspiration (ET) through water demand, on the other side to plant water content through leaf water potential. Residual energy balance approaches based on remote sensing allow to estimate the spatial distribution of daily actual ET at plant scale, representing an useful tool to detect its spatial variability across different cultivars and even within each parcel. Moreover, the connection between actual ET and leaf water potential is still not well assessed, especially under water stress conditions, even if farmers use leaf water potential to plan irrigation. However residual energy balance methods are based on the hypothesis that storage terms are negligible, at least during the remote sensor overpass. Indeed, energy balance approaches estimate daily actual ET from the instantaneous value at the overpass time using a daily integration method. The paper first verifies this latter assumption using field data acquired by a flux tower on a whole phenological period. Then, the actual ET values measured by eddy covariance tower were analyzed together with water potential measured using a Scholander chamber; the analysis highlights that, under water stress conditions, daily actual ET is inversely linearly related with water potential. These results suggest the possibility to use remote sensing-based ET as support for irrigation management at plot scale.

High resolution airborne multispectral and thermal infrared imagery was acquired over the Mojave River, California with the Utah State University airborne remote sensing system integrated with the LASSI imaging Lidar also built and operated at USU. The data were acquired in pre-established mapping blocks over a 2 day period covering approximately 144 Km of the Mojave River floodplain and riparian zone, approximately 1500 meters in width. The multispectral imagery (green, red and near-infrared bands) was ortho-rectified using the Lidar point cloud data through a direct geo-referencing technique. Thermal Infrared imagery was rectified to the multispectral ortho-mosaics. The lidar point cloud data was classified to separate ground surface returns from vegetation returns as well as structures such as buildings, bridges etc. One-meter DEM's were produced from the surface returns along with vegetation canopy height also at 1-meter grids. Two surface energy balance models that use remote sensing inputs were applied to the high resolution imagery, namely the SEBAL and the Two Source Model. The model parameterizations were slightly modified to accept high resolution imagery (1-meter) as well as the lidar-based vegetation height product, which was used to estimate the aerodynamic roughness length. Both models produced very similar results in terms of latent heat fluxes (LE). Instantaneous LE values were extrapolated to daily evapotranspiration rates (ET) using the reference ET fraction, with data obtained from a local weather station. Seasonal rates were obtained by extrapolating the reference ET fraction according to the seasonal growth habits of the different species. Vegetation species distribution and area were obtained from classification of the multispectral imagery. Results indicate that cottonwood and salt cedar (tamarisk) had the highest evapotranspiration rates followed by mesophytes, arundo, mesquite and desert shrubs. This research showed that high-resolution airborne multispectral and thermal infrared imagery integrated with precise full-waveform lidar data can be used to estimate evapotranspiration and water use by riparian vegetation.

Vegetation indices (VI) combine mathematically a few selected spectral bands to minimize undesired effects of soil background, illumination conditions and atmospheric perturbations. In this way, the relation to vegetation biophysical variables is enhanced. Albeit numerous experiments found close relationships between vegetation indices and several important vegetation biophysical variables, well known shortcomings and drawbacks remain. Important limitations of VIs are illustrated and discussed in this paper. As most of the limitations can be overcome using physically-based radiative transfer models (RTM), advantages and limits of RTM are also presented.

The capability of models to predict vegetation biophysical variables is usually evaluated by means of one or several goodness-of-fit measures, ranging from absolute error indices (e.g. the root mean square error, RMSE) over correlation based measures (e.g. coefficient of determination, R²) to a group of dimensionless evaluation indices (e.g. relative RMSE). Hence, the greatest difficulty for the readers is the lack of comparability between the different models' accuracies. Therefore, the objective of our study was to provide an overview about the quantitative assessment of biophysical variable retrieval performance. Furthermore, we aimed to suggest an optimal set of statistical measures. This optimum set of statistics should be insensitive to the magnitude of values, range and outliers. For this purpose, a literature review was carried out, summarizing the statistical measures that have been used to evaluate model performances. Followed by this literature review and supported by some exemplary datasets, a range of statistical measures was calculated and their interrelationships analyzed. From the results of the literature review and the test analyses, we recommend an optimum statistic set, including RMSE, R², the normalized RMSE and some other indicators. Using at least the recommended statistics, comparability of model prediction accuracies is guaranteed. If applied, this will enable a better intercomparison of scientific results urgently needed in times of increasing data availability for current and upcoming EO missions.

A significant amount of research has been performed to develop efficient methods for monitoring of vegetation dynamics. A prevailing part of the work is devoted to multispectral data transformation techniques such as spectral bands ratios and linear combinations (vegetation indices) as a mean for vegetation parameters estimation. Vegetation cover fraction and chlorophyll assessment is a main objective in vegetation monitoring. In agriculture, for instance, vegetation amount is related to plant growth monitoring, stress detection and yield forecasting. Here we use colorimetric analysis of spectral reflectance data to examine the sensitivity of vegetation chromaticity features to chlorophyll and canopy fraction changes. Two main factors influence vegetation visible and near infrared reflectance: plant senescence, i.e. chlorophyll inhibition due to plant maturing or as a stress symptom, and soil spectral properties varying with soil type and surface properties. The work was conducted in order to reveal plant senescence effects and soil background impact on vegetation reflectance response and colorimetric characters behaviour.

Precision agriculture, forestry and environmental remote sensing are applications uniquely suited to the 8 bands that DigitalGlobe's WorldView-2 provides. At the fine spatial resolution of 0.5 m (panchromatic) and 2 m (multispectral) individual trees can be readily resolved. Recent research [1] has shown that it is possible for hyper-spectral data to invert plant reflectance spectra and estimate nitrogen content, leaf water content, leaf structure, canopy leaf area index and, for sparse canopies, also soil reflectance. The retrieval is based on inverting the SAIL (Scattering by Arbitrary Inclined Leaves) vegetation radiative transfer model for the canopy structure and the reflectance model PROSPECT4/5 for the leaf reflectance. Working on the paper [1] confirmed that a limited number of adjacent bands covering just the visible and near infrared can retrieve the parameters as well, opening up the possibility that this method can be used to analyze multi-spectral WV-2 data. Thus it seems possible to create WV-2 specific inversions using 8 bands and apply them to imagery of various vegetation covered surfaces of agricultural and environmental interest. The capability of retrieving leaf water content and nitrogen content has important applications in determining the health of vegetation, e.g. plant growth status, disease mapping, quantitative drought assessment, nitrogen deficiency, plant vigor, yield, etc.

The Sahelian belt of West Africa is a region characterized by wide climate variations, which can in turn affect the survival of local populations especially in rangeland, as happened during the dramatic food crisis in the 70-80s caused by severe drought. This work has been carried out in the framework of the EU FP7 Geoland2 project as a contribution to the ECOWAS component (Economic Community Of West African States) of the AMESD (African Monitoring of the Environment for Sustainable Development) programme with the purpose of establishing the reliability of Dry Matter Productivity (DMP) developed by Flemish Institute for Technological Research (VITO), a spatial estimation of dry matter (DM) obtained from remotely sensed data. DMP can be of great help in monitoring savanna pasturelands in a region characterized by food insecurity and a significant variability of biomass production, linked to climate variations, which can in turn affect the survival of local populations. The evaluation of DMP was carried out thanks to the Centre de Suivi Ecologique (CSE) and Action Contre la Fame (ACF), the partners who provided the field biomass measurements. The paper shows the correlation of DMP with field measurements of herbaceous biomass, and discusses the differences among the different sites where ground data were collected. The analysis of other environmental variables (land cover, rainfall), which can be influential on rangeland biomass production, is presented in order to better explain the variance of field measurements among the different years.

Mapping of Landscape Protection Areas with regard to user requirements for detailed land cover and biotope classes has been limited by the spatial and temporal resolution of Earth observation data. With the new spatial high resolution RapidEye data providing an additional channel in the red-edge region potentially new possibilities for vegetation mapping should be investigated. The presented work is part of the ENVILAND-2 project, which focuses on the complementary use of RapidEye and TerraSAR-X data to derive land cover and biotope classes as needed by the environmental agencies. The goal is to semi-automatically update the corresponding maps by utilising more Earth observation data and less field work derived information. The red-edge spectral region located between the red and near infrared (NIR) wavelengths, has proven to held valuable information on vegetation type, age and condition. In this study the goal is to evaluate the red-edge spectral information compared to the shorter and longer wavelength of the RapidEye sensor. This is done with regard to the classification capability of different land cover classes. Four RapidEye images were used covering two study sites: 1. Rostocker Heide, Mecklenburg-Vorpommern and 2. Elsteraue, Saxony. The spectral bands were analysed for redundant information by using regression and hypothesis testing. For the rededge band and for every class combination present in the study area different separability measurements like divergence or Bhattacharyya distance were computed. As result there are for every class a separability values. The separability values are provided for all spectral bands. A comparison of the values showed the applicability of the red-edge for the classification. Results have shown that additional red-edge information leads to similar class separability for vegetation classes as using red and NIR spectral information. Some specific classes can be classified with a higher accuracy by additional using the red-edge information.

We conducted a remote sensing analysis to discern features and patterns of areas affected by salt. Maximum likelihood classification (MLC), Support Vector Machine (SVM), and Minimum Distance (MD) with four classes (slightly, moderately, high and extreme saline soil) are applied to classify the salt affected areas. 102 samples, collected from the investigated region, are used as input data set . Soil properties, land use and ground water table are selected as the main parameters affecting soil salinity. These parameters are used to understand the spatial distribution of the different classes of soil salinity. Our approach was applied on hyperspectral data from the EO-1 Mission. The present study highlighted that gypsum soil is obviously fitting with class of extreme and high saline soil. Thus, high content of gypsum in soil is the most important parameter controlling the soil salinity in this region. Moreover, water logging is lightly affecting the soil salinity through the rising of the water table level by sea water seeping; especially in the irrigation areas located no more than 5 km from the coast line. Computed accuracy from the classification gave different but encouraging accuracy results varying between 46% and 75%. SVM is showing the best performance in extracting patterns and features of soil salinity classes (kappa coefficient of 63% and overall accuracy of 75%). Furthermore, this work reveals the high potential of hyperspectral data in discerning areas that are highly and extremely affected by salinity.

Agro-drought usually refers to the shortage of water for crop irrigation in a short period, creating serve impacts on crop production due to insufficient soil moisture. This phenomenon has been considered as a challenge for rice farmers in the Lower Mekong Basin (LMB), especially in the dry season (from November to April). Thus, information on agro-drought is important for water scheduling to mitigate adverse impacts on rice production. The main objective of this study is to investigate the applicability of the monthly MODIS normalized difference vegetation index (NDVI) and land surface temperature (LST) data for drought monitoring from 2008 to 2010. The data was processed for the dry season because this period is usually suffered from droughts. A simple temperature vegetation difference index (TVDI) was used to estimate the surface soil moisture content. We investigated the sensitivity between the preliminary TVDI results and TRMM (Tropical Rainfall Measuring Mission) precipitation. The results revealed good agreement between the two datasets. TVDI was declined during or after rain events indicating greater soil moisture content, but increased again later indicating less soil moisture content. The results by analysis of TVDI showed that the moderate and serve droughts were spatially scattered over the region from November to March and returned to normal condition by the end of the dry season (April) with the onset of rainy season. The drought was found more serve and extensive in plains of Thailand and Cambodia. The larger area of serve drought was especially observed for the 2008-2009 dry seasons compared to 2010. The results achieved from this study could be useful for crop irrigation scheduling.

In Central Asia, more than eight Million ha of agricultural land are under irrigation. But severe degradation problems and unreliable water distribution have caused declining yields during the past decades. Reliable and area-wide information about crops can be seen as important step to elaborate options for sustainable land and water management. Experiences from RapidEye classifications of crop in Central Asia are exemplarily shown during a classification of eight crop classes including three rotations with winter wheat, cotton, rice, and fallow land in the Khorezm region of Uzbekistan covering 230,000 ha of irrigated land. A random forest generated by using 1215 field samples was applied to multitemporal RapidEye data acquired during the vegetation period 2010. But RapidEye coverage varied and did not allow for generating temporally consistent mosaics covering the entire region. To classify all 55,188 agricultural parcels in the region three classification zones were classified separately. The zoning allowed for including at least three observation periods into classification. Overall accuracy exceeded 85 % for all classification zones. Highest accuracies of 87.4 % were achieved by including five spatiotemporal composites of RapidEye. Class-wise accuracy assessments showed the usefulness of selecting time steps which represent relevant phenological phases of the vegetation period. The presented approach can support regional crop inventory. Accurate classification results in early stages of the cropping season permit recalculation of crop water demands and reallocation of irrigation water. The high temporal and spatial resolution of RapidEye can be concluded highly beneficial for agricultural land use classifications in entire Central Asia.

Being recognized as a powerful tool in many scientific and application fields, remote sensing enters recently still wider into its utilization stage when the goal is to bring the up-to-now investigation results to an operational use. Agricultural monitoring is among the priorities of remote sensing observations supplying early information on the development and growth conditions of crops. Various approaches have been used for crop behavior assessment in order to provide objective, timely and quantitative yield forecasts at regional and national scales. Among these approaches are phenology tracking, agro-meteorological modeling, remote sensing data implementation. On the other hand, continues the research to improve the reliability of the results by implying, for instance, different sampling strategies, different statistical data analysis and extrapolations, different data integration from various sources. In this paper we test an approach for yield forecasting and verification of the predictions with consideration of plant phenology. It comprises the development of simple yield prediction models based on key crop bioparameters; the development of crop spectral-biophysical relationships for crop variables retrieval and yield prediction from multispectral reflectance data; verification of the spectral predictions via crop yield agronomical indicators.

Rice is a major economic crop in the Lower Mekong Subregion (LMS). Information on rice growing areas is thus vital for agricultural planning for the sake of food security. This study aimed to map rice cropping systems in LMS from timeseries MODIS NDVI data for 2010. We processed the time-series NDVI data using wavelet transform and crosscorrelation. The classification results were assessed using ground verification data. The results indicated that smooth NDVI profiles derived from wavelet transform reflected the temporal characteristics of rice crop phelonogy under different rice cropping systems, enabling us to select proper training patterns used in cross-correlation based classifier. The comparison between the classification map and ground verification data revealed that the results achieved from this classification approach was promising for regional mapping of rice cropping patterns. The overall accuracy and Kappa coefficient were 79.5 and 0.72 respectively.

In recent years, the wide-spreading of vineyard cultivation in the Apulia Region (Italy) has showed negative consequences on the hydrogeological balance of soils as well as on the visual quality of rural landscape which has been significantly altered by the heavy diffusion of artificial plastic coverings. In order to monitor and manage this phenomenon, a detailed site mapping has become essential. With the increase of spatial resolution, pixel based approaches no longer capture the characteristics of classification targets. Consequently, classification accuracy is poor. Object-based image classification techniques overcome this issue by first segmenting the image into meaningful multipixel objects of various sizes and then assigning segments to classes using fuzzy methods and hierarchical decision keys. In this study an object-based classification procedure from Very High Spatial Resolution (VHSR) true color aerial data was developed on a test area located between the Apulian municipalities of Ginosa and Palagiano in order to support the update of the existing land use database aimed at plastic covered vineyard monitoring.

Human activities contaminate both coastal areas and open seas, even though impacts are different in terms of pollutants, ecosystems and recovery time. In particular, Mediterranean offshore pollution is mainly related to maritime transport of oil, accounting for 25% of the global maritime traffic and, during the last 25 years, for nearly 7% of the world oil accidents, thus causing serious biological impacts on both open sea and coastal zone habitats. This paper provides a general review of maritime pollution monitoring using integrated approaches of remote sensing and hydrodynamic modeling; focusing on the main results of the MAPRES (Marine pollution monitoring and detection by aerial surveillance and satellite images) research project on the synergistic use of remote sensing, forecasting, cleanup measures and environmental consequences. The paper also investigates techniques of oil spill detection using SAR images, presenting the first results of "Monitoring of marine pollution due to oil slick", a COSMO-SkyMed funded research project where X-band SAR constellation images provided by the Italian Space Agency are used. Finally, the prospect of using real time observations of marine surface conditions is presented through CALYPSO project (CALYPSO-HF Radar Monitoring System and Response against Marine Oil Spills in the Malta Channel), partly financed by the EU under the Operational Programme Italia-Malta 2007-2013. The project concerns the setting up of a permanent and fully operational HF radar observing system, capable of recording surface currents (in real-time with hourly updates) in the stretch of sea between Malta and Sicily. A combined use of collected data and numerical models, aims to optimize intervention and response in the case of marine oil spills.

Impact of jellyfish in human activities has been increasingly reported worldwide in recent years. Segments such as tourism, water sports and leisure, fisheries and aquaculture are commonly damaged when facing blooms of gelatinous zooplankton. Hence the prediction of the appearance and disappearance of jellyfish in our coasts, which is not fully understood from its biological point of view, has been approached as a pattern recognition problem in the paper presented herein, where a set of potential ecological cues was selected to test their usefulness for prediction. Remote sensing data was used to describe environmental conditions that could support the occurrence of jellyfish blooms with the aim of capturing physical-biological interactions: forcing, coastal morphology, food availability, and water mass characteristics are some of the variables that seem to exert an effect on jellyfish accumulation on the shoreline, under specific spatial and temporal windows. A data-driven model based on computational intelligence techniques has been designed and implemented to predict jellyfish events on the beach area as a function of environmental conditions. Data from 2009 over the NW Mediterranean continental shelf have been used to train and test this prediction protocol. Standard level 2 products are used from MODIS (NASA OceanColor) and MERIS (ESA - FRS data). The procedure for designing the analysis system can be described as following. The aforementioned satellite data has been used as feature set for the performance evaluation. Ground truth has been extracted from visual observations by human agents on different beach sites along the Catalan area. After collecting the evaluation data set, the performance between different computational intelligence approaches have been compared. The outperforming one in terms of its generalization capability has been selected for prediction recall. Different tests have been conducted in order to assess the prediction capability of the resulting system in operational conditions. This includes taking into account several types of features with different distances in both the spatial and temporal domains with respect to prediction time and site. Moreover the generalization capability has been measured via cross-fold validation. The implementation and performance evaluation results are detailed in the present communication together with the feature extraction from satellite data. To the best of our knowledge the developed application constitutes the first implementation of an automate system for the prediction of jellyfish appearance founded on remote sensing technologies.

A field study of optical properties of inland water quality was performed in Asprokremmos Dam in Cyprus. The field campaign last from May 2010 to October 2010. Field spectroradiometric measurements were taken using a handheld spectro-radiometer GER1500 equipped with a fibre optic probe. Spectral range of the instrument is 299-1088nm. Reflectance was calculated as a ratio of the target radiance to the reference radiance. The target radiance value was the measured value taken on the water of the reservoir and the reference radiance value was the measured value taken on the standard Spectralon panel, which represent the sun radiance which rich the earth surface-without atmospheric influence. From this campaign spectral signatures of the water were retrieved in several depths. The appearance of water color can be determined through the analysis of the retrieval spectral signatures, irradiance reflectance R(λ), and the optical properties of the water, backscattering coefficient (bb) and absorption coefficient (a). Constituents and their concentration in the water can directly affect the optical properties of the water so optical properties values can be used in order to evaluate the type of water and to determine water quality parameters such as turbidity.

For many lives that inhabit inland water bodies such as lakes and reservoirs, water temperature is an important environmental factor-the shift of water temperature may cause to replace some species by others in an ecosystem. On the other hand, some reports indicate that the surface temperatures of some lakes or reservoirs have been increasing with time due to global warming. Thus, water temperature monitoring for inland waters like lakes and reservoirs is important as aspects of biodiversity conservation and global warming monitoring. However, many water bodies except for some large lakes have not fully or never been monitored on water temperature. We therefore have been developing a satellitebased lake and reservoir temperature database (SatLARTD) since 2009. As of August 2011, SatLARTD in Japan (SatLARTD-J) has been nearly completed using thermal infrared (TIR) imagery observed by the ASTER instrument onboard the Terra satellite, providing water surface temperatures for 899 Japanese water bodies greater than a size of 270 m by 270 m (3 by 3 pixels in ASTER/TIR), with daily max/min air temperatures. In the future version, other satellite data like Terra/MODIS will be added for improvement of the temporal resolution. We also wish to extend the target area from only Japan to Asia or the world.

Mangrove forest is an important ecosystem located in coastal area that provides various important ecological and economical services. One of the services provided by mangrove forest is the ability to act as carbon sink by sequestering CO₂ from atmosphere through photosynthesis and carbon burial on the sediment. The carbon buried on mangrove sediment may persist for millennia before return to the atmosphere, and thus act as an effective long-term carbon sink. Therefore, it is important to understand the distribution of carbon stored within mangrove forest in a spatial and temporal context. In this paper, an effort to map carbon stocks in mangrove forest is presented using remote sensing technology to overcome the handicap encountered by field survey. In mangrove carbon stock mapping, the use of medium spatial resolution Landsat 7 ETM+ is emphasized. Landsat 7 ETM+ images are relatively cheap, widely available and have large area coverage, and thus provide a cost and time effective way of mapping mangrove carbon stocks. Using field data, two image processing techniques namely Vegetation Index and Linear Spectral Unmixing (LSU) were evaluated to find the best method to explain the variation in mangrove carbon stocks using remote sensing data. In addition, we also tried to estimate mangrove carbon sequestration rate via multitemporal analysis. Finally, the technique which produces significantly better result was used to produce a map of mangrove forest carbon stocks, which is spatially extensive and temporally repetitive.

During floods following rainfall events characterized by long return period, rivers bring to their mouths the higher concentration of sediments. This paper deals with a qualitative assessment of coastal water and turbidity load in estuarine waters as a consequence of an intense rainfall event occurred on the 16t^h and 17^th September 2003 in the eastern part of Sicily. Although empirical relationships to estimate turbidity using remote sensing can be found in literature, however models parameters need to be calibrated through in situ measures acquired via intensive field campaigns. The algorithm used within this research was calibrated using field data acquired during three periods in 2008 (at the beginning of July, August and September). Plumes caused by two rivers, the Simeto and Anapo, were spatially and temporally analyzed. The rivers belong to two catchments characterized by different pedology and land cover. It was proved that the turbidity plume at the estuary has a strict correlation with distance from river mouth, however it strongly depends on catchment characteristics. A multitemporal analysis of MODIS images with 250 m spatial resolution showed that nephelometric turbidity (NT) rises as the flow discharge reaches the gulf, then it sharply decreases tending to an asymptotic base value approximately after a decade. Finally, the coarse spatial resolution of MODIS images was judged not appropriate to characterize plume dynamic close to the mouths of the rivers under investigation.

The site for this study - located in Rhineland-Palatinate, Germany ("Bitburger Gutland") - covered different geological substrates and agro-pedological zones. In total, 42 plots were sampled in the field; soil samples from the top horizon were analysed in the laboratory for total organic carbon (OC), hot water-extractable C (HWE-C) and microbial C (Cmic). In parallel to the ground campaign, a data set of the HyMap^TM airborne imaging sensor was acquired on 27th of August 2009. After pre-processing, HyMap spectra were used to assess the contents of OC, HWE-C and Cmic. As calibration method we used partial least squares regression (PLSR), as it allows a handling of large input spaces and noisy patterns. Since calibration quality was poor for HWE-C and Cmic (cross-validated r₂ values were less than 0.5), we additionally combined PLSR with a genetic algorithm (GA) to preselect an optimum set of spectral features instead of using the full spectrum. With this GA-PLSR approach, results improved considerably for all constituents in the crossvalidation (r² ≥ 0.72). Very similar GA selection patterns for all carbon fractions suggest that spurious (indirect) correlations may be relevant for assessing HWE-C and Cmic. For the GA approach, some overfitting due to a selection based on chance correlations between C fractions and spectral variables cannot be excluded.

Monitoring the global gross primary production (GPP) is relevant to understanding the global carbon cycle and evaluating the effects of interannual climate variation on food and fiber production. GPP, the flux of carbon into ecosystems via photosynthetic assimilation, is an important variable in the global carbon cycle and a key process in land surface-atmosphere interactions. The Moderate-resolution Imaging Spectroradiometer (MODIS) is one of the primary global monitoring sensors. MODIS GPP has some of the problems that have been proven in several studies. Therefore this study was to solve the regional mismatch that occurs when using the MODIS GPP global product over Korea. To solve this problem, we estimated each of the GPP component variables separately to improve the GPP estimates. We compared our GPP estimates with validation GPP data to assess their accuracy. For all sites, the correlation was close with high significance (R² = 0.8164, RMSE = 0.6126 g·C·m^-2·d^-1, bias = -0.0271 g·C·m^-2·d^-1). We also compared our results to those of other models. The component variables tended to be either over- or under-estimated when compared to those in other studies over the Korean peninsula, although the estimated GPP was better. The results of this study will likely improve carbon cycle modeling by capturing finer patterns with an integrated method of remote sensing. Keywords: VEGETATION, Gross Primary Production, MODIS.

Surface of the earth temperature of the earth caused phenomenon that rise and is global warming as greenhouse gas concentration into waiting by continuous discharge of greenhouse gas increases since passing industrial revolution. While gravity about climate fluctuation is risen worldwide, place that can diminish successively biggest surface of the earth change by global warming is high latitude area of polar regions. This study observed distribution of vegetation to confirm change of tundra-taiga boundary. Tundra-taiga boundary is used to observe the transfer of vegetation pattern because it is very sensitive to human activity, natural disturbances and climate change. The circumpolar tundra-taiga boundary could observe reaction about some change. Reaction and confirmation about climate change were definite than other place. This study used Leaf Area Index(LAI) 8-Day data in August from 2000 to 2009 that acquire from Terra satellite MODerate resolution Imaging Spectroradiometer(MODIS) sensor and used Köppen Climate Map, Global Land Cover 2000 for reference data. This study conducted analysis of spatial distribution in low density vegetated areas and inter-annual / zonal analysis for using the long period data of LAI. Change of LAI was confirmed by analysis based on boundary value of LAI in study area. Development of vegetation could be confirmed by area of grown vegetation(730,325km² ) than area of reduced vegetation(22,372km² ) in tundra climate. Also, area was increased with the latitude 64°N~66° N as the center and around the latitude 62° N through area analysis by latitude. Vegetation of tundra-taiga boundary was general increase from 2000 to 2009. While area of reduced vegetation was a little, area of vegetation growth and development was increased significantly.

The knowledge of spatial and temporal variability of land cover is important to manage water resources for yield forecasting, water stress prediction, irrigation water management and flood protection. Cloud cover dramatically reduces the temporal resolution of optical data thus limiting their operational use; in addition, the spatial resolution is often inadequate for applications in heterogeneous areas. On the other hand, algorithms based on Synthetic Aperture Radar (SAR) implemented to retrieve vegetation parameters are not yet fully validated. New SAR missions (COSMO-Skymed and Terrasar-X) may represent a suitable source of data for operational uses due to the high spatial and temporal resolution, although X band is not optimal for agricultural and hydrological applications. This paper reports the influence of soil-vegetation variables (especially biomass indices) on X-band COSMO-Skymed data using Ping Pong products. The study is carried out over two different sites: the SELE plain (in the south-eastern part of Campania, Italy) that is mainly characterized by herbaceous plants and tree crops; and the Campobello-Castelvetrano area (in the south-western part of Sicily, Italy) mainly covered by olive trees, vineyards and woods. The sensitivity analysis is performed by comparing vegetation indices (NDVI or LAI) derived by Landsat TM 5 and ETM+ 7 with COSMO-Skymed (CSK) images acquired in May-June 2010 within the project COSMOLAND (Use of COSMO-SkyMed SAR data for LAND cover classification and surface parameters retrieval over agricultural sites) funded by the Italian Space Agency (ASI). Sensitivity analysis results address to develop algorithms to retrieve vegetation biomass maps from CSK X band characterized by high temporal and spatial resolution.

Minimization of the risk associated with spray applications requires a proper understanding of the spray drift phenomenon. This fact has led to the development of several techniques to measure the deposition on horizontal surfaces as well as the airborne spray profiles. Assessment of airborne spray drift is particularly difficult because this phenomenon is subject to variable micrometeorological conditions. However the monitoring of airborne drift has a great importance since it can be carried over long distances. This paper reviews main sampling techniques currently used to asses the airborne spray drift, based on passive collectors and tracers. Theoretical principles that determine the efficiency of passive samplers are studied as well as the performance of different types of tracers. On the other hand, this paper shows new airborne spray drift assessment techniques based on lidar technology, reviewing its principle of operation as well as its practical application in several spray drift trials. It is concluded that the lidar technique has significant advantages over conventional methods, especially in terms of time consumption and monitoring capabilities. However, the future adoption of lidar technology for airborne spray drift studies will be subjected to the development of lidar instruments really adapted to this application.

The survey of Landsat satellite image is effective in the continuous monitoring of a vast area during long periods of time. It is increasingly being used to derive and analyze spatial distribution data of both the normalized difference built-up index (NDBI) and land surface temperature (LST) that are major indicators for an analysis of urban environment. Especially, LST is one of the key parameters in physics and meteorology of land surface processes on regional and global scales. Satellite remote sensing has been expected to be effective for obtaining thermal information of the earth's surface with a high resolution. Meanwhile as more than 50% of the populations are situated in cities, urbanization has become an important contributor to global warming due to remarkable urban heat island (UHI) effect. UHI effect is meteorological phenomenon that the air temperature increases in urban area than the suburbs because grows with the progress of urbanization. UHI effect has been affected to the regional climate and environment. This study aims to examine relationships of LST with NDBI, and with surface moisture using Landsat TM and ETM+ imagery obtained for the city of Chungbuk in Korea; and to quantitatively compare the patterns and intensity of UHI with land-use/land-cover (LULC) types. Landsat TM (thematic mapper) and ETM+ (enhanced thematic mapper plus) imagery, respectively acquired in 1991, 1994, 2000 and 2006, were utilized to assess urban area thermal characteristics in Cheongju, the city of Chungbuk province in Korea. In order to accurately estimate urban surface moisture, tasseled cap model (TCM) was utilized to generate the proportion of surface moisture. The results indicate urbanization is an accurate indicator of UHI effects with strong linear relationships between LST and NDBI. This implies that surface moisture can be used to analyze temperature quantitatively for UHI studies validated by NDBI. And this suggests that surface moisture, combined with LST, and NDBI, can quantitatively describe the spatial distribution and temporal variation of urban thermal patterns and associated LULC types.

Phoenix canariensis represents one of the most relevant ornamental plants within Mediterranean environment. In the last few years the infestation of a curculio coleopteron, namely the Rhynchophorus ferrugineus, caused a widespread decimation of these palms. Unluckily damages caused by the insect are evident only in the advanced phase of the disease making futile almost any plant treatment. Early warning of this disease may represents the only way to setup efficient actions to fight the coleopteron in trees where it takes over, thus limiting its spreading in contiguous palms. This research aims to achieve the former result by processing: i) short and long-wave images of the crown acquired during day-time by a balloon platform, and ii) a time series of thermal images of the trunk recorded during night-time on the field. The research is based on the hypotheses that: j) the disease induces changes of both transpiration processes and crown shape, because the damages of vascular tissues; jj) the local increase of temperature within the trunk, due to anaerobic fermentation established within the palm, extends up enough to surface to be diachronically analyzed to localize the disease core.

The main objective of this study was to analyze the spatial and temporal variation of the Douro river plume (DRP) dimension, for one hydrological year, based on image segmentation of MERIS data and to modeling the plume dimension based on different environmental parameters that should be related to the DRP. The adopted methodology consisted in the development/implementation of an algorithm based on region growing approach, to automatically select the region seed and the threshold values for each image. In this algorithm two options may be used to select the seed and the threshold values. In order to relate the DRP dimension with several environmental parameters, the Douro river discharges (water flows), tide level and wind speed values were computed. The parameter that most directly influences DRP dimension is water flows. A seasonal study was also performed, considering summer and winter periods separately. The best results were obtained for the winter period with a correlation coefficient of 0.45. The plume derived from MERIS data represents DRP only when the river flow exceeds a certain threshold. Only considered the data with a corresponded water flows higher than 300 m³/s, a positive correlation of 0.74 was found between the DRP and water flows.

The tropical coastal landscape of Tulum in Quintana Roo, Mexico has a high ecological, economical, social and cultural value, it provides environmental and tourism services at global, national, regional and local levels. The landscape of the area is heterogeneous and presents random fragmentation patterns. In recent years, tourist services of the region has been increased promoting an accelerate expansion of hotels, transportation and recreation infrastructure altering the complex landscape. It is important to understand the environmental dynamics through temporal changes on the spatial patterns and to propose a better management of this ecological area to the authorities. This paper addresses a multi-temporal analysis of land cover changes from 1993 to 2000 in Tulum using Thematic Mapper data acquired by Landsat-5. Two independent methodologies were applied for the analysis of changes in the landscape and for the definition of fragmentation patterns. First, an Iteratively Multivariate Alteration Detection (IR-MAD) algorithm was used to detect and localize land cover change/no-change areas. Second, the post-classification change detection evaluated using the Support Vector Machine (SVM) algorithm. Landscape metrics were calculated from the results of IR-MAD and SVM. The analysis of the metrics indicated, among other things, a higher fragmentation pattern along roadways.

The incident solar radiation is one of the component of the land surface energy budget and constitutes an essential input for several applications. An accurate estimation of this variable on large areas requires a dense network of ground sensors and continuous knowledge of the cloud cover, that are rarely available. A valid alternative in this respect is constituted by the remote sensing. In this work a simple algorithm is used in order to integrate the LSA-SAF (Land Surface Analysis Satellite Applications Facility) products of shortwave incident radiation obtained from MSG-SEVIRI imagery with ground radiometers observations. A statistical approach is followed in order to define a criterion for accept or reject the ground sensors observations, by modelling the mean daily error between the observations and a theoretical radiation time series and the cloud cover observations with probability distribution functions. Such distributions is used for the ground sensors selection criterion. The analysis is used to produce a dataset of corrected solar radiation maps on the whole Italian territory for a period of 5 years (2005-2009).

In Korea, rapid industrialization and economic growth have led to serious problems including reduced open space, environmental degradation, traffic congestion, and urban sprawl. These problems have been exacerbated by the absence of effective conservation and governance, and have resulted in various social conflicts. This study analyzed ecosystem service value over the past 20 years using previously reported coefficients. In addition, using logistic regression, we projected the land-cover distribution in 2020 according to the Environmental Conservation Value Assessment Map and analyzed ecosystem service value based on land use changes. Between 1985 and 2005, built-up areas had increased threefold. In the same time period, forest, grassland, and wetland areas decreased. Because of these trends, ecosystem service value decreased from 7,300 million USD to 6,525 million USD. By analyzing land-cover distribution with 2020 land-cover maps, we determined that farmland, grassland, and bare land areas had declined by approximately 24.3%. The 2020 land-cover map with considering ECVAM showed a decrease of 89 million USD in ecosystem service value, while the map without considering ECVAM showed a decrease of 165 million USD. The results of this analysis indicate that environmentally sustainable systems and urban development must be applied to achieve sustainable development and environmental protection.

This research dealt with a daytime integration method with the help of Simple Biosphere Model, Version 2 (SiB2). The field observations employed in this study were obtained at the Yingke (YK) oasis super-station, which includes an Automatic Meteorological Station (AMS), an eddy covariance (EC) system and a Soil Moisture and Temperature Measuring System (SMTMS). This station is located in the Heihe River Basin, the second largest inland river basin in China. The remotely sensed data and field observations employed in this study were derived from Watershed Allied Telemetry Experimental Research (WATER). Daily variations of EF in temporal and spatial scale would be detected by using SiB2. An instantaneous midday EF was calculated based on a remote-sensing-based estimation of surface energy budget. The invariance of daytime EF was examined using the instantaneous midday EF calculated from a remote-sensing-based estimation. The integration was carried out using the constant EF method in the intervals with a steady EF. Intervals with an inconsistent EF were picked up and ET in these intervals was integrated separately. The truth validation of land Surface ET at satellite pixel scale was carried out using the measurement of eddy covariance (EC) system.

Climate change has been an important issue particularly in recent years. Climate change has been reported as a phenomena caused by human activities as identified in the IPCC Fourth Assessment Report (AR4) in 2007, and in order to prevent negative impacts to our planet, conscious efforts to reduce greenhouse gases are necessary worldwide. In addition, Korea's interest in global climate change is growing. In reality, symptoms of global warming on the Korean Peninsula are visible in the air, on the land and in changes patterns to the normal levels and contents of Korea's oceans. Impacts of global warming result in abnormal temperature fluctuation, typhoons, regional flooding and desertification with such extreme and that are arguably more frequent occurrences of natural disasters quickly becoming a general problem for the community as a whole. On the other hand, the development of IT technology and the improvement and use of satellite technology have ensured better access to RS technique and utilization. Due to RS technology is ability to monitor it has become widely used in farming applications, environment prediction and planning and ecology studies and analysis. The purpose of this study is to assess emission and absorption in relation to geographical features and to be better able to deliver environment information to produce a spatial map of carbon dioxide in Chungbuk by using RS and GIS with a focus on carbon dioxide emission and its direct absorption caused by tree growth according to energy consumption.

Satellite observation collocation algorithms are generally used to spatially match observations or products from different satellite systems. The spatially matched and integrated satellite datasets are commonly used in integrated retrievals, satellite instrument inter-calibration and satellite observation validation. Instrument physical based collocation algorithms are developed at NOAA/NESDIS/STAR to support the development of the satellite observation integration system. The algorithms are applied within the Geostationary satellite & Polar satellite (GEO-LEO) integration system for IASI/SEVRI and will applied in the future CrIS/GOES-R observation integration system. In this paper, the details of the algorithms for IASI/SEVERI and AIRS/SEVIRI collocation are described and some results for both are presented.

The time series of various parameters of satellite imagery (NDVI/EVI, temperature) during the growing season were considered in this work. This means that satellite images were considered not like a number of single scenes but like temporal sequences. Using time series enables estimating the integral phenological properties of vegetation. The basis of the developed technique is to use one of the methods of transformation of the multidimensional space in order to get the principal components. The technique is based on considering each dimension of the multidimensional space as satellite imagery for a specific date range. The technique automatically identifies spatial patterns of vegetation that are similar by phenology and growing conditions. Subsequent analysis allowed identification of the belonging of derived classes. Thus, the technique of revealing the spatial distribution of different dynamical vegetation patterns based on the phenological characteristics has been developed. The technique is based on a transformation of the multidimensional space of states of vegetation. Based on the developed technique, areas were obtained with similar interannual trends.

Gross Primary Production (GPP) is the sum of carbon absorbed by plant canopy. It is a key measurement of carbon mass flux in carbon cycle studies. Remote sensing based light use efficiency model is a widely used method to estimate regional GPP. In this study, MODIS-PSN was used to estimate GPP in Heihe River Basin. In order to better the model accuracy, maximum light use efficiency (ε0) in MODIS-PSN is estimated using local observed carbon flux data and meteorological data. After adjustment of parameter ε0, MODIS-PSN can correctly estimate GPP for major vegetation type in the Heihe River Basin. Then, yearly GPP over Heihe River Basin was estimated. The results indicated that about 1.4*10¹³g carbon enter terrestrial ecosystem through vegetation photosynthesis in the Heihe River Basin one year. In contrast, there is just 5.73*10₁₃g carbon enter terrestrial ecosystem according to the standard MODIS GPP product, which is greatly underestimated GPP in the Heihe River Bain.

It is very necessary to validate MODIS land surface temperature (LST) for its application, especially in the arid and semi-arid regions. In this study, the Terra and Aqua MODIS 1km daily LST products (MOD/MYD11A1) are validated using ground based longwave radiation observation. The longwave radiation ground measurements during 2008 to 2009 were collected from four automatic weather stations in the Heihe River Basin. In this validation process, the land surface broadband emissivities of the validation stations were obtained from ASTER Spectral library. Then the ground-measured LSTs of validation stations were converted from surface longwave radiation based on Stefan-Boltzmann's law and thermal radioactive transfer theory. The validation results indicated that: except for DYKGT station, the mean bias was less then 1K and the mean absolute error (MAE) range was about 2-3K; MYD11A1 LSTs from Aqua have larger biases, MAEs, and RMSDs than that of MOD11A1 LSTs from Terra in most cases. The comparisons with ground measured LSTs show that the MAEs and RMSDs from daytime MOD/MYD11A1 comparisons are larger than that from nighttime MOD/MYD11A1 comparisons.

Global climate changes as well as abnormal climate phenomena have affected the agricultural environment on a great scale. Thus, there is a strong need for countermeasures by making full use of agriculture related information. As agricultural lands in South Korea are mostly operated by private farmers on a small parcel level, it is difficult to gather information for an overview on changing crop condition and to construct database necessary for disease management, production estimation and compensation measures on a regional or governmental level. The objective of this study is to evaluate the multispectral reflectance characteristics of RapidEye image data to classify agricultural land cover as well as crop condition in South Korea. As the RapidEye sensor offers the spectral information in red edge range as a first multispectral satellite system, we focus on the usefulness of red edge reflectance for identifying crop species and for interpreting crop growth or stress condition.

In mountainous regions, the radiometric signal recorded at the sensor depends on a number of factors such as sun angle, atmospheric conditions, surface cover type, and topography. In this paper, a computer simulation model of radiation transfer is designed and evaluated. This model implements the Monte Carlo ray-tracing techniques and is specifically dedicated to the study of light propagation in mountainous regions. The radiative processes between sun light and the objects within the mountainous region are realized by using forward Monte Carlo ray-tracing methods. The performance of the model is evaluated through detailed comparisons with the well-established 3D computer simulation model: RGM (Radiosity-Graphics combined Model) based on the same scenes and identical spectral parameters, which shows good agreements between these two models' results. By using the newly developed computer model, series of typical mountainous scenes are generated to analyze the physical mechanism of mountainous radiation transfer. The results show that the effects of the adjacent slopes are important for deep valleys and they particularly affect shadowed pixels, and the topographic effect needs to be considered in mountainous terrain before accurate inferences from remotely sensed data can be made.

The effect of vegetation cover on climatic change has not yet observed in Egypt. In the current study, Ismailia Governorate was selected as a case study to assess the impact of the vegetation cover expansion on both land surface and air temperature during twenty-eight years from 1983 to 2010. This observation site was carefully selected as a clear example for the highly rate of reclamation and vegetation expansion process in Egypt. Land Surface Temperature (LST) that were extracted from NOAA/AVHRR satellite data and air temperature (T_air) data that were collected from ground stations, were correlated with the expansion of vegetation cover that was delineated using Landsat TM and Landsat ETM+ data. The result showed that (LST) decreased by about 2.3°C while (T_air) decreased by about 1.6°C with the expansion of the cultivated land during twenty-eight years.

As for the shortcoming that traditional interpolation methods often cause the over-smooth problem, or couldn't fully take the variety of the terrain detail into account, this paper proposed a DEM densification method by using shape from shading (SFS) based on spectral information from single highly spatial resolution satellite image. In accordance with the idea of introducing SFS into DEM interpolation, a method is put forward, which is under the condition of the unknown light source in spatially heterogeneous area. Surface relative shape was reconstructed at first, and the second order edgeoriented image interpolation method was applied to generate a high-resolution DEM grid. Spectral information of the unknown points was used to reveal the actual surface reflection properties, and land surface could be accurately modeled compared traditional SFS method, which use a constant reflectance in the whole region. Experiments proved that the algorithm is very effective for the sparse grid DEM interpolation and offer a new way for DEM densification.

Based on the protensive GIMMS NDVI data set and meteorological data during 1982-2009 in the Heihe River Basin, a novel multiple time-scale analysis method, Empirical Mode Decomposition (EMD), is used to diagnose the periodicities of NDVI, air temperature and precipitation data. At the same time, the relationship among these three elements is performed. The results indicate that SINDVI, temperature and precipitation have the similar 3 and 10 years quasiperiodic in the upper reaches of the Heihe River Basin. SINDVI and temperature have the similar 3 and 10 years quasiperiodic, SINDVI and precipitation have the similar 3, 6, 8 and 15 years quasi-periodic in the middle reaches of the Heihe River Basin. In the meantime, in the lower reaches of the Heihe River, SINDVI and temperature have the similar 3 and 10 years quasi-periodic, SINDVI and precipitation have the similar 3 and 6 years quasi-periodic. It is indicated that the temperature and precipitation are both the driving factor affecting the vegetation in the Heihe River Basin. In addition, the EMD method can be effectively used to analyze the relationship between time series data and the meteorological data.

In this paper the results of spatio-temporally collocated polarization measurements of snow and bare soil (covered by old and dry lying stems of wheat and weed) microwave reflective (radar backscattering coefficient) and emissive (brightness temperature) characteristics angular dependences at ~37GHz are presented. As well as a structure and operational features of utilized Ka-band, multi-polarization, combined scatterometer-radiometer system, measuring and calibration facilities are discussed.

Taihu Basin is located in the lower reach of the Yangtze River basin. Recent years, severe pollution in Lake Taihu was frequently occurred, which need to evaluate overall water balance of the basin. Evapotranspiration (ET) and precipitation are the key elements in water balance estimation that give scientifically sound information on water availability. Currently, satellite remote sensing is widely used for estimation of these two parameters. In this study, precipitation and ET from remote sensing and observing runoff are used to estimate annual variations in the water budget of the Taihu Lake Basin from 2005 to 2007. The Global Satellite Mapping of Precipitation (GSMaP) data was applied to estimate precipitation in spatial and temporal variability of the Taihu Basin. The surface temperature-normalized difference vegetation index (Ts-NDVI) triangle method with topographic correction was used to estimate ET from MODIS datasets in this study. Runoff was observed from hydrological station. The ET is the largest consumption in water budget components over Taihu Basin. For the whole basin, the ratio of ET/Rainfall is about 0.85-0.95 from 2005 to 2007, and it is about 1.2-1.4 for Lake Taihu. In general, the income terms of water balance in the basin including precipitation and inflow from Yangtze River should be equal to outgo terms including ET, outflow and water storage. But the income terms is mostly larger than outgo terms in Taihu Basin, the imbalance percentage is about 0.4-9.6% for the whole basin, and 0.5-3.7% for Lake Taihu.

A good understanding of interactions of electromagnetic radiation with soil surface is important for a further improvement of remote sensing methods. In this paper, a radiosity-based analytical model for soil Directional Reflectance Factor's (DRF) distributions was developed and evaluated. The model was specifically dedicated to the study of radiation transfer for the soil surface under tillage practices. The soil was abstracted as two dimensional U-shaped or V-shaped geometric structures with periodic macroscopic variations. The roughness of the simulated surfaces was expressed as a ratio of the height to the width for the U and V-shaped structures. The assumption was made that the shadowing of soil surface, simulated by U or V-shaped grooves, has a greater influence on the soil reflectance distribution than the scattering properties of basic soil particles of silt and clay. Another assumption was that the soil is a perfectly diffuse reflector at a microscopic level, which is a prerequisite for the application of the radiosity method. This radiosity-based analytical model was evaluated by a forward Monte Carlo ray-tracing model under the same structural scenes and identical spectral parameters. The statistics of these two models' BRF fitting results for several soil structures under the same conditions showed the good agreements. By using the model, the physical mechanism of the soil bidirectional reflectance pattern was revealed.

Leaf area index (LAI) is very often a critical parameter in process-based models of vegetation canopy response to global environment change. This paper made an assessment of the Collection 5 MODIS LAI product (MCD15A2) using field sample data in cropland areas. One of the major problems for validating MODIS LAI product using field measurements is the scale mismatch between ground 'point' measurements and the MODIS resolutions. In heterogeneous areas, we need to transform field measurements to the scale of MODIS due to scale effect caused by the heterogeneity of land surface. In this study, we performed the scale transformation through fractal method. LAI was measured with the LAI- 2000 plant canopy analyzer. The LAI-2000 measurements were multiplied by a clumping index to get true LAI values. The field data was related to 30-m resolution TM images using empirical methods to create reference LAI map. Fractal dimensions for each MODIS pixel were calculated using a triangular prism method based on reference LAI map. Then the field LAI values were upscaled to 1km spatial resolution using the fractal dimension theory. The MODIS LAI product validation results shown that, MODIS LAI are lower than the ground measurements without scale effect correction, but quite close to the upscaled field measured LAI. The conclusion is the fractal dimension theory can be used to solve the scale problem caused by spatial heterogeneity in LAI products validation.