This PDF file contains the front matter associated with SPIE Proceedings Volume 7824, including the Title Page, Copyright information, Table of Contents, and the Conference Committee listing.

In the standard MODIS Collection 5 aerosol retrieval algorithm, estimates of the surface albedo between the VIS and MIR channels must be made. Unfortunately, the operational model used is not suitable for urban areas and efforts to modify the required VIS-MIR surface spectral ratios for urban areas are needed to remove aerosol retrieval biases. To address these issues, we use results based on the ASRVN product to provide regionally tuned surface reflection ratios Using these values removes retrieval bias and improves resolution to 1.5 km. In addition, we note explore the relationships for multiple urban sites and illustrate a general correspondence between the surface reflection ratiosn and biases in AOD retrieval. Further validations of the surface reflection differences in urban areas are illustrated using high resolution LANDSAT 7 imagery for vegetation / urban boundaries.

Remote sensing offers the opportunity to produce land cover classifications for large and remote areas on a yearly basis and is an important tool in regions that lack these information. However often training and validation data to generate annual land cover maps are not available in necessary quantity - being from one year only or covering only a small extent of the region of interest. This study was focused on land use classifications at regional scale with a special emphasize on annual updates under the constraint of limited sampling data. Often, sampling is reduced to one year or to an unrepresentative area extend within the region of interest. The investigations for the period between 2004 and 2009 were conducted in the irrigation systems of the Amu Darya Delta in Central Asia, where reliable information on crop rotations is required for sustainable land and water management. Annual training and validation data were extracted from high resolution land use classifications. For classification, statistical features based on MODIS time series of vegetation indices, reflectance and land surface temperature (LST) were calculated and a random forest algorithm was applied. By a combination of training data from different years, the accuracy could be enhanced from an overall accuracy of 70% to more than 90% for a focused subregion and also good consistency with high resolution images for the other parts of the delta, which has to be confirmed using quantitative validation. A combination of a different number of years was tested. Already two years can be sufficient to generate a robust and transferable random forest to produce yearly land use maps. The study shows the possibility to combine training data from different years for the annual classification of irrigated croplands on a regional scale.

Studies of impact of human activity on the vegetation dynamics in the Sahel belt of Africa are recently re-invigorated due to a new scientific findings that highlighted the primary role of climate in the drought crises of the 70s-80s. Time series of satellite observations allowed identifying re-greening of the Sahel belt that indicates no sensible human effect on vegetation dynamics at sub continental scale from 80s to late 90s. However, several regional/local crises related to natural resources occurred in the last decades underling that more detailed studies are needed. This study contribute to the understanding of climate/human impact on pasture vegetation status in the Sahel region in the last decade (1999- 2008). The use of a time-series of SPOT-VGT NDVI and FEWS-RFE rainfall estimates allowed to analyze vegetation and rainfall trends and identify local anomalous situation in the region. Trend analysis has been conducted to map a) areas where vegetation has been significantly decreased or increased due to rainfall pattern and b) anomalous zones where vegetation dynamics could not be fully explained by rainfall pattern by. The identified hot-spots areas have been compared with spatial information on the reported humanitarian-food crisis events in order to understand chronic situation where ecosystems carrying capacity is endangered. The results of this study show that even if a general positive re-greening situation is evident for the entire Sahel, some serious hot spots exist in areas where cropping system and pasture activity are conflicting.

With the launch of the German hyperspectral satellite mission 'Environmental Mapping and Analysis Program' (EnMAP), anticipated in 2014, unprecedented opportunities will open up for a wide range of applications. Along with different areas of application, the agricultural sector will particularly benefit from the availability of such observation capability. Information about state and dynamics of the (non-)vegetated land surface, expressed by biophysical variables, is required for instance in irrigation water determination, stress detection or in advanced crop production modeling. In the context of the mission, a toolbox will be provided to determine these variables from hyperspectral imagery. Algorithms to be implemented will range from empirical methods, such as hyperspectral vegetation indices, to physically based approaches, involving the inversion of canopy reflectance models. In this study, potential techniques for the EnMAP toolbox are selected and tested using data from two field campaigns conducted in two different geographic regions. One of the campaigns was carried out in summer 2009 at the German agricultural 'Landau test site' as a first step towards the scientific preparation of the EnMAP mission. During the campaign, data of the airborne hyperspectral scanner HyMap were acquired concurrently with ground measurements of canopy water content and other variables. The second campaign was conducted in the Cuga river basin in Sardinia (Italy) during summer 2007. First results of data analyses will be presented and discussed, emphasizing in particular the benefits of multi-temporal and multi-seasonal hyperspectral data availability over current operational systems.

Land surface biophysical parameters such as the fraction of photosynthetic active radiation (fPAR) and leaf area index (LAI) are keys for monitoring vegetation dynamics and in particular for biomass and carbon flux simulation. This study aimed at deriving accurate regression equations from the newly available RapidEye satellite sensor to be able to map regional fPAR and LAI which could be used as inputs for crop growth simulations. Therefore, multi-temporal geo- and atmospherically corrected RapidEye scenes were segmented to derive homogeneous patches within the experimental fields. Various vegetation indices (VI) were calculated for each patch focusing on indices that include RapidEye's red edge band and further correlated with in situ measured fPAR and LAI values of cotton and rice. Resulting coefficients of determination ranged from 0.55 to 0.95 depending on the indices analysed, object scale, crop type and regression function type. The general relationships between VI and fPAR were found to be linear. Nonlinear models gave a better fit for VI-LAI relation. VIs derived from the red edge channel did not prove to be generally superior to other VIs.

Sustainability of world crop production and food security has become uncertain. The authors have developed an environmental research system called Remote Sensing Environmental Monitor (RSEM) for treating carbon sequestration by vegetation, grain production, desertification of Eurasian grassland, and CDM afforestation/ reforestation to a background of climate change and economic growth in rising Asian nations. The RSEM system involves vegetation photosynthesis and crop yield models for grains, including land-use classification, stomatal evaluation by surface energy fluxes, and daily monitoring for early warning. This paper presents a validation method for RSEM based on carbon partitioning in plants, focusing in particular on the effects of area sizes used in crop production statistics on carbon fixation and on sterility-based corrections to accumulated carbon sequestration values simulated using the RSEM photosynthesis model. The carbonhydrate in grains has the same chemical formula as cellulose in grain plants. The method proposed by partitioning the fixed carbon in harvested grains was used to investigate estimates of the amounts of carbon fixed, using the satellite-based RSEM model.

Global warming and climatic changes due to human activities impact on marine and terrestrial ecosystems, which feedbacks to climate system. These negative feedbacks amplify or accelerate again global climate change. In particular, life cycle of vegetation sensitively vary according to global climate change. This study attempts to analyze quantitatively vegetation change in Korea peninsula using harmonic analysis. Satellite data was extracted from SPOT/VEGETATION S10 MVC (Maximum Value Composite) NDVI (Normalized Difference Vegetation Index) products during 10 years (1999 to 2008) around Korea peninsula. This NDVI data set was pre-processed to correct noise pixels cause by cloud and ground wetness. Variation of vegetation life cycle was analyzed through amplitudes and phases of annual harmonic components (first harmonic components) per year for two land cover types (cropland and forest). The results clearly show that the peak of vegetation life cycle in Korea peninsula is brought forward to early. Especially, it represents that the phases over low latitudes area between 32.8°N and 38°N steadily decrease every year both forest and cropland. The study estimated that phase values moved up approximately 0.5 day per year in cropland and 0.8 day per year in forest.

In the face of global change, concepts for sustainable land management are increasingly requested, among others to cope with the rapidly increasing energy demand. High resolution land use classifications can contribute spatially explicit information suitable for land use planning. In this study, the coverage of cereal crops was derived for two regions in Baden-Wuerttemberg and Rhineland-Palatinate - Germany, as well as in the Alsace - France, by classifying multitemporal and multi-scale remote sensing data. The presented methodology shall be used as basic input for high resolution bio-energy potential calculations. Segmentation of pan-merged 15 m Landsat 7 ETM+ data and pre-classification with CORINE data was applied to derive homogenous objects assumed to approximate the field boundaries of agricultural areas. Seven acquisitions of moderate resolution IRS-P6 AWiFS data (60 m) recorded during the vegetation period of 2007 were used for the subsequent classification of the objects. Multiple classification and regression trees (random forest) were selected as classification algorithm due to their ability to consider non-linear distributions of class values in the feature space. Training and validation was based on a subset of 1724 samplings of the official European land use survey LUCAS (Land Use/ Cover Area Frame Statistical Survey). Altogether, the object based approach resulted in an overall accuracy of 74 %. The use of 15 m Landsat for mapping field objects were identified to be one major obstacle caused by the characteristically small agricultural units in Southwest Germany. Improvements were also achieved by correcting the LUCAS samples for location errors.

In general, agricultural drought always occurs under the circumstance of the comprehensive interactions among the factors of nature, economy and society. The loss due to agricultural drought in China is huge every year. Therefore the timely monitoring of agricultural drought is critical to help reduce the loss. The information of agricultural drought early warning is helpful for local governmental officials and farmers in preparation for coping with the likely happening drought. The paper presents an approach and findings of an early warning of agricultural drought which has been successfully conducted in the semiarid and rainfed farming area in Ningxia autonomous region in the northwest of China.

Remote sensing technology has been developed and applied to provide spatiotemporal information on crop stress for precision management. A series of multispectral images over a field planted cotton, corn and soybean were obtained by a Geospatial Systems MS4100 camera mounted on an Air Tractor 402B airplane equipped with Camera Link in a Magma converter box triggered by Terraverde Dragonfly® flight navigation and imaging control software. The field crops were intentionally stressed by applying glyphosate herbicide via aircraft and allowing it to drift near-field. Aerial multispectral images in the visible and near-infrared bands were manipulated to produce vegetation indices, which were used to quantify the onset of herbicide induced crop stress. The vegetation indices normalized difference vegetation index (NDVI) and soil adjusted vegetation index (SAVI) showed the ability to monitor crop response to herbicide-induced injury by revealing stress at different phenological stages. Two other fields were managed with irrigated versus nonirrigated treatments, and those fields were imaged with both the multispectral system and an Electrophysics PV-320T thermal imaging camera on board an Air Tractor 402B aircraft. Thermal imagery indicated water stress due to deficits in soil moisture, and a proposed method of determining crop cover percentage using thermal imagery was compared with a multispectral imaging method. Development of an image fusion scheme may be necessary to provide synergy and improve overall water stress detection ability.

The SMOS (Soil Moisture and Ocean Salinity) satellite provides soil moisture data at about 40 km resolution globally. Validation of SMOS data using in situ measurements is complicated due to the large integrated scale of remote sensing observations. Nevertheless, different approaches can be used to circumvent the direct comparison. One is to upscale ground measurements using aggregation rules. Another is to downscale (or disaggregate) remote sensing data at the representativeness scale of ground measurements. This study combines both approaches to make ground and remote sensing data match at an intermediate spatial scale. On one hand, the local-scale in situ soil moisture data collected during the first AACES (Australian Airborne Calibration/validation Experiments for SMOS) are aggregated to 4 km resolution. On the other hand, a disaggregation methodology of SMOS data based on 1 km resolution MODIS (MODerate resolution Imaging Spectroradiometer) data is implemented at 4 km resolution over the Murrumbidgee catchment, the site of the AACES campaign. Results indicate a correlation coefficient between disaggregated and ground observations of 0.92. The y-intercept of the linear regression between disaggregated and ground observations is very close to 0. However, the slope of that line is 0.44 only. This seems to highlight an issue with either the dielectric constant model or the roughness parameter value currently used in the SMOS retrieval algorithm.

Since it's launch, the ESA's Soil Moisture and Ocean Salinity (SMOS) satellite, is delivering new data from its LBand 1.4Ghz 2D interferometer [1]. The observations from SMOS are used to retrieve soil moisture in the first centimeters and ocean salinity at the surface of the water. The observations are multi-angular with a 3 days maximum revisit time. The spatial resolution of SMOS data is 40km. In this paper we present on event detection algorithm implemented at CATDS (Centre Aval de Traitement des Données SMOS) the CNES level 3 and level 4 SMOS enter. This algorithm is a three stage change detection algorithm. At stage one the possibility/probability of occurrence of the event is evaluated. This is done via spatiotemporal constraints maps. These maps are obtained from the analysis of NSIDC's freezing index products over the last century. Climate data from ancillary files are tested will taking into consideration the uncertainty of the data. Some selected retrieved variables are also tested. At stage two a time series analysis is applied. In the current version of the algorithm a direct change detection algorithm is used. The tests make use of available variables of polarization index, retrieved soil moisture...Finally at stage three a simple fuzzy logic approach is used to decide if the event occurred. This approaches takes into consideration the separation time of the data. Ascending and descending orbits are taken into consideration. In this study freezing detection is presented over central CONUS. The temporal and angular signature of SMOS will be presented. Comparison is done with the SCAN network

The Soil Moisture and Ocean Salinity (SMOS) satellite is a 2D interferometer in L-band (1.4 GHz). Over land, it enables to probe the earth surface emissivity related to soil moisture in the first centimeters of the soil, with an aimed accuracy better than 4m³.m^-3 and an average spatial resolution of 40km. The European Space Agency's (ESA) ground segment provides half-orbit soil moisture products at level 2. The Centre National d'Etudes Spatiales (CNES) in France has developed the CATDS (Centre Aval de Traitement des Données SMOS) ground segment in order to produce global maps, known as level 3 and 4 products. Over land, the algorithm is based on the level 2 soil moisture ESA's prototype. The major enhancement of the CATDS concerns the use of multi-orbit retrieval. The level 3 Soil moisture (SM) products are global maps of soil moisture, and other geophysical products (vegetation optical thickness, albedo, soil dielectric constant or surface temperature). For a particular point, the revisit time is between 1 and 3 days, and the entire Earth's surface is covered by SMOS field of view in 3 days. Level 3 SM products are available over different time periods. First, a 1 day global product is generated for each day. Then, 1 day global maps are aggregated in 3-day global products, 10- day and monthly products.

Polarimetric SAR data at L-band are known to be particularly well adapted for estimating moisture content and roughness. However, many agricultural fields are generally covered by a short vegetation layer that hampers this analysis. In fact, many applications of surface parameter retrieval methods using polarimetric SAR data over agricultural sites revealed that parameters are underestimated over most of the fields covered by short vegetation (e.g. grass, clovers, winter wheat). This bias is due to the electromagnetic contribution of the vegetation which significantly modifies the polarimetric response. An identification of different kind of vegetation is necessary in order to determine the feasibility to estimate soil moisture. The AgriSAR campaign, Agricultural Bio-/Geophysical Retrievals from Frequent Repeat SAR and Optical Imaging, was conducted for ESA in 2006 in order to study the agricultural vegetation. The multi-temporal datasets were acquired with the DLR's E-SAR sensor in Görmin (Germany). From this campaign, many ground measurements were obtained: Leaf Area Index (LAI), wet and dry biomass and soil moisture. Thus, using all information, eight agricultural vegetation classes could be characterized independently of soil moisture. This paper presents this identification necessary to elaborate an original mapping technique allowing localizing agricultural fields having a vegetation layer. A classification based on the support vector machine (SVM) and on the analysis of polarimetric parameter behavior is developed using multi-temporal images over fields covered by vegetation. The obtained vegetation maps allow the analysis of the temporal evolution of plants. This classification has high product and user accuracy which are presented. The technique is shown to perform well over the AgriSAR dataset.

In August 2009, the TropiSAR campaign was conducted in French Guiana with the ONERA airborne system SETHI in order to support the Phase A of the Earth Explorer candidate mission, BIOMASS. Several SAR data acquisitions at P-band are now available for analysis over tropical forest. This paper presents one of the four acquisition sites, Paracou and two related studies performed over this dataset. The first interrogation focuses on the radiometric stability at P-band of the forest backscatter. This stability is an essential point if the backscatter is expected to be used for forest biomass estimation. Moreover, the compatibility of the current BIOMASS mission design, relying on repeat pass interferometry for forest height retrieval, to tropical forest and the related temporal decorrelation is then explored.

Tillage practices can affect the long term sustainability of agricultural soils as well as a variety of soil processes that impact the environment. The benefits of reduced tillage and no-till practices over agriculture fields are well documented and include: (1) significant reductions in wind and water erosion mitigating nutrient and pesticide runoff into waterways; (2) increasing and/or maintaining soil organic matter; (3) increasing biological activity and improving soil structure; and (4) increasing soil carbon and its sequestration. Information on tillage activities assists in implementing policies and programs to promote beneficial management practices (BMPs), and in monitoring the success of these initiatives. Agriculture and Agri-Food Canada supports environmentally responsible agriculture and has identified this as one of their priorities. Thus, tillage information requirements have become increasingly important to a number of programs and policies within the department. Rapid, accurate and objective methods are required to map and monitor tillage activities. Earth observing satellites can assist with targeting and monitoring land management activities. For the last decade, research has clearly demonstrated that complementary information provided by both optical and radar satellite sensors are fundamental in developing an agricultural land management monitoring system. Launched in June 2007, the TerraSAR-X is a radar satellite acquiring data at the X-band frequency (9.6 GHz). The application of TerraSAR-X data for conservation tillage mapping has been somewhat limited, and thus this study investigates its use in determining tillage occurrence. An HH-HV TerraSAR-X image was acquired on November 4, 2009 and ground data were also collected characterizing tillage conditions at the time of acquisition. Backscatter responses were analyzed to identify tillage occurrence and to differentiate between untilled, chiseled and moldboard ploughed fields. Preliminary analysis showed that HH polarization can better contribute to tillage discrimination than compared to HV polarization and that the backscatter response can be used to discriminate untilled fields from ones that are moldboard ploughed. However, chiseled fields were often confused with highroughness (rms height~1.30 cm) untilled fields and moldboard ploughed fields. Fully polarimetric X-band radar datasets could potentially contribute more information to mapping tillage conditions.

Tropical forests are complex ecosystems where the potential of remote sensing has not yet been fully realized. The increasing availability of satellite metric imagery along with canopy altimetry from airborne LiDAR open new prospects to detect individual trees. For this objective, we optimized, calibrated and applied a model based on marked point processes to detect trees in high biomass mangroves of French Guiana by considering a set of 1m pixel images including 1) panchromatic images from the IKONOS sensor 2) LiDAR-derived canopy 2D altimetry and 3) reflectance panchromatic images simulated by the DART-model. The relevance of detection is then discussed considering: (i) the agreement in space of detected crown centers locations with known true locations for the DART images and also the detection agreement for each pair of IKONOS and LiDAR images, and (ii) the comparison between the frequency distributions of the diameters of the detected crowns and of the tree trunks measured in the field. Both distributions are expected to be related due to the allometry relationships between trunk and crown. Results are encouraging provided that crown sizes sufficiently large compared to 1m pixels.

Use of ground based remote sensing technologies such as scanning lidar systems (light detection and ranging) has gained traction in characterizing ambient aerosols due to some key advantages such as wide area of regard (10 km²), fast response time, high spatial resolution (<10 m) and high sensitivity. Energy Dynamics Laboratory and Utah State University, in conjunction with the USDA-ARS, has developed a three-wavelength scanning lidar system called Aglite that has been successfully deployed to characterize particle motion, concentration, and size distribution at both point and diffuse area sources in agricultural and industrial settings. A suite of massbased and size distribution point sensors are used to locally calibrate the lidar. Generating meaningful particle size distribution, mass concentration, and emission rate results based on lidar data is dependent on strategic onsite deployment of these point sensors with successful local meteorological measurements. Deployment strategies learned from field use of this entire measurement system over five years include the characterization of local meteorology and its predictability prior to deployment, the placement of point sensors to prevent contamination and overloading, the positioning of the lidar and beam plane to avoid hard target interferences, and the usefulness of photographic and written observational data.

During last two decades the increasing availability of remotely sensed acquisitions in the thermal infrared part of the spectrum has encouraged hydrologist community to develop models and methodologies based on these kind of data. The aim of this paper is to compare three methods developed to assess the actual evapotranspiration spatial distribution by means of remote sensing data. The comparison was focused on the differences between the "single" (SEBAL) and "two" source (TSEB) surface energy balance approaches and the S-SEBI semi-empirical method. The first assumes a semiempirical internal calibration for the sensible heat flux assessment; the second uses a physically based approach in order to assess separately the soil and vegetation fluxes. Finally, the last one is based on the correlation between albedo and surface temperature for evaporative fraction estimations. The models were applied using 7 high resolution images, collected by an airborne platform between June and October 2008, approximately every 3 weeks. The acquired data include multi-spectral images (red, green and near infrared) and thermal infrared images for surface temperature estimation. The study area, located in the south-west cost of Sicily (Italy), is characterised by the presence of typical Mediterranean cultivations: olive, vineyard and citrus. Due to irrigation supplies and rainfall events, the water availability for the crops varies in time and this allowed to perform the comparison in a wide range of the modelled variables. Additionally, the availability of high spatial resolution images allowed the testing of the models performances at field scale despite the high vegetation fragmentation of the study area. The comparison of models performance highlights a good agreements of model estimations, analyzed by means of MAD (Mean Absolute Differences) and MAPD (Mean Absolute Percent Differences) indices, especially in terms of study area averaged fluxes. The analysis in correspondence of various crop fields highlights higher differences for low vegetation coverage and for scarce water availability.

A national system for monitoring the population increase of agricultural pest "Lobesia Botrana" (vine moth/fly that attacks grapes) in Cyprus has been developed. The system comprises of automated delta traps with GPS that use wireless(Wi-Fi) camera, automated image analysis for identification of the specific fly species, Wi-Fi technology for transferring the data using mobile telephony network to a central station for result presentation and analysis. A GIS database was developed and included details of the pilot vineyards, environmental conditions and daily data of the number of captured flies from each automated trap. The results were compared with MODIS and LANDSAT satellite thermal images since the appearance of the vine fly is greatly dependent on the microclimate temperatures (degree days). Results showed that satellite data can estimate accurately the appearance of the vine fly. The proposed system can be an important tool for the improvement of a national Integrated Pest Management (IPM) system and it can also be used for monitoring other agricultural pests and insects.

A GER-1500 field spectro-radiometer was used in this study in order to retrieve the necessary spectrum data of the spring potatoes for estimating the spectral vegetation indices (SVI's). A field campaign was undertaken with measurements of LAI, and crop height using the Sun-Scan instrument, acquired simultaneously with the spectroradiometric measurements between March and April of 2009. Several regression models are tested for determining the best 'regression models' that to relate LAI/crop height and SVI's. The results showed that there is a strong statistical relationship between LAI/crop height and SVI's which can be used at the estimation of evapotranspiration (ET_c). Direct comparison and validation of proposed models with Landsat image data is also made. The study area was the Mandria Village in Paphos district in Cyprus.

The quantitative effect of crop canopy reflected spectrum by leaf area index (LAI) and average leaf angle (ALA) was studied. Effect of ALA on canopy reflected spectrum can not be ignored with inversion of LAI and monitoring of crop growth condition by remote sensing technology. It indicated that canopy reflected spectrum has significant difference between erective and horizontal cultivars by radiative transfer model and measured experiment data. Investigations have been made on identification of erective and horizontal cultivars by bidirectional canopy reflected spectrum. The bidirectional reflectance of visible and near infrared bands at 15°, 30°and 45°field of view for the main viewing plane could be used for identification of plant structural types based on bidirectional data. For erective varieties, the bidirectional canopy reflectance at near infrared was f45°>f15°> f30°, at visible band was f45°>f15°≈f30°; For middle varieties, the bidirectional canopy reflectance at near infrared and visible band was f15°>f45°> f30°; For loose varieties, the bidirectional canopy reflectance at near infrared and visible band was f45°>f30°> f15°. So, it is feasible to identify loose, middle and erective varieties of wheat by bidirectional canopy reflected spectrum. The result indicates that the application of EVI is affected by LAD, so LAD should be considered when retrieve LAI using enhanced vegetation index (EVI).

The vegetation covering land surface is main component of biosphere which is one of five significant spheres on the earth. The vegetation plays a very important role on the natural environment conservation and improvement to keep human being's living environment evergreen while the vegetation supplies many natural resources to human living and development continuously. Under the background of global warming, vegetation is changing as climate changes. It is not doubt that human activities have great effects on the vegetation dynamic. In general, there are two aspects of the interaction between vegetation and climate, the climatic adaptation of vegetation and the vegetation feedback on climate. On the base of the research on the long term vegetation growth dynamics, it can be found out the vegetation adaptation to climate change. The dynamic change of vegetation is the direct indicator of the ecological environment changes. Therefore, study on the dynamic change of vegetation will be very interest and useful. In this paper, the vegetation change in special region of China will be described in detail. In addition to the methods of the long term in-situ observation of vegetation, remote sensing technologies can also be used to study the long time series vegetation dynamic. The widely used NDVI was often employed to monitor the status of vegetation growth. Actually, NDVI can indicate the vigor and the fractional cover of vegetation effectively. So the long time series of NDVI datasets are a very valuable data source supporting the study on the long term vegetation dynamics. Since 1980, a series of NOAA satellites have been launched successfully, which have already supplied more than 20 years NOAA/AVHRR satellites data. In this paper, we selected Ningxia Hui autonomic region of China as the case study area and used 20 years pathfinder AVHRR NDVI data to carry out the case study on the vegetation dynamics in order to further understand the phenomena of 20 years vegetation dynamics of the whole Ningxia region. Ningxia Hui autonomic region is one of provinces in west china. Ningxia is a small region with square area of about 66, 4000 km2. Ningxia has special land cover with irrigated crop land in north and natural grass land in central and south. In addition to NDVI data, we also collected land cover and land use data and administrative border vector data with the scale of 1:4,000,000 and other data. The results show that (1)vegetation dynamic of Ningxia presents the characters of one season per year with the length of the growth season from the first decade May to the middle decade October and the range of NDVI value 0.05-0.25; the season characters vary with the local area; the max value of NDVI in the central dry area is only 0.2 and the date of reaching the peak of time series NDVI in the irrigation area is the latest while that in the south mountain area is the earliest; the Helan mountain area presents the characters of forest and the range of NDVI is narrower than those in the irrigation area and the south mountain area and higher in winter than those in two area above; in recent 18 years, the length of growth season in whole Ningxia has prolonged one decade, mainly in spring one decade in advance.(2) from 1982 to 1999, the trend of the whole Ningxia mean NDVI is increasing and presents the stable or better of vegetation growth; compared to NDVI in 1980's, NDVI in 1990's has increased already and the anomaly of growth season mean NDVI is mainly negative in 1980's while mainly positive in 1990's; NDVI in the central dry area is the lowest while NDVI in the Helan mountain is the highest; the values of NDVI in the irrigation area, the Helan mountain area and the south mountain area are higher than that of the whole Ningxia; the increasing trend of vegetation dynamic in the irrigation area, the south mountain area and the central dry area is similar with the whole Ningxia while the trend in the Helan mountain area is increasing from 1982-1988 but decreasing after 1988.

The use of satellite remote sensing images could be a valid alternative to the classical methods of bathymetric measurements for depths less than 30 meters. In this work, several pixels corresponding to different depths are considered to numerically evaluate the relation between the water spectral response and the real depth, in the Douro River Estuary (Porto, Portugal). The main concept relies on principal components analysis, which allows for combining the information of the n available spectral bands from the image into an equal number n of principal components. The dataset is composed by an IKONOS-2 image and bathymetric values. An initial analysis was performed in order to determine the viability of the data for bathymetric study of the Douro River estuary. It was proved that it was not possible to find any direct relationship between the DNs of the IKONOS-2 image and depth values. Therefore, a simple linear regression of the bathymetric values on the IKONOS-2 image principal components was considered. A significant correlation was found between the first principal component and the real depths. In the future, the use of simultaneous data and the use of other statistical models such as decision trees may also provide important contributes to improve this methodology.

The SudMed project aims since 2002 at modelling the hydrological cycle in the Tensift semi arid watershed located in central Morocco. To reach these modelling objectives, emphasis is put on the use of high and low resolution remote sensing data, in the visible, near infrared, thermal, and microwave domains, to initialize, to force or to control the implementation of the process models. Fundamental studies have been conducted on Soil-Vegetation-Atmosphere Transfer modelling (SVAT), especially related to the various means of incorporating both ground and remote sensing observation into them. Satellite data have been used for monitoring the snow dynamic which is a major contribution to runoff issued from the mountains. Remote sensing image time series have also been used to map the land cover, based on NDVI time profiles analysis or temporal unmixing of low resolution pixels. Subsequently, remote sensing time series proved to be very valuable for monitoring the development of vegetation and the crop water status, in order to estimate of evapotranspiration, key information for irrigation management.

Natural Resource Monitoring in Africa (NARMA) is one of the Core Information Services of EU-FP7 project Geoland2 addressing important sectoral policies that concern with the development of an environmental monitoring capacity over African countries for the needs of the European Commission (EC) services and for regional and continental EC partners in African countries. Congo basin is one of the target area where NARMA has to contribute to the development of AMESD/CICOS services in support to management of water resources focusing on environmental aspects of watersheds. In this contest and to better understand dynamics that occur in the watershed, an analysis has been conducted on the relation between precipitation, river discharge and vegetation dynamics by exploiting available time series of Earth Observation data. Rainfall dynamics has been described using FEWS-NET RFE estimations, river discharge has been monitored using ENVISAT radar altimeter data provided by LEGOS laboratory and vegetation dynamics have been examined through vegetation indices available from long term series of SPOT-VGT data. The comparison between river discharge measured at Bangui (Central African Republic), gauging station and radar altimeter virtual station data demonstrated that these data can be used to estimate river discharge. This result allowed to focus a preliminary analysis on the Uele watershed, Ubangi sub basin, using radar data as a proxy of river discharge, comparing these trends to seasonal rainfall estimates and trying to disentangling the effect of vegetation on discharge-rain relation. Results showed that a strong positive correlation is obtained between rain data and river discharge only at the end of the vegetation season when plants have reduced water demand for evapotranspiration and less intercept rain. Trend analysis on the considered time windows are provided and the contribution of these finding for river water alert monitoring system is discussed.

Today, different carbon sources are producing more carbon dioxide than is being absorbed by carbon sinks, contributing towards the instability in the natural balance of carbon dioxide. The goal of the SnowCarbo-project is to improve the model predictions of carbon dioxide by using a variety of Earth Observation, GIS and in situ data in constraining and calibrating the models. The aim of this article is to present different alternatives for land cover data needed in climate and carbon balance modeling, and some preliminary evaluation in the context of climate modeling. The regional climate model REMO developed at Max Planck Institute has been used to simulate the past, present and future climates over wide range of spatial resolutions. These models use Olson ecosystem classification as land cover data, which represents Finnish environment quite badly. Therefore, new versions of land cover data have been constructed based on higher resolution GlobCover and Corine Land Cover classifications as well as classifying different MODIS-products. The results are preliminary, but new versions seem to work better.

In order to construct a predictive scheme of short-term torrential rainfall events over NE China a case study is made of the development of a meso-β-scale convective system (MCS) that produced exceptionally heavy rainfall there on August 10, 2006, together with meso-scale environment, by means of automatic station data, satellite imagery and conventional observations. As an event of 100-yr return character, the downpour occurring in the central-western portion of NE China produced the maximal 1-hr precipitation of 90.8 mm at Tailai, with 82 mm thereof in the latter half hour. IR satellite and high-resolution visible cloud maps are employed to investigate how the MCS evolved from a meso-γ into a meso-α MCC (Meso Convective Complex). Analysis shows that associated with higher than 33 mm in 30 min at 6 cityand county-level regions, the MCS was separated into 2 phases. In the first phase, i.e., before the MCC formation, the MCS moved mainly eastward, merging finally into the MCC, and in the second phase, i.e., the MCC mature stage, the MCS were in the southwestern fringe of the MCC, with the strongest precipitation observed there. Inspection of even higher-resolution visible images yields that where the northern and western cumulus lines met, the MCS developed vigorously enough to produce downpour. Examination of the MCS intensification and precipitation happening indicates that 1) high temperature, high humidity and convectively unstable stratification existed over the rainbelt, in conjunction with significant increase in convective available potential energy and decline of lifted condensation level and free convective height, in favor of the torrential rains; 2) mergence of meso-βscale cloud clusters allowed MCS to develop quickly for rainfall; 3) the northern and western cumulus lines corresponded to two convergence lines on the surface wind field and at their meeting point strong convergence resulted in sufficiently intensely growing meso-βcloud clusters to produce rainfall. Analysis of MCS propagation southwestward in the MCC yields that the movement of the rainstorm-producing MCS was dependent on the shift direction and velocity of the convergence lines.

Soil moisture is difficult to quantify because of its high spatial variability. Consequently, great efforts have been undertaken by the research community to develop practical remote sensing approaches to estimate the spatial distribution of surface soil moisture over large areas and with high spatial detail. Many methodologies have been developed using remote sensing data acquiring information in different parts of the electromagnetic spectrum. Conventional field measurement techniques (including gravimetric and time-domain reflectometry) are point-based, involve on-site operators, are time expensive and, in any case, do not provide exhaustive information on the spatial distribution of soil moisture because it strongly depends on pedology, soil roughness and vegetation cover. The technological development of imaging sensors acquiring in the visible (VIS), near infrared (NIR) and thermal infrared (TIR), renewed the research interest in setting up remote sensed based techniques aimed to retrieve soil water content variability in the soil-plant-atmosphere system (SPA). In this context different approaches have been widely applied at regional scale throughout synthetic indexes based on VIS, NIR and TIR spectral bands. A laboratory experiment has been carried out to verify a physically based model based on the remote estimation of the soil thermal inertia, P, to indirectly retrieve the soil surface water content, θ. The paper shows laboratory retrievals using simultaneously a FLIR A320G thermal camera, a six bands customized TETRACAM MCA II (Multiple Camera Array) multispectral camera working in the VIS/NIR part of the spectrum. Using these two type of sensors a set of VIS/NIR and TIR images were acquired as the main input dataset to retrieve the spatial variability of the thermal inertia values. Moreover, given that the accuracy of the proposed approach strongly depends on the accurate estimation of the soil thermal conductivity, a Decagon Device KD2 PRO thermal analyzer was used to verify the remotely estimate of thermal conductivity. Remotely estimated water contents were validated using the gravimetric method. The considered thermal inertia approach allowed prediction of the spatial distribution of the soil water with a satisfactory level of accuracy.

Soluble salts in soils seriously compromise agricultural productivity around the world. Arid and semi-arid regions are most prone to salinization. Careful monitoring and surveying of salt-affected soils is needed to ensure sustainable development in such regions. Remote sensing techniques are being increasingly applied to investigate this phenomenon. Our approach is to map low and moderately salt-affected soils in northeast Brazil through the combination of remote sensing data and geochemical ground-based measurements. Spectral properties, salinity, vegetation and brightness indices were used to extract salinization features and patterns from the Brazilian soils. MODIS Terra data were selected to cover the 1.7 million km2 area and the images were taken during the summer 2008 sampling campaign. The electrical conductivity (EC) from 112 sites was determined (1:5 soil/water suspension method) to test the capability of each indicator to identify salt-affected areas based on correlations between indicators and electrical conductivity (ground truth). Eighteen indices emerged from the MODIS Terra images. A moderate correlation was found between electrical conductivity and the spectral indices. Salinity emerged as the most significant index. Spectral properties were used to define soil classes based on their degree of salinization. Near infrared (NIR) region from the electromagnetic spectrum showed high potential to separate different categories of salt-affected soil from MODIS multispectral data. A low correlation between vegetation indices and electrical conductivity indicates that these indices are inadequate when trying to discern features and patterns of salt affected areas on a large scale

Hyperspectral and multi-angular data provides an opportunity to accurately retrieve LAI and other biophysical parameters for vegetation monitoring. Based on a vegetation canopy BRDF model, the Directional Second Derivative (DSD) method has been obtained. In order to further evaluate the performance of the method, canopy reflectance spectra at different LAI values and different view angles are simulated using PROSAIL models first. Then LAI are retrieved from the spectra using DSD, SD (Second Derivative) and BRDF model inversion method. Results show that DSD is the most accurate and stable method compared with the other two. It also indicates that the optimal direction for LAI retrieval is near the hot spot. Finally, the single-angular Hyperion/EO-1 and multi-angular CHIRS/PROBA images are used to demonstrate the application of DSD method. The retrieved values are validated by ground measurements. The results are in good agreement with numerical simulations.

In this paper we present an analytical model for the computation of radiation transfer of discontinuous vegetation canopies. Some initial results of gap probability and bidirectional gap probability of discontinuous vegetation canopies, which are important parameters determining the radiative environment of the canopies, are given and compared with a 3- D computer simulation model. In the model, negative exponential attenuation of light within individual plant canopies is assumed. Then the computation of gap probability is resolved by determining the entry points and exiting points of the ray with the individual plants via their equations in space. For the bidirectional gap probability, which determines the single-scattering contribution of the canopy, a gap statistical analysis based model was adopted to correct the dependence of gap probabilities for both solar and viewing directions. The model incorporates the structural characteristics, such as plant sizes, leaf size, row spacing, foliage density, planting density, leaf inclination distribution. Available experimental data are inadequate for a complete validation of the model. So it was evaluated with a three dimensional computer simulation model for 3D vegetative scenes, which shows good agreement between these two models' results. This model should be useful to the quantification of light interception and the modeling of bidirectional reflectance distributions of discontinuous canopies.

In Situ hyperspectral data analysis for varied coverage estimation of submerged plant is very important for the interpretation of remote sensing images. In this study, the hyperspectral reflectance of Vallisneria spiralis was measured using a Spectroradiometer with spectral range of 350-1050 nm and resolution of 3 nm in Hangzhou bay wetland and the cover of submerged plant was measured. The results showed that the reflectance rate and the "red edge peak" of the first derivation of Vallisneria spiralis spectrum rose with its increasing coverage. The relationships between the coverage of Vallisneria spiralis and the spectral reflectance, spectral indices and red edge at the wavelengths were carried out and analyzed respectively. These results showed a clear linear relationship between the coverage of Vallisneria spiralis and spectrum, and the coverage of Vallisneria spiralis could be quantitatively estimated from the reflectance measured in situ. The hyperspectral remote sensing have a ability and potential to distinguish and monitor the distribution and dynamics of submerged vegetation on a large scale.

The spectra were measured with hand-spectral instrument in Hangzhou Bay, and the water samples were collected in situ and analyzed in the lab. The correlations between chlorophyll-a and total suspended matter and the measured spectral reflectivity were analyzed, and chlorophyll and total suspended matter concentrations were estimated by using the combination of the field measured spectral reflectance and Quickbird image, respectively. Simulating Quickbird bands which would be validated for the estimation of chlorophyll and total suspended matter content were described. The estimation regression models were constructed and discussed in this paper. Two of the most precise estimation models were used to estimate the chlorophyll and total suspended matter concentration. The results showed that Quickbird images are the appropriate data resources in the high spatial remote sensing to estimate the chlorophyll-a and total suspended matter, and it is more precise to estimate chlorophyll content with the two bands of Quickbird image. The combination of band 3 and band 1 are better to estimate chlorophyll content, and the correlation between band 4 and band1 and total suspended matter is also very close, and the most precise band to estimate total suspended matter is band 4 and band 1.

Change detection using satellite imagery has been increasing the need for effective land management, land environmental changes. Utilizing remote sensing data analysis is high application possibility about management in the field of environmental changes, because relatively wide area in a short-term is to get the visual information. The principal objective of this study was to provide that statistic approaches to determine dynamic thresholds for detection of significant change using image differencing of NDVI (Normalized Difference Vegetation Index). Dynamic threshold look-up-table obtained from statistics (per-pixel standard deviations over 10 years) of 10-year wide-swath satellite data (SPOT/VEGETATION) was used to apply Landsat-based change detection. Two areas is utilized in research using Landsat 7 ETM+ images that have resolution 30×30 m. When achieve changed detection taking advantage of image differencing technique which is one of the changed detection technique, it choose more dynamic critical value taking advantage of middle and low resolution satellite data. As a result, it is effective that takes advantage of NDVI value more than reflection value and method to decide change standard is effective that take advantage of statistics.

In this paper, the field-derived hyperspecral reflectance of the Tamarix spp vegetation and background soil parameters (soluble salt content and eight base ions) of associated saline soil at the lower reaches of the Tarim River were measured and studied. The spectral responses of the Tamarix spp canopy with different level saline soil, and the relationship between soil salt variables and vegetation spectral indices were analyzed in order to understand the sensitive bands and the appropriate vegetation spectral indices. The results revealed that:(1) the spectra at band ranges 600-750nm,1350-1550nm change regularly with the increasing salt content of the background soil. (2) The removal continuum reflectance shows that the three absorb troughs with band ranges 460-560nm, 560-760nm,1050-1300nm, 1300-1700nm, can play an important role in indicating increasing changes of soil salt. Theses sensitive bands just reflect the characteristics of the chlorophyll content, moisture content of the vegetation. It presents a negative correction between the absorption valley changing trend and the salt content in the background soil. When the soil salinity content is higher, the corresponding absorption valley value that reflects vegetation growth condition characteristic is not more obvious. (3) Further analysis are conducted on the absorption width at the band range 1300-1700nm, the absorption width at the band range 560-760nm, λ_red, absorption depth at the band range 1050-1300nm, R₁₄₅₀, MSAVI, trough area of the band range 560-760nm, λ_red and the absorption depth at the band range 560-760nm, these nine indices can be used to predict salt values of saline soil and evaluate the degree of soil salinity.

A high resolution imaging millimetre wave SAR delivers three key parameters important for precision farming applications, namely range, reflectivity and polarization state. The reflectivity gives information upon the type of crop and its humidity. Especially in the millimeter wave region young growing green plants exhibit a considerably higher reflectivity than older, dry leaves. Dependent on the transmit-receive polarization also indications are given upon the humidity of the underlying soil. Polarimetry also allows to judge the ripeness of the grain as the geometry of the ear is changing during the ripening process.

Egypt is experiencing a water crisis because all resources are used and the Nile is not able to provide for the increasing needs especially for irrigation. In order to better manage the available resources, an accurate quantitative assessment of water fluxes, and especially evapotranspiration, is needed to improve the management of irrigation water. We show here how MODIS low resolution satellite images (250m) were used, jointly with climatic data interpolated from stations, to compute the annual irrigation consumption of the Nile delta based on the FAO-56 method. The results are used to estimate of the Water budget of Egypt and give insights about irrigation efficiency.

Due to significant anthropogenic changes that have occurred in the last several decades in Bucharest city's landscape, urbanization has become an important factor affecting urban surface parameters, hence in the surface-atmosphere interaction processes, with a great potential to alter the local climate. Land use and land cover (LULC) influence a variety of processes important in characterizing urban /periurban biophysical parameters' quality, including aerosol deposition rates, biogenic emissions, albedo, surface temperatures, climatic parameters and other. Analysis of surface biophysical parameters changes in urban/periurban areas of Bucharest town based on multi-spectral and multi-temporal satellite imagery (Landsat TM, ETM and IKONOS) for 1989 - 2009 period provides the most reliable technique of environmental monitoring regarding the net radiation and heat fluxes associated with urbanization at the regional scale. Investigation of radiation properties, energy balance and heat fluxes is based on information derived from various satellite sensors and in-situ monitoring data, linked to numerical models and quantitative biophysical information extracted from spatially distributed NDVI-data and net radiation. This study attempts to provide environmental awareness to urban planners suggesting that future changes in urban land cover could substantially affect climate by altering biophysical land-atmosphere interactions.

Shadow exists obviously in high resolution remote sensing images. Automatic extracting shadow is quite important for removing shadow as noise or for mining shadow information. A new method of IKONOS shadow extraction in urban region was presented in this paper based on the principal component (PC) fusion information distort. First, the NIR (near infrared) band with more shadow information was selected for shadow extraction, and the information distort of PC fusion was assessed; it was found that shadow was sensitive to difference index. Second, a relative difference index was structured to enhance shadow information, as a result the values of relative difference index in shadow region were higher and the ones in non-shadow region were lower. Third, possible shadow was distinguished from non-shadow by threshold. Finally standard deviation was used to differentiate shadow from water for possible shadow, and the shadow was extracted. The results show that this shadow extraction method was simple with high accuracy, not only the shadow of high building but also that of low trees were all detected.

Urban heat island(UHI) effect is the temperature increase in urban areas compared to that in surrounding rural areas and is caused by a number of factors, such as land use / land cover (LULC)change, increase fuel consumption and lack of vegetation in urban core areas. The replacement of natural surface types from soil and vegetation to impervious materials such as asphalt and concrete structures affects the albedo and runoff characteristics of the urban land surface. The impervious materials have a lower albedo than soil and vegetation and hold more solar energy, which increase land surface temperature (LST) during the summer season. UHI effects on the center region of South Korea were analyzed using remotely sensed data. The objectives of this study are to examine the summer-time thermal environment of the Cheongju city in Korea, review the satellite assessment of the thermal environment of LULC, and compare thermal environment in 1991 to 2006. Chang detection of thermal environment is performed to determine whether a significant change has occurred. The average of LST of study area has increased 2.7°C during 15years because of changed land cover from paddy field and forest to barren, factory, and concrete. This case study indicates that barren, factory, and residential apartment over on the Cheongju and Ochang increased in the late 1990s and that vegetation area are changing predominantly in the direction of decreased forest and paddy fields. Decreasing forest and paddy fields are an important result, as it suggests that directional changes are occurring on the Cheongju and Ochang that are consistent with experimental urban warming. The most influential factors for controlling the summer-time thermal environment are the distribution of surface cover characteristics (e.g. LULC) and urban morphology, such as urban consistence materials, geometry, development stage, and density.

In December 2006 blooms of Oscillatoria rubescens were found in the reservoir Prizzi in Sicily. Oscillatoria is a genus of filamentous alga comprising approximately 6 species, between these the O. rubescens is sadly famous since this organism produces microcystins which are powerful hepatotoxins. Firstly found in Europe in 1825 on Geneva lake, recently (2006) those algae has been find out in Pozzillo, Nicoletti e Ancipa reservoirs (Enna Province), as well as in Prizzi (Palermo Province) and Garcia reservoirs (Trapani Province). Toxins produced by those bacteria (usually called microcystine LR-1 and LR-2) are highly toxic since they can activate oncogenes cells causing cancer pathologies on liver and gastrointestinal tract. Even if water treatment plants should ensure the provision of safe drinking water from surface waters contaminated with those toxic algae blooms, the contamination of reservoirs used for civil and agricultural supply highlights human health risks. International literature suggests a threshold value of 0.01 μgl^-1 to avoid liver cancer using water coming from contaminated water bodies for a long period. Since O. rubescens activities is strongly related to phosphate and nitrogen compounds as well as to temperature and light transmission within water, the paper presents the comparison between optical properties of the water of an infested reservoir and those of a reservoir characterized by clear water. Field campaigns were carried out in February-March 2008 in order to quantify the spectral transparencies of two water bodies through the calculation of the diffuse attenuation coefficient, measuring underwater downwelling irradiance at different depths as well as water spectral reflectance. Results show that diffuse attenuation coefficient is reduced by approximately 15% reducing light penetration in the water column; coherently reflectance spectral signature generally decreases, exhibiting a characteristic peak around 703 nm not present in uncontaminated waters. Latter findings highlight the possibility to detect O. rubescens infestations using their spectral characteristics by means of multitemporal remote sensing techniques.

The severe erosion phenomena affecting the Mediterranean coasts are strictly related to geophysical characteristics and socio-economic pressures. This suggests the need of monitoring and modelling the phenomenon in order to quantify its strength. In fact, the shoreline position, as well as its temporal evolution, provides important information for designing defence structures and for the development of a coastal management plan. The shoreline has a dynamic nature as it changes both in the short and long period. Those changes are caused by geo-morphological (e.g. bars and barrier island development etc.) and hydrodynamic (wave motion, tides and flows) processes, as well as by sudden and fast events such as sea storms, earthquakes and tsunamis. The research examines the uncertainty in positioning the shoreline coupling remotely sensed images and a hydro-maritime model. Although the assessment accuracy strongly relies on data availability and consistency, the resulting assessment of the shoreline erosion and accretion is crucial for an overall understanding of the hydro-maritime geo-morphological interaction. The study case is the Marsala coastline (western coast of Sicily, Italy), named 12th island physiographic unit. It is characterized by a low coast with sandy sediments from Holocene age. These sediments are in continuity of sedimentation on whitish debris composed by organogenic limestone from Pleistocene age. The diachronic analysis was carried out on both emerged and submerged parts of the beach and involves two distinct phases. In the first phase, geo-morphological in situ data have been compared with maps and georeferenced remote sensing images referred to the period 1994-2006. It allowed the identification of shoreline indicators [2] such as the beach cross-section and the shoreline positioning including its spatial and temporal variations. It should be noted that the comparison between EO (Earth Observation) images and cartographic maps is subjected to several uncertainties, due to graphic error, geo-referencing accuracy and spatial resolution. Moreover tidal and climate waves data refer to an acquisition time different to that of the EO images. In the second phase, a maritime hydraulic modelling accounting for sea fluctuations has been performed. The run-up is related to wave's amplitude and phase, as well as to the composition and particle size of the beach sediments determining the beach slope [3]. Prior to run-up calculation, an investigation aiming to evaluate how the waves propagate from offshore to inshore (a third-generation spectral wave numerical model, SWAN - Simulating WAves Nearshore), has been carried out. Wave data have been acquired by a buoy belonging to the National Network Waves Data, located at the SW of the Mazara del Vallo harbour (Trapani), while tide data were recorded by the national marigraph of Porto Empedocle (Agrigento). The results allowed assessing the uncertainty and the consequent accuracy in the shoreline positioning for given slope, highlighting that it is not always possible to assess the shoreline rise and fall, for values lower than 10-15 m.

NE China is at the Northern Hemisphere (NH) extratropical latitudes, whose climate shares many features with the climate at these latitudes. The study shows that since the 1880s the temperature of NE China has been rising in a fluctuating manner, with one peak in the 1940s and since the 1980s to the present the temperature has broken the record of the past 100 years, arriving at a new level. Statistical evidence indicates that since the 1880s NE China has experienced the temperature rise of 0.6~1.1⁰C, a trend in agreement with that of the NH change. On the other hand, the precipitation has not shown pronounced variation in this period but exhibited dry and wet stages, with the dominant periods spanning ~11 and ~22 years. In the past 30 years the heat condition in this region has been improved greatly, where ≥10.0⁰C cumulative temperature values have been increased remarkably. Following research results available it is inferred that the factors affecting the climate change in this region probably include CO₂, circulation, sea surface temperature, anthropogenic activities etc. Climate change will impact inevitably on agriculture and ecology, and initial assessment is made of the climate impact, together with countermeasures and suggestions presented.

A radiative transfer code to calculate the light field in the stratified hydrologic system is described and validated. The matrix-operator method is used. Chlorophyll-a and CDOM fluorescence and Raman scattering has also been incorporated into the code. Special emphasis is put on the treatment of phase function characterized by a sharp peak using δ-fit method. The code is validated by a model intercomparison for selected radiative transfer problems in the hydrologic system. Several profile data sets of inherent optical properties is obtained by using instruments of AC-s, Hydroscat-6 and others. These data sets were made in different hydrologic systems as Yangtze Estuary, and the East China sea. We use the IOPs measured in situ as input of radiative transfer code to simulate the light field in the hydrologic system. The preliminary result is presented in this paper.

In this paper, according to the flood risk-influencing hazard of disaster-formatire factors, the stability of hazard-pregnant environment and the vulnerability of hazard bearing body, the indexes of precipitation, runoff, river, terrain, population and economy are considered. Taking, Henan Province as the focus area, and county as the administrative unit, both the hazard assessment map and the vulnerability assessment map of the flood disaster are acquired based on GIS and AHP integrated method. Finally, the comprehensive hazard evaluating map of the flood disaster was drawn. The case study shows that the GIS-based category model is effective in flood risk zonation. Therefore the paper has a certain theoretical and practical significance.

Soil humidity plays a key-role in hydrological and agricultural processes. In the rainfall-runoff processes the knowledge of its spatial distribution is fundamental to accurately model these phenomena. Furthermore in agronomy and agricultural sciences, assessing the water content of the root zone is required in order to optimize the plant productivity and to improve the irrigation systems management. Despite the importance of this variable the in situ measurements techniques based on Time Domain Reflectometry (TDR) or on the standard thermo-gravimetric methods, are neither cost-effective nor representative of its spatial and temporal variability. Indirect estimations via Earth Observation (EO) images include the triangle method, which shows that Land Surface Temperature (LST) is prevalently controlled by surface and root zone humidity in bare and vegetated soils respectively. The effects of pre-processing techniques correcting for altimetry and seasonality are analyzed by means of shortwave and longwave airborne images acquired on a vineyard during a whole phenological period. The paper also discusses the advantages induced by replacing the absolute temperatures with relative values, that were obtained subtracting the temperatures measured by micrometeorological station or the surface temperature of high thermal inertia surfaces (as small irrigation reservoir) chosen as reference values. The validation with in situ data also highlights that a higher spatial resolution not necessarily imply a higher accuracy.

A field experiment was performed in winter wheat farmland at Baoding (Hebei province, China) from 13 Feb. 2009 to 19 Feb. 2009. In the experiment, brightness temperature of 8 channels (6.925GHz, 10.65GHz, 18.7GHz and 36.5GHz, horizontal and vertical polarization of each frequency), soil moisture, land surface temperature, snow character, and sublayer temperature are measured. The original purpose of the experiment is to character the drought monitoring ability of the microwave radiometer's channels. But during the experiment, there is a small snowfall process. So we got the radiometry characteristic corresponding with 4 statement of soil: wet soil, dry soil, frozen soil and snow covered soil (dry snow cover and wet snow cover). We do the analysis of all of the former cases. We also do the comparison of satelliteborne radiometer (AMSR-E) and the ground-based radiometer that we got using the 7days-measurement.The trend of the 2 measurement fit well.

In the last years, the availability of new technologies of Earth Observation encouraged researches to use integrated approaches for environmental monitoring. Even for agro-hydrological applications, remotely sensed data are available on wide areas allowing the retrieval of cost-effective and representative estimation of high spatial and temporal variability of the soil-vegetation system variables. In particular, soil water content plays an important role determining the partition of precipitation between surface runoff and infiltration and, moreover, influences the distribution of the incoming radiation between latent and sensible heat flux. As a consequence, distributed soil water content maps are essential data for watershed applications such as flood prediction and crop irrigation scheduling. Since cloud cover has been highlighted as the main limitation of SW/TIR traditional techniques, this research is focused on the applicability of soil moisture models based on active microwave. In particular, a Semi Empirical Coupled Model (SECM) is proposed. Reliable assessments of both surface roughness and dielectric constant (thus soil moisture) are retrieved by means of two iterative modules, without any calibration phase. The validation with in situ soil moisture, taken at a depth comparable to the RADAR penetration, gives a good agreement for bare-sparse vegetation coverage. The research is carried out on the 24 km² test-site of DEMMIN (Görmin farm, Mecklenburg Vorpommern), in the North-East of Germany. Data were acquired within the ESA-funded AgriSAR project, between April and July 2006. The implemented model uses HH, VV and HV polarized L-bands, acquired by the German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt - DLR) using an airborne platform.

Height is one of important parameters for evaluating winter wheat growth. It can be not only used to indicate growth status of winter wheat, but also play a very important role in wheat growth environmental simulating models. Remote sensing images can reflect vegetation information and variation trend on different spatial scales, and using remote sensing has become a very important means of retrieving crop growth indices such as H(height), F(vegetation coverage fraction), LAI(leaf area index) and so on. In the paper, firstly LAI was estimated with a gradient-expansion algorithm by combining remote sensing images of Landsat5 TM with field data of winter wheat measured in Shunyi&Tongzhou District, Beijing in 2008, and then applied the dimidiate pixel model with NDVI (Normalized Difference Vegetation Index) from landsat5 TM to calculate F(vegetation coverage fraction), lastly taking the ratio of LAI and F as the factor built the model to estimate winter wheat growth height. The result displayed that the determinant coefficient R² arrived at 0.48 between the field measured and the fit value by the wheat height estimating model, which showed it was feasible to apply the model with multispectral remote sensing images to estimate the wheat height.

As climatic variability and anthropogenic stressors are growing up continuously, must be defined the proper criteria for forest vegetation assessment. In order to characterize current and future state of forest vegetation satellite imagery is a very useful tool. Vegetation can be distinguished using remote sensing data from most other (mainly inorganic) materials by virtue of its notable absorption in the red and blue segments of the visible spectrum, its higher green reflectance and, especially, its very strong reflectance in the near-IR. Vegetation reflectance has variations with sun zenith angle, view zenith angle, and terrain slope angle. To provide corrections of these effects, for visible and near-infrared light, was used a developed a simple physical model of vegetation reflectance, by assuming homogeneous and closed vegetation canopy with randomly oriented leaves. A simple physical model of forest vegetation reflectance was applied and validated for Cernica forested area, near Bucharest town through two ASTER satellite data , acquired within minutes from one another ,a nadir and off-nadir for band 3 lying in the near infra red, most radiance differences between the two scenes can be attributed to the BRDF effect. Other satellite data MODIS, Landsat TM and ETM as well as, IKONOS have been used for different NDVI and classification analysis.

In monitoring vegetation change and urban planning, the measure and the mapping of the green vegetation over the Earth play an important role. The normalized difference vegetation index (NDVI) is the most popular approach to generate vegetation maps for remote sensing imagery. Unfortunately, the NDVI generates low resolution vegetation maps. Highresolution imagery, such as IKONOS imagery, can be used to overcome this weakness leading to better classification accuracy. Hence, it is important to derive a vegetation index providing the high-resolution data. Various scientific researchers have proposed methods based on high-resolution vegetation indices. These methods use image fusion to generate high-resolution vegetation maps. IKONOS produces high-resolution panchromatic (Pan) images and low-resolution multispectral (MS) images. Generally, for the image fusion purpose, the conventional linear interpolation bicubic scheme is used to resize the low-resolution images. This scheme fails around edges and consequently produces blurred edges and annoying artefacts in interpolated images. This study presents a new index that provides high-resolution vegetation maps for IKONOS imagery. This vegetation index (HRNDVI: High Resolution NDVI) is based on a new derived formula including the high-resolution information. We use an artefact free image interpolation method to upsample the MS images so that they have the same size as that of the Pan images. The HRNDVI is then computed by using the resampled MS and the Pan images. The proposed vegetation index takes the advantage of the high spatial resolution information of Pan images to generate artefact free vegetation maps. Visual analysis demonstrates that this index is promising and performs well in vegetation extraction and visualisation.

Sunshine duration is a way to characterize the climate of particular region, and used in tourism and agriculture. The method of retrieved sunshine duration from satellite derived cloud index was evaluated for north China by FY-2C visible images, measured data. The results clear show that the FY-2C data can be used for mapping sunshine duration over north China. The FY-2C data is useful for sunshine duration retrieval for where the sunshine duration is not available.

Fraction of vegetation (Fv) plays an important role in ecosystems. Estimation of Fv is essential for drought monitoring, natural resources studies, estimation of soil erosion volume etc. The aim of this study is to estimate Fv in an arid area in Iran using ALOS Imagery (June 2008). In order to find the best index for estimation of Fv, Seventeen vegetation indices (ARVI, DVI, EVI, GEMI, IPVI, MSAVI1, MSAVI2, NDVI, PVI, SAVI, SARVI, SARVI2, SR, TSAVI, WDVI) were used. The canopy cover percentage of 52 sample plots (50m by 50m) was measured in the field in June 2009. Regression models were used to assess the relationships between the field data and the calculated Fv. The 52 sample plots were randomly divided two times to 30 calibrations and 22 validations, and to 35 and 17 samples. Results revealed that selecting the calibration and validation data randomly leads to different results. Therefore, cross-validation method was used to reduce random division effect. Results indicated that, among all indices, vegetation indices such as MSAVI1, PVI, WDVI and TSAVI which are based on soil line have higher R² and lower RMSE (R² > 0.63, RMSE ≈ 3%). The results confirm the dominant effect of soil reflectance in arid areas.

AVHRR NDVI time-series datasets were used to investigate the variations in the key phenology events in cropland of North China over the past 20 years. The method based on Savizky-Golay filter was used to reconstruct NDVI time-series data set, and reduce the effects time-series of cloud contamination and the Bidirectional Reflectance Distribution Function (BRDF). Based on NOAA / AVHRR NDVI 10-day time-series data, we estimated that the growing season duration of summer maturing crop has lengthened, primarily through an earlier onset date of the start of the growing season (SGS) and a later onset date of the end of the growing season (EGS) during period of 1982-2000 in north China. The autumn maturing crop has not showed the seminar characterization in area with double crop system. The onset date of SGS is earlier, and the onset date of EGS has slightly delayed in area with one crop system.

An uneven growing winter wheat will be slower to reach full ground cover and will be lead to uneven yield and quality for cropland. The traditional investigation of crop uniformity is mainly depends on manpower. Remote sensing technique is a potentially useful tool for monitoring the crop uniformity status for it can provide an area global view for entire field within the crop growth season with scathelessness. The objective of this study was to use remote sensing imagery to evaluate the crop growth uniformity, as well as the yield and grain quality variation for a winter wheat study area. One Quickbird image on winter wheat booting stage was collected and processed to monitoring the uniformity of wheat growth. The results indicated that the spectrum parameters of Quickbird image can reflect the spatial uniformity of winter wheat growth in the study areas. Meanwhile the spatial uniformity of wheat growth in early stage can reflect the uniformity of yield and grain quality. The wheat growth information at the booting stage has strong positive correlations with yield, and strong negative correlation with grain protein. The correlation coefficient between OSAVI (optimized soil adjusted vegetation index) and wheat yield was 0.536. It was -0.531 for GNDVI (Greeness-normalized difference vegetation index) and grain protein content. The study also indicated that diverse spectrum parameters had different sensitivity to the wheat growth spatial variance. So it is feasible to use remote sensing data to investigate the crop growth and quality spatial uniformity.

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 Vegetation Index (NDVI) and Land Surface Temperature (LST) that are major indicators for an analysis of vegetation-environment. Likewise, NDVI and LST are essential in order to detect, as well as to monitor, the environmental changes in arable land. Therefore, the relationship between NDVI and LST should be quantified for the accuracy improvement of agricultural statistical data based on Remote Sensing. This study has intended to analyze the characteristics of NDVI and LST using Landsat imagery of arable land in Cheongju City, to quantify the relationship between NDVI and LST. The results indicated that time seasonal change of raster data for four times of the highest group of LST and the lowest group of vegetation located in the Cheongju city, Chungcheongbuk-do, Korea, are observed and analyzed their correlations for the change detection of land cover. This experiment, based on proposed algorithms, detected a strong and proportional correlation relationship between the highest group of LST and the lowest group of vegetation index which exceeded R=(+)0.9. Therefore, the proposed Correlation Analysis Model between the highest group of LST and the lowest group of vegetation index will be able to give proof of an effective suitability to the land cover change detection and monitoring.

To correct erroneous data arising from a variety of methods for monitoring soil drought, the paper presents the analysis of the crop-canopy spectral characteristics and measured field moisture in the mid-late stage of wheat grain filling by means of observations of synchronously monitoring drought at Satellite -airborne - in situ observation in an experiment made in the low land of the Yellow River reach in a Zhoukou farm of the province on 23 May, 2009. Results suggest that (1) In the later time of wheat grain filling, there was no clear absorption valley in the domain of 1175nm, and it is different from the spectral chart in the period of turning green to heading. (2) There are data distortions in the domain of 1541nm and 2053nm which make out that the spectral in these domain are disable for retrieved the wheat canopy character. (3) The relationship of one depth to adjacent is better and the soil moisture in deeper depth could be deduced from its relationship with surface water content. (4) The retrieved results of FY-3A are not better than MODIS', but the accuracy has been to meet the current demand for services, and can be applied to operation.

In this paper, crop growth monitoring methods are described elaborately. The crop growth models, Netherlands-Wageningen model system, the United States-GOSSYM model and CERES models, Australia APSIM model and CCSODS model system in China, are introduced here more focus on the theories of mechanism, applications, etc. The methods and application of remote sensing monitoring methods, which based on leaf area index (LAI) and biomass were proposed by different scholars at home and abroad, are highly stressed in the paper. The monitoring methods of remote sensing coupling with crop growth models are talked out at large, including the method of "forced law" which using remote sensing retrieval state parameters as the crop growth model parameters input, and then to enhance the dynamic simulation accuracy of crop growth model and the method of "assimilation of Law" which by reducing the gap difference between the value of remote sensing retrieval and the simulated values of crop growth model and thus to estimate the initial value or parameter values to increasing the simulation accuracy. At last, the developing trend of monitoring methods are proposed based on the advantages and shortcomings in previous studies, it is assured that the combination of remote sensing with moderate resolution data of FY-3A, MODIS, etc., crop growth model, "3S" system and observation in situ are the main methods in refinement of dynamic monitoring and quantitative assessment techniques for crop growth in future.

In this paper, the analysis methods, which used to descript the winter wheat growing features, have been found of satellite remote sensing coupled with the data of LAI, dry matter weight, etc. the results showed that the revised rate of remote sensing to in-situ observation is different in different developmental stages of winter wheat. Mainly manifested in the following aspects (1) In the early stage of growth and development of winter wheat (in March), the leaf area index LAI of winter wheat is small, due to the impact of soil background, the winter wheat NDVI which retrieved from MODIS data (leaf area index LAI can be calculated from NDVI) are vary greatly from in-situ observations, the revised coefficient is relatively large. (2) In the rapid vegetative growth stage (April), the ground was completely covered by winter wheat, the influence of soil background decreased, and LAI which retrieved from remote sensing closing to the data in-situ observation accordingly and the revised coefficient is smaller than in the early stage of winter wheat. (3) The LAI decreased sharply in the later stage of winter wheat. So the LAI accuracy of remote sensing retrieval as well as reduced. The differences are largest between the remote sensing retrieval and in-situ observation, and the revised coefficient is largest in all growing stage.

The endurance of summer maize to flood and the impacts of the flood on maize yields and its components were studied in this paper. Obtain maize model for quantitative assessment of flood disasters. Based on the GIS and EOS/MODIS water monitoring data, access to the flood disaster loss model can be rapid quantitative assessment. The results showed that flood had obvious impacts on the maize density, maize greenery, ear lengthen and thick, and the yields. The method can be used for fast, accurate, continuous, dynamic monitoring and evaluation of flood hazards, for the development of post-disaster planning.