A proper determination of the BRDF is of interest for land surface studies in different topics such as albedo estimation, correction of anisotropy effects, and retrieval of vegetation parameters by defining optimal geometries. In this paper, we evaluate a set of parametric models widely-used for BRDF characterisation (Roujean model, Ambrals combinations, non-linear RPV and the empirical Walthall's model). These models are inverted and tested against atmospherically-corrected BRF measurements acquired with the CHRIS (Compact High Resolution Imaging Spectrometer) instrument on board the PROBA (Project for On-Board Autonomy) satellite over an agricultural test site located in Barrax (Spain) during the SPARC (SPectra bARrax Campaign) 2003 campaign. The study area presents different land crops with high variability in LAI values from 0 to 6. The objectives of the present study are to determine how well the different parametric BRDF models are able to fit CHRIS/PROBA's observed multiangular reflectances in order to determine the nadir-zenith reflectance, which is the optimal geometry to retrieve the fractional vegetation coverage (FVC), and to describe the anisotropy of vegetation canopies, which can be useful to estimate accurately the leaf area index (LAI). To do so, performance indicators are obtained for the different models. The results of this study show that all the tested models are fairly accurate in the entire spectral range (RMS<0.016 at 674 nm and RMS<0.025 at 803 nm) and thus are suitable for normalisation purposes. However, most of them are not able to describe BRDF features such as the hot spot, which hampers the use of these models for exploiting the directional information. There are no significant differences, for the experimental conditions, among those evaluated although the best models appear to be the linear Ross-Li model (low RMS) and the non-linear RPV model (more realistic BRDF).

In the context of the Common Agricultural Policy (CAP) there is a strong interest of the European Commission for counting and individually locating fruit trees. An automatic counting algorithm developed by the JRC (OLICOUNT) was used in the past for olive trees only, on 1m black and white orthophotos but with limits in case of young trees or irregular groves. This study investigates the improvement of fruit tree identification using VHR images on a large set of data in three test sites, one in Creta (Greece) and one in the south-east of France with a majority of olive trees and associated fruit trees, and the last one in Florida on citrus trees. OLICOUNT was compared with two other automatic tree counting, applications, one using the CRISP software on citrus trees and the other completely automatic based on regional minima (morphological image analysis). Additional investigation was undertaken to refine the methods. This paper describes the automatic methods and presents the results derived from the tests.

This paper studies the detection of vegetation stress in orchards via remote sensing. During previous research, it was shown that stress can be detected reliably on hyperspectral reflectances of the fresh leaves, using a generic wavelet based hyperspectral classification. In this work, we demonstrate the capability to detect stress from airborne/spaceborne hyperspectral sensors by upscaling the leaf reflectances to top of atmosphere (TOA) radiances. Several data sets are generated, measuring the foliar reflectance with a portable field spectroradiometer, covering different time periods, fruit variants and stress types. We concentrated on the Jonagold and Golden Delicious apple trees, induced with mildew and nitrogen deficiency. First, a directional homogeneous canopy reflectance model (ACRM) is applied on these data sets for simulating top of canopy (TOC) spectra. Then, the TOC level is further upscaled to TOA, using the atmospheric radiative transfer model MODTRAN4. To simulate hyperspectral imagery acquired with real airborne/spaceborne sensors, the spectrum is further filtered and subsampled to the available resolution. Using these simulated upscaled TOC and TOA spectra in classification, we will demonstrate that there is still a differentiation possible between stresses and non-stressed trees. Furthermore, results show it is possible to train a classifier with simulated TOA data, to make a classification of real hyperspectral imagery over the orchard.

Rice is one of the most important crops in the whole world, providing staple food for more than 3000 million people. For this reason FAO declared the year 2004 as The International Year of Rice promoting initiatives and researches on this valuable crop. Assessing the Net Primary Production (NPP) is fundamental to support a sustainable development and to give crop yield forecast essential to food security policy. Crop growth models can be useful tools for estimating growth, development and yield but require complex spatial distributed input parameters to produce valuable map. Light use efficiency (LUE) models, using satellite-borne data to achieve daily surface parameters, represent an alternative approach able to monitor differences in vegetation compound providing spatial distributed NPP maps. An experiment aimed at testing the capability of a LUE model using daily MODIS data to estimate rice crop production was conducted in a rice area of Northern Italy. Direct LAI measurements and indirect LAI2000 estimation were collected on different fields during the growing season to define a relationship with MODIS data. An hyperspectral MIVIS image was acquired in early July on the experimental site to provide high spatial resolution information on land cover distribution. LUE-NPP estimations on several fields were compared with CropSyst model outputs and field biomass measurements. A comparison of different methods performance is presented and relative advantages and drawbacks in spatialization are discussed.

The objective of this study was to investigate the potential of the synergy between the biophysical/ecophysiological models and remote sensing signatures for dynamic estimation of key biophysical variables at the ecosystems-atmosphere interface. We obtained a long-term and comprehensive data set of micrometeorological, plant, and remote sensing (optical and thermal domains) measurements over well-managed uniform agricultural fields. The net ecosystem CO₂ flux (NEE_CO2) was measured by the eddy covariance method (ECM). A soil-vegetation-atmosphere transfer (SVAT) model was used to describe the energy balance, water budget, and physiological processes in the soil-vegetation-atmosphere system that allowed simulating the seasonal change of CO₂ and water fluxes as well as biomass, photosynthesis, soil water, and surface temperatures. Both remotely sensed surface temperature and spectral reflectance were useful to effectively tune the process-based model, so that biomass, evapotranspiration, and CO₂ flux were accurately simulated. Simulated NEE_CO2 agreed nicely with those measured by ECM, while simulated biomass agreed well with independent measurements. The synergy of remote sensing and process-based modeling was quite effective in utilizing infrequent and multi-source remote sensing data. This approach would have great potential for quantitative and dynamic assessment of multiple variables in terrestrial ecosystems.

Irrigation Advisory Services (IAS) are the natural management instruments to achieve a better efficiency in the use of water for irrigation. IAS help farmers to apply water according to the actual crop water requirements and thus, to optimize production and cost-effectiveness. The project DEMETER (DEMonstration of Earth observation TEchnologies in Routine irrigation advisory services) aims at assessing and demonstrating how the performance and cost-effectiveness of IAS is substantially improved by the incorporation of Earth observation (EO) techniques and Information Society Technology (IT) into their day-to-day operations. EO allows for efficiently monitoring crop water requirements of each field in extended areas. The incorporation of IT in the generation and distribution of information makes that information easily available to IAS and to its associated farmers (the end-users) in a personalized way. This paper describes the methodology and selected results.

ERA-40 stands for ECMWF Re Analysis and refers to the rerun of the European Centre of Medium Range Weather Forecast (ECMWF) Global Circulation model for the period September 1957- August 2002 utilizing all state-of-the-art information and satellite data input presently available. Here, a selection of the ERA-40 atmospheric output data at the surface is used to force the catchment hydrological model LISFLOOD to simulate historic river flows for the whole of Europe on a 5km grid resolution. Once evaluated against observed rainfall and point flow data, the output constitutes an extensive and coherent 40+ year database of pan-European calibrated river flow time series, providing a wealth of information and potential for a range of evaluation purposes. For example, statistical analyses could serve to detect flooding and/or drying frequency trends. Alternatively, the output dataset could be used to assess flood alert levels in a consistent and uniform manner at any point for any river in Europe. The set would also be extremely useful as a basis for scenario studies, investigating the impact of policy and decision making such as de/aforestation and water reservoir management on flow regimes. Further, in the context of the general scientific consensus of an expected increasing trend in extreme events, the database may serve as a resource of information for extrapolated future scenarios.

Lake Distos is situated Central Evia, Greece near to the Aliveri city. An ancient city was has been built at the shore of the lake. During the seventies a big part of the lake has been drained. As a result the lake's extent varies during the year. It covers a small area during the summer period and floods a quite big area during the winter. The objective of this study was to process multitemporal and multisensor satellite data in order to monitor the changes of the lake extent and the environmental consequences. In order to detect those changes we used multitemporal and multisensor satellite images. The former image that we used is a Landsat 2 MSS subscene acquired on July 1975. The newer image is an Envisat SAR image acquired on July 2003. We also used six Landsat TM and ETM images covering the period from 1975 to 2003. All the images covering the area of study have been geometrically corrected taking into account more than 100 ground control points distributed in whole images. The resembling method for warping the data was nearest neighborhood interpolation and the new pixel size for all the images was 30 meters. The general conclusion is that we can use satellite data from different sensors for the lake extent mapping and the environmental monitoring.

Water transparency (Secchi depth) is a basic parameter that describes the optical property of water, and it is a traditional item measured in situ. The traditional method of monitoring water transparency is the in-situ measurement by ship. However, because of its inherent shortcoming, this in situ method can not satisfy the requirement of the large-scale, quick and real-time monitoring of the water transparency. Therefore, it must be combined with the remote sensing technology to fulfill the monitoring of the water transparency. This paper studies the water transparency monitoring in China Sea by using SeaWiFS satellite sensor. First, the inversing algorithm of water transparency is introduced briefly, which based on the radiative transfer theory and bio-optical model of water. Second, the accuracy of the algorithm is validated by using the large-scale in-situ data from the Japan Meteorological Agency (JMA), which covered most of the Northwest Pacific ocean. The result shows the inversing relative error of water transparency is 22.6% by using the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) data, and it is even better in the open sea. Third, using this algorithm and SeaWiFS data, a remote sensing product data set of water transparency in China Sea was generated. Finally, we present the analysis of seasonal distribution and fluctuation patterns of water transparency in China Sea by using the generated remote sensing product collection of water transparency.

This paper is the second part of two-part set which proposes a methodology in order to validate the LSA SAF vegetation products (LAI/FVC/fAPAR) derived from SEVIRI/MSG. The main objective of this methodology refers to assessing the uncertainty of SEVIRI/MSG products by analytical comparison to in situ measurements. The scaling problem is solved in this work by considering high-resolution maps in order to make the direct comparison between ground truth and coarse-resolution products. A detail description of the measurement acquisition and estimates was presented in a first document whereas the estimation of the high-resolution biophysical maps from this in situ data set is undertaken in this paper. This work attempts to evaluate the capabilities of a geostatistical approach in the estimation of high-resolution LAI/FVC/fAPAR maps. The geostatistical approach is based on collocated cokriging, which allows to derive high-resolution maps from in situ measurements over a small area (5x5 km2), centred at Barrax test site. This technique takes into account the spatial dependence of the data, the neighbouring information, densely sampled auxiliary information and the variance estimation as opposed to empirical functions. The method has shown to be appropriate for the spatial extension of in situ measurements. An important contribution of this work is the analysis of the uncertainties associated to the method which provides an appreciation of the varying precision of the cokriged estimates due to the irregular disposition of informative points by means of the estimated variance. On the other hand, a flag image is also provided by using the convex hull tool in order to account for possible uncertainties in previous steps to the final cokriging output.

Many rainfall estimation techniques and algorithms are developed for a particular region and for very different time-space scales. Instantaneous rain rates may vary from fractions of mm to over 100mm per hr. and the rainfall intensity with duration varies from region to region. We need to understand the errors structure for a variety of instruments and algorithms that are in use today or will be in use tomorrow over different regions. Bangladesh is the country that suffers from flooding in most of the year because of highly intensive rainfall within and outside of the country. The performance of satellite rainfall is an important issue for hydro-meteorological application in Bangladesh. In this study, the first space-borne Precipitation Radar (PR) launched by Tropical Rainfall Measuring Mission (TRMM) satellite data is used, which produces rain/no rain flag, vertical rain rate profile, near surface rain etc. However, only those gauge stations are considered in this study that falls inside the instantaneous field of view of particular TRMM observations. The preliminary result shows that Bangladesh is distinct from the other region in USA. Passive Microwave calibrated IR (3B41RT) performs better than TMI-2A12 rain product over Bangladesh. The main reason could be summer rain in Bangladesh that comes mainly from extensive mid-level stratiform clouds. We could also observe from PR reflectivity profile using contoured frequency by altitude display (CFAD), higher detection error are those areas where stratiform rain is dominant, or constitute a significant proportion.

Passive microwave sensors onboard satellites can provide global snow water equivalent (SWE) observations day or night, even under cloudy conditions. However, there are both systematic (bias) and random errors associated with the passive microwave measurements. While these errors are well known, they have thus far not been adequately quantified. In this study, unbiased SWE maps, random error maps and systematic error maps of Eurasia for the 1990-1991 snow season (November-April) have been examined. Dense vegetation, especially in the taiga region, and large snow crystals (>0.3 mm in radius), found in areas where the temperature/vapor gradients are greatest, (in the taiga and tundra regions) are the major source of systematic error. Assumptions about how snow crystals evolve with the progression of the season also contribute to the errors. In general, while random errors for North America and Eurasia are comparable, systematic errors are not as great for Eurasia as those observed for North America. Understanding remote sensing retrieval errors is important for correct interpretation of observations, and successful assimilation of observations into numerical models.

The importance of soil moisture on many scientific fields like hydrology, meteorology, crop growth or soil erosion has been addressed frequently. Its characterisation has been a difficult task because of its high spatial and temporal variability. Several point based measurement techniques have been developed with different degree of success, but their conversion to spatially distributed values depends on complex geostatistical techniques. Furthermore, sensor installation and maintenance can be quite tedious. In this background, SAR remote sensing sensors provide valuable information on land surface parameters. The backscattering of the SAR signal depends amongst others on the dielectric constant of the observed surface, which is mainly related to the soil surface water content. It also gives spatially distributed information with a resolution adequate for different spatial scales: from medium or small watersheds to agricultural fields. Its periodicity can be appropriate for calibrating, on a monthly basis, the simulations of distributed hydrologic modelling tools. The present paper reports the first results of an ongoing research of which the main objective is the development of a simple methodology for the calibration of the soil moisture component of distributed hydrological models using SAR data. Five RADARSAT-1 images, acquired between 27/02/2003 and 02/04/2003 over the Navarre region (Northern Spain) have been processed. The calculated backscattering values have been compared to soil moisture and surface roughness ground measurements. Empirical linear regression models have been fitted at three different scales: point scale, field scale and catchment scale, showing acceptable correlation between calculated backscattering values and ground measured soil moisture specially at field and watershed scale. However, consistent trends have not been found probably due to differing local conditions such as surface roughness or vegetation cover. Seeking for a more consistent approach, the physically based Integral Equation Method (IEM) model has been applied. Yet, simulations run by the IEM have not been completely successful probably due to an inadequate characterisation of surface roughness.

A global data base of daily surface soil moisture has been compiled by applying a recently developed land parameter retrieval algorithm to a nine year historical data set of brightness temperatures from the Scanning Multichannel Microwave Radiometer (SMMR). The instrument, flew on-board the Nimbus-7 satellite, and made daily daytime and nighttime global observations of brightness temperature at five frequencies and two polarizations from 1978 to 1987. Spatial distributions of global soil moisture are examined, and they compare well with corresponding observations of global precipitation and global vegetation indices.

In this paper Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (Ts) were combined to indicate different land-cover types based on the fact that the biome has a similar seasonal trajectory in the NDVI/Ts space. Normalized Temperature-Vegetation Angel and Norm (NTVA &TVN) based on NDVI/Ts space, were put forward as input parameters for regional-scale land-cover classification. Remote sensing data used in this study are MODIS data products: MOD13 and MOD09, firstly the monthly Ts and NDVI were produced by the maximum value composite; secondly the monthly NDVI/Ts spaces were created; then NTVA &TVN were calculated for each of the 12 months. The monthly NTVA, TVN, NDVI, Ts were dealt with Principal Component Analysis (PCA) method, and their first three principal components were assembled to four groups as input parameters for classification. Remotely sensed land-cover system for China Based on land-ecosystem and maximum likelihood classifier were adopted to classify with four different input parameters. The classification accuracy for different inputs were compared and analyzed, and the results showed that combination of NDVI and Ts can indicate different land-cover types well; as input parameters, NTVA and TVN are applicable to macro land-cover classification, and can work well to improve classification accuracy at coarse spatial scales without other accessorial data.

As a result of the accomplish experiments determine, that by a method of a laser-induced fluorescence of chlorophyll it is possible to spot for cedar an early stage of the stressful factor, bound with presence in ground ≪petroleum dirt≫. In our case the laboratory researches provided learning a quantitative contents chlorophyll for plants found in normal and stressful conditions on a basis spectrophotometrical of a method. Natural measurement the observations behind dynamics of a photosynthetic state means of wood plants in vivo enable. For an estimation of this state the fluorescence of chlorophyll on wavelength 685 and 740 nm was used. The optical model of a green leaf was developed for methods of a laser-induced fluorescence of chlorophyll. A experiments series on remote research of processes violation of mineral power supply and exchange in plants is carried spent. Was considered the change of the ratios of intensity of a fluorescence of chlorophyll and carotenoids at deficiency. Was designed technique for detection infringement processes of mineral nutrition and change surveyed acidity grounds on laser-induce fluorescent responses of deciduous plants.

Recently, when the ninth and tenth were mined in Feiching city mining area, several mine wells occurred on water invasion. Based on systematic interpretation of TMimages in Fei Cheng mining area, authors find that there are five zones of NS trending lineaments, which nearly distribute in radial in TM images. Image processing can be divided into three types, they are spectrum enhancement, spatial filtering and data fusion, the useful methods in this area are auto-adaptive enhancement, density slicing and K-L transform. With ninth and tenth seam coals mined, three mines of east area have broken out serious accidents of water. Statistical materials and the test of water quality drawing off five limestone indicates water-yielding zone near NS, NNE, and NW trending faults, or near intersection point of its and others. In order to solve the problem, using remote sensing and other techniques, we try to find some influential factors on mine flow. Further analyses, such as, the exploration of geology on earth, and microcosmic from rock slice, the authors find that there are some reasons which lead to water invasion such as geological structure, karsts, index and so on, in which the main reason might be north-south deep fracture which is the pathway of well water's distribution, migration and recharge of mine water. There being more complicate geologic structure in the west of mine area, at last, with RS authors point out important zone of mine water invasion which the prevention-control of hazards from mine water and some measures to avoid water blast in future.

In this article, the information development techniques and methods of characteristic spatial and spectral dimensions applied to distinguish separability of the similar spectrums and the relationship with soil physical and chemical characteristic in the conjoind field of the desert foreland and the Loess Plateau of China. The soillines parameters were primarily proved up the relations with soil orginal matter and soil water content,and had better availability to the markedly different soils; Canonical Discriminant can efficaciously distinguished the soils which were similar each other in the spectral and chemic-physical characteristic.The two principal components were extracted from PCA. Thereinto the soil water and whole Fe³⁺/Fe²⁺ contributed to Fac1 and accounted for 72%;Fac2 for 27%. Further,a unitary quadratic equation was modelestablished with the Fac1 as causal variable and soil water content as independent variable based on the above-mentioned results.

The Satellite Application Facilities on Land Surface Analysis (LSA SAF) is aimed to produce and disseminate geophysical products using data from EUMETSAT satellites such as the geostationary MSG1 and the polar orbiting METOP. One of the main scientific objectives for LSA SAF validation activities is to provide the User Community with measures of uncertainty for all derived products. In this context, this document is the first of a two-part set which proposes a consistent methodology for the validation of the LSA SAF vegetation products (LAI/FVC/fAPAR) derived from SEVIRI /MSG . The methodology includes (1) an appropriate field data sampling strategy over different test sites, (2) derivation of high-resolution biophysical variable maps over a larger area (approximately the same size as the SPOT4-HRVIR2 multispectral image) along with an associated uncertainty, and (3) up-scaling to medium and coarse (MSG) resolution scales. This paper aims at developing the stage (1) of the methodology at the specific test site of Barrax, an agricultural area in Central Spain (39°3'N, 2°12'W), whereas the part (2) is addressed in a second document (this issue) and the part (3) will be addressed for future tasks. This work includes a detailed description along with an exhaustive analysis of the vegetation product estimates by the hemispherical camera during the SPARC'03 field campaign, which took place in July 2003 at Barrax test site. The hemispherical photographs have proved to provide accurate estimates of biophysical parameters in crop canopies with significant advantages such as the possibility to evaluate the gap fraction in all viewing direction. On the other hand, a test analysis of the (CAN-EYE) software package used for the hemispherical photographs processing was undertaken. This paper also includes the intercomparison with another ground data set collected by the optical instrument LI-COR LAI2000 during the same campaign.

Seasonal evapotranspiration is an essential measure to model crop growth and hydrological balances particularly for irrigation agriculture in semi-arid environments. Hydrological models traditionally integrate single-spot measurements of meteorological stations to estimate potential evapotranspiration. During the last years, the application of thermal remote sensing data in combination with meteorological data of soil-vegetation-atmosphere models facilitated the estimation of actual evapotranspiration on a large scale. This study employed multi-temporal Moderate Resolution Imaging Spectroradiometer (MODIS) data to apply the Surface Energy Algorithm for Land (SEBAL) model to the heterogeneous environment of the Khorezm region, Uzbekistan. Further meteorological data was used to extrapolate actual evapotranspiration to seasonal actual evapotranspiration. The validation of the modeled actual evapotranspiration showed acceptable accuracy when compared to the limited point-based ground truth data. The integration of a rule-based land use classification with higher spatial resolution revealed the necessity to include sub-pixel knowledge of land use distribution to interpret the modeling results. First evaluations of the water distribution and consumption situation were achieved by interpretation of modeled seasonal actual evapotranspiration with hydrological GIS information.

A technique to quantify the amount of dew on grassland with an L-band (1.4 GHz) passive microwave radiometer has been presented. The horizontal polarized brightness temperature is sensitive to dew and morning dew can increase the temperature up to 5 K. This is in contrary to recent published results, where they expect that dew does not have any effect on L band (1.4 GHz) observations. By using both the horizontal and vertical polarized brightness temperature in combination with measured soil moisture conditions we were able to estimate the amount of dew. The results compared well with another remote sensing technique to measure dew using a spectral reflectance sensor. In addition, a simple comparison study was done to study the sensitivity of the microwave emission on dew events and changes in internal water. This study showed that the microwave emission at L band is more sensitive to changes in dew than to changes in internal vegetation water content when the soil is wet. When the soil is dry, the microwave emission is more sensitive to internal vegetation water.

This paper aims to develop a remote sensing method of monitoring grain production in the early stages of crop growth. It is important to oversee the quantity of grain in production at an early stage in order to raise the alarm well in advance if a poor harvest is looming, especially in view of the rapid population increase in Asia and the long-term squeeze on water resources. Grain production monitoring would allow orderly crisis management to maintain food security in Japan, which is far from producing enough grain for its own population. We propose a photosynthesis-based crop production index CPI that takes into account all of: solar radiation, effective air temperature, vegetation biomass, the effect of temperature on photosynthesis by leaves of grain plants, low-temperature sterility, and high-temperature injury. These later factors, which extend the model of Rasmussen, are significant around the heading period of crops. The proposed photosynthesis-based crop production index CPI has accurately predicted the rice yield expressed by the Japanese Crop Situation Index in three years, including the worst yield in recent years, at a test site in Japan.

By means of GIS method, the digital elevation model (DEM) of Binjiang Basin was established, and the boundary of the basin was drawn. Based on SCS model, the Grid SCS model was established; with witch the flood volumes were computed. This research indicates that the Grid SCS model can calculate the spatial distribution of the runoff and the calculation precision of runoff yield is improved.

Here, we introduce an interactive communication and management system, Scope Water, which is constructed to establish a transfer of results from research work directed towards the solving of a specific problem. To proceed step by step towards this goal, the system uses a structured approach. Starting with the global exploration of knowledge, expertise, and ideas from experts, passing an objective assessment of this information and leading finally to a coopertive making up of a concept for problem solving by specialists. Scope Water has been developed on the basis of recent advances in cybernetic management experienced in team meetings and was successfully launched as a tool to gain quick access to recent results from research work on water by Strategic Science Consult Ltd. (SSC). SSC now plans to broaden the application of SCope Water by adding a platform which allows scientists on remote sensing to offer their results, knowledge and ideas as a service to help to solve specific problems on studying/monitoring aquatic systems. Single scientists, working groups and research instituitions are invited to participate in such a service metwork on remote sensing and are asked to ceclare their interest by sending an e-mail to the authors address given above.