Crop growth and production can be simulated by models for the whole canopy as a function of intercepted radiation, water availability, air temperature and nitrogen availability. Simulation models supply quantitative outputs starting from quantitative inputs and they need quite complex databases to run simulations. In practice, the more complex and physically based these tools are, the more inputs are required for their application. In most cases such data are not available. This is the reason why, for large scale evaluations, simplified models are often applied and satellite data are used as input. In particular, multi-temporal Earth Observation data represent a valid tool to define crop phenological stages and derive temporal and spatial variability of vegetation biophysical parameters, such as the Leaf Area Index (LAI). In 2003 and 2004 two intensive field campaigns were conducted over different areas of the Italian Rice Belt, Northern Italy, with the objective of collecting data for growth model calibration. Field spectroradiometer measurements and LAI estimation, retrieved by LAI2000, have been used to study the best Vegetation Index (VI) for rice growth monitoring. VI vs LAI relationship has been scaled up to MODIS data to produce LAI map for the entire growing season and the key phenological rice events have been detected by multitemporal MODIS analysis. Preliminary results of rice production estimation using a Light Use efficiency model that ingests spatially distributed phenological information are presented. Comparison with CropSyst model phenological parameters are provided and the contribution of multi-temporal EO data for regional crop monitoring is discussed.

Previous experiments demonstrated the relationships between the radar backscattering coefficient, σ_o and crop parameters such as fresh biomass, plant height and Leaf Area Index (LAI). Topsoil water content also influences the backscattered signal and is as such a required input parameter in the physical and semi-empirical models that extract vegetation parameters from σ_o. In an operational environment, it is not possible to measure soil moisture over an entire agricultural region. As the vegetation cover hampers the radar remote sensing of soil moisture, near surface soil moisture can be simulated using a hydrological model. In this paper, it is investigated whether soil moisture values obtained through the hydrological model TOPLATS can be used in a crop parameter retrieval algorithm. The data set used for this investigation was collected from March to September 2003 in the Loamy Region, Belgium. During this period, 18 agricultural fields were sampled for vegetation parameters and soil moisture. In addition, 11 ERS-2 images of that period were acquired of which 6 coincided with the field measurement dates. Because the necessary catchment data were not available, TOPLATS was calibrated on a point scale for every field with in situ soil moisture. The calibrated TOPLATS model was applied to simulate soil moisture values at the ERS-2 acquisition dates for which no soil moisture field measurements were available. In parallel, the Water Cloud model was calibrated using the biophysical parameters measured on the field in order to retrieve LAI estimates from ERS SAR time series. In a second step, the simulated soil moisture values corresponding to the SAR acquisition dates were used as input in the Cloud model as substitutes of field measurements, and the propagation of the soil moisture estimate error in the LAI retrieval algorithm was studied. Finally the experimental results were discussed in the perspective of a regional crop monitoring system and the operational feasibility is assessed.

El-Qaa plain lies in the southern part of Sinai Peninsula extending along the Gulf of Suez. It occupies an area of about 3300 km2. El-Qaa plain is suffering from seasonal flashflood that can roll boulders, tear out trees, destroy local people buildings, and scour out new channels. Flashfloods are among the most frequent and costly natural disasters in terms of human hardship and economic loss. Terrain units of ElQaa plain were interpreted by draping satellite ETM+ image over Digital Terrain Model (DTM).These units could be categorized into sand sheet, outwash plain, inter-ridged sand flat, wadi bottom, wadi outlet, dry valley, delta,dry &wet sabkhas, ridge, rocky hill, cuesta, peniplain, rockoutcrop, footslope, bajada & alluvial fans, inclined lime stone, marine spits & heads and water bodies. On the other hand soils of ElQaa plain were classified into the following sub groups: Calcic Haplosalids, Gypsic Haplosalids, Lithic Haplocalcids, Lithic Torripsamments, Typic Aquisalids, Typic Haplodurids, Typic Torripsamments, and Typic Haplocalcids. Arc Hydro Model was used with the aid of DTM for deriving slope, flow direction, basins, flow length and flow accumulation. These derivations influence directly the flashflood behavior. Universal Soil Loss Equation (RUSLE) was used to estimate soil loss in ElQaa plain using GIS spatial analyses. The minimum mean soil loss belonging to water erosion was determined by 1.23 ton\hectare\year, meanwhile the maximum one was estimated by 5.08 ton\hectare\year. Alternative management and potential cropping system was suggested to adequate conservation measures in farm planning system of ElQaa plain. These measures could be grouped under 1-Selection of appropriate landuse. 2-Maintaining organic matter. 3-Reducing tillage. 4-Using zero tillage or direct seeding. 5-Growing forages and using crop rotations.

Environmental studies have always been associated with fieldwork, which is still carried out in rather conventional ways. At the same time it is well known that these fieldworks consume considerable parts of the research budgets while collected data has often not the desired quality and data collection methods tend to interrupt the whole research process. Now the usage of mobile GIS systems is drastically altering the outdoor work and solving or reducing many of the associated problems. This paper describes how the usage of mobile GIS technology during a hydrological fieldwork campaign in the Algarve (Portugal) addresses several of the traditional fieldwork problems and how the involvement of students can have a double positive side-effect. The evaluation showed that the new method enables researchers to collect and record reliable spatial data in a more uniform and efficient way, which saves time that could be used to analyze and process data during the fieldwork. Even more, using the mobile GIS system, the researcher is able to retrieve important hydrological information when he or she is in the field. Further work is planned on the improvement of field communication and data-exchange possibility for guidance and feedback from specialists at the office.

Radar remote sensing of bare soil surfaces has shown to be very useful for retrieving soil moisture. However, the error on the retrieved value depends on the accuracy of the roughness parameters (RMS height and correlation length). Several studies have revealed that these parameters show a high variability within a field, and therefore, a lot of soil roughness profiles need to be measured to obtain accurate measurements of soil roughness. Yet, in an operational mode, soil roughness measurements are not available and therefore, for different tillages, possibility distributions of roughness values can be defined. Through inverting the Integral Equation Model, possibility distributions for soil moisture are determined. After transferring these possibilities into probabilities, mean soil moisture values and the uncertainty hereupon (given by the standard deviation on the retrieved soil moisture values) are obtained. The effect of different roughness types on the retrieval accuracy is assessed. It is found that the accuracy depends on the wetness state of the soil.

Recent field experiments showed that dew has a significant effect on L-band (1.4 GHz) microwave observations. At an experimental grass site in the Netherlands (ELBARA2003), and at an experimental fallow site in France (SMOSREX) several dew events were able to increase the horizontal polarized brightness temperature up to 10 K. The Microwave Polarization Difference Index (MPDI) was shown to be a powerful index to describe the effect of dew. Current satellite missions (i.e. TRMM and SSM/I) but also future missions (i.e. HYDROS and SMOS) observe the Earth surface when dew is likely, between 6-8 AM. These observations are used in soil moisture retrieval methodologies, and ignoring of the dew effect may lead to a significant underestimation of soil moisture. Therefore we started, as a follow up of the previous field studies, an investigation of the effect of dew on microwave observations at satellite scale. Two months of TRMM data were selected to study the diurnal variations of the microwave signal and their relation to morning dew. Between February and March 1998 distinct diurnal MPDI patterns were detected from space. The MPDI values at X band (~10 GHz) were significantly higher in the afternoon, compared to the morning for several agricultural regions in the northern part of the state of Oklahoma in the United States. These diurnal MPDI variations from space were similar as the patterns as detected by the dew affected field observations at L-band, leading us to conclude that TRMM data at X-band is as well affected by dew.

ERA-40 stands for ECMWF Re Analysis and refers to the rerun of the European Centre of Medium Range Weather Forecast (ECMWF) Numerical Weather Prediction (NWP) model for the period September 1957- August 2002 utilizing all state-of-the-art information and satellite data input presently available. From this dataset the top layer volumetric soil water is extracted and evaluated against surface moisture retrievals derived from the SMMR instrument on board the Nimbus-7 satellite for the European window. The evaluation of NWP model output with observed data is relevant to the initialization of land surface conditions in these models, which is important for accurate short term to long range meteorological and hydrological prediction. Because land surface parameters are highly integrated states, errors in land surface forcing, model physics and parameterization tend to accumulate in the land surface stores, such as soil moisture, of these models This has a direct effect on the model's water and energy balance calculations, and will eventually result in inaccurate weather predictions. It is expected that improved accuracy in defining initial conditions for NWPs along with continuous internal bias corrections for baseline data generated by uncoupled Land Surface Models (LSM), will lead to highly improved shortterm to long-range weather forecasting capability. Preliminary analysis presented here reveals the off set between the two data sets, although distinct, is relatively constant, which suggests a potential for improved initialization and bias correction by an optimized accuracy and spatial representation of the soil moisture data fields.

The SMOS mission, planned for launch in 2007, will carry an L-band (1.4 GHz) multi-angle, dual-polarisation interferometric microwave radiometer for global monitoring of soil moisture (and ocean salinity). For routine processing of global scale SMOS soil moisture data, the τ-ω zero-order transfer model has been selected. However, at a global scale, a high percentage of SMOS pixels is infected with fractional forest, whereas for most forest types the radiative transfer properties at L-band are practically unknown. This paper presents some preliminary results of the Bray'04 experimental campaign, which was held with the objective of studying the angular and polarisation characteristics of a coniferous forest, as knowledge of the effects of forests on the microwave signal is essential for solving the problem of heterogeneity. The main focus of these results is parameter retrieval of the single scattering albedo (ω), the effective dielectric constant (κ) of the emitting surface layer and the vegetation optical depth (τ).

Forest fires can be the cause of serious environmental and economic damages. For this reason considerable effort has been directed toward forest protection and fire fighting. The means traditionally used for early fire detection mainly consist in human observers dispersed over forest regions. A significant improvement in early warning capabilities could be obtained by using automatic detection apparatus. In order to early detect small forest fires, the use of a dial system will be considered. A first evaluation of the lowest detectable concentration will be estimated by a numerical simulation. The theoretical model will be used also to get the capacities of a dial system in fire surveillance of wooded areas. Fixing the burning rate for several fuels, the maximum range of detection will be evaluated. The results of these simulations will be reported in the paper.

The lack of information on vegetation dryness prior to the use of fire as a management tool often leads to a significant deterioration of the savanna ecosystem. This paper therefore evaluated the capacity of SPOT VEGETATION time-series to monitor the vegetation dryness (i.e., vegetation moisture content per vegetation amount) in order to optimize fire risk assessment in the savanna ecosystem of Kruger National Park in South Africa. The integrated Relative Vegetation Index approach (iRVI) to quantify the amount of herbaceous biomass at the end of the rain season and the Accumulated Relative Normalized Difference vegetation index decrement (ARND) related to vegetation moisture content were selected. The iRVI and ARND related to vegetation amount and moisture content, respectively, were combined in order to monitor vegetation dryness and optimize fire risk assessment in the savanna ecosystems. In situ fire activity data was used to evaluate the significance of the iRVI and ARND to monitor vegetation dryness for fire risk assessment. Results from the binary logistic regression analysis confirmed that the assessment of fire risk was optimized by integration of both the vegetation quantity (iRVI) and vegetation moisture content (ARND) as statistically significant explanatory variables. Consequently, the integrated use of both iRVI and ARND to monitor vegetation dryness provides a more suitable tool for fire management and suppression compared to other traditional satellite-based fire risk assessment methods, only related to vegetation moisture content.

Large-scale wild fires have direct impacts on natural ecosystems and play a major role in the vegetation ecology and carbon budget. Accurate methods for describing post-fire development of vegetation are therefore essential for the understanding and monitoring of terrestrial ecosystems. Time series analysis of satellite imagery offers the potential to quantify these parameters with spatial and temporal accuracy. Current research focuses on the potential of time series analysis of SPOT Vegetation S10 data (1999-2001) to quantify the vegetation recovery of large-scale burns detected in the framework of GBA2000. The objective of this study was to provide quantitative estimates of the spatio-temporal variation of vegetation recovery based on remote sensing indicators. Southern Africa was used as a pilot study area, given the availability of ground and satellite data. An automated technique was developed to extract consistent indicators of vegetation recovery from the SPOT-VGT time series. Reference areas were used to quantify the vegetation regrowth by means of Regeneration Indices (RI). Two kinds of recovery indicators (time and value- based) were tested for RI's of NDVI, SR, SAVI, NDWI, and pure band information. The effects of vegetation structure and temporal fire regime features on the recovery indicators were subsequently analyzed. Statistical analyses were conducted to assess whether the recovery indicators were different for different vegetation types and dependent on timing of the burning season. Results highlighted the importance of appropriate reference areas and the importance of correct normalization of the SPOT-VGT data.

The success of any decision support system for managing wildfires lies on its ability to simulate fire evolution. Therefore, accurate information on the natural fuel material in any area of interest is necessary. The present study aims to provide methodological tools to explore in depth the potential of new Earth Observation data for horizontal mapping of vegetated areas. Two approaches are mainly described. The first one deals with the classification of ASTER visible, near- and short-wave infrared images in a detailed nomenclature including both different species and tree densities. This is important for wildfire studies since the same vegetation classes may represent completely different risk ignition levels depending on their morphological characteristics (i.e., trees height and density). The improvement of class separability using hyperspectral images acquired by Hyperion is also investigated. The second approach refers to a pattern recognition software tool for single tree counting using a very high spatial resolution image acquired by IKONOS-2 satellite. According to this approach, the regions dense in plants are identified by applying a suitable thresholding on the image. The resulted regions are further processed in order to estimate the number and location of single trees based on a pre-specified crown size per stratified zone. The outcome of the latter approach may provide direct evidence of tree density relating to ground biomass. Finally, the two approaches are used in a complementary manner to explore the possibilities offered by new sensor technology to override past limitations in species and fuel classification due to inadequate spectral/spatial resolution. The pilot application area is Mount. Pendeli and the east side of Mount. Parnitha, in the prefecture of Attiki, Greece.

In the framework of MONIDRI (Development of DSS on water use monitoring and optimisation, based on the integration of calculation and evaluation models) research project (financed on the basis of Italian research Ministry strategic program), two Airborne remote sensing sensors have been experimented. The Compact Airborne Spectrographic Imager (CASI) allows to a very accurate crop recognition. This is specially required in crop water requirements (CWR) calculation, that could change between different varieties of the same cultivation. A significant results has been obtained in Abruzzo region, where the use of CASI allows to recognise different vineyards varieties (red and white). Thanks to the use of this platform it's possible to define clusters of crops with similar WR and spectral firm. These clusters could be used in LANDSAT TM image processing for agricultural water use monitoring. The ALTM1210 (Airborne laser altimeter) allows to produce DEM with extraordinary geometric accuracy (elevation points each 20 cm). In two case studies the ALTM has been utilised to produce DEM with 3 m of geometric resolution. This data have been integrated to a soil erosion calculation model to simulate the future volume losses of artificial lakes for irrigation. The other ALTM application consists of the integrated use of DEM with LANDSAT TM to monitor the seasonal level and surface of irrigation lakes, and to estimate the available volumes on the basis of surfaces/volumes diagrams, according to the lake project data.

In the present work we show the potential of multiangular hyperspectral PROBA-CHRIS data to estimate aerosol optical properties over dense dark vegetation. Data acquired over San Rossore test site (Pisa, Italy) have been used together with simultaneous ground measurements. Additionally, spectral measurement over the canopy have been performed to describe the directional behavior of a Pinus pinaster canopy. Determination of aerosol properties from optical remote sensing images over land is an under-determined problem, and some assumptions have to be made on both the aerosol and the surface being imaged. Radiance measured on multiple directions add extra information that help in reducing retrieval ambiguity. Nevertheless, multiangular observations don't allow to ignore directional spectral properties of vegetation canopies. Since surface reflectivity is the parameter we wish to determine with remote sensing after atmospheric correction, at least the shape of the bi-directional reflectance factor has to be assumed. We have adopted a Rahman BRF, and have estimated its geometrical parameters from ground spectral measurements. The inversion of measured radiance to obtain aerosol optical properties has been performed, allowing simultaneous retrieval of aerosol model and optical thickness together with the vegetation reflectivity parameter of the Rahman model.

Remote sensing offers a very interesting means to estimate the soil moisture state of a hydrological system. However, practical use for small scale agricultural applications is still limited. Ground truth data remain necessary to validate the inversion from the measured quantities to soil moisture content, to understand small scale processes in the horizontal plane, and to assess the distribution of water over a soil profile. Additionally, land surface models offer basic knowledge of the physical and physiological processes affecting the soil moisture state. A combination of both sources of information yields an optimal estimate of the system state and offers the best knowledge available to decision makers. In this study, ground measurements of soil moisture in the Optimizing Production Inputs for Economic and Environmental Enhancement (OPE³) field (near Washington D.C.) of the United States Department of Agriculture (USDA) were assimilated into the Community Land Model (CLM2.0). Some practical problems that prevent optimal state estimation are discussed, such as the presence of bias in the model or observations, and the limited knowledge of the correlation structure of e.g. model error. Some case studies revealed that the influence of assimilation of upper layer soil moisture, as provided by remote sensing, improves the model results, but is not as persistent for profile estimation as assimilation of soil moisture in deeper layers.

Many studies in the past have used remote sensing techniques to estimate evapotranspiration from cropped and naturally vegetated surfaces. These studies have reported differences between estimated values and ground measured evapotranspiration rates using a variety of methods such as Bowen ratio and eddy covariance flux systems. The mixed results of this body of research could be due to the fact that remotely sensed latent heat fluxes generally were integrated using simple arithmetic averages of pixel values, around the reference flux stations. Recently, techniques have been developed¹ to properly weight and integrate remotely sensed distributed heat fluxes according to the contributing areas upwind from ground-based flux stations. This paper describes the application of a 2-D source area (footprint) function for the integration of remotely sensed latent and sensible heat fluxes estimated from short-wave and thermal high-resolution airborne multispectral digital imagery as well as Landsat Thematic Mapper imagery. Comparisons are made against heat fluxes measured over corn and soybean fields using thirteen eddy covariance flux towers. The data were collected over a period of three weeks during the summer of 2002 close to Ames, Iowa as part of the SMACEX/SMEX02 experiment funded by NASA. The rain fed corn and soybean crops were in their vegetative stage of growth during the period, presenting considerable surface heterogeneity in plant cover and leaf area. Results show that remotely sensed fluxes integrated using the footprint functions compared well to the ground measured fluxes and that the methodology was valid for satellite-based fluxes.

A physically-based soil temperature model using remote sensing inputs is being developed. The model uses the standard soil heat transfer equation together with remote sensing-based estimates of the surface temperature and incoming radiation to calculate soil temperature at various depths in the profile. Vertical polarization microwave brightness temperatures at a frequency of 37 GHz are used to estimate the near-surface soil temperature. Incoming radiation is derived from surface solar irradiance values acquired from the International Satellite Cloud Climatology Project (ISCCP) data archives. Experimental field observations were used first to develop the temperature model.

Accurate representation of land surface temperature (LST) at a large-scale is of great concern in numerous environmental studies. Simply averaging of LST measured at a small-scale into the large-scale may lead to misrepresentation due to spatial variability in LST and uncertainty in surface heterogeneity. The satellite-derived LST may be more comprehensive in large-scale observation, yet it often has the satellite-view biases, which is especially true to terrain area. To account for these factors, an approach for LST scaling was proposed, based on the Stefan-Boltzmann law and terrain correction approach used in remote sensing. It was further modified oriented to satellite-derived LST. A terrain area over the Loss Plateau of China was selected for examination. The Lipton-Ward (L-W) approach, which was originally developed for addressing the satellite-view biases in retrieved LST in mountain area, was also adopted in the examination from the perspective of scaling. Incorporated with 90-m topographical data, 90-m LST and emissivity products from The Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) were scaled up to 900-m using the two approaches. Results showed that the proposed approach smoothed the LST difference and reduced the standard deviation of LST but the L-W approach did not. This was further confirmed from the comparison with the terrain corrected 900-m LST, which was produced from the LST products of The MODerate resolution Imaging Spectroradiometer (MODIS) onboard the same satellite platform with ASTER sensor.

In the framework of the European CAP (Common Agricultural Policy), the European Commission imposes on member states to prevent irregularities, and recommends the control with remote sensing (CwRS) of the declared crops and declared area of crop fields. In the framework of remote sensing procedure, the European Commission, by the way of his Joint Research Centre, advises the use of very high spatial resolution (VHR) satellite data. These data are extraordinary from the point of view of the spatial resolution but the use of these kinds of data involves some problems in the traditional per-pixel classification like the salt and pepper effect and the poor spectral resolution of the VHR data. The region-based classification could solve these problems and allows the use of other features on top of spectral features in the classification process. This study present the potential of the VHR data region-based classification to the classification of the agricultural and rural land cover in the framework of the remote sensing control of the European Union CAP.

Monitoring crop conditions and assessing nutrition requirements is fundamental for implementing sustainable agriculture. Rational nitrogen fertilization is of particular importance in rice crops in order to guarantee high production levels while minimising the impact on the environment. In fact, the typical flooded condition of rice fields can be a significant source of greenhouse gasses. Information on plant nitrogen concentration can be used, coupled with information about the phenological stage, to plan strategies for a rational and spatially differentiated fertilization schedule. A field experiment was carried out in a rice field Northern Italy, in order to evaluate the potential of field radiometric measurements for the prediction of rice nitrogen concentration. The results indicate that rice reflectance is influenced by nitrogen supply at certain wavelengths although N concentration cannot be accurately predicted based on the reflectance measured at a given wavelength. Regression analysis highlighted that the visible region of the spectrum is most sensitive to plant nitrogen concentration when reflectance measures are combined into a spectral index. An automated procedure allowed the analysis of all the possible combinations into a Normalized Difference Index (NDI) of the narrow spectral bands derived by spectral resampling of field measurements. The derived index appeared to be least influenced by plant biomass and Leaf Area Index (LAI) providing a useful approach to detect rice nutritional status. The validation of the regressive model showed that the model is able to predict rice N concentration (R²=0.55 [p<0.01]; RRMSE=29.4; modelling efficiency close to the optimum value).

After a review of the current state of the art in LAI retrieval with optical and radar remote sensing data, this study investigates the capabilities of satellite remote sensing imagery in operational crop growth monitoring. This study demonstrated that the availability of an extensive crop field delineation database (like existing for the entire Belgian country) is of crucial in interest in order to retrieve crop specific information. LAI remote sensing retrieval was achieved during the year 2003 on a large Belgian agricultural area (4500 km²) for Sugar beet, Winter wheat and Maize crops. In order to increase the monitoring temporal frequency, an integration of SPOT-HRV, ENVISAT-MERIS and ERS2-SAR sensors was carried out, with a good level of accordance. The retrieval results were compatible with the concurrent field measurements as well as with the outputs given by the WOFOST crop growth model.

The Leaf Area Index is a key parameter that is indispensable for many biophysical and climatic models. LAI is required for modeling crop water requirements for precision farming and agricultural resource management. The objective of this study was to investigate different approaches for estimating LAI from EO data. To this aim multiangular CHRIS/PROBA data, from SPARC 2003 and 2004, were used in the inversion of PROSPECT-SAILH models using a numerical optimization technique based on Marquardt-Levenberg algorithm. The optimal spectral sampling to estimate LAI was investigated using a sensitivity analysis. From the same data set, the reflectance in the red and near-infrared bands, from the closer to nadir image, was considered in order to estimate the LAI using an empirical approach based on the CLAIR model. The LAI obtained from the empirical approach was finally employed as prior information in the physical based model. LAI values retrieved with the combined approaches were realistically estimated with a good accuracy (RMSE is 0.51 m2m-2).

In spite of the insistent need for comparable data, structural analysis of forest canopies with trees as their main components often is subjective, and thus insufficiently standardised. Current methods seldom are capable of describing the three-dimensional structure of a forest canopy, and the tree in particular, in an efficient, repeatable, accurate, complete and comprehensive manner. Laser technology provides a rising tool which creates the possibility to generate a unique and comprehensive mathematical description of deciduous tree structure. The terrestrial laser system used in this study consisted of the commercially available SICK Laser-Measurement-System-200 (LMS200). The laser scans the surrounding vegetation obtaining three-dimensional structural datasets with high resolution. The objective of this research was to investigate the influence of the geometric laser measurement pattern on the accuracy of the quantitative mathematical description of tree structure. Laser measurement variability along angular viewing differences is caused by the physical laser pulse/object interactions and an intrinsic characteristic of the laser device: a transmitted laser pulse is reflected by the first object it encounters. Therefore, spatial information, related to the position of the vegetative elements located behind the target object, was not available (shadow effect). These background objects had to be measured from different angles to obtain comprehensive laser coverage. An artificial tree was constructed in an experimental setup, thereby creating the possibility to arrange the structural key elements (leaves and branches) according to predetermined patterns. The multidirectional accuracy and quality of the laser measurement setups subsequently were compared based on the development of a mathematical description of each case.

The experimentation takes place in the Maya Biosphere Reserve, in the heart of the Peten region in Guatemala. In this natural area intermingled rivers and lakes, the forest which was in balance with environmental conditions dominated all the space. However, the landscape has just suffered a real transformation for the last 15 years. Since 1987, populating has settle up regularly by succesive waves. They have appropriated, cleared and changed the native forest in pasture and milpa (field of corn). This process of systematic deforestation by large fires, permits the creation of new rural societies, a new area of distinctly diverse uses. But the sudden and non control setting up of these populations threaten environment conditions. A conflict for the land has been appeared around and inside of the Maya Biosphere Reserve, whitch is itself threatened. The State of Guatemala, as the NGO need a local and regional perception. And yet, faced with this speedy phenomenon non finished, the lack of updated cartographic data in a area little known and badly statistical informed, high resolution remote sensing becomes an irreplaceable tool to understand such radical transformations. To understand spatio temporal process of this new rural pioneer front, to make a dynamic diagnosis, to date, to follow, to map, to update environmental and statistical data, the method of image processing proposed is based on satellite data fusions--Landsat-TM and Spot--by multidated approaches (11 images over 15 years), multi-scale (from local to regional) and multispectral (only one image resultant of 41 georeferenced channels) ; the results have been ratified by field work.

Deforestation is a result of complex causality chains in most cases. But identification of limited number of factors shall provide comprehensive general understanding of the vital phenomenon at a broad scale, as well as projection for the future. Only two factors -- human population size (N) and relief energy (R: difference of minimum altitude from the maximum in a sampled area) -- were found to give sufficient elucidation of deforestation by nonlinear logit regression models, whose functional forms were suggested by step functions fitted to one-kilometer square high precision grid-cell data in Japan (n=6825). Likelihood with spatial dependency was derived, and several deforestation models were selected for the application to East Asia by calculating relative appropriateness to data. For the measure of appropriateness, Akaike's Information Criterion (AIC) was used. Logit model is employed so as to avoid anomaly in asymptotic lower and upper bounds. Therefore the forest areal rate, 0 < F < 1. To formulate East-Asian dataset, landcover dataset estimated from NOAA observations available at UNEP, Tsukuba for F, gridded population of the world of CIESIN, US for N, and GTOPO30 of USGS for R, were used. The resolutions were matched by taking their common multiple of 20 minutes square. It was suggested that data of full forest coverage, F=1.0, which were not dealt in calculations due to logit transformation this time, should give important role in stabilizing parameter estimations.

Over the past few years, NASA has had a great interest in exploring the feasibility of using Unmanned Aerial Vehicles (UAVs), equipped with multi-spectral imaging systems, as long-duration platform for crop monitoring. To address the problem of predicting the ripeness level of the Kauai coffee plantation field using UAV aerial images, we proposed a neural network algorithm based on a nested Leaf-Canopy radiative transport Model (LCM2). A model-based, multi-layer neural network using backpropagation has been designed and trained to learn the functional relationship between the airborne reflectance and the percentage of ripe, over-ripe and under-ripe cherries present in the field. LCM2 was used to generate samples of the desired map. Post-processing analysis and tests on synthetic coffee field data showed that the network has accurately learn the map. A new Domain Projection Technique (DPT) was developed to deal with situations where the measured reflectance fell outside the training set. DPT projected the reflectance into the domain forcing the network to provide a physical solution. Tests were conducted to estimate the error bound. The synergistic combination of neural network algorithms and DPT lays at the core of a more complex algorithm designed to process UAV images. The application of the algorithm to real airborne images shows predictions consistent with post-harvesting data and highlights the potential of the overall methodology.

The Medium Resolution Imaging Spectrometer, MERIS, on board of ENVISAT-1 fulfils the information gap between the current high and low spatial resolution sensors. In this respect, the use of MERIS full resolution data (300 m pixel size) has a great potential for regional and global land cover mapping. However, the spectral and temporal resolutions of MERIS (15 narrow bands and a revisit time of 2-3 days, respectively) might be further exploited in order to get land cover information at a more detailed scale. The performance of MERIS for extracting sub-pixel land cover information was evaluated in this study. An iterative linear spectral unmixing method designed to optimize the number of endmembers per pixel was used to classify 2 MERIS full resolution images acquired over The Netherlands. The latest version of the Dutch land use database, the LGN5, was used as a reference dataset both for the validation and for the selection of the endmembers. This dataset was first thematically aggregated to the main 9 land cover types and then spatially aggregated from its original 25m to 300m. Because the fractions of the different land cover types present in each MERIS pixel were computed during the aggregation, a sub-pixel accuracy assessment could be done (in addition to the traditional assessment based on a hard classification). Results pointed out that MERIS has a great potential for providing sub-pixel land cover information because the classification accuracies were up to 60%. The correct number of endmembers to unmix every pixel was adequately identified by the iterative linear spectral unmixing. Future research efforts should be put in making use of the high revisit time of the MERIS sensor (temporal unmixing).

A hyperspectral virtual forest scene of a Fagus sylvatica stand is presented. An off-the-shelf tree architectural software package (Bionatics) was used to generate a biologically accurate Fagus tree, while leaf BRDF data were acquired with the use of a hyperspectral Compact Laboratory Spectro-Goniometer (CLabSpeG). The goal behind the virtual forest scene is to create a Virtual Imaging System using measured BRDF data of vegetative material in order to improve existing canopy reflectance models. This imaging system is the first step towards the creation of a hyperspectral virtual laboratory that will be used to research and better understand earth solar interaction principles.

Early detection of biotic and abiotic stresses and subsequent steering of agricultural systems using hyperspectral sensors potentially could contribute to the pro-active treatment of production-limiting factors. Venturia inaequalis (apple scab) is an important biotic factor that can reduce yield in apple orchards. Previous hyperspectral research focused on (i) determining if Venturia inaequalis leaf infections could be differentiated from healthy leaves and (ii) investigating at which developmental stage Venturia inaequalis infection could be detected. Logistical regression and partial least squares discriminant analysis were used to select the hyperspectral bands that best define differences among treatments. It was clear that hyperspectral data provide the contiguous, high spectral resolution data that are needed to detect subtle changes in reflectance values between healthy and stressed vegetation. The research was extended to include tree-based modeling as an alternative classification method. Results suggested that good predictability could be achieved when classifying infected plants based on this supervised classification technique. It was concluded that the spectral domain around 1600 nm was best suited to discriminate between infected and non-infected leaves immediately after infection, while the visible spectral region became more important at a well-developed infection stage. Research was focused on young leaves, because of the decreased incidence of infection in older leaves, the so-called 'ontogenic resistance'. Additional research was performed to gain a better understanding of the processes occurring during the first days after leaf unfolding and to evaluate the natural spectral variability among leaves. An undisturbed 20-day growth profile was examined to assess variations in the reflectance spectra due to physiological changes at the different growth stages of the leaves. Results suggested that an accurate distinction could be made between different leaf developmental stages using the 570 nm, 1940 nm, and 1460 nm wavelengths, and the red edge inflection point. Based on these results and the outcome of some existing chlorophyll indices, it was concluded that the chlorophyll content in leaves increased remarkably during the first 20 days after unfolding.

This study reports the methods used and the results from the mapping of the wild reindeer range Forollhogna in Central Norway. Landsat 7 ETM and IRS 1C satellite imageries were used in the mapping. The spatial resolution of the IRS 1C data was enhanced to 5 meter by merging the panchromatic and the multi-spectral bands together. A 'new' method for mapping of the accessible winter pastures was developed and tested in this project. Our assumption is that the areas without or with thin snow cover late in the winter and in the calving period is the most accessible winter pastures during the whole winter period. We used the Normalized Difference Snow Index (NDSI) image based on a Landsat ETM+ image acquired on 19^th of May 2002 in order to mask these areas. The areas of the vegetation types within these masked areas were estimated (km²), and these data were used in the estimation of the reindeer carrying capacity. The winter carrying capacity for wild reindeer in Forollhogna using the NSDI-index was estimated to be more than 4200 reindeer, while the winter capacity without this snow mask was estimated to be more than 6700 reindeer. Based on this study, we have recommended an increase of the number of reindeer from the actual population level of 1750 reindeer to more than 2000 reindeer.

Rapid, reliable and objective estimations of leaf area index (LAI), defined as one half the total intercepting area per unit ground surface area, are essential for numerous studies of atmosphere-vegetation interaction, as LAI is very often a critical parameter in process-based models of canopy response to global environmental change. The usefulness of indirect optical LAI measurements by means of hemispherical canopy photography has already been demonstrated in that context. LAI is then calculated by gap fraction inversion. As a standardized protocol is needed for digital hemispherical canopy photography, virtual 3-D canopy stand models can be used to develop, validate and optimize data processing from these photographs.

The objective of this study is to find an efficient method of crop classification based solely on satellite microwave data. Microwave data can sometimes be the only non-contaminated satellite data available for a selected area, where frequent cloudiness makes optical data useless. Hence, this approach has to be studied at least as an alternative method for application when other data are missing. Although ASAR is not a fully polarimetric instrument, a selection of dual polarization modes and a selection of incidence angles are available when using ASAR alternating polarization product (APP). Larger incidence angle and cross-polarized data are better for crop discrimination, while lower incidence angle and co-polarized data contain more information on soil moisture. In the years 2003 and 2004 a sequence of ASAR IS2, IS4 and IS6 images acquired during a growth season was analyzed. Supervised classification of ASAR APP was performed with ground truth data collected for 700 plots covered by homogeneous crops. For growth seasons of 2003 and 2004 various combination of images were tested in order to find the best set of data providing the highest accuracy of crop classification.

Leaf area index (LAI) is a key variable of up-scaling or down-scaling in global climate change research. The Qinghai-Tibetan Plateau is an ideal place to study and model interactions between natural ecosystem and climate change because there are unique interactions between ecosystems and environments on the extremely high plateau where vegetation remains undisturbed. In this study, we present field data for leaf area index(LAI) in 42 field plots located along an altitudinal gradient from 2800m to 4000m around Gonghe basin in Qinghai-Tibetan plateau using a global positioning system during 2003-2004. The vegetation types of these field plots included grasslands and grass-shrub mixed lands. We also acquired MODIS data (product MOD13Q1) over the study area between January and December, 2003. These products consisted of 15-days composite of vegetation indices (EVI and NDVI) at 250m spatial resolution. We developed a MODIS-based Leaf Area Index Model of Grass land using 20 site-specific measurement data from the 42 field plots, and validated the model using other 22 field plots measurement data. Using MODIS-derived Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI), Data analyses have shown that EVI had a stronger linear relationship (R²=0.8226, n=20) with LAI at Gonghe Basin than did the NDVI (R²=0.7885, n=20). The simulation of the model was conducted at Gonghe Basin of Qinghai-Tibetan plateau. The predicted LAI values agreed well (EVI model R²=0.621, n=22) (NDVI model R²=0.612, n=22) with observed LAI of grassland at Gonghe Basin. This study demonstrated the potential of the model for scaling-up of LAI of grasslands in China.

This research aims to develop a remote sensing method for monitoring grain production in the early stages of crop growth in Japan and Asia. A photosynthesis based crop production index CPI for rice is proposed that takes into consideration the solar radiation, the effective air temperature, and NDVI as a factor representing vegetation biomass. The CPI index incorporates temperature influences such as the effect of temperature on photosynthesis by grain plant leaves, low-temperature effects of sterility, cool summer damage due to delayed growth, and high-temperature injury. These latter factors are significant at around the heading period of crops. The CPI index for rice was validated at ten monitoring sites in the central and northern half of Japan. The method is based on routine observation data, allowing automated monitoring of crop production at arbitrary sites without any special observations. The CPI index is applied to rice production in five regions of China, using solar irradiation data from the Japanese Geostationary Satellite, the Normalized Vegetation Index (NDVI) derived from NOAA AVHRR, and world weather data.

Estimates of forest biomass are needed to account for carbon at the tree, stand and regional scales. Sample plots of national forest inventories provide the basic database for these estimates. At the tree scale, a common estimation method is the use of an allometric equation that relates a tree's predicted compartment biomass y_i (i = foliage, branches, stem wood or stem bark) with easily obtained non-destructive measurements, i.e., diameter at breast height (D): y_i=b_i1D^bi2 or with both D and tree height (H): y_i=b_i1D^bi2H^bi3, b_ik being the parameters estimated. A common paradigm observed in biomass literature considers that parameter values vary between stands and regions. At the regional scale, however, when comparing national biomass equations to regional biomass equations, our results showed no significant differences between both types of equation. These results contribute to strengthening the allometric theory as an organizing principle for quantifying the relationship between tree size and biomass across spatial scales. In tandem with the allometry theory, we used a soil-canopy model based on Li-Strahler's approach for up-scaling biomass from the tree to stand scale in a mixed hardwood-coniferous forest. Our results indicated that the shadow fraction of Landsat TM reflectance was correlated with stand biomass. However, this model is indebted with heteroscedasticity, meaning that its error increases appreciably when stand biomass density is high.

In the context of precision agriculture, several recent studies have focused on detecting crop stress caused by pathogenic fungi. For this purpose, several sensor systems have been used to develop in-field-detection systems or to test possible applications of remote sensing. The objective of this research was to evaluate the potential of different sensor systems for multitemporal monitoring of leaf rust (puccinia recondita) infected wheat crops, with the aim of early detection of infected stands. A comparison between a hyperspectral (120 spectral bands) and a multispectral (3 spectral bands) imaging system shows the benefits and limitations of each approach. Reflectance data of leaf rust infected and fungicide treated control wheat stand boxes (1sqm each) were collected before and until 17 days after inoculation. Plants were grown under controlled conditions in the greenhouse and measurements were taken under consistent illumination conditions. The results of mixture tuned matched filtering analysis showed the suitability of hyperspectral data for early discrimination of leaf rust infected wheat crops due to their higher spectral sensitivity. Five days after inoculation leaf rust infected leaves were detected, although only slight visual symptoms appeared. A clear discrimination between infected and control stands was possible. Multispectral data showed a higher sensitivity to external factors like illumination conditions, causing poor classification accuracy. Nevertheless, if these factors could get under control, even multispectral data may serve a good indicator for infection severity.

Strict control of the amount of water in soils is one of the most important environment factors for crop growth, so it would be useful to be able to determine the amount of water distributed through the soil at all times. We have therefore proposed a new system for measuring the amount of water in the soils. It achieves this by measuring transitions in the temperature distribution in the soil using infrared ray sensors. The ground surface temperature obtained from infrared ray sensors is different with and without the presence of water. Therefore, we can determine the amount of water that is present from transitions in the temperature distribution when using in this system. Because it uses infrared ray sensors, this system has the great advantage that is enables non-contact, real time measurements of the distribution of water in the ground. If this system were to be developed further, increased efficiency might be expected in some areas of agricultural fields.

We have applied a Land Surface Temperature algorithm to the whole Pathfinder AVHRR Land (PAL) database, aiming at studying the evolution of the vegetation at a global scale. The Land Surface Temperature parameter, along with NDVI, will allow retrieving vegetation changes between July 1981 and September 2001. We have also built a classification which takes into account both vegetation variations and thermal patterns, from NDVI and Air Temperature at 2 meters height data. This classification allows differentiating areas which present close vegetation changes throughout the year, but totally different climates, as for example in mountainous and semiarid regions. The main quality of this classification is that it does not need any a priori information on the encountered vegetation, and thus can evolve from year to year. Through the 20 years of data, the evolution of Land Surface Temperature shows to be strongly affected by orbital drift and satellite changes. This will require an adequate correction to allow deeper study. On the other hand, NDVI does not show this trend, but aerosol absorption from Mount Pinatubo's eruption in June 1991 seems to corrupt temporarily the data in the northern hemisphere.

The purpose of this study is to analyze the monthly mean spatial and temporal distribution of evapotranspiration (ET) during the 1981-2000 period in Brazil using remotely sensed data. The methodology involves the use of a relationship between ET and Normalized Difference Vegetation Index (NDVI). ET was estimated for the main Brazilian biomes through the Penman-Monteith method using climatological data from 194 Brazilian meteorological stations during the 1961-1990 period. NDVI data for the July 1981 to July 2000 period was obtained from Advanced Very High Resolution Radiometer (AVHRR) sensor on board the NOAA satellite. A relatively high correlation coefficient between ET and NDVI (r=0.81) was found, showing a near linear relationship involving these variables. Also, the monthly mean ET over Brazil was estimated using NDVI data. The results showed that the ET rate in the Amazon region is not well defined because the maximum values occur after the rainy season, while for the Northeast Brazil, the highest ET values occur in according to period of rainy season. The annual cycle of ET is most defined in the Central region, with maximum values occurring in January to May period and minimum in September. In the South and Southeast regions, the annual cycle ET does not change very much. Finally, this study suggests that NDVI is an important variable for indirectly monitoring ET over large areas, thus with great potential for agronomical and climatic applications.

The photochemical reflectance index (PRI) derived from narrow bands reflectance at 531 and 570 nm is related to the light use efficiency (LUE) of terrestrial vegetation. However, the satellite sensor that has these two spectral bands has not existed so far. Therefore, it is necessary to substitute PRI using spectral bands of current satellite sensors. In this study, first we investigated the effectiveness of PRI alternatives by the band combination and the multiple regression analyses that used the MODIS land and ocean band reflectance simulated from ground observation data. In the band combination analysis, it was possible to substitute PRI when each growth stage of vegetation was separately analyzed. But, it was difficult to substitute PRI by a single equation in all growth stages of vegetation. In the multiple regression analysis, it was possible to substitute PRI by using the logarithms of MODIS land and ocean band reflectance even when all growth stages of vegetation were analyzed at the same time. Second we verified accuracy of alternative by the multiple regression analysis that used the logarithm of MODIS ocean band reflectance to apply the different observation periods and sites data. It was found that the error was 0.011 in RMSE and this corresponds to 10% error of LUE. Finally we applied this PRI alternative to the actual MODIS. It was possible to show the changes of the PRI alternative by spatial pattern and land surface cover types.

Remote sensing at optical wavelengths provides information on agricultural crop status, therefore being a useful tool for the detection and monitoring of drought stress in crop production. In the project "crop drought stress monitoring by remote sensing" (DROSMON) led by the University of Natural Resources and Applied Life Sciences in Vienna, which started in January 2005, remote sensing methods for drought stress classification were based on physical models of canopy reflectance using a combination of SAILH and PROSPECT. Spectral reflectance of maize and wheat were measured in situ using a field spectroradiometer FieldSpec Pro FR for different crop development stages and drought stress levels at a test site in Vienna, Austria. An extensive validation program was carried out measuring various physiological properties of the crops. A significant difference in reflectance was observed between the canopies experiencing distinct drought stress levels. The observed differences could be confirmed by model simulations based on the measured biophysical variables. These suggest that there will be a change in spectral reflectance in drought stressed crops, varying according to the different growth stages. This is most marked in the near (NIR) and mid (MIR) infrared wavelength region, probably due to modifications of leaf internal structure, variations in leaf inclination (e.g. due to wilting) and leaf area index. We present initial results from this research, which partly support these ideas. Further investigations are necessary.

Satellite based monitoring of agricultural activities requires a very high temporal resolution, due to the highly dynamic processes on viewed surfaces. The solitary use of optical data is restricted by its dependency on weather conditions. Hence, the synergetic use of SAR and optical data has a very high potential for agricultural applications such as biomass monitoring or yield estimation. Synthetic Aperture Radar data of the ERS-2 offer the chance of bi-weekly data acquisitions. Additionally, Landsat-5 Thematic Mapper (TM) and high-resolution optical data from the Quickbird satellite shall help to verify the derived information. The Advanced Synthetic Aperture Radar (ASAR) of the European environmental satellite (ENVISAT) enables several acquisitions per week, due to the availability of different incidence angles. Moreover, the ASAR sensor offers the possibility to acquire alternating polarization data, providing HH/HV and VV/VH images. This will help to fill time gaps and bring an additional information gain in further studies. In the present study the temporal development of biomass from two winter wheat fields is modeled based on multitemporal and multisensoral satellite data. For this purpose comprehensive ground truth information (e.g. biomass, LAI, vegetation height) was recorded in weekly intervals for the vegetation period of 2005. A positive relationship between the normalized difference vegetation index (NDVI) of optical data and biomass could be shown. The backscatter of SAR data is negatively related to the biomass. Regression coefficients of models for biomass based on satellite data and the collected biomass vary between r²=0.49 for ERS-2 and r²=0.86 for Quickbird. The study is a first step in the synergetic use of optical and SAR data for biomass modeling and yield estimation over agricultural sites in Central Europe.

Evapotranspiration (ET) in regional scale is not only a major component of energy and water balance, but also a linking medium between ecological system and climatic system. Due to the increased needs from hydrological, climatological and ecological communities, more interest has been paid on developing algorithms to estimate ET over larger scales by means of combining remote sensing measurements of land surface parameters during the last decades. Compared to all previous remote sensing algorithms for heat fluxes estimations, the Surface Energy Balance System (SEBS) developed by Su (2002) has the most important advantage of its inclusion of a physical model for the estimation of the roughness height for heat transfer which is the most critical parameter in the parameterization of heat fluxes of land surface. In this paper, first, SEBS has been utilized to estimate the surface fluxes over HuangHuaiHai Plain in Northern China by using MODIS/TERRA images, in combination of meteorological data collected in meteorological stations distributed over the study area. The estimated fluxes by SEBS in clouds free days are first compared with the measurements from QRSLSP/Shunyi Campaign near Beijing (Liu et al.2002) and then compared to the measurements by Large Aperture Scintillometers (LAS) in Zhengzhou LAS station located in Henan Province, China. Both the comparisons show that the estimated fluxes from SEBS have a good agreement with the measurements. Based on the validation of the model, an extended modular of SEBS has been utilized to estimate daily ET over the study area and the results showed that the extended SEBS can be used to estimate daily ET over regional scale. Finally, limitations and special care in using SEBS are discussed.

Radar based surface soil moisture retrieval has been subject of intense research during the last decades. However, several difficulties hamper the operational estimation of soil moisture based on actually available space borne sensors. The main difficulty experienced so far consists of the parameterization of other surface characteristics, mainly roughness, which strongly influences the backscattering coefficient and harms the soil moisture inversion. This fact, along with the high spatial variability of the surface roughness parameters, makes it necessary to perform intensive roughness measurements in order to invert soil moisture values with an adequate accuracy, what reduces the applicability of the approach. This paper reviews an approach, proposed by Pauwels et al.⁸, in which a combined application of two well documented backscattering models, i.e. the Integral Equation Method model and the Oh model, is carried out following an iterative scheme. The approach can be applied to single configuration scenes acquired over homogeneous roughness conditions and yields estimates of both soil moisture and roughness parameters without performing ground measurements of soil moisture or roughness. The proposed algorithm was applied to a set of five RADARSAT-1 scenes acquired over Navarre (Spain) between February and April 2003. Inverted soil moisture and surface roughness parameters were compared to ground measured reference values over an experimental watershed. Results are encouraging and the possibility of simultaneously estimating both variables opens new application scenarios for radar remote sensing on the study of numerous processes at the soil surface.

In this work a classification of the main irrigated crops of the Piave river basin and an estimation of crop water requirements during the growing season are presented. The work is divided into two parts. The first includes recognition, mapping and quantification of the main irrigated crops for thematic map production and a database creation. MIVIS hyperspectral airborne data, Landsat-TM/ETM+ multispectral satellite data and ground truth data were used for crop classification. A specific method of knowledge-based image classification was designed and used. The proposed method was compared with other per point conventional classification methods. In the second part the crop water need estimation is discussed. Ground-climatological data of the study area ground-climatological stations were used. The water balance equation parameters were estimated on a ten-days basis. A spatial interpolation method was used to propagate these parameters at pixel spatial resolution to study area. Soil water deficit map for irrigation was produced and a flow rate estimation was performed.

The present study is focusing on the following main objectives that are: 1) to compare the spectral and radiometric characteristics of AVIRIS data from Alfalfa crops with the spectra measured by FieldSpecR ASD radiometer; 2) to study the impact of growth regulators that applied on alfalfa in comparison of data collected from AVIRIS seen and; 3) to build a spectral library for the alfalfa crop that exposed to growth regulators that were studied. AVIRIS data from Blythe were acquired in June 1997 to study the agricultural spectra from different crops and for identification of crops in other areas with similar environmental factors and similar spectral properties. On June 26-28, 2001 spectra were collected from Alfalfa fields using the FieldSpecR ASD spectrometer at Blythe area, California (at the 114o 33.52 W Longitude and 33o 39.76 N Latitude to Longitude 114o 33.54 W and Latitude 33 39.88 N). The alfalfa crop fields were treated with different chemicals of growth regulators. These growth regulators were AuxiGro, Apogee and Messenger. These chemicals were used in different concentrations. Environmental parameters were studied such as the soil water content (WC), pH, and organic matter (OM). The results of this study showed that there is a significant correlation between the data that were collected by AVIRIS image scene in 1997 and spectral data collected by the FieldSpec spectrometer in the same places that were scanned by AVIRIS. This correlation allowed us to build a spectral library to be used in ENVI-IDL software. Furthermore, using IDL algorithms of Spectral Angle Mapper classification (SAM), Spectral Feature Fitting (SFF) and Spectral Binary Encoding (SPE) showed an excellent agreement between the traced spectra from the AVIRIS image and the spectral radiometer data collected from the alfalfa crops treated with growth regulators (i.e. the correlation is between 75 - 94% match). Three widely used vegetation indices such as NDVI, WBI, and PRI, showed that there are significant correlations between WBI and NDVI (r²= 0.44 - 0.88 for alfalfa crops treated by growth regulators. Further use of the AVIRIS images can be of a value to crops identification or crops yield for commercial use.