Monitoring regional agricultural crop condition has traditionally been accomplished using NOAA AVHRR (1 km) data. New methods are developed for assessing crop yields by retrieving biophysical parameters from remotely sensed imagery and integrating with crop simulation models. The MODIS imagery with its 250 m resolution and a potentially daily coverage offers an opportunity for operational applications. The objective of this research was to assess the potential application of MODIS data for operational crop condition and yield estimates. A field study was conducted during the 2000 crop season in McLean county Illinois (IL), USA. Twenty corn and soybean fields were monitored with measurements for crop reflectance, Leaf area Index (LAI) and other crop growth parameters. A radiative transfer model was used to independently develop the LAI from the MODIS 250-m data. Crop growth parameters retrieved from the imagery were integrated in a crop yield simulation model. The magnitude and spatial variability of estimated LAI and the NASA product was partly due to differences in the classification of crop type and the pixel resolutions. A comparison with the NASA derived MODIS vegetation parameters and independently derived parameters are presented.

AVIRIS data from Blythe,Calfornia , 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. In this respect; the main objectives of this study are: 1) to compare the spectral and radiometric characteristics of AVIRIS data from agriculture crops with the spectra measured by FieldSpecR ASD radiometer; 2) to explore the use of AVIRIS images in identifying agriculture crops; and; 3) to build a spectral library for the crops that were studied. A long-term goal is to extend the spectral library for different vegetation or crops in different stages of growth. In order to support our study, on July 18-19, 2000 we collected spectra using the FieldSpecR ASD spectrometer from selected fields planted with different crops at Blythe area, California (at the Longitude 114 degree(s) 33.28 W and Latitude 33 degree(s) 25.42 N to Longitude 114 degree(s) 44.35 W and 33 degree(s) 39.77 N Latitude). 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 FieldSpecR spectrometer. This correlation allowed us to build a spectral library to be used in ENVI_IDL software. This leads to identification of different crops and in particular the visible part of the spectra. Furthermore, using IDL-ENVI algorithms of Spectral Angle Mapper classification (SAM), Spectral Feature Fitting (SFF) and Spectral Binary Encoding (SPE) showed that there is an excellent agreement between the predicted and the actual crop type (i.e. The correlation is between 85-90% match). Further use of the AVIRIS images can be of a value to crops identification or crops yield for commercial use. Kenaf crop spectra were studied. The kenaf varieties (Tainung 2, Everglades 41) were significantly differentiated by both the spectral data from AVIRIS and from the hand-held radiometer.

The objective of the research presented here is to obtain crop yield forecasts basing on the information of NOAA- AVHRR/NDVI and CORINE land cover data. The methodology described in Genovese et al. (2001) consists of extracting yield indicators from CNDVI (CORINE-NDVI) time series at a regional scale. In Genovese et al. (2001), a preliminary study on Spain for a four year span (1995-1998) has shown that indicators extracted from the CNDVI profiles can be more closely related to crop yield performances than indicators based on simple NDVI profiles. To prove the validity of this approach, a more complete experiment was realised on France for the same period. Linear regressions were calculated using regional CNDVI-based indicators versus regional wheat yield data (EUROSTAT NEW CRONOS database). A French national wheat yield forecast was then derived by aggregation of regional results. The goodness of the results confirms the advantages of such approach. The combination of a CNDVI-based indicator with the linear trend observed on yields between 1975 and 1997 led to very good regression criteria (coefficient of determination higher than 86%) and allowed a satisfying prediction of wheat yields.

What is lacking in precision farming at present, are more comprehensive and non-destructive methods for obtaining the data needed to prescribe variable treatments. A farmer needs to be informed in order to be efficient, and that includes having the knowledge and information products to forge a viable strategy for farming operations. Current remote sensing (satellite images) sources are too coarse in multispectral spatial resolution and too infrequent in time to allow detailed tracking of phenological stages during the growing season. In this research very detailed and automated on-the-go optical monitoring of the crop is used for detecting and managing zones with different crop yield potential on a seasonal scale. In particular, reflectance properties are used to identify and evaluate optical indicators of the nutritional status of the crop. These indicators should allow site-specific in-seasonal correction of N-application to come to optimal crop yield all over the field. Based on these indicators, site-specific fertilization is done with a variable fertilizer equipped with DGPS. At the end of the season, the crop was harvested with a combine harvester, equipped with precision farming sensors to map final crop yield. In this way final results could be evaluated and analyzed.

Geographic Information Systems have become indispensable tools for watershed-scale hydrologic analysis and modeling. The integrative capabilities of GIS can emulate real-world complexity, facilitating interdisciplinary research and communication. Landsat Thermal Mapper raster images represent topography, land use, land cover, as well as spatial data of surface and ground water hydrology, weather, and soils types are all integrated in GIS themes such as views, tables, charts, and layouts. GIS is being used in data visualization, processing, and management. This paper presents a case study on the integration between the Geographical Resources and Assessment Support System (GRASS) and the distributed parameter and physical process watershed model, Soil and Water Assessment Tool (SWAT) for the management of land practices. This integration proved to be effective and efficient for input data extraction and management for simulating the baseline conditions as well as other scenarios. SWAT is capable of continuous time simulation and flexible domain delineation. The spatial distribution of SWAT output results was successfully presented by using Geomedia-Intergraph software. By using SWAT-Geomedia integration, areas under sever water quality problems could be identified. Sediment and nutrients loads were studied in order to determine a better management of surface water resources in large agricultural watershed.

The SMOS L-band 2-D interferometer is based on an innovative concept of bi-dimensional aperture synthesis method. The instrument is a Y shaped structure consisting of 3 coplanar arms. The sensor has new and very significant capabilities especially in terms of multi-angular view configuration.. This paper will describe the SMOS concept in terms of instrument (characteristics) investigates the main aspects of the retrieval capabilities of the 2-D microwave interferometer for monitoring soil moisture, vegetation biomass and surface temperature. The analysis is based on model inversion taking into account the instrument characteristics. The standard error of estimate of the surface variables is computed as a function of the sensor configuration system and of the uncertainties associated with the spatial measurements. The inversion process is based on a standard minimisation routine that computes both retrieved variables and standard error associated with the retrievals. The potential of SMOS, depending on the view angle configuration and the use of the sole 1.4 GHz is investigated. These questions are key issues to define the observation configuration of SMOS that meets the scientific requirements and the technical constraints of the spatial missions.

Iran's most obvious hydroclimatic problems are compounded of the disadvantages of scanty and highly seasonal precipitation and a surface configuration which tends to concentrate moisture on the periphery of the country, leaving its vast heart an area of irreconcilable sterility. Most of the central Iran has arid conditions with dry and hot summer months, when streams with and the land is parched. Nowhere in Iran is there an annual surplus of water, and significant seasonal surpluses occur in only the wishbone of high mountains that encloses the central plateau on the north and west. In most parts (about 80 percent of the total of country) the nature of human activity depends upon the availability of surface water that can be tapped by wells and qantas. Runoff is episodic and occurs only because the precipitation, meagre as it is momentarily exceeds the infiltration capability of the surface. Such precipitation is not of course capricious in terms of quantity, location and distribution in time. For more accurate investigation, remote sensing data was used to overcome the large area. Finally for arid basins, combined data from remote sensing (Cosmos and Aerial photographs) data and topography maps provided significant results.

We have recently developed a new, fully automated method for detecting and visualizing narrow rivers in Amazon forests from 3-look JERS-1 SAR images using strong isolated scatterers lined up intermittently along rivers. The resulting approximate range of waterways correspond astonishingly well with waterways visible in the cloudless areas of the near IR images of JERS-1 VNIR data observed one week later. We applied this method to more than 250 images of multi-temporal 4 look JERS-1 SAR scenes of tropical forest areas, such as the Amazon from May, 1996 to February, 1997, New Guinea from November, 1995 to March 1996, and Congo Basin of February and November, 1996. The resulting change in brightness (or length) of rivers corresponds quite well with the change in the average monthly rainfall data of the nearby areas. We therefore conclude that the brightness of the waterways in the JERS-1 SAR images are qualitative indicators of water flow in these rivers.

Artificial neural networks (ANN) constitute a powerful class of nonlinear function approximates for model-free estimation. Neural network models are characterized by topology, activation function and learning rules. The wavelet neuron model is obtained by replacing an activation function with wavelet bases in the traditional neuron model. The wavelet is a localized function that is capable of detecting some features in signals. A wavelet basis function is assigned for each neuron and each synaptic weight is determined by learning. Wavelet neural networks are used in this study to process remotely sensed data and classify soil based on its moisture content. To evaluate the effectiveness of the wavelet neural networks, a soil moisture data set consisting of 750 vectors, each with three components (surface temperature, brightness temperature at L-Band (TB-L) and at S-Band (TB-S)) and some remotely sensed images are evaluated in the experiments. A comparison with Backpropagation networks is investigated for the supervised training of remotely sensed data and classification of soil moisture.

The recent availability of multi-band thermal infrared imagery from the Advanced Spaceborne Thermal Emission & Reflection radiometer (ASTER) on NASA's Terra satellite has made feasible the estimation of evapotranspiration at 90 meter resolution. One critical variable in evapotranspiration models is surface temperature. With ASTER the temperature can be reliably determined over a wide range of vegetative conditions. The requirements for accurate temperature measurement include minimization of atmospheric effects, correction for surface emissivity variations and sufficient resolution for the type of vegetative cover. When ASTER imagery are combined with meteorological observations, these requirements are usually met and result in surface temperatures accurate within 1-2 C. ASTER-based evapotranspiration estimates were made during September 2000 over a sub-humid regions at the USDA/ARS Grazinglands research laboratory near El Reno in central Oklahoma. Daily evapotranspiration was estimated by applying instantaneous ASTER surface temperatures, as well as ASTER-based vegetation indices from visible-near infrared bands, to a two-source energy flux model and combining the result with separately acquired hourly solar radiation data. The estimates of surface fluxes show reasonable agreement (within 50-100 W/m²) with ground-based Bowen Ratio Energy Balance measurements and illustrate how ASTER measurements can be applied to heterogeneous terrain. There are some significant discrepancies, however, and these may in part be due to difficulty quantifying fractional cover of senescent vegetation.

For hydrology and terrestrial ecosystem studies, topography has a significant influence on the amount of intercepted solar radiation, the surface and sub-surface water movements, the type and distribution of vegetation and the microclimate. Processing Digital Elevation Model (DEM) data to extract hydrological features becomes a routine, but the numerous DEMs available stress the importance of their quality assessment. Radar interferometry (InSAR) technique is a promising approach to generate digital elevation models. The goal of this research is to verify to what extend the InSAR DEM can be used as a topographic database for deriving hydrological informations. This study was realised over the Dendre and the Lesse watersheds. Because DEM quality cannot be determined by a single criterion, the quality assessment should be application oriented. In this study, the NGI (National Geographic Institute of Belgium) DEM was chosen as a topographic reference for the quality assessment of the InSAR DEMs. The Root Mean Square (RMS) of the altitude difference between the NGI and the InSAR DEM was used as general quality measurement. The mean slope value has been calculated to characterise the relief of the basin. For both of the basins, watershed borders and hydrographic network were generated with GIS technique. The results obtained were compared between them and with digitised hydrographic network. Hydrographic network derived from InSAR DEM was not found accurate enough in flat wide valley. For the studied areas, the InSAR DEMs are precise enough for large-scale hydrological investigation where information like watershed border or relief is needed. However, InSAR DEMs is not suitable for hydraulic models, because they require extreme accuracy.

A new approach for retrieving surface soil moisture from satellite microwave brightness temperature is described. The approach uses radiative transfer theory together with a non- linear optimization routine to partition the observed microwave signal into its soil and vegetation components. Vegetation optical depth is derived directly from the microwave polarization difference index, while the soil component is solved in terms of the soil dielectric constant. A global data base of soil physical properties is then used to derive soil moisture from the soil dielectric constant. The approach is tested with historical SMMR data to produce time series of surface soil moisture over several global test sites. Comparisons with ground observations of soil moisture are made. Preliminary results over several global test sites are provided.

There is an increasing need for accurate measurement of forest cover both in terms of its spatial and temporal variation for improvements in forest management. Forest cover can be estimated using Normalised Difference Vegetation Index (NDVI) from satellite data. The objective of this study was to assess leaf area of the forest cover from the spectral reflectance measured by satellite. This paper analyses the relationship between NDVI and different ages of managed pine plantation from 1994 - 1997 in the Kings Forest, East Anglia, UK. The Satellite data used were four - SPOT 2 HRV2 multi spectral images for June 1994, 1995, 1996 and 1997. Secondary data on forest management were derived from compartment records supplied by the Forestry Commission. Age is an important variable in forest growth. Therefore this study investigated changes of NDVI in different age groups <8, 8-18, 18-38, 38-58 and >58 years. The study found that during the period 1994- 1997 NDVI changed fairly little from 0.4-0.7. However in each age group slight changes can be easily recognised. There was a significant high positive correlation between NDVI and age in all four years with R2 values of 0.68, 0.66, 0.79 and 0.78 for 1994, 1995, 1996 and 1997 respectively. The results show that the NDVI changes provide important information on vegetation growth in a plantation forest. The results of comparing NDVI and age were also encouraging but further work is needed to determine the potential use of earth observation data in forest growth and management.

A general multichannel algorithm for land surface temperature estimation is described. It has been set up by simulations of NOAA / AVHRR-2 measurements in order to parameterize the temperature correction due to atmospheric water vapor absorption and deviation of surface emissivity from unity at 11 and 12 micrometers wavelengths. The parameterization is based on model calculations performed with FASCODE, a line - by - line radiative transfer program. A comparison with one particular algorithm1 selected by ESA to produce global thermal maps of the Earth2 shows no significant difference in the retrieved surface temperature for water vapor around 2.5 g/cm². For greater deviations from this value of water vapor content the difference in the results can be remarkably large. Model calculations have been performed for deviations of each parameter (water vapor and surface emissivity) individually and for both parameters simultaneously. The approach used allows the application of the algorithm to other sensors. In fact, the next step of our activity on this subject envisages its application to the appropriate bands of the MODIS sensor.

The fluorescence signal emitted from vegetation is directly linked to the photosynthesis and as such may be used as an indicator for plant functioning, stress and vitality. Observation of solar induced fluorescence from space is proposed by measuring the weak signal contribution in the Fraunhofer line wavelengths. In an ESA funded study various aspects of measuring the fluorescence signal from space have been analysed for it's feasibility. Both scientific and instrumental aspects have been considered in the analysis. The scientific requirements have been studied in detail, looking to aspects such as the selection of Fraunhofer lines, the solar induced fluorescence radiance, measurement accuracy, spatial resolution, atmosphere influence, etc. This has resulted in instrument requirements, that are the basis for a trade off study of optical observation techniques. The main choice was between applying a grating spectrometer or a filter spectrometer, each having advantages and disadvantages for Fraunhofer line detection (FLD). From both spectrometer types a preliminary optical design has been made. Besides a model has been developed to evaluate the different configurations for S/N, integration time, radiance level etc. For these calculations it appeared, that the information about solar excited fluorescence intensity of vegetation is minimal. In the study of feasibility of Fraunhofer line detection from space is demonstrated, albeit, that the observation strategy will depend on the real amount of the solar excited fluorescence intensity. The results of the study are a good basis for further development of a spaceborn Fraunhofer line detector.

The effects of nitrogen (N) application level on temporal development of ground cover (GC) and reflection curves of leaf strata within the canopy were studied with an experimental imaging spectroscopy set-up. This set-up has a high spatial (0.16-0.26mm²) and spectral resolution (5-13 nm) within the 405-1650 nm range and can distinguish leaves in height and leaf angle related intensity classes from 1 meter distance. Between June and late October 2000, a greenhouse experiment was conducted with Lolium perenne L. mini swards with 5 N treatments (0, 30, 60, 90, 120 kg N per cut), 4 cutting dates and 3 replicates. Images were recorded twice a week. Ground cover of green material increased up to cutting dates. Nitrogen treatments could be separated by development of GC and the width of the broad chlorophyll absorption band of leaves around 680 nm (CAW), with small changes in GC and CAW for the 0 N and large changes for high N treatments. Two weeks after cutting, CAW reached its maximum with a subsequent decrease for the lower N treatments in the last week before cutting. The width of the CAW showed a unique relation with the relative yield deficit for all cutting dates (R²equals0.95). Changes in edge position with vertical position in the canopy were stronger for blue edge and green edge when compared to red edge. Darker green leaves were present at the bottom of the canopy and lighter green leaves at the top.

The determination of phytoplankton primary production is one key future application of remote sensing data in Lake Constance. One of the main influencing factors of the maximum photosythetic rate in waters is the bulk temperature. From satellite data like NOAA-AVHRR, the skin temperature can be retrieved at high temporal resolution. However, as parameter for primary production, the bulk temperature in the mixed water layer is needed. In this study, an extensive multi-temporal set of AVHRR data is used to set up a relationship between lake skin temperature from satellite data and the bulk temperature in 0.1 m depth from in situ temperature profiles. Temperature profiles exist for a fixed station in Lake Constance in 20 min intervals over the last twelve years. From 245 cloudless AVHRR-scenes for the years 1995 until 1999, the skin temperature was derived using the split window technique. Different approaches of this technique are intercompared for their accuracy for Lake Constance. In a future step, the bulk temperature in the upper 4 m and the depth of the mixed layer will be determined for input in a primary production model. Multiple regression analysis as well as a neural network approach will be used for the prediction of the bulk temperature from the skin temperature for Lake Constance.

On several days in 2000 & 2001 the Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER) on the Terra satellite obtained data over the Jornada Experimental Range test site along the Rio Grande river and the White Sand National Monument in New Mexico. ASTER has 14 channels from the visible (VNIR) through the thermal infrared (TIR) with 15 m resolution in the VNIR and 90 m in the TIR. The overpass time is approximately 11 AM (MST). With 5 channels between 8 and 12 micrometers these multispectral TIR data from ASTER provide the opportunity to separate the temperature and emissivity effects observed in the thermal emission from the land surface. Ground measurements during these overflights included surface temperature, vegetation type and condition and limited surface emissivity measurements. Preliminary results indicate good agreement between ASTER emissivities and ground measures. Analysis of earlier aircraft data has shown that the multispectral TIR data are very effective for estimating both the surface temperature and emissivity. These results will be compared with those obtained from the ASTER data for this site. With multispectral thermal infrared observations provided by ASTER it is possible for the first time to estimate the spectral emissivity variation for these surfaces on a global basis at high spatial resolution.

A coupled leaf/canopy radiative transfer model, LCM2, has been developed with NASA and AFRL funding to investigate the feasibility of detecting spectral signatures from within and under a vegetation canopy. The model is unique in that it features direct radiative transfer coupling between the leaf scattering elements and the canopy reflectance, through the within-leaf radiative transfer model, LEAFMOD. The leaf radiative transfer model characterizes photon scattering within a participating homogeneous leaf taking into account both biochemical composition and leaf thickness. The leaf model is incorporated as the scattering element in a canopy reflectance model (CANMOD) assuming a bi-Lambertian leaf scattering phase function. The primary distinction between conventional and canopy radiative transfer is canopy architecture, which is introduced through leaf angle distributions. A Lambertian partially reflecting background is assumed to lie beneath the canopy. The influences of biochemical composition, average leaf thickness, reflecting background, and canopy architecture on canopy reflectance can therefore be investigated. The model is extended here to include azimuthal dependence by considering collided and first collided radiances separately. The LCM2 model with the new azimuthal correction will be coupled to an atmospheric radiative transfer code, MODTRAN4, to simulate a satellite imager response at orbital altitude.

Global Models of the Earth - Atmosphere System describe the role of the terrestrial biosphere using increasingly complex Land Surface Models (LSM). These models mimic the exchange of energy, water and carbon between the land and the atmosphere, with emphasis on the role of terrestrial vegetation. Literature shows a clear trend towards fully interactive LSM-s, i.e. accounting for the dynamic response of vegetation to weather and climate. The latter may not be limited to biomass accumulation and address slower changes in vegetation type and composition. Improving the performance of such models require addressing two broad questions: Can we measure vegetation properties with the accuracy required by model sensitivity? How do we measure vegetation properties over the grid size of Global Models and are we able to incorporate the inherent spatial heterogeneity of terrestrial vegetation? The role of terrestrial vegetation in the land - atmosphere exchanges of energy, water and carbon is determined by properties, such as albedo, fAPAR, LAI and chlorophyll, related to spectro - directional radiance in the range 0.4 im - 14 im.. These variables and their spatial patterns can, therefore, be determined with accurate observations of spectro - directional radiance at selected view - angles and wavelengths. The paper summarizes results of several field experiments and airborne campaigns in Spain and France dedicated to these scientific objectives during the period 1998 - 2000. Examples are presented of the use of multi- angular hyper-spectral measurements to determine LAI, fAPAR, cholorophyll and heat fluxes with both field and airborne measurements. Particular attention is dedicated to illustrate the need for multi-angular observations in the entire spectral range 0.4im - 14 im..

This paper reports the operational implementation of radargrammetry for the production of Digital Elevation Models, or DEMs, to areas of rugged topography. The Southern Ethiopian Highlands east of lake Abaya, with elevations between ca. 900 and 4,400 meters, were mapped. Currently available topographical maps are of insufficient quality to assist a study of the area's unique land use system, which is arguably the oldest and most durably sustained land use system of the planet. Without external inputs or terracing, the land use system maintains soil fertility and staves-off hunger. It has been doing so during the past 30 years of unrest and civil war, in one of the most crowded regions of Ethiopia. However, the central role of the staple crop enset within the land use system and its production cycles has hardly been the subject of scientific study. Understanding of this system is most likely to be relevant to enhancement of health and productivity in many regions of the world. Upon the request of the Agricultural Bureau for Gedeo Zone, geocoded and georeferenced topographical maps with accuracy of 20 meters (x, y and z) were made by PRIVATEERS N.V. on the basis of RADARSAT multi-incidence (S2/S7) images. These maps are now incorporated as the basic layer within the Bureau's GIS system. Map production techniques proved to be cost effective and relevant; especially for mountainous areas with poor accessability where correct geographic information is not available. The ease of orientation proved of invaluable help to rationalize execution and planning of cost-effective environmental field work and reporting.

Information based on satellite data is used for evaluation of crop growth conditions what is essential for proper management of agricultural fields. The database of satellite data used for this application consists of optical and radar data from ERS. Soil moisture has been assessed using two different approaches. First one concerned the application of soil moisture index based on sensible and latent heat calculated from surface temperature (ATSR) and meteorological data (H/LE) and backscattering coefficient calculated from SAR data. Second one concerned the application of modified semiemperical water-cloud model to simulate backscattering coefficients of C-VV of ERS and L-HH of JERS as a function of LAI, Leaf Water Area Index and Vegetation Water Content. The final results gave the possibilities of comparison of the modeled soil moisture values with field measurements. The two-way attenuation of vegetation in three models for C-VV band and L-HH band has been examined.

This paper discusses the potential of radar signal to characterise the stone cover over bare soil surfaces in arid rocky regions. A high correlation was observed between radar measurements and stone cover percentage. Numerical Moment method based on integral equations of backscattering has been used to study the influence of the stone cover on simulated radar signal. Simulations are validated with measurements acquired in two arid sites. A good agreement is demonstrated between numerical simulations and radar ERS2 measurements. An empirical model is then proposed to inverse radar measurements and retrieve stone cover percentage.

A study was conducted to assess the soil moisture distribution in the northern regions of Kazakhstan, which is the major agricultural area for spring wheat grain production. A model for calculating the available soil moisture in the top one-meter stratum of soil for the third decade of April was developed. This technique is based on the analysis of current information about the amount of precipitation during the cold period and the time period (dates) when the snowmelt occurs which are derived from NOAA AVHRR imagery (1 km resolution). The multiyear average maps of available soil moisture and snowmelt dates are taken into account for calculating the current year soil moisture maps. A map of the maximum NDVI in July was correlated with the calculated soil moisture maps for the month of April for agricultural regions during 1997-2000. From the results of this study, the available soil moisture map at 1: 2.5 million scale was developed for the current year.

For various applications in soil science, hydrology and climatology it is important to estimate soil hydraulic properties over large areas. The methods to quantify them have been of limited success so far. One of the main difficulties is the influence of soil structure on infiltration and drainage of soils. The classical soil physical methods cannot be applied because they yield local measurements of properties, which are spatially averaged over sampling volumes of soil columns but not over plots or entire fields. One way out of this dilemma may be the use of microwave radiometry to quantify the dynamics of surface water contents and use this information as a proxy variable to estimate the hydraulic properties of the underlying soil. This study reports on a field experiment conducted from April to July 2001. Beside measuring the dynamics of the water content in the topsoil by a ground-based 11.4 GHz microwave radiometer, we carried out in-situ measurements of water content, temperature and matric potential. The infiltration pattern, visualized with a blue dye tracer added to the infiltrating solution, showed that both soils were strongly susceptible to preferential flow. The changes in topsoil water content seemed to be well comparable to the changes in the measured reflectivities.

The Simple Soil-Plant-Atmosphere Transfer - Remote Sensing (SiSPAT-RS) model was developed to simulate soil-vegetation-atmosphere energy and water transfers as well as remote sensing measurements at field scale, in the visible and thermal infrared domains. This model has been validated on the Alpilles-ReSeDA (Remote Sensing Data Assimilation) experimental database gathering micrometeorological data and remote sensing observations. Sensitivity and uncertainty studies were performed with the SVAT model and the radiative transfer models using stochastic technics like Monte Carlo's methods. The results permit us to quantify the model uncertainties linked to parameters and initial conditions uncertainties, such information which is essential to carry on assimilation studies. Here, we present the results concerning the sensitivity analysis and the results of the calibration on an experimental agricultural field of ReSeDA experiment.

The issue of deriving cross-scale aggregation rules has been heavily investigated during the past two decades. The widely used approach consists of formulating grid-scale surface fluxes using the same equations that govern the patch-scale behavior but whose arguments are the aggregate expressions of those at the patch-scale. Such approach has been used in the past derive area-averaged or effective radiative surface temperature as it might be observed using low spatial resolution satellite data. The problem however is such satellite data exhibits large directional effect and none the past studies have addressed this issue. The present work tackles this issue of the combined effects of surface heterogeneity and view angle variations on surface temperature measurements. The directional effects are modeled on surfaces having a known heterogeneity. Then, the angular properties of local surfaces, assumed homogeneous, are calculated according a multiple scattering model. By applying the principle of aggregation, the equivalent angular radiance of the whole heterogeneous scene is then defined. This made it possible to show that this radiance is particularly sensitive to the directional effects, in particular when the spatial variation of surface temperature is significant and when there is a vegetation component in the heterogeneous land surface. The structure of the vegetation component is also a significant factor of directional effect on equivalent angular radiance.

Lidar is a promising tool for forest-fire monitoring because this active detection technique allows efficient location of tenuous smoke plumes resulting from forest fires at their early stages. For the technique to be generally usable instrumentation must be eye-safe, i.e. it must operate within the spectral range λ<0.4 or λ>1.4 micrometers . In this paper the lidar efficiency at the wavelengths 0.3472 micrometers (second harmonic of the ruby laser) and 1.54 micrometers (Er:glass laser) are compared using a theoretical model. The results of calculations show that the energy required for smoke-plume detection using 0.3472 micrometers becomes greater than the corresponding value for 1.54 micrometers when the distance exceeds some threshold, which ranges between 2 and 6 km depending on other parameters. Being caused by relatively higher absorption of the UV radiation in the atmosphere, this result is valid for any wavelength in the vicinity of 0.35 micrometers , for example, the third harmonic of Nd:YAG laser and the second harmonic of Ti:sapphire laser.

The main goal of this study was to evaluate the environmental condition of two watersheds, called Ribeirao das Cabras and Ribeirao Piracicamirim, both part of the 'Piracicaba' River Watershed (Sao Paulo State - Brazil), using Remote Sensing and Geographic Information System (GIS), in order to provide subsidies to land use planning. Tendencies and potentialities were identified in each area in order to study the land use adequacy using different scenarios and propose a land use planning for both regions using environmental planning concepts. All information layers were integrated and analysed to generate land use capacity maps. These maps, together with maps of possible protected areas, according to the environmental laws, were compared to the actual land use, leading to maps of conflict areas for both watersheds, which were studied and compared through methods and specifics criteria to identify tendencies and potentialities for both areas. The natural and cultural attributes, main degradation processes, development tendencies and potentialities identified in each area, and the available legal instruments, were the basis for a guideline for conservation and protection of these areas through environmental planning concepts, which showed to be very important and adequate to the occupation planning process.

We studied the importance of land surface heterogeneity on climate models using the MOSIAC Land-Surface Model (LSM). Preliminary analysis of results indicated there were errors in surface heat fluxes for certain geographical regions with contrasting cover such as forests, grasses, and crops when using only one cover class per grid. For spatially varying areas, two to four classes per grid typically captured most of the variation in surface energy and water fluxes. A Minimum Percent Cutoff approach to select the number of classes per grid (or tiles) was found the most efficient in terms of computer time and accuracy. In a comparison between 1/8 degree versus 1-degree grid resolutions, the finer resolution land cover data were more important than finer resolution atmospheric forcing data (e.g. precipitation and radiation) on latent heat flux estimation.

In the U.S. Northern Great Plains, growing seasons are short but extremely productive. Farms and ranches are large, so many of precision agriculture's early adopters reside in the region. Crop yield maps at season's end reveal sizable variations across fields. Farm management relying upon uniform chemical applications is ineffective and wasteful. We provided information about crop and range status in near- real-time, so that in-season decisions could be made to optimize final yields and minimize environmental degradation. We created learning communities, in which information is shared among scientists, farmers, ranchers, and data providers. The new information for agricultural producers was satellite and aerial imagery. Value-added information was derived from ETM+, AVHRR, IKONOS, and MIDOS sensors. The emphasis was on reducing the time between acquisition of data by a satellite and delivery of value-added products to farmers and ranchers. To distribute large spatial data sets in short times to rural users we relied upon satellite transmission (Direct PC). Results include: (1) management zone delineation, (2) variable-rate fertilizer applications, (3) weed detection, (4) irrigation efficiency determination, (5) detection of insect infestation, (6) specification of crop damage due to inadvertent chemical application, and (7) determination of livestock carrying capabilities on rangelands.

Data assimilation methods applied to hydrologic models can incorporate spatially distributed maps of near surface temperature, especially if such measurements can be reliably inferred from satellite observations. Uncalibrated thermal IR imagery sometimes is scaled to temperature units to obtain such observations using the assumption that dense forest canopies are close to air temperature. For fully leafed deciduous forest canopies in the summer, this approximation is usually valid within 2C. In a leafless canopy, however, the materials views are thick boles and branches and the forest floor, which can store heat and yield significantly higher variations. Winter coniferous forests are intermediate with needles and branches being the predominant viewed materials. The US Dept of Energy's Multispectral Thermal Imager (MTI) is an experimental satellite with the capability to perform quantitative scene measurements in the reflective and thermal infrared region respectively. Its multispectral thermal IR capability enables quantitative surface temperature retrieval if pixel emissivity is known. MTI is pointable and targets multiple times in the winter and spring of 2001 at the Howland, Maine AmeriFlux research site operated by the University of Maine. Supporting meteorological and optical depth measurements also were made from three towers at the site. Directional thermal models of forest woody materials and needles are driver by the surface measurements and compared to satellite data to help evaluate the relationship between air temperature and satellite thermal measurements as a function of look angles, day and night.

The knowledge of the resistance of overland flow is an integral and necessary part of modeling runoff in arid land. The arid overland flow is affected by land cover especially composed by stubs and variability of stone cover. Radar SAR images and Optics remotely sensed data offer spatial and temporal information of large variety of environmental procedures which determine land surface process. An experiment aimed at evaluating the potential of these technology is arid area was carried out within the framework of the EC project FLAUBERT (Flood in Arid Unites By Earth Remote sensing Techniques). The measurements of soil irregularities by the mean of a pin profilometer have permitted the evaluation of the Manning roughness resistance which is an important input to hydrological models (Kineros, etc). Analysis of coherence maps showed a separation between few land classes and to point out some variations due to vegetation cycles. Geographical Information System GIS technologies offer many new opportunities for hydrological modeling. These technologies are used to increase the degree of definition of spatial subunits numbers and description details and to form spatially distributed models of watershed. GIS-hydrological model coupling offers also the potential of multidisciplinary data management and provide interface between the models and their users.