An Impedance Probe (IP) was used during the Southern Great Plain 1997 field experiment to support the remote sensing soil moisture mapping effort in Oklahoma, USA. Soil moisture sampling techniques used as ground-truth for remote sensing are most often not adequate due to the time and cost associated with them. The need for fast, accurate, less expensive, and less destructive measurement techniques within the remote sensing discipline is increasingly growing. The IP offers an alternative for measuring soil moisture in remotely- sensed regions where the logistics of gravimetric sampling can restrict the number of samples obtained. The gravimetric technique has been the traditional and most accurate technique used to validate remotely measured soil moisture. This technique is very destructive and time consuming, thereby limiting our ability to apply it over large areas expected by coarse resolution microwave remote sensing products. Recent advances in the Time Domain Reflectometery (TDR) techniques have resulted in sensors with less noise and require minimum human intervention. Field sampling and laboratory experiments were conducted to evaluate and calibrate soil moisture data measured using an IP. The objectives were: to examine the best calibration technique that can be used to estimate the spatial distribution of soil moisture and/or a representative mean moisture level for a given location, and to evaluate the correlation of soil moisture values using the probe and gravimetric measurement techniques. Results indicated that a site specific calibration curve developed by using a pressure plate apparatus provided a representative mean value was equivalent to the gravimetric technique. Both, the simulated and manufacturer calibration techniques provide soil moisture values that are spatially correlated with the gravimetric measurement technique. Our results indicated that, the instrument can provide reproducible results comparable to the gravimetric technique. This instrument can be utilized to meet the requirement of measuring the soil moisture content in the upper 6-cm of the soil depth.

The aim of the present project was to identify the capabilities of multitemporal ERS SAR interferometric coherence and PRI images to evaluate soil moisture, to estimate crop height and to identify crops for four crop types (winter wheat, potato, sugar beet and maize) and for different pilot fields. The coherence images acquired during the winter and spring seasons can be used to identify bare or nearly bare fields with a threshold value, and then PRI images were used to quantify soil moisture value for each bare field. The coherence images acquired during the growing season were used to evaluate crop height for each studied crop type. Moreover, the coherence image provided some additional information to PRI images for the crop type identification. This study was carried out in the framework of the PRODEX program financed by the Belgian Federal Office for Scientific, Technical and Cultural affairs (OSTC) and the European Space Agency (ESA).

Soil moisture plays a key role in the hydrological cycle. Because of its heterogeneous distribution satellite measurements can be favorable for arriving at mean areal values. For bare soil such values of soil moisture can be derived from active microwave measurements. However a commonly recognized method for estimating soil moisture under vegetation using remote sensing data does not exist at present. Carlson proposed a method to estimate soil moisture of the upper layer under the vegetation. This method involves using VIS-, NIR-, and TIR data and simulations of a Soil- Vegetation-Atmosphere-Transfer (SVAT)-model. The original method is enhanced and tested in theory and practical application. Comparison of derived soil moisture with field measurements shows good agreement. It is shown that microwave measurements can be included in the analysis in a meaningful way.

Based on a series of studies conducted in Botswana and preliminary results from an ongoing study in Spain, developments in microwave remote sensing by satellite which can be used to monitor near real-time surface moisture and also study long term soil moisture climatology are described. A progression of methodologies beginning with single polarization studies and leading to both dual polarization and multiple frequency techniques are described. Continuing analysis of a nine year data set of satellite-derived surface moisture in Spain is ongoing. Preliminary results from this study appear to provide some evidence of long term decertification in certain parts of this region. The methodologies developed during these investigations can be applied to other regions, and have the potential for providing modelers with extended data sets of independently derived surface moisture for simulation and validation studies, and climate change studies at the global scale.

A new approach for calculating mesoscale soil moisture maps from coarse resolution (500 m - 1 km) SAR data utilizing spatially reduced ERS data is presented. The processing of the radar data is described which includes a correction for the impact of surface roughness, plant water content and soil texture upon of the backscatter intensity. First, the portion of the pixel which does not provide soil moisture information (forest, built-up areas, water) is corrected, then the corrected backscatter intensity is normalized to a reference landuse (cereals). The required landuse map was derived from LANDSAT data. However, these landuse classes which must be distinguished to account for differences in surface roughness and plant water content, may also be derived from AVHRR spectro-temporal unmixing. Using model results and measurements together with the landuse map, the impact of the plant water content was corrected. Finally, the soil texture was taken into account to calculate the mesoscale surface soil moisture. The resulting soil moisture was validated quantitatively using ground truth measurements. A qualitative validation of the spatial patterns was carried out through by comparison of the calculated soil moisture with precipitation patterns. A good agreement was found between ground based and satellite derived soil moisture (RMSQ less than 5 VOL%).

Remote sensing can be a very interesting source of distributed data for large or medium scale hydrological modeling, where soil status and land conditions can be extremely different from one zone to another and a large amount of in-situ measurement would be necessary. In this study two Landsat TM images of the lower part of the Arno basin (Tuscany, Italy) taken in 1991 have been processed using several techniques. Cluster analysis gave interesting results in monitoring the state of soil and vegetation in the two different periods of the year. Clusters obtained have been compared with the distribution of different pedological classes and soil use and with geomorphological information derived from the DTM. Landsat data have been used also to obtain several soil water content indexes, and produce maps of soil moisture. A principal component analysis has been used to obtain data that are directly dependent on soil and as less influenced as possible by other factors like vegetation. Finally, an algorithm to retrieve soil hydraulic properties (permeability, gravitational storage, capillary storage) from geomorphologic data (slope, aspect) and pedological class has been studied, using Monte Carlo simulation and optimization techniques. The spatially distributed hydraulic properties of soil have been applied in a physically based hydrological model. The results have been compared with soil water content indexes obtained from Landsat data analysis on two sub-basins of the Arno river.

More than 80% of the total water consumption in Mediterranean countries is used for agricultural practices. When this water comes from subterranean reservoirs (aquifers), the evaluation of extracted volumes is very complex using the traditional methods. The exploitation of subterranean waters in Span has become, in some cases, an environmental problem since it has affected highly valuable wetlands and river flows by depressing the water table levels. In this work, we introduce a methodology based on Remote Sensing and Geographic Information Systems (GIS) techniques that allows to evaluate the extracted water volumes for irrigation, with high accuracy levels and relatively low economic costs. The developed system is based on the elaboration of crop maps that are obtained from the analysis of multitemporal Landsat TM images. The used procedure is a supervised classification of images that combines maximum likelihood algorithms and decision-tree criterions. The resulting data are integrated in a GIS and crossed with the values of water volumes needed per crop. The obtained output allows quantifying not only the spatial distribution of the water consumption, but also its evolution through time.

The spatially distributed, physically based snowsubmodel ESCIMO (Energy Balance Snow Cover GIS Integrated Model) is presented which is integrated in the multiscale modeling shell PROMET. ESCIMO calculates the spatial snow cover distribution and snowmelt runoff by an energy and mass balance assertion. The meteorological input parameter fields are derived from generally available measurements of the German Weather Service (DWD) climatic station network ensuring the transferability of the model. These measurements of precipitation, temperature, wind speed and cloudiness are temporally and spatially interpolated. To take the influences of different vegetation into account spatial landuse information is derived by a multitemporal spectral unmixing procedure of NOAA-AVHRR satellite data from the vegetation period 1995. Necessary model parameters are taken from literature and no calibration is conducted. The temporal distribution of the snow cover and snow water equivalent was modeled for the winter season of 1993/94 in the Weser catchment. The modeled snow water equivalent is verified with measurements. The resulting coefficient of determination between model and measurement is 0.62. The spatial performance of ESCIMO in the Weser catchment is validated by a comparison of the modeled with the snow cover derived from a NOAA-AVHRR image. The correspondence between the two images is 86.3%.

A model is described to compute the surface evaporative fraction from remotely sensed data. The scheme is based on the fact that much of the variability of the evaporative fraction is determined by the fractional cover of green vegetation, the remaining variability being caused by variations in soil wetness. Vegetation transpiration is obtained from an AVHRR vegetation index whereas soil wetness is obtained from the SSM/I microwave polarization difference temperature. Comparison of remotely sensed evaporative fraction with ground-based data from FIFE yield an average absolute error of 0.036 which is within experimental error. It is also shown that the remotely sensed evaporative fraction is largely independent of soil textural composition.

Spatially distributed inland quality parameters, such as suspended sediments, chlorophyll load, water transparency and surface temperature were evaluated in the four majors lakes of the sub-alpine region located in northern Italy. These lakes alone account for 90% of the inland water resource of the entire country. Due to the agricultural and industrial uses of the lake water and their main economic relevance, linked to the recreational vacation of the entire sub-alpine environment, a large monitoring program for their conservation is justified. Two Landsat-5 Thematic Mapper data acquired in March 1997 were used to compare the different behavior of the lakes water in Spring, when the main algae bloom occurs. Water quality parameters were extracted by means of the chromaticity analysis: Landsat Chromaticity System (LCS) uses absolute radiance values from the satellite and derive the trophic state of the waters in terms of 'redness' X, 'greenness' Y, and 'blueness' Z of water color, related to the suspended materials, to the chlorophyll concentration and to the water turbidity, respectively. Moreover water surface temperature was derived from radiometrically corrected TM band 6 and the influence of water volume and altitude on water surface temperature and the relation among them were formalized.

Classifying surface water bodies according to trophic status by remote sensing techniques has had limited success in lakes with relatively high nonalgal turbidity levels. Since the trophic status of a lake is typically defined based on its chlorophyll-a concentration, and since relatively high suspended solids concentrations masks chlorophyll absorption and reflectance peaks, determining trophic status remotely is typically only partially successful. Hoer, we were interested in exploring hyperspectral data analysis for estimating trophic status. Hyperspectral data (10 nm resolution between 262 and 850 nm) of light attenuation were measured in Lake Texoma (USA) at the surface, 0.1, 0.5, 1.0 and 1.5 meters in depth, while simultaneously analyzing the water column for chlorophyll-a and suspended solids concentration. Data were collected at five sampling stations, each representative of a major zone in the 36,000 hectare lake, approximately monthly, during 1996/97 hydrologic year. Downwelling and upwelling vertical attenuation coefficients were calculated using Bouger-Lambert's law. First and second order derivatives, as well as higher order derivatives were applied to the spectral data. The results showed a clear correlation between first order derivatives and turbidity, while the second order derivatives were correlated to chlorophyll-a concentrations.

A method is presented for extracting wetland areas in northern high-latitude zones using Normalized Difference Vegetation Index (NDVI) and land surface temperature (T_s) calculated from midday NOAA/AVHRR data. Wetland areas have been distinguished from other land-cover types using signatures on a scattergram of NDVI vs. T_s. The method was applied for extracting wetland areas in the basin of the Ob River in the west Siberian lowland. The result have been verified with ground-truth data and land-cover classification results obtained from high-resolution satellite images.

Several meteorological conditions are known to cause anomalous propagation (AP) of microwave radiation. The effect of AP in weather radar measurements my be important as spurious echoes from distant ground targets may appear as precipitation leading to wrong rainfall estimations. AP may also affect dramatically the quality of clear air radar observations. In this study, more than one hundred radiosonde ascents are examined to evaluate the occurrence of AP at the coastal site of Barcelona (Spain). Temperature and humidity profiles are used to calculate refractivity gradients and to estimate the existence of ducting layers. Ducts represent the worst case of super refraction and within them microwaves travel trapped like in a waveguide. To detect thin AP features a vertical resolution higher than that given by standard operational radiosonde data is desirable. For this reason, radiosonde data recorded every 10 s have been used. Results are compared against standard operational radiosonde analysis revealing a significantly higher number of AP layers. The output of a mesoscale numerical weather prediction model is also used to derive refractivity gradients. The ability of the model to simulate the propagation conditions is overviewed in order to assess the feasibility of an operational diagnostic AP product.

Simple models relating growth to photosynthetically active solar radiation absorbed by forest canopies are a necessary tool for scaling-up functional parameters to ecosystem scale. The present study examined the effects of coastal erosion and of distance from the seaside on the physiology and growth of 63-year-old Pinus pinea stands. The effects of radiation interception, stomatal limitations and carbon allocation on stand above-ground increments were analyzed. The assessment of the relative importance of each term and the relation between growth and intercepted light by the canopy was the starting point for RS analysis by means of vegetation indices based on TM images, once confounding effects had been eliminated by pairwise analysis. The analysis of reflectance spectra also allowed to quantify the impact of erosion on ecosystem diversity and integrity.

This work presents the results of the PEICRE project, which monitors desertification in Niger. A previous project (PIK), carried out various interventions between 1984 and 1996, in the region of the Keita village in Niger, in order to reduce the effects of the desertification and land degradation processes. The CeSIA Institute evaluated whether the field interventions achieved the expected results. A Landsat Thematic Mapper image of 1984 and a Spot Multispectral image of 1995, were used to compare the two different types of vegetation coverage. A second aspect of the developed work concerned the erosion analysis, using GIS techniques and multisource data integration models. Particularly, satellite, meteorological and terrain data were fused to estimate the potential erosion in the studied area in relation to different years. Accordingly, both the image processing and spectral classification procedures and the developed hydrological models will be described. The models were applied to data collected both before and after the project development and the results will be discussed in this paper, considering the effects of the project in the land degradation processes.

The multistage procedure of multichannel image pre- and post- processing is proposed. It includes nonlinear image-to-image and image-to-topology map superimposing with optimization of transform parameters, image separate and vector filtering for remained error reduction and image enhancement. Then the image preliminary recognition is to be performed for determined homogeneous regions, two possible ways of data interpretation based on the use of radiophysical models or supervised learning neural networks are discussed. It is shown that the proposed procedure provides a satisfactory determination of field lots with different erosion and other useful results.

The thermal inertia, P, is defined as a measure of the resistance offered by materials to change their temperature. P is the most important single thermal property which governs surface temperature variation. Therefore thermal inertia is of great interest to geological and hydrological studies and climate modeling. An attractive and unique way to map and monitoring this parameter over large scale is to use space observation from satellite in the visible and thermal infrared bands. In this paper we present a new algorithm, based on Xue and Cracknell's model, which allows to obtain the thermal inertia combining afternoon and morning NOAA satellites. The algorithm was tested with a set of measurements made on a region of Niger in the frame of HAPEX-Sahel experiment. The behavior of the model was analyzed by comparing the predicted surface temperatures with the measured ones every ten minutes along the daytime, and by comparing the predicted and measured maximum and minimum surface temperature values as well as their times in the daytime. Our results indicate that for the 90 per cent of the cases the absolute difference between predicted and measured surface temperature is lower than 2 K, with a standard deviation of 1.5 K that improves to 1 K when predicting the maximum and minimum surface temperatures. In this situation the FTM predicts also their respective times with a standard deviation lower than 30 minutes, this makes possible building images of minimum surface temperature and their date from NOAA data. This fact is of great interest in the case of frosting with clear sky conditions. Following the proposed algorithm a map of thermal inertia of the Iberian peninsula is presented. The results are consistent with the known properties of this area.

Satellite observations of agricultural and other plant canopies in the thermal IR regime have generally been at spatial scales of tens to hundreds of meters. Use of the thermal IR at higher resolutions is confounded by the mixture problem and other associated scaling issues. Advances in sensor technology will extend our capabilities for IR measurements to shorter wavelengths and yield improved spatial resolutions. However, experience with aircraft remote sensing observations has indicated that care must be exercised in understanding the interaction effects of viewing geometry at these higher resolutions. The utilization and scaling of observables with multi-resolution remote sensing data sets remain a difficult problem. At high spatial resolution the three-dimensional character of scene components contained within a pixel must be considered. In this paper, we explore the variability in brightness temperature and the co-variation of NDVI with brightness temperature as a function of viewing geometry and changing spatial resolution. Using three- dimensional models for both canopy reflectance and thermal IR exitance, we employ a theoretical analysis for an agricultural scene where previous comparisons and measurements were available.

A comparative study of a selection of classification methods for agricultural fields in sequences of aerial images is presented. The image sequences are acquired by an RGB-CCD video camera which is assumed to be on board of an airplane, moving linear over the scene. The objects in the scenes being considered are agricultural fields. The classes of agricultural fields to be distinguished are determined by the type of crop, e.g. potatoes, sugar beet, wheat, etc. In order to recognize and classify these fields obtained from the aerial sequences of images, a common approach is in the use of surface texture. Textural features are extracted from the images to effectively characterize the vegetation. Methods based on Circular Symmetric Auto-Regression, Co-Occurrence Matrix and Local Binary Patterns are selected for the comparative study. The experiments are carried out with image sequences taken from a scaled model of a landscape and a selection from the Brodatz set. A few training images are used to set up the model bases for the three methods. The methods are tested using the same regions from other images of the sequence, and other sequences of images of similar fields. Comparison fa the methods is based on the confusion matrix. Sensitivity to variations in flight direction, variations in altitude and luminance conditions are being considered.

This work analyzes the spatial and temporal variation of Normalized Difference Vegetation Index (NDVI) and rainfall in the Northeast region of Brazil (NEB), between 1 degree to 18 degrees South and 35 degrees to 47 degrees West. Studied were the dry years of 1982 and 1983 and the rainy years of 1984 and 1985, covering the intense El Nino-Southern Oscillation (ENSO) event of 1982 - 83. Ten test-areas representing prevailing and different phitophysiognomies of the region were used for temporal analysis. The spatial analysis was based on a grid of 5,571 rainfall data points spaced by 0,25 degrees latitude and longitude, interpolated from the original rain-gauge measurements. In relation to previous NEB studies, the subdivision of the vegetation in classes and the use of correlations with time lags were introduced. The results showed that: (1) NDVI and rainfall monthly data follow similar patterns on a temporal and spatial basis, being statistically correlated where the NDVI can depict the rainfall regime in periods of unusual droughts or rain in NEB; (2) the best correlation was found between the combined rainfall of two consecutive months with the NDVI of the latter these two months; (3) there was also an NDVI increase in the rainy season for the extremely dry year of 1983; (4) NDVI and rainfall better represented the vegetation hydric potential in the form of a ratio, or Rain Greenness Ration (RGR), rather than separately, and; (5) the largest NDVI variation occurred for the urban area class. The results indicate the potential use of satellite NDVI imagery to monitor drought occurrences as well as to study climatic variability on a regional scale.

Vegetation monitoring has been one of the main targets of remote sensing study. A vegetation-soil-water Index (VSWI) is defined to monitor land cover conditions based on empirical fact that a visible red versus near infrared scattergram of any satellite data generally forms a triangular distribution. Three vertices of the triangular distribution (end member triangle) correspond to end members for vegetation, bare soil and water (or dark target). The VSWI measures the distances from data point to three edges of the end member triangle. For vegetative component, the distance from data point to the opposite side of end member for vegetation, and so on. The VSW index has possibility to monitor not only vegetation conditions but also soil and water conditions as well. In the present study, the VSW index was used to monitor land cover conditions of mountainous region in Japan with LANDSAT TM images. A change in VSWI around MT. Fuji was also examined using two LANDSAT TM scenes.

A reliable estimation of primary production of terrestrial ecosystems is often a prerequisite for carrying out land management, while being important also in ecological and climatological studies. At a regional scale, grassland primary production estimates are increasingly being made using satellite data. In a currently used approach, regional Gross, Net and Above-ground Net Primary Productivity (GPP, NPP and ANPP) are derived from the parametric model of Monteith and are calculated as the product of the fraction of incident photosynthetically active radiation absorbed by the canopy (f_APAR) and gross, net and above-ground net production (radiation-use) efficiencies ((epsilon) _g, (epsilon) _n, (epsilon) _an); f_APAR being derived from indices calculated from satellite measured reflectances in the red and near infrared. The accuracy and realism of the primary production values estimated by this approach therefore largely depend on an accurate estimation of (epsilon) _g, (epsilon) _n and (epsilon) _an. However, data are scarce for production efficiencies of semi-arid grasslands, and their time and spatial variations are poorly documented, leading to often large errors on the estimates. In this paper a modeling approach taking into account relevant ecosystem processes and based on extensive field data, is used to estimate sub- seasonal and inter-annual variations of (epsilon) _g, (epsilon) _n and (epsilon) _an of a shortgrass site of Arizona, and to quantitatively explain these variations by these of plant water stress, temperature, leaf aging, and processes such as respiration and changes in allocation pattern. For example, over the 3 study years, the mean (epsilon) _g, (epsilon) _n, and (epsilon) _an were found to be 1.92, 0.74 and 0.29 g DM (MJ APAR)^-1 respectively. (epsilon) _g and epsilon_n exhibited very important inter- annual and seasonal variations mainly due to different water stress conditions during the growing season. Inter-annual variations of (epsilon) _an were much less important, while for periods shorter than a growing season, (epsilon) _an exhibits very contrasting values from re-growth to senescence. Therefore the calculation of ANPP based on Monteith's approach seems less prone to errors due to environmental effects when computed on an annual basis, whereas for periods shorter than the growing season the computation of either GPP, NPP or ANPP is delicate.

The feasibility of the inversion of optical remote sensing products to measure critical biophysical properties of Eucalyptus Forests at regional scales is investigated here. The biophysical variables used were leaf area Index, LAI, Diameter at Breast Height, DBH, Height and Age of Eucalyptus stands pertaining to a combination of different genetic materials (E. urophylla x E. grandis hybrids) and propagating systems (seeds or cuttings) and management system (planting and coppicing). The field sampling was done daily during 3 months, from April to June 1997, and covered 130 stands of minimum sizes of 9 hectares, within an Eucalyptus farming area of about 800 km², centered at 19 degrees South, 42 degrees West, Brazil. The stands ranged from 12 to 84 months old. The measurements of LAI were done using two pairs of LAI-2000 (LICOR) under conditions of diffuse light. The Normalized Difference Vegetation Index, NDVI, and the Soil Adjusted Vegetation Index, SAVI, were derived from a LANDSAT-TM image acquired on June 5, 1997. Furthermore, a mixture model technique was applied to derive three new parameters: fraction of green vegetation, F_GV, fraction of shadow, F_SH, and fraction of soil, F_S. Regression analysis were done between biophysical variables and remote sensing products. Linear correlation with coefficients of determination, R², as high as 0.8 were found between LAI versus F_GV and LAI versus SAVI, on all genetic materials. In general, SAVI was shown to give better estimates of LAI than NDVI, which is explained by the openings in the canopy as the Eucalyptus grow older. The correlation with the other biophysical variables (Height and DBH) were also shown to be significant, although the R² ranged from 0.4 to 0.6. The correlation between F_GV and SAVI was higher than 90% such that they can be used to estimate Eucalyptus biophysical parameters with the same statistical significance.

Optical images from the Biosphere reserve of Menorca (Spain) are processed to analyze the spatial pattern of the landscape and to determine whether the spatial configuration influences its dynamics. Indices of the landscape pattern are calculated to describe patch shape and interactions among land covers. The descriptors are computed at a patch level: the number and the edge of the patches, and its shape complexity using the fractal dimension; and at a class level: the heterogeneity index, the largest patch index and the probability of adjacency among classes. We use neutral models to compare the results of real landscapes with those generated by simulation. Two kinds of neutral models are performed: random landscapes and landscapes with contagion. Moreover, a Bayesian approach, reveals how the spatial pattern influences the land cover dynamics. We estimate the probability of change from the class i to the class j in a time step, conditioned to the state of its 4 nearest neighbors. This kind of analysis is represented in a three dimensional matrix we termed spatio-temporal matrix. In the case of Menorca, and during the 1984 - 1992 period, we can conclude that land cover changes are not strictly Markovian, the change of state of a cell is not simply a function of its current state but it is influenced by surrounding cells.

For estimating the biomass of meadows in the Ammer catchment (test site) a terrain corrected multitemporal ERS-1/-2 data set consisting of 30 descending and ascending scenes and their coherences from 1995 and 1996 is used. The radar backscatter coefficient received from grassland is influenced by canopy geometry, plant water and soil moisture content. The biomass is a measure for the plant water content and therefore has an influence on the dielectric constant. The geometrical factors in the canopy depend on the plant height, the vegetation density and species distribution. Moreover, the parameters growth height and biomass correlate strongly for the investigated meadows. To validate the investigations, a data base consisting of weekly measurements of plant height as well as wet and dry biomass on selected fields was built up. Statistical analysis is successfully applied to the backscatter values of the test meadows to relate the average backscatter value of each investigated meadow to the measured plant parameters. Furthermore, coherence data were used to investigate their potential to detect temporal biomass changes.

Airborne imaging spectrometers, like DAIS (Digital Airborne Imaging Spectrometer) sensor, are powerful tools to monitor surface biophysical parameters, but need atmospheric correction to be compared to reference spectra. The aim of this work is the atmospheric correction of the DAIS images acquired by DLR during a flight campaign in the area of Les Gavarres (Girona, NE Spain). Experimental atmospheric measurements of direct and global irradiances together with downward radiance allow us to monitor the atmospheric radiative properties in order to carry out the atmospheric correction. An operational algorithm has been developed to process the DAIS images at low-time cost in the visible and near infrared; range (from 500 to 1100 nm).

In this study we propose a methodology for understory characterization based on the isolation of the forest reflectance that is exclusively due to the background. This is achieved by extracting the contribution of the forest canopy from the overall forest reflectance using forward modeling based on forest reflectance models and existing data on forest structure. Then, the understory can be characterized by applying any methodology developed for open-sky shrub lands, such as vegetation indices or linear mixture models. The developed methodology was successfully applied to simulated data of maritime pine forests with different biophysical stand characteristics and with different understory characteristics.

We developed a simple technique to evaluate the importance of non-linear mixing in coniferous forests with different overstory structural characteristics and different backgrounds. The methodology consists on using a hybrid forest reflectance model for reflectance simulation, and on using factor analysis and target testing for unmixing the forest reflectance into three components: a forest canopy component (free of background effects), a background component (free of canopy effects) and a background-and-canopy dependent component. This third component is considered responsible for non-linearities since it depends simultaneously on the background reflectance and on the canopy transmittance. After running the model, the contribution of the third (non-linear) component to the total forest reflectance was evaluated and compared over different forest scenarios parameterized with data collected on maritime pine stands in Central Portugal.

Traditional farming usually resorts to the use of large amounts of fertilizers and other chemicals to meet the needs for food production. These practices ultimately cause dreadful harm to human lives. Precision agriculture has emerged as a valuable and a promising aid to mitigate these adverse effects by helping farmers increase crop yield while sustaining a clean environment. As an outgrowth of the efforts exerted in this field, we conducted a study, using hyperspectral remotely-sensed data, GIS, and GPS to adequately manage the nitrogen (N) fertilizer applications to optimize the crop yield while protecting the environment. Three nitrogen rates (i.e., 40, 80, and 120 kg/ha) from different sources were applied to four replications, with 20 plots (i.e., treatments) in each replication and data were collected at different growth stages of cotton. Statistical analyses indicated significant correlation between the hyperspectral data and the leaf chlorophyll content and crop yield. The results also indicated no significant difference in yield between 80 and 120 kg/ha N application rates, which suggests that high rates of N does not benefit the farmer more than it hurts the environment. GIS analysis visually revealed site specific (using GPS) relationship among these elements. Through this study, our ultimate goal of predicting the N content from non- destructive remotely-sensed observations in order to adequately and rapidly manage the utilization of N fertilizer was achieved, which will benefit farmers and help protect the environment.

The practical application of current airborne and future satellite or space station based high spectral resolution (hyperspectral) imagery to vegetative canopies (sparse or dense) and resulting derived bio-physical variables will depend upon our ability to rapidly apply scientifically based algorithms. Key to these rapid assessments is the selection of the best or optimal channels or bands for detection of plant stress or dysfunction. Previous work has demonstrated the potential of utilizing high spectral resolution optical signatures for detecting plant stress related to the vegetation's moisture within the leaf structure. Future algorithms and techniques need to discriminate plant species as well as any plant dysfunction or stresses in terms of leaf chemistry or other canopy bio-physical variables in order to improve operational advances in the use of hyperspectral imagery for environmental surveillance, agriculture and earth system science management. Second derivative imagery based upon derivative algorithms and selected bands are presented for AVIRIS imagery of Kennedy Space Center, Cape Canaveral and the Satellite Beach region of central Florida. The algorithms show potential for being used as the basis for firmware or 'silicon strategy' based algorithms in the future.

As a part of the environmental air quality assessment studies, we have conducted spectral characterizations of steady state fluorescence and canopy level reflectance measurements in responses to the air quality treatments. These studies have been carried out under well watered and restricted soil moisture regimes. Our primary objective was to evaluate fluorescence technique for the nondestructive assessment of plants grown in elevated CO₂, elevated O₃, and both elevated CO₂ and O₃ environments under normal and restricted soil moisture conditions. The experimental field site is located at the South Farm, USDA/BARC, Beltsville, Maryland. We used field open-top chamber systems to simulate four altered combinations of tropospheric CO₂ and O₃ environments under well watered and restricted soil moisture conditions. The simulated gaseous environments included: (1) charcoal-filtered (CF) ambient air as the control chamber; (2) elevated CO₂ consisted of CF ambient air plus 150 plus or minus 10 (mu) l/l CO₂; (3) elevated O₃ that consisted of Non-Filtered (NF) ambient air and addition of 35 plus or minus 5 nl/l O₃; and (4) high CO₂ and O₃ chamber which consisted of NF ambient and additions of 150 plus or minus 10 (mu) l/l CO₂ and 35 plus or minus 5 nl/l O₃. The CO₂ was supplied for 18 hours (3:00 to 21:00 h EST) per day from a CO₂ bulk tank. In the elevated O₃ treatment chambers, 35 plus or minus 5 nl/l O₃ was supplied during seven hour period from 9:00 to 16:00 h EST per day. Both gases were supplied for the entire growing season from early July to late-October. Two soybean cultivars 'Essex' and 'Forrest' were planted in the OTC. Fluorescence images were acquired at F450, F550, F680, and F740. The results showed that fluorescence responses of 'Essex' and 'Forrest' were significantly different in all four bands. Restricted soil moisture treatment had significant effects in that means for F450 and F550 bands significantly increased in both cultivars. The increases in fluorescence intensities at F450 and F550 for 'Essex' due to restricted soil moisture were much greater than those of 'Forrest.' At F680 and F740 bands, only 'Essex' was significantly affected by soil moisture treatment. The most significant effects of elevated CO₂ and O₃ were observed at F450 and F550 bands. Means for elevated O₃ in ambient air in both cultivars were significantly higher in both soil moisture regimes. However, no significant differences between CF and NF+CO₂+O₃ within a soil moisture treatment were observed. Means for CF+CO₂ was significantly lower for both cultivars under well watered soil condition at F450. In general, none of the treatment means were significantly affected by elevated O₃ treatments at F680 and F740. Although visible stress symptoms such as chlorosis, discoloration, or necrosis were not evident in the leaves used in this study, F450 and F550 fluorescence images depicted the effects of elevated O₃, partial compensation of elevated O₃ effects by the elevated CO₂, and positive physiological effects of elevated CO₂. Plant stresses that cause localized damage may not be easily detected with small field of view point measurements. Imaging a whole leaf may provide better means to study the effects of non-systemic stresses. We are also currently investigating reflectance techniques for a possible complementary use with fluorescence measurements in the remote assessment of above treatment effects.

Remotely sensed reflectance from stressed and non-stressed crop vegetative cover can be predicted from two combination of spectral bands as a ratio or as normalized vegetation indices. The most common spectral bands used lie in the red and infrared region (350 - 800 nm) and are dominated by the absorption of chlorophyll and other accessory pigments. In addition, reflectance in the middle infrared is dominated by absorption from liquid water contained in plant's tissues. The objectives of the present work are: (1) to evaluate the reflectance data from frequently irrigated and water stressed alfalfa using a handheld radiometer and assess the spectral correlation with the ground-truth and; (2) to evaluate the applications of a Hyperspectral Structure Component Index (HSCI) proposed by Shakir and Girmay-Gwahid (1998). The experiment was designed to collect reflectance data from alfalfa (pure alfalfa stand and a plot where alfalfa was mixed with sedge grass) planted at the Blythe Research Station, California. The size of the plots was 30 X 50 ft². With a field spectrometer, the scan over each treatment was made at 1 hr intervals between 10:00 a.m. and 2:00 p.m. Pacific Day Time (PDT). Vegetative samples were taken from the two treatments during the initial sampling for purposes of conducting chemical analysis. Soil samples were collected to determine the amount of available soil moisture differences in the two treatments. The results of this experiment showed that in the 850 - 1150 nm wavelength range the stressed alfalfa plots showed lower reflectance than unstressed plots. However; the reflectance of stressed alfalfa was higher than the unstressed stands above the 1150 nm. This is probably due to the absorption from liquid water contained in the unstressed plant tissues. The analysis of data using the (HSCI) model showed that the stressed pure alfalfa plots have values less than 1 and under unstressed alfalfa plots have the value greater than 1. This means that the model is differentiating between the stressed and unstressed vegetation. Additional work will evaluate the reflectance peaks and their relationship to other parameters that were collected and are relevant to the applications of the model.

The goal of CALIS is to monitor vegetation condition and to evaluate the impact of climate hazards on both agriculture production and environment. CALIS will warn the end-users, e.g. insurance companies, when crop damage arises, and help them in evaluating compensation for farmers who have been hit. CALIS target calamities are: night frost in early spring, drought in late spring and early summer, heat excess. Impact of climate hazards on vegetation will be assessed through use of meteorological and Earth Observation satellite data, as well as complementary information such as agricultural statistics, tables of local phenological stages, and ground data measurements. A specific methodology has been set for each climatic hazard. The project has been very much a collaborative, interdisciplinary one. End-users have been responsible for defining the goals, selecting meteorological events and checking the results. Scientists provided expertise in selecting and tuning the methods, while value added companies brought their knowledge and experience in the thematic area to specify the system, to generate and interpret the products. Before the end of 1998, the system will be operational for assessing frost damage over the test sites (France, Greece and Spain).

Estimate of the organic carbon content in soil is critical for global change modeling activities. Therefore, the predictive model for estimating soil carbon would provide an important tool for the scientific community. We used remotely sensed TM imaginary data together with the soil profiles and moss layer carbon data for the Northern Study Area (NSA) of the BOREAS project. Different classification and functional models of the carbon dependency on remotely sensed data were developed. The complexity of the models was scrutinized. Based on these techniques, we have developed a set of analysis tools. These tools and an Internet based access to some of these tools will be presented.

The passive remote method of microwave radiometry and its instrumental realization for express diagnostics of radioactive elements in the atmosphere have been discussed. Analysis of the microwave radiation due to ionization and dissociation of atmospheric components interacting with radioactive elements is carried out. The photochemical processes resulting in background microwave radiation power have been discussed. As an example, the results of natural experiment of detecting the atomic hydrogen radiation in the plume of emissions of nuclear cycle processing plants have been presented.

The spatial distribution of birds is an important parameter in the analysis of terrestrial systems; in this study its relationship with satellite-derived vegetation data has been demonstrated. In the present work four Landsat Thematic Mapper (TM) images were used to obtain the Normalized Difference Vegetation Index (NDVI) and the Leaf Water Content Index (LWCI), plus three structure indices based on a land use map. A hierarchical cluster analysis of these indices allowed the identification of four main clusters that were compared to the results of a set of vegetation and ornithological samples, taken in the period of May-June 1997. The procedure used in this study has allowed the individuation of environmental topologies with ecological relevance from satellite imagery. The typification of the environment leads to the formulation of faunistic prediction models in terms of ornithic composition. The avifauna predictability permits, at last, the set-up of proceedings for the environment quality evaluation in terrestrial systems, whenever standard scores for the species would be stated.

We address the problem of estimating fire impact in Mediterranean forests based on Landsat-TM imagery, for which we have used a forest fire in Andalucia (S. Spain) as a case of study. We processed two LANDSAT-TM scenes, acquired before and after the date of the fire. The post-fire scene was segmented and a table of segment statistics was submitted to a hierarchical model-based agglomerative clustering. The boundary defined by the classification closely matches the boundary defined by helicopter. We also produced an image classification of the pre-fire image based on image segmentation and canonical analysis and studied the trajectories of burnt and not-burnt centroids in the Kauth- Thomas plane. The trajectories indicated the existence of a differential response to fire and to phenologic change. We modeled the post-fire conditions as if the region had not been burnt and defined as Index of Fire Impact as the difference between the actual and the modeled second Kauth-Thomas component. The Index of Fire Impact is significantly related to field estimates, but with a scatter that introduces uncertainty in the inversion.

The most commonly used fire detection methods based on AVHRR (Advanced Very High Resolution Radiometer aboard NOAA satellites) observations have been applied, in this work, to the Italian Peninsula, in order to assess their effectiveness and robustness in an operational scheme for fire monitoring in different areas. The analysis developed so far shows that unsuccessful results obtained from existing detection methods mainly depend on the use of generalized, fixed threshold values, in the fire-detection tests. A new fire-detection technique, based on the most general RAT (Robust AVHRR Technique) approach, is proposed in this work. It seems able to join good performances, typically associated with techniques based on locally tuned thresholds, with high operational exportability, achieved by an automated implementation scheme completely based on AVHRR data hands. Improvements achievable by using this new approach over the Italian Peninsula, have been evaluated (for several forest- fire events and different observational conditions) by comparison with the historical records of the Italian Forestry Service.

Medial graphs are interesting tools that can be used to describe arbitrary shapes. This representation is based on a theoretical framework in which the skeleton of the shape is extracted from a compact homogeneous area. Given a sequence of images, it is possible to detect moving objects from the analysis of graph deformations. In order to facilitate the matching of the graphs, specific constraints are used, in particular the type of the node, that corresponds to a major center or a narrow part of the shape. The method is applied to the matching of smoke areas, extracted from a sequence of aerial images of forest fires. An estimate of the local direction and speed of the wind can be inferred from the result of the graph matching.

The spectral reflectance of surface water bodies was measured with high spectral resolution radiometer. The water bodies were also sampled to assess the concentrations of total suspended solids and chlorophyll-a in laboratory. An exponential model using the reflectance ratio of TM3 and TM1 bands was derived for predication of the total suspended solids concentration. TM image of Lake Taihu was acquired at a different time. The distribution of total suspended solids in Lake Taihu was estimated and compared with the field measurements.

This paper is a description of the methodology applied and results obtained in a study of the relations among the yearly evolution of the NDVI, derived from NOAA-AVHRR images, and meteorological variables. NOAA images are captured daily by the receivers located at our Remote Sensing Laboratory and processed by means of our own software. Air temperature and rainfall recorded by the Spanish Meteorological Institute (INM) have been interpolated into the spatial resolution of the AVHRR images. Solar radiation is calculated using Meteosat images, also received at our Laboratory, and a model developed by us. Three forest classes (deciduous forest, evergreen forest and pines) were chosen for 5-year study (1993 - 1997). These classes have an important presence in Castilla y Leon and also a well differentiated ecological significance. Models to relate the variables have been developed and an ecophysiological interpretation has been done. Finally, the improvement of the models by means of separating the different climatic zones using the classification CLATERES and also by introducing new variables capable to better measure hydric stress is addressed.

In this paper four indices suitable to estimate fire danger due to vegetation water stress are studied. These indices are calculated and analyzed by combining NOAA-AVHRR images, received and processed daily in our Remote Sensing Laboratory, and meteorological data. Precipitation and air temperature, recorded by the Spanish Meteorological Service (INM), are interpolated into the spatial resolution of the AVHRR images. Solar radiation derived from Meteosat images has also been used. The first two danger indices are based on the analysis of the NDVI temporal evolution, its decrease is supposed to be related to an increase in water stress and, consequently, in fire danger. The other two indicators are calculated using surface temperature, derived from the AVHRR thermal channels, air temperature and the NDVI and are thought to be related to evapotranspiration or hydric stress. The indices are calculated, tested (using ground data generated by the Spanish Forestry Service), compared and analyzed by means of their relation with meteorological variables. Three of them can be used operationally and they are calculated on a daily basis in our Laboratory and sent to the Spanish Forestry Service during months of risk within the framework of an agreement developed over the last four years.

Operational monitoring of vegetative cover by remote sensing currently involves the utilization of vegetation indices (VIs), most of them being functions of the reflectance in red (R) and near-infrared (NIR) spectral bands. A generalized soil-adjusted vegetation index (GESAVI), theoretically based on a simple vegetation canopy model, is introduced. It is defined in terms of the soil line parameters (A and B) as: GESAVI equals (NIR-BR-A)/(R + Z), where Z is related to the red reflectance at the cross point between the soil line and vegetation isolines. Z can be considered as a soil adjustment coefficient which let this new index be considered as belonging to the SAVI family. In order to analyze the GESAVI sensitivity to soil brightness and soil color, both high resolution reflectance data from two laboratory experiments and data obtained by applying a radiosity model to simulate heterogeneous vegetation canopy scenes were used. VIs (including GESAVI, NDVI, PVI and SAVI family VIs) were computed and their correlation with LAI for the different soil backgrounds was analyzed. Results confirmed the lower sensitivity of GESAVI to soil background in most of the cases, thus becoming the most efficient index. This good index performance results from the fact that the isolines in the NIR-R plane are neither parallel to the soil line (as required by the PVI) nor convergent at the origin (as required by the NDVI) but they converge somewhere between the origin and infinity in the region of negative values of both NIR and R. This convergence point is not necessarily situated on the bisectrix, as required by other SAVI family indices.

Recovery of land surface temperature (LST) from remotely sensed data requires correction for atmospheric effects and decoupling surface temperature and emissivity. In this study, we have applied the Temperature Emissivity Separation (TES) method to several flight lines of the Thermal Infrared Multispectral Scanner (TIMS) acquired as part of the HAPEX- Sahel experiment. Atmospheric correction of at-sensor radiances is done by means of nearly coincident radiosondes and the MODTRAN radiative transfer code. The sensitivity of the method to the atmospheric corrections has been checked by using different radiosonde data. Even for low altitude flights, ignorance of atmospheric correction can lead to large errors in the retrieved emissivities and temperatures. Errors depend on the surface type, but in all cases channel 1 and 6 of TIMS are the most affected. The TES method is based on an empirical relationship relating the maximum-minimum emissivity difference (or contrast) with the minimum value for the 6 TIMS channels. Residual atmospheric effects dictate the max-min difference, especially for flat targets (e.g. vegetation). Since channels 1 and 6 have shown a greater sensitivity to atmospheric effects, a modified version using only the 4 central channels has been proposed and applied to the TIMS scenes. Preliminary results suggest that this modified version yields better values for vegetation targets, with emissivities around 0.98 and very little spectral variation.

Radiometric characterization of the infrared emission of forest fires has been performed for different primary carriers (grass, shrub, and slash) and different ambient conditions in a combustion tunnel (temperature, relative humidity and wind speed). Fourier Transform-based FTIR spectroradiometer (2.2 - 18 micrometer range) as well as a thermal camera have been used in the study. Also a 'classical' characterization, based on the fuel weight before and after the burn, and the measurement of soil and fire temperatures, has been made. Temperature profiles at different heights in different points of the heating zone have been measured by using an automatically controlled thermocouple network. The thermocouples measure the temperatures each 5 seconds in the 0 - 1200 degrees Celsius range. Parameters defining the fire behavior, mainly determined by the fuel consumption ratio, rate of spread, angle and length of flames, and the fire lineal intensity, are obtained from this characterization. Comparison between these results and the radiometric measurements has been done. Information from the camera is used to obtain infrared emissivities of the fires.

Multi-angle algorithms for estimating sea and land surface temperature with ATSR data require a precise knowledge of the angular variation of surface emissivity in the thermal infrared. Nowadays, very few measurements do exist of this variation. In this work an experimental investigation of the angular variation of the infrared emissivity in the thermal infrared (8 - 14 micrometer) band of some representative samples has been made at angles of 0 degrees - 65 degrees (at 5 degree increments) to the surface normal. The results show a decrease of the emissivity with increasing viewing angles, being water the substance with highest angular dependence (about 7% from 0 degree to 65 degree views). Clay, sand, slimy and gravel show variations about 1 - 3% in the same range of views while an homogeneous grass cover does not show angular dependence. Finally, we include an evaluation of the impact that these data can produce in the algorithms for determining land and sea surface temperature from double angle views.

Investigations of radar backscattering signal intensity from different vegetation covers are rather actual, especially for solving the problem of vegetation biomass characteristics determination according to reflected signal. The article presents the attempt of 'water cloud' model application for description of backscattering signal intensity from two- layered (vegetation and soil) object.

In dryland, yield of crop varies substantially in space, often changing by an order of magnitude within few meters. Precision agriculture aims at exploiting this variability by changing agriculture management practices in space according to site specific conditions. Thus instead of managing a field (typical area 50 to 100 hectares) as a single unit using average conditions, the field is partitioned into small pieces of land known as management units. The size of management units can be in the order of 100 to 1,000 m² to capture the patterns of variation of yield in the field. Agricultural practices like seeding rate, type of crop, and tillage and fertilizers are applied at the scale of the management unit to suit local agronomic conditions in unit. If successfully practiced, precision agriculture has the potential of increasing income and minimizing environmental impacts by reducing over application of crop production inputs. In the 90s, the implementation of precision agriculture was facilitated tremendously due to the wide availability and use of three technologies: (1) the Global Positioning System (GPS), (2) the Geographic Information System (GIS), and (3) remote sensing. The introduction of the GPS allowed the farmer to determine his coordinate location as equipments are moved in the field. Thus, any piece of equipment can be easily programmed to vary agricultural practices according to coordinate location over the field. The GIS allowed the storage and manipulation of large sets of data and the production of yield maps. Yield maps can be correlated with soil attributes from soil survey, and/or topographical attributes from a Digital Elevation Model (DEM). This helps predicting variation of potential yield over the landscape based on the spatial distribution of soil and topographical attributes. Soil attributes may include soil PH, Organic Matter, porosity, and hydraulic conductivity, whereas topographical attributes involve the estimations of elevation, slope, aspect, curvature, and specific catchment area. Finally remote sensing provided a means of assessing soil and crop conditions over large scales from the air, without excessive sampling on the ground. There are two objectives for this work. The first objective is to analyze the spatial variability of yield across a spectrum of scales to identify the spatial characteristics of yield variation; in essence, we are trying to answer the following questions, at what scale of management unit we should resolve the field level variability and what is the relationship between this resolution and the observed variability form a yield map? The second objective is to identify the soil and topographical attributes that control yield variation over the landscape topography. We already know that, because erosion and deposition are major processes in the formation of a catena, soil variations occur in response to surface and subsurface flow over the landscape. Also landscape positions corresponding to low elevation tend to have high catchment area which usually results in high soil water content in the root zone and thick A horizon. Can topographical attributes explain yield variation observed in the landscape? Will topographical attributes extracted from a DEM compensate for the relatively poor spatial resolution from a soil survey?