The clouds of Earth are fundamental to most aspects of human life. Through production of precipitation, they are essential for delivering and sustaining the supplies of fresh water upon which human life depends. Clouds further exert a principal influence on the planet's energy balance. It is in clouds that latent heat is released through the process of condensation and the formation of precipitation affecting the development and evolution of the planet's storm systems. Clouds further exert a profound influence on the solar and infrared radiation that enters and leaves the atmosphere, further exerting profound effects on climate and on forces that affect climate change (Stephens, 2005). It is for these reasons, among others, that the need to observe the distribution and variability of the properties of clouds and precipitation has emerged as a priority in Earth observations. Most past and current observational programs are contructed in such a way that clouds and precipitation are treated as separate entities. Nature does not work this way and there is much to be gained scientifically in moving away from these artificial practices toward observing clouds and precipitation properties jointly. We are now embarking on a new age of remote sensing of clouds and precipitation using active sensors, starting with the tropical rainfall measurement mission (TRMM) and continuing on with the A-Train (described below). This new age provides us with the opportunity to move away from past and present artificial observing practices offering a more unified approach to observing clouds and precipitation properties jointly.

In this paper we show changes in the dates of snow disappearance in the Arctic between the late 1960s and the early 2000s, from arbitrary but consistent boundaries, using National Oceanic Atmospheric and Administration (NOAA) satellite observations. The date the snowline retreats during the spring (when it first moves north of the 60° and 70° parallels) has occurred approximately a week earlier in recent decades compared to the late 1960s, for many Arctic locations. During this same period, substantial portions of the Arctic have been experiencing higher temperatures and a conspicuous diminution of sea ice, especially in the past 10 years. Our results generally agree with these observations -- tendency toward earlier snowmelt was sustained until about 1990. Since that time, however, the date of snow disappearance has not been occurring noticeably earlier, and snow cover has actually been forming earlier in the autumn.

Land surface temperature (LST) is of importance in controlling most physical, chemical, and biological processes of the Earth system. Satellite-derive LST provides large-scale observation and is very useful to environmental studies. Among numerous satellite sensors, the MODerate resolution Imaging Spectroradiometer (MODIS) and the Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) are onboard the same satellite platform TERRA. MODIS MOD11_L2 and ASTER AST_08 LST products have a spatial resolution of 1-km and 90-m, respectively. Our previous scaling study revealed 2.2K on-average differences between the MODIS and the upscaled ASTER LST over a heterogeneous semiarid area in the Loess Plateau of China (SPIE Proc., 5967: 58670O-1-8). Because the retrieval algorithm for MODIS 1-km LST product is subject to uncertainty in emissivity estimate over semiarid and arid areas, this paper uses ASTER emissivity data to reduce the LST inconsistency. Based on the MODIS LST retrieval algorithm, a new algorithm is derived. The algorithm does not rely on the coefficients used in the MODIS algorithm such that it can be implemented without acquisition of the raw MODIS datasets. The MODIS LST and band-31 emissivity as well as the upscaled ASTER emissivity are the necessary inputs to the proposed algorithm. Using the new approach, the rectified MODIS LST achieved the satisfied agreement with the upscaled ASTER LST. This study also suggested that the uncertainty in LST induced by retrieval algorithm could be larger than the scale induced uncertainty.

A methodology to retrieve the land surface infrared properties of snow and ice is presented that takes advantage of the high spectral resolution observations of a new generation of polar orbiting weather satellites. The NASA Atmospheric Infrared Sounder (AIRS) on the EOS Aqua platform was the first of a series of high spectral resolution sensors that includes the operational Infrared Atmospheric Sounding Interferometer (IASI) on Europe's METOP platforms and the Cross-track Infrared Sounder (CrIS) on the U.S. National Polar Orbiting Environmental Satellite System platforms. Application of the method described here is intended for the high latitude polar regions, e.g. Scandinavia, Siberia, Alaska/Yukon, and Antarctica. These regions provide challenges for the identification and removal of cloud effects during the polar winter. A methodology is described for the identification of clear fields of view of the sounder over snow and the estimation of surface infrared emission. An example over the Greenland ice sheet is presented. The effective identification of clear sounder fields of view and the correct interpretation of the surface emission is an important component of making high latitude data over land useable in Numerical Weather Prediction data assimilation.

Improved accuracy in defining initial conditions for fully-coupled numerical weather prediction models (NWP) along with continuous internal bias corrections for baseline data generated by uncoupled Land Surface Models (LSM), is expected to lead to improved short-term to long-range weather forecasting capability. Because land surface parameters are highly integrated states, errors in land surface forcing, model physics and parameterization tend to accumulate in the land surface stores of these models, such as soil moisture and surface temperature. This has a direct effect on the model's water and energy balance calculations, and will eventually result in inaccurate weather predictions. Surface soil moisture and surface temperature estimates obtained with a recently improved retrieval algorithm from the Advanced Microwave Scanner Radiometer (AMSR) aboard NASA's Earth Observing System (EOS) Aqua satellite are evaluated against model output of the Community Noah Land Surface Model operated within the Land Information System (LIS) forced with atmospheric data of the NCEP Global Data Assimilation System (GDAS). The surface temperature retrievals and Noah LSM output are further evaluated against local measurements from the Mesonet observational grid in Oklahoma. Preliminary analysis presented here shows a potential to improve simulated surface temperature estimates of the Noah model by assimilating satellite derived surface temperature fields. The potential for updating (top) soil moisture seems to be more restricted, mainly as a result of the relatively thick top soil layer of the model as compared to the passive microwave emanation depth.

Seasonal and interannual vegetation trends in the last eleven years were analyzed for two macro-regions, South Italy and North Africa, in search for evidence of climate changes and associated desertification processes. The South Italy macro-region comprises Apulia, Campania, Basilicata, Calabria and Sicily regions, while the North Africa one covers the northern part of Lybia. Vegetation index data for the whole Europe and North Africa can be retrieved from the DLR archive of thematic maps in the form of monthly composite Normalized Difference Vegetation Index (NDVI) maps. The DLR archive dates back to 1995, thus the analysis could only be carried out for the last eleven years. The analysis of temporal vegetation variations was performed by implementing specific routines which provide objective measurements of vegetation trends and anomaly. Rainfall data for the same periods and geographic areas, were also analyzed in order to investigate the correlation between the two phenomena. Results for the two selected macro-regions, from 1995 to 2006, are presented and discussed. In a successive phase, this study will focus on distinguishing vegetation variations at regional level, in order to compare different local trends.

The additional heating of the air over the city is the result of the replacement of naturally vegetated surfaces with those composed of asphalt, concrete, rooftops and other man-made materials. The temperatures of these artificial surfaces can be 20 to 40 ° C higher than vegetated surfaces. This produces a dome of elevated air temperatures 5 to 8 ° C greater over the city, compared to the air temperatures over adjacent rural areas. This effect is called the "urban heat island". Urban landscapes are a complex mixture of vegetated and non-vegetated surfaces. It is difficult to take enough temperature measurements over a large city area to. The use of remotely sensed data from airborne scanners is ideal to characterize the complexity of urban albedo and radiant surface temperatures. The National Aeronautics and Space Administration (NASA) Airborne Thermal and Land Applications Sensor (ATLAS) operates in the visual and IR bands was used in February 2004 to collect data from San Juan, Puerto Rico with the main objective of investigating the Urban Heat Island (UHI) in tropical cities. In this presentation we will examine the techniques of analyzing remotely sensed data for measuring the effect of various urban surfaces on their contribution to the urban heat island effect.

A historical data set of continuous satellite derived global land surface moisture and land surface temperature is being developed jointly by the NASA Goddard Space Flight Center and the Vrije Universiteit Amsterdam. The data will consist of surface soil moisture retrievals from observations of both historical and currently active satellite microwave sensors, including Nimbus-7 SMMR, DMSP SSM/I, TRMM TMI, and AQUA AMSR-E. The data sets will span the period from November 1978 through December 2005. The soil moisture retrievals are made with the Land Parameter Retrieval Model, which was developed jointly by researchers from the above institutions. The various sensors have some different technical specifications, including primary wavelength, radiometric resolution, and frequency of coverage. Consequently, the soil moisture sensing depth also varies between the different sensors. It is expected that the data will be made available for download by the general science community within about six months. Specifications and capabilities of different sensors and how they affect soil moisture retrievals are discussed.

The synergistic use of multi-temporal and multi-spectral remote sensing data offers the possibility of long-term forest change monitoring. Due to natural and anthropogenic disturbances in Romania, forested ecosystems are undergoing accelerated change. Change detection is a remote sensing technique used to monitor and map landcover change between two or more time periods and is now an essential tool in forest management activities. We compared the ability of Multitemporal Spectral Mixture Analysis (MSMA), and maximum likelihood (ML) classification, Principal Components Analysis (PCA) techniques to accurately identify changes in vegetation cover in a south-eastern part of Romania study area between 1984 and 2004 for Landsat TM, ETM and SAR images. Fuzzy logic approach provides a mathematical formalism for combining evidence from various sources to estimate the significance of a detected change Supervised classification accuracy results were high ( > 68% correct classification for four vegetation change classes and one no-change class).For spatial patterns of changes assessment, has been applied change vector analysis .Classification accuracies are variable, depending on the class and the comparison method as well as function of season of the year. To solve urgent needs in application of remote sensing data, forest cover changes must be detected based on monitoring spatial and temporal regimes across landscapes. Specific aim of this paper is to assess, forecast, and mitigate the risks of forest system changes and its biodiversity as well as on adjacent environment areas and to provide early warning strategies on the basis of spectral information derived from satellite data.

The main objective of the current work is to recognize the dominant and predominant clay minerals of South Port Said plain soils, Egypt using the high advanced remote sensing techniques of hyperspectral data. Spectral analyses as one of the most advanced remote sensing techniques were used for the aforementioned purpose. Different spectral processes have been used to execute the prospective spectral analyses. These processes include 1-The reflectance calibration of hyperspectral data belonging to the studied area, 2- Using the minimum noise fraction (MNF) transformation. 3 -Creating the pixel purity index (PPI) which used as a mean of finding the most "spectrally pure", extreme, pixel in hyperspectral images. Making conjunction between the Minimum Noise Fraction Transform (MNF) and Pixel Purity Index (PPI) tools through 3-D visualization offered capabilities to locate, identify, and cluster the purest pixels and most extreme spectral responses in a data set. To identify the clay minerals of the studied area the extracted unknown spectra of the purest pixels was matched to pre-defined (library) spectra providing score with respect to the library spectra. Three methods namely, Spectral Feature Fitting (SFF),Spectral Angle Mapper (SAM) and Binary Encoding (BE) were used to produce score between 0 and 1, where the value of I equal a perfect match showing exactly the mineral type. In the investigated area four clay minerals could be identified i.e. Vermiculite, Kaolinite, Montmorillinite, and Illite recording different scores related to their abundance in the soils. In order to check the validity and accuracy of the obtained results, X-ray diffraction analysis was applied on surface soil samples covering the same locations of the end-members that derived from hyperspectral image. Highly correlated and significant results were obtained using the two approaches (spectral signatures and x-ray diffraction).

Tanaka and Nishii (2005) figured out that deforestation can be elucidated quantitatively by nonlinear logit regression models in four East Asian test fields: forest areal rate F as a target variable, and human population size (N) and relief energy (R: difference of minimum altitude from the maximum in a sampled area) as explanatory variables, whose functional forms had been suggested by step functions fitted to one-kilometer square high precision grid-cell data firstly in Japan (n=6825): log(F/(1 - F)) = β₀ + g(N) + h(R) + error, where g(N) and h(R) are regression functions of explanatory variables N and R, respectively. Likelihood functions with spatial dependency were derived, and several deforestation models were selected for the application to four regions in East Asia by calculating relative appropriateness to data. For the measure of appropriateness, Akaike's Information Criterion (AIC) was used. To formulate East-Asian dataset, landcover dataset estimated from NOAA observations available at UNEP, Tsukuba for F, gridded population of the world of CIESIN, US for N, and GTOPO30 of USGS for R, were used. The resolutions were matched by taking their common multiple of 20 minute square. Tanaka and Nishii (ibid.) omitted the data with F = 0.0 and F = 1.0 to employ the logit models. Unfortunately the reduction of the data size for regression led to instability of parameter estimation. As for the test field in Harbin, China, n = 76 for 0.0 < F < 1.0, but n = 504 for 0.0 less than or equal to F less than or equal to 1.0. In this study, we therefore compare the models based on all data, especially with F = 1.0, by the following extended logit transformation with two additional positive parameters of κ and λ: log((F + κ)/(1 - F + λ)) = β₀ + g(N) + h(R) + error. Obvious improvements in terms of relative appropriateness to data are observed in extended logit models.

Motivated by the increasing importance of hyperspectral remote sensing, this study investigates the potential of the current-generation satellite hyperspectral data for coastal water mapping. Two narrow-band Hyperion images, acquired in summer 2004 within a nine day period, were used. The study area is situated at the northern sector of south Evvoikos Gulf, in Central Greece. Underwater springs, inwater streams, urban waste and industrial waste are present in the gulf. Thus, further research regarding the most appropriate methods for coastal water mapping is advisable. In situ measurements with a GPS have located the positions of all sources of water and waste. At these positions groundspectro-radiometer measurements were also implemented. Two different approaches were used for the reduction of the Hyperion bands. First, on the basis of histogram statistics the uncalibrated bands were selected and removed. Then the Minimum Noise Fraction was used to classify the bands according to their signal to noise ratio. The noisiest bands were removed and thirty-eight bands were selected for further processing. Second, mathematical and statistical criteria were applied to the in situ radiometer measurements of reflectance and radiance in order to identify the most appropriate parts of the spectrum for the detection of underwater springs and urban waste. This approach has determined nine hyperspectral bands. Τhe Pixel Purity Index and the n-D Visualiser methods were used for the identification of the spectra endmembers. Both whole (Spectral Angle Mapper or Spectral Feature Fitting) and sub pixel methods (Linear Unmixing or Mixture-Tuned Matched Filtering) were used for further analysis and classification of the data. Bands resulting from processing the groundspectro-radiometer measurements produced the highest classification results. The spatial resolution of the Hyperion hyperspectral data hardly allows the detection and classification of underwater springs. Contrary, inwater streams and chlorophyll are satisfactorily classified. The SAM classification method seems to work better as the number of endmembers increases. The Linear Unmixing classification method gives better results as the number of endmembers decreases.

Structure parameters for characterization of vegetation canopies are often estimated from remote optical measurements. Existing methods include those based on measurements of gap fraction, spectral vegetation indices, or the inversion of spectral canopy reflectance models. This paper proposes a novel method based on inversion of multiangular measurements of the abundances of light scattering components, which may be estimated using spectral unmixing. An algorithm is described for predicting the abundances of various scattering components using Monte Carlo simulation with a Poisson canopy model and an ellipsoidal leaf angle distribution. The method was tested using simulated data from ray-traced images in a ground-based measurement scenario. Model fit surfaces were calculated for 20 different values of leaf area index (LAI) and mean leaf angle (MLA). The experiments generally showed good correspondances between observations and predictions, except for high values of LAI and low values of MLA. Future work should include experiments on real data and robust unmixing of scattering components.

The objective of this study, which is part of the project "crop drought stress monitoring by remote sensing" (DROSMON), is to assess the potential of hyperspectral imagery to determine drought stress of crops due to heterogeneous soil composition by estimating the leaf area index (LAI). LAI, which characterizes the actual status of the crops and therefore the potential yield, may be seen as the most important parameter indicating medium term drought stress. As a result of former river meanders, the soils in the Marchfeld region are interrupted by bands of lighter soil. The higher content of sand in the bands leads to a lower water storage capacity and consequently to a decrease in plant growth. An airborne HyMap image was acquired in June 2005 during anthesis stage of wheat. Inversion of a radiative transfer model by means of a look-up-table (LUT) approach was performed to retrieve LAI and other canopy parameters from wheat canopy reflectance. Additionally, the LAI was estimated by establishing empirical relationships between LAI and spectral indices (MSAVI, TVI and MTVI2). Both ways of LAI estimation showed a reasonable correlation to final yield measurements obtained one month after the image data acquisition. However, there was a slightly better agreement of model inversion results. The results suggest the applicability of hyperspectral imagery to map potential drought risk of (wheat) fields.

In the context of vegetation studies Earth Observation (E.O.) data have been extensively used to retrieve biophysical parameters of land surface. In some cases, thanks to the availability of near-real-time data, tools and applications have been developed and implemented in the fields of precision agriculture, water resources monitoring and management. So far, empirical approaches based on vegetation indices (VIs) have been successfully applied. They may provide a satisfactory level of accuracy in the estimation of important vegetation biophysical parameters (e.g. LAI, fractional ground cover, biomass, etc). Such methods, however, require a reliable reference data-set to calibrate empirical formulas on different vegetation types; furthermore, they are generally based on a few spectral bands, with a consistent under-exploitation of the full spectral range available in new generation sensors. Alternative approaches based on inversion of radiative transfer models of vegetation represent a challenging opportunity for the estimation of vegetation parameters from data with high dimensionality.

This paper describes research undertaken on the improvement of within-field late season yield forecasting for crops such as wheat using multi-temporal visible/infrared satellite imagery and multi-polarization radar satellite imagery. Experiments have been carried out using ASAR imagery from Envisat combined with nine bands of ASTER imagery from the NASA Terra satellite. An experimental test site in an agricultural area in the county of Lincolnshire, UK, has been used. The satellite imagery has been integrated using artificial neural networks which have been trained as predictors of the spatial distributions of yield per unit area in a variety of fields. Ground truth data in the form of yield maps from GPS-enabled combine harvesters have been used to train the neural networks and to evaluate accuracy. The results show that the combinations of ASTER and ASAR imagery can provide enhanced yield predictions with overall correlations of up to 0.77 between predicted and actual yield patterns. The results also show that the use of dual polarization radar data alone is not sufficient to give reasonable yield predictions even in a multi-temporal mode. It has also been shown that varying the architectures of the neural networks with ensembles can improve the overall results.

Forest fires are one of the major environmental issues in large areas of Southern Italy, and more generally in Mediterranean Europe. Biomass burning reduces carbon fixation in terrestrial vegetation, while risk of soil erosion increases in burned areas. The premier action against fires is prevention, and in this context fire risk mapping is an invaluable tool. Various factors, either static or dynamic, contribute to the definition of fire risk. Among them, vegetation moisture plays a key role, since forests susceptibility to fire increases with increasing plant water stress and biomass dryness. A tool is needed to allow a timely detection of such forest conditions, and space-borne and airborne remote sensing can be very effective to this end. Many authors have demonstrated the role of remote sensing in the assessment of vegetation moisture. Various multi-spectral systems have been reported to be useful, such as Landsat TM, SPOT or NOAA AVHRR. We have recently started a research to evaluate fire risk in the rural environment of Southern Italy using the Moderate Resolution Imaging Spectrometer (MODIS), carried on board of EOS Terra and Aqua satellites. The MODIS systems have 20 spectral wavebands covering the visible, the near infrared and the shortwave infrared with a spectral resolution of 10-50 nm. This paper describes the results of a preliminary experiment to identify the most useful bands or band combinations (spectral indexes) for the detection of biological indicators of plant water stress. PROSPECT radiative transfer code has been adopted to simulate leaf reflectance as a function of leaf properties. Results highlighted the potential of single and combined simulated MODIS bands in the retrieval of vegetation moisture indicators related to fire risk.

Chlorophyll fluorescence (Chf) emission allows estimating the photosynthetic activity of vegetation - a key parameter for the carbon cycle models - in a quite direct way. However, measuring Chf is difficult because it represents a small fraction of the radiance to be measured by the sensor. This paper analyzes the relationship between the solar induced Chf emission and the photosynthetically active radiation (PAR) in plants under water stress condition. The solar induced fluorescence emission is measured at leaf level by means of three different methodologies. Firstly, an active modulated light fluorometer gives the relative fluorescence yield. Secondly, a quantitative measurement of the Chf signal is derived from the leaf radiance by using the Fraunhofer Line-Discriminator (FLD) principle, which allows the measurement of Chf in the atmospheric absorption bands. Finally, the actual radiance spectrum of the leaf fluorescence emission is measured by a field spectroradiometer using a device that filters out the incident light in the Chf emission spectral range. The diurnal cycle of fluorescence emission has been measured for both healthy and stressed plants in natural and simulated conditions. The main achievements of this work have been: (1) successful radiometric spectral measurement of the solar induced fluorescence; (2) identification of fluorescence behavior under stress conditions; and (3) establishing a relationship between full spectral measurements with the signal provided by the FLD method. These results suggest the best time of the day to maximize signal levels while identifying vegetation stress status.

The project DEMETER (DEMonstration of Earth observation TEchnologies in Routine irrigation advisory services) was designed to assess and demonstrate improvements introduced by Earth observation (EO) and Information and Communication Technologies (ICT) in farm and Irrigation Advisory Service (IAS) day-to-day operations. The DEMETER concept of near-real-time delivery of EO-based irrigation scheduling information to IAS and farmers has proven to be valid. The operationality of the space segment was demonstrated in three different pilot zones in South Europe during the 2005 irrigation campaigns. Extra-fast image delivery and quality controlled operational processing make the EO-based crop coefficient maps available at the same speed and quality as ground-based data (point samples), while significantly extending the spatial coverage and reducing service cost. The new online Space-Assisted Irrigation Advisory Service (e-SAIAS) is the central outcome of the project. Its key feature is the operational generation of irrigation scheduling information products from a virtual constellation of high-resolution EO satellites and their delivery to farmers in near-real-time using leading-edge on-line analysis and visualization tools. First feedback of users at IAS and farmer level is encouraging. The paper gives an overview of the project and its main achievements.

The structure of vegetation is paramount in regulating the exchange of mass and energy across the biosphereatmosphere interface. In particular, changes in vegetation density affected the partitioning of incoming solar energy into sensible and latent heat fluxes that may result in persistent drought through reductions in agricultural productivity and in the water resources availability. Limited research with citrus orchards has shown improvements to irrigation scheduling due to better water-use estimation and more appropriate timing of irrigation when crop coefficient (Kc) estimate, derived from remotely sensed multispectral vegetation indices (VIs), are incorporated into irrigation-scheduling algorithms. The purpose of this article is the application of an empirical reflectance-based model for the estimation of Kc and evapotranspiration fluxes (ET) using ground observations on climatic data and high-resolution VIs from ASTER TERRA satellite imagery. The remote sensed Kc data were used in developing the relationship with the normalized difference vegetation index (NDVI) for orange orchards during summer periods. Validation of remote sensed data on ET, Kc and vegetation features was deal through ground data observations and the resolution of the energy balance to derive latent heat flux density (λE), using measures of net radiation (Rn) and soil heat flux density (G) and estimate of sensible heat flux density (H) from high frequency temperature measurements (Surface Renewal technique). The chosen case study is that of an irrigation area covered by orange orchards located in Eastern Sicily (Italy) during the irrigation seasons 2005 and 2006.

One of the approaches of estimating crop evapotranspiration over large areas using remote sensing is the use of canopy reflectance (vegetation indices) derived from multi-temporal satellite imagery to estimate and update evapotranspiration crop coefficients. When this method is applied after the irrigation season is over, a spectral crop classification using one or more of the images can be conducted to produce a crop type map of the entire area, allowing the application of the appropriate crop coefficients on a field-by-field basis. However, if the application is to be run in real-time during an irrigation season using satellite images as they become available, a different classification scheme is required as early season images might not be optimally suited for a traditional spectral classification. This paper presents a real-time method of classification based on a combination of spectral classification and logic using the prior knowledge of the crop types and growth curves in the region. The method is applied to images acquired every two weeks over the 2004 irrigation season at the Lebrija Irrigation District on the Guadalquivir River in Southern Spain. Ground truth information was provided by the local irrigation district.

The evapotranspiration (ET) is one of the most important components of the water cycle in semi-arid Taihang Mountain region of North China. Due to significant changes in topography, the ET of this semi-arid region tends to vary dramatically both in time and space, which renders the accurate estimation of yearly or seasonal ET a difficult task. In current study, based on rGIS-ET v1.0, a regional ET model, by adding module of adjusting surface temperature in terrain, solar radiance terrain correction, and shaded relief, we improved the rGIS-ET a remote sensing model on ArcGIS platform for mapping ET distribution in such a semi-arid mountain area. With DEM of 30 meter and climate data, we run the model to estimate daily ET in mountain area using Landsat data. The ET distribution pattern estimated from Landsat data by rGIS-ET v2.0, was integrated into SWAT method to calculated daily ET at a high spatial resolution in the sub-watersheds. With input of the daily ET, SWAT simulated the annual flow of the component of water balance at sub-watersheds from 1995 to 2003. The variations of flows of annual ET were significant amount the sub-watersheds and the variations of ET flow may cause the variation of other components flow.

During the last the two decades, the scientific community developed detailed mathematical models for simulating land surface energy fluxes and crop evapotranspiration rates by means of a energy balance approach. These models can be applied in large areas and with a spatial distributed approach using surface brightness temperature and some ancillary data retrieved from satellite/airborne remote sensed imagery. In this paper a district scale application in combination with multispectral (LandaSat 7 TM data) and hyperspectral airborne MIVIS data has been carried out to test the potentialities of two different energy balance models to estimate evapotranspiration fluxes from a set of typical Mediterranean crops (wine, olive, citrus). The impact of different spatial and radiometric resolutions of MIVIS (3m x 3m) and LandSat (60m x 60m) on models-derived fluxes has been investigated to understand the roles and the main conceptual differences between the two models which respectively use a "single-layer" (SEBAL) and a "two-layer" (TS) schematisation.

Remote sensing of evapotranspiration has become more common during the last decade. Two of the approaches being used are the reflectance-based crop coefficient method and the energy balance method. In the energy balance approach, surface temperature is used to calculate sensible heat flux and long wave radiation and depending on the complexity of the model, different methods are used to handle the aerodynamic temperature term. This paper compares two energy balance approaches with different methodology for estimating sensible heat flux (H): (i) one layer energy balance model and (ii) two-layer energy balance model called the Two Source Model. The results from the two approaches are compared using a set of comprehensive field and remote sensing measurements of model input data and actual evapotranspiration measured with a dense network of eddy covariance stations during the SMACEX campaign in central Iowa during the 2002 growing season. Results show that both methods of estimating H perform well using calibrated Landsat Thematic Mapper imagery as remotely sensed inputs.

This paper describes a methodology, which is currently being tested, aimed at generating flow hydrographs and the operational simulation of existing pressurized irrigation delivery networks in southern Italy. The approach involves a combined use of high-resolution remote sensing and of a simplified agro-hydrological model to simulate the soil water balance at the level of each delivery node of the irrigation network. High resolution multispectral satellite imagery was acquired on a single date overpass for an existing irrigation district located in southern Italy. The use of high-resolution imagery enables mapping the land use, identifying the specific extent of different cropped areas, and accounting for the spatial variability of parameters related to crop evapotranspiration over the irrigated areas. In the rationale of the proposed methodology, the agro-hydrological model uses spatially-distributed inputs derived from the high-resolution multispectral image and, by maintaining a soil-water balance in the cropped fields downstream from the delivery hydrants, would allow generating disaggregated information on soil water deficits and thus on timing and volumes of irrigation demand for the area served by the distribution network. The aggregation of hydrant hydrographs would allow generating the flow hydrographs at the upstream end of the irrigation network. Results from simulations will then be compared with historical datasets of irrigation events that will be obtained by downloading data records from multiuser electronically-fed hydrants equipped with the AcquaCARD^(R) technology. These comparisons will allow investigating any deviation of actual water deliveries to farm fields from the simulation results, accounting for various uncertainties, including the ones related to farmers' water management strategy. The overall objective of the study being conducted is validating the reliability of the proposed methodology for modernization of large-scale irrigation systems.

A technique is presented for detecting vegetation crop nutrient stress from hyperspectral data. Experiments are conducted on peach trees. It is shown that nutrient deficiencies that caused stress could an be detected reliably on hyperspectral spectra. During an extensive field campaign, foliar and crown reflectance has been measured with a portable field spectroradiometer. Airborne hyperspectral imagery is acquired over the orchard with the AHS hyperspectral sensor. The multi-level approach (leaf level and top of canopy) enabled the assessment of vegetation indices and their relationship with pigment concentration at both leaf and canopy levels, showing the potential and limitations of hyperspectral remote sensing on the different levels. Stress on the peach orchard is was treated with iron chelates to recover from iron chlorosis conditions. Blocks of trees treated with iron chelates created a dynamic range of chlorophyll concentration as measured in leaves. A relationship is obtained between the measured spectra and estimated biochemical parameters via inversion of a linked directional homogeneous canopy reflectance model (ACRM) and the PROSPECT leaf model. Numerical model inversion was conducted by minimizing the difference between the measured reflectance samples and modeled values. An improved optimization method is presented. Results are compared with a simple linear regression analysis, linking chlorophyll to the reflectance measured at the leaf level and Top of Canopy (TOC). Optimal band regions and bandwidths are analyzed.

This study aims to ascertain how well remote sensing data can characterize fuel type at different spatial scales in fragmented ecosystems. For this purpose, multisensor and multiscale remote sensing data such as, hyperspectral (Multispectral Infrared and Visible Imaging Spectrometer) MIVIS and Landsat- Temathic Mapper (TM) acquired in 1998 were analysed for a test area of Southern Italy characterized by mixed vegetation covers and complex topography. Fieldwork fuel type recognition, performed at the same time as remote sensing data acquisitions, were used to assess the results obtained for the considered test areas.. The method comprised the following three steps: (I) adaptation of Prometheus fuel types for obtaining a standardization system useful for remotely sensed classification of fuel types and properties in the considered Mediterranean ecosystems; (II) model construction for the spectral characterization and mapping of fuel types; (III) accuracy assessment for the performance evaluation based on the comparison of satellite-based results with ground-truth. Two different approaches have been adopted for fuel type mapping: the well-established classification techniques and spectral mixture analysis. Results from preliminary analysis have showed that the use of unmixing techniques allows an increase in accuracy at around 7% compared to the accuracy level obtained by applying a widely used classification algorithm.

Detection of fire smoke plume with a compact cheap rangefinder based on 905 nm laser diode (2 μJ pulse energy, slashed oh 2 cm telescope and 720 m solid-target range) is demonstrated. Reliable detection of small experimental fires (20×25 m² fire plot, burning rate of ~3 kg/s) is achieved for the range of about 255 m. A theoretical model of the mixing of burning products with air in the wind, based on three-dimensional system of Navier-Stokes equation and commercial software PHOENICS, is developed. The model predicts 220 m range of smoke detection by the rangefinder, indicating good agreement between the theoretical and experimental data. On the basis of this theoretical model an estimation of the smoke detection efficiency for a longer range (20 km for solid targets) instrument, based on a 1540 nm laser with a pulse energy of 8 mJ and a 4 cm telescope, is made. The obtained smoke-detection range estimation, 6 km, indicate that more powerful rangefinders can be used not only in shot-range applications, such as fire detection in premises, tunnels and storage yards, but in more demanding areas, such as wildland fire surveillance, as well.

Evapotranspiration (ET) is an important component of hydrological cycle and large-scale ET is of great concern in numerous studies of global environmental change. The large-scale ET can be estimated using remotely sensed data and the energy balance based approach, in which the homogeneous land surface is assumed. The difficulty in application of the approach with the homogeneity assumption to the heterogeneous land surface can be released by spatial scaling approach. On the other hand, measurement error always exists even over the homogeneous surfaces, and the error unavoidably propagates into the scaled ET. However, error propagation in scaling received rare attention. To this issue, this paper describes the energy balance based approach and the physics-based scaling functions for expanding the application to heterogeneous surface. Surfaces at a fine-scale are assumed to be homogeneous enough so as to represent a heterogeneous surface at a coarse-scale. From error analysis, a general form of error propagation in ET is derived and then applied to ET at the two scales. Multi-scale analysis results suggest that error in ET estimate, introduced by measurement errors in the relevant variables at the fine-scale, would decline rather than be enhanced, when the variables are scaled up into the coarse-scale using the scaling functions. Therefore, the coarse-scale value would be more accurate with the proper scaling approach adopted.

Different empirical and physically based methods were employed to derive the vegetation water content of summer barley plots (n=22) from spectroradiometric measurements (ASD FieldSpec II). Data were acquired for two different phenological stages in May and June 2005. For the empirical approaches, a ratio index using the reflectances at 1355 and 710 nm and the partial least squares regression provided the best estimation results (r² > 0.90). Canopy radiative transfer modeling was performed by coupling the PROSPECT and SAIL models. The retrieved values for C_w (equivalent leaf water thickness) × LAI were highly correlated (r² = 0.86) with the measured canopy water contents, but showed distinct underestimates. For the vegetative phenological stage investigated in May, the PROSAIL results were very close to the measured water contents of the leaf fraction, but this was not valid for the data collected in June. Obviously, different phenological stages need specific model calibration, as the presence of undetectable water in non-leaf tissues is variable. All approaches were applied to synthetic HyMap data generated by resampling the spectroradiometer readings. Estimation results did not differ significantly; thus, by neglecting spatial scaling effects, the pure spectral information provided by both data sets is almost equivalent.

In this research, an attempted has been made to map paddy rice of the southern coastal areas of Caspian Sea using multi-temporal Radarsat and Landsat TM optical images acquired in different seasons of the year 1998. Two major tasks including land use map preparation and Change detection studies were implemented. Due to the presence of clouds, some parts of the TM based land use map were not completed. So, land use maps prepared with the use of Radarsat and TM fused images derived based on multiplicative, Brovey and principal component algorithms. For change detection studies, image differencing algorithm used to derive the changed areas. Most of the changed areas were observed to be within agricultural lands (Paddy fields). Results of this study show that fused image based on Brovey algorithm has not only eliminated the cloudy areas of TM data but also has more information on land use land cover in comparison with Radarsat and TM images alone. Comparison between land use change map and image derived from change detection studies showed a high correlation between agricultural lands (Paddy fields) and changed areas within the change image. The results of this study could be useful to agricultural and land use land cover planners.

High spatial resolution ASTER data have 5 thermal bands, of which band 13 and 14 are especially suitable for land surface temperature (LST) estimation. Generally, LST retrieval from two thermal bands is done through so-called split window technique. In the past two decades above 17 split window algorithms have been proposed. However, such algorithm for ASTER data has not been reported, probably due to the new availability of the data for environmental application. In the study, a new split window algorithm has been developed for LST retrieval from ASTER data. Our algorithm only involves two essential parameters for LST retrieval while keeping the same accuracy as those having more parameters. Detailed derivation of the split window algorithm has been given in the paper, which including formulation of thermal radiation transfer equation, determination of algorithm constants, and estimation of the essential parameters. Comparison of our algorithm with the existing ones for validation of its accuracy and applicability in the real world indicates that our algorithm has an average root mean square (RMS) error of 0.67°C when transmittance has an error of 0.05 and emissivity has an error of 0.01. Thus we can conclude that our algorithm is a very good alternative for accurate LST retrieval from ASTER data. Application of the algorithm to Wuxi-Suchou region in Yangtze River Delta produces a very reseasonable LST image of the region, hence confirms the applicability of the algorithm.

In this study, investigation was designed to find an effective method for estimating chlorophyll and nitrogen concentration in the canopies of rice from hyperspectral EO-1 Hyperion image. Continuum-removal analysis enables the isolation of absorption features and minimizes the background influence, thus absorption features stand out. We applied stepwise regression analysis and absorption feature analysis to the field measured foliage and canopy continuum-removed spectra. The results showed that the continuum-removed spectra from the whole range could be broke down into four isolated wavelength ranges and the first wavelength range was centered at 670nm. The area of the wavelength range centered at 670nm based on the BNC spectra was strongly correlated with the chlorophyll and nitrogen concentration. It was validated by EO-1 Hyperion image data, the results showed that the multiple correlation coefficients (R²) between the area of the wavelength range centered at 670nm based on the BNC image spectra and chlorophyll and nitrogen concentration were 0.485 and 0.783 separately. Then the estimation equations were applied to the rice pixels of image which were recognized through Normalized Difference Vegetation Index (NDVI), Land Surface Water Index (LSWI) and Enhanced Vegetation Index (EVI). Thus the chlorophyll and nitrogen concentration distribution maps were obtained. The values in the maps were quite consistent with those of field measurements.

Forest ecosystem is an important component of terrestrial ecosystem and plays an important role in global changes. Aboveground biomass (AGB) of forest ecosystem is an important factor in global carbon cycle studies. The purpose of this study was to retrieve the yearly Net Primary Productivity (NPP) of forest from the 8-days-interval MODIS-LAI images of a year and produce a yearly NPP distribution map. The LAI, DBH (diameter at breast height), tree height, and tree age field were measured in different 80 plots for Chinese fir, Masson pine, bamboo, broadleaf, mix forest in Liping County. Based on the DEM image and Landsat TM images acquired on May 14th, 2000, the geometric correction and terrain correction were taken. In addition, the "6S"model was used to gain the surface reflectance image. Then the correlation between Leaf Area Index (LAI) and Reduced Simple Ratio (RSR) was built. Combined with the Landcover map, forest stand map, the LAI, aboveground biomass, tree age map were produced respectively. After that, the 8-days- interval LAI images of a year, meteorology data, soil data, forest stand image and Landcover image were inputted into the BEPS model to get the NPP spatial distribution. At last, the yearly NPP spatial distribution map with 30m spatial resolution was produced. The values in those forest ecological parameters distribution maps were quite consistent with those of field measurements. So it's possible, feasible and time-saving to estimate forest ecological parameters at a large scale by using remote sensing.

The objective of this study is to improve and modify the evapotranspiration module designed with Penman-Monteith (PM) approach embedded in SWAT2000 distributed hydrological model so as to improve the precision of evapotranspiration (ET) estimation in hydrological process simulation. For PM approach in SWAT2000 ET₀ simulation, some of its daily parameters such as air pressure, albedo and soil heat flux were simplified, for overcoming this shortcoming, an improved Weather Generator (WGEN) were proposed. By means of the observed and WGEN simulated meteorological parameters, systematic study on the ET estimations with PM approach for arid, semi-arid Heihe River Basin and humid, precipitation-rich Hanjiang River Basin was conducted. It was found that the computed ET with PM approach fairly correlates with the field observed ones, but biased to be a little lower in quantity than the observed ones. Further study indicated a strong linear function between the slope of the linear function and the elevation of the station, which lead us to correct the PM approach yielded result with the elevation of the study plot. Consequently, the modified PM approach with improved WGEN was applied to the estimation of ET for the area where observed meteorological data were absent or missing. In annual time scale the relative error between simulated observed ET was found less than 3% and 20% respectively in both monthly time scale and annual totals, which suggested the reliability of the modifications for both WGEN and PM ET₀ module in hydrological process studies. But in daily time scale, further improvements are required.

Water yielding in the hydrologic cycle is a temporally and spatially varied process. However, water yielding mechanics expressed in hydrological simulations seldom accurately characterize such dynamic processes thus weakens the simulation capabilities of present hydrological modeling systems. In this study a conceptual distributed hydrological model entitled ESSI (infiltration Excess and Saturation excess Soil-water Integration model for hydrology) was developed for flooding simulation and long term water resource management studies by means of RS, GIS and data mining techniques. This distributed hydrological modeling system has three significant characteristics: 1) capable of determining temporally and spatially varied water yielding mechanics over the most basic simulated grid by comparing with real-time computed rainfall and soil water variables; 2) excellent weather adoptability to ensure the model perform excellently for either wet and dry watershed conditions; 3) fully distributed simulating capabilities enable the model output about 20 distributed hydrological process components in different time scales, i.e. evapotranspiration (potential and actual), canopy storage, and soil moisture contents in different soil depth etc. Calibration and validation of the modeling system was conducted on two carefully selected climatologically typical watersheds in China, one located in the typical humid climate condition of upper stream of the Hanjiang river Basin, gauged by the Jiangkou hydrometric station (drainage area: 2413 km²), and another the Yingluoxia watershed (drainage area: 10029 km²), situated in typical cold and arid Heihe Mountainous region. With the calibrated model parameters and the appropriate combination of hydrological simulating module, ESSI successfully reproduced the flooding events and long term hydrological processes for the both experiment watershed, which implies the model an excellent hydrological simulation tool under various weather conditions.