This PDF file contains the front matter associated with SPIE Proceedings Volume 7472, including the Title Page, Copyright information, Table of Contents, and the Conference Committee listing.

Many marsh areas in Southern Spain were dewatered during the 1950's for agricultural purposes. These actions were not successful due to the high salinity trend exhibited in such soils, especially notable during the long dry summers in these locations. Recently, many attempts to restore the marshes have been made to try to return the original flooding cycles to the dewatered areas, and promote the development of spontaneous vegetation suitable to salty environments. This work deals with the monitoring of the increase of the flooding area in the San Pedro River marshes (Cádiz) in Spain after the demolition of a dam near the mouth, from the analysis of Landsat TM images with a linear mixture spectral model. Three different components (vegetation, dry soil and wet soil) were quantified in the area over the two years following the destruction of the dam and the increase in tidal entry to the marsh and compared to the results from a previous date. The results were calibrated with field data measured directly on the terrain surface. The model used was capable of discriminating such components with satisfactory accuracy, providing data on the evolution of the flooding area throughout the year and the increase in vegetation distribution one year after the dam break. Differences in the tidal advance along tidal creeks in the main reach of the river before and after the demolition were successfully identified. The impact of the dam action on the development of vegetation was also quantified; the results showed the potential to restore this degraded marsh land.

The Modified Normalizes Differences Vegetation Index is defined by: MNDVI = (c.NIR-Red)/(c.NIR+Red). c is a real number, which generally takes values between 0.1 and 10. NIR and Red are the reflectances at the Near Infrared and Red channels, respectively. In the present paper the performance of the MNDVI vegetation index is studied, using an ALOS image over a burnt forest area of Greece. For each produced MNDVI image, the statistical parameters of the histogram (standard deviation and entropy), the semivariogram and the frequency spectrum are calculated. It is observed that the entropy and standard deviation present a peak at a characteristic c value which depends on the statistical parameters of the NIR and Red channels. The semivariogram also changes with c and presents the most rapid increasing tendency with distance at the same characteristic c value. Therefore, changing c in the MNDVI produces images with different tonality contrasts and spatial variations, which may help the potential user to broaden the spectrum of the available vegetation index images and detect targets of interest.

The hydrological effects of the conversion of forested land to other land uses (and vice versa) are to a large degree unknown. The present study investigate the effect of natural regrowth of forest on the regional hydrological cycle, and in particular the effects on streamflow. The Dragonja catchment (covering SW Slovenia and NW Croatia) was chosen because the land use changed significantly in this region over the last 50 years. Satellite data and field observations were used to study the hydrological effect of land use change. Historical remote sensing data from Landsat and ASTER revealed a significant change from agriculture to forest within the catchment. From 1973 to 2002 26% changed from agricultural field to forest. In the same period both the baseflow and the storm and flood frequency dropped significantly. A large part of the streamflow changes may probably be linked to precipitation changes in this region, making the hydrological study on reforestation a difficult task. Until now no significant link between reforestation and changes in the hydrological cycle was found and more research is needed to fully understand the hydrological system in this region.

Hydrological predictions in ungauged or poorly gauged basin are crucial for sustainable water management and environmental changes study induced by climate change. Application of remote sensing technology has retrieved lots of spatio-temporal dataset during the past decades for references. In this study, TRMM/PR and MODIS LST data were introduced to get spatial patterns of precipitation and temperature changes by Empirical Orthogonal Function (EOF) technique in a mountainous watershed, southern Tianshan. An input variable group was attempted to be constructed for the Artificial Neural Networks (ANN) to model the stream flow change based on the patterns achieved above. The results indicate that the spatial variability patterns of meteorology can be well recognized from the remote sensing data by EOF analysis. The stream flow process can be satisfyingly simulated with input variables captured from the leading modes during the study period. While, since the probabilistic model was not based on full physical mechanisms, and often times, also limited by the amount of input data, uncertainties often implicated in the output. As an example, it is discussed through the rapidly glaciers melting phenomena induced by climate warming, which is expected to cause change in the flow generation mechanism.

The current work aimed at testing a methodology which can be applied to low spatial resolution satellite data to assess interannual crop area variations on a regional scale. The methodology is based on the assumption that within mixed pixels such variations are reflected by changes in the related multitemporal Normalised Difference Vegetation Index (NDVI) profiles. This implies that low resolution NDVI images with high temporal frequency can be used to update land cover estimates derived from higher resolution cartography. More particularly, changes in the shape of annual NDVI profiles can be detected by a Neural Network trained by using high resolution images for a subset of the study years. By taking into account the respective proportions of the remaining land covers within a given low resolution pixel, the accuracy of the net can be further increased. The proposed methodology was applied in a study region in central Italy to estimate area changes of winter crops from low resolution NDVI profiles. The accuracy of such estimates was assessed by comparison to official agricultural statistics using a bootstrap approach. The method showed promise for estimating crop area variation on a regional scale and proved to have a significantly higher forecast capability than other methods used previously for the same study area.

The systematic use of satellite images to support crop damage assessment has been hampered by two reasons: lack of satellite systems with high revisit frequency have made it difficult to image affected areas within the necessary time windows, and second, the high cost of satellite images with appropriate resolution have made their integration into existing processes too costly to be valuable. RapidEye has developed an operational service to produce damage assessment maps on insured fields in the U.S. corn belt that overcomes these problems. The service will be capable of delivering the maps in a suitable time frame by taking advantage of the RapidEye constellation. The production of these maps is based on analysis of after-storm images only, thus reducing the cost of the service. The processing is based on a six step process that includes: pre-processing of the images; estimation of agricultural land vegetation parameters from the imagery; separation of corn from soybean fields; estimation of the statistical descriptors for corn and soybean fields separately; inner-field classification into damage classes; and integration of the results in maps with road and land cover data. Results from the 2008 and 2009 crop seasons are presented.

In Mexico, and everywhere else, the ecosystems are constantly changing either by natural factors or anthropogenic activity. Remote sensing has been a key tool to monitoring these changes throughout history and also to understanding the ecological dynamics. Hence, sustainable development plans have been created in order to improve the decisionmaking process; thus, this paper analyses deforestation impact in a very important natural resourcing area in Mexico, considering land cover changes. The study area is located in the coast of Jalisco, Mexico, where deforestation and fragmentation as well as high speed touristic development have been the causes of enormous biodiversity losses; the Chamela-Cuixamala Biosphere Reserve is located within this area. It has great species richness and vast endemism. The exploitation of this biome is widespread all over the country and it has already had an impact in the reserve. The change detection multi-temporal study uses Landsat satellite imagery during the 1970-2003 time period. Thus, the objective of change detection analysis is to detect and localize environmental changes through time. The change detection method consists in producing an image of change likelihood (by post-classification, multivariate alteration detection) and thresholding it in order to produce the change map. Experimental results confirmed that the patterns of land use and land cover changes have increased significantly over the last decade. This study also analyzes the deforestation impact on biodiversity. The analysis validation was carried out using field and statistic data. Spatial-temporal changing range enables the analysis of the structural and dynamic effects on the ecosystem and it enhances better decision-making and public environmental policies to decrease or eliminate deforestation, the creation of natural protected areas as a biodiversity conservation method, and counteracting the global warming phenomena.

The area of study is the broader area of North Western Athens near to Thrakomakedones village. Due to the 2004 Olympic Games a lot of changes occurred in that area. The aim of this study was to detect and map these changes, estimate the urban expansion rate and the human interferences in the natural landscape, using remote sensing data and GIS techniques. Airphotos and high resolution satellite data covering the period 2000-2007 and topographic maps of 1:5.000 scale were used for the urban growth mapping. Finally the qualitative and quantitative results of this study are presented in this paper.

Landsat SLC-off imagery has been downloaded for the whole of Africa, with the imagery being acquired by the satellite during the period September 2007 to May 2008. This imagery is intended to be used for the production of a land cover map of Africa at Landsat ETM+ image resolution. The quantity of data (>1000 scenes, each containing ~40 million pixels) means that automated image analysis is required in order to achieve this. Manual identification of land cover classes has been carried out and a classification system developed that is based on the FAO's LCCS. This classification system is designed to provide a level of detail that will be useful to land managers, farmers and environmentalists alike. Prior to mapping the entire continent it is necessary to determine whether or not the method selected will produce sufficiently accurate maps. Here a test of the classification method, which uses neural network classification followed by nearest-neighbour interpolation, is described. Results show that the mapping accuracy is greater than 85% for all classes where the pixels were available, and that interpolation of missing pixels post-classification gives accuracy greater than 80% for all classes (the number of classes varied between scenes, but was generally between 5 and 10). In addition, key requirements for the development of a continental land cover map of Africa have been identified during this work.

Fire detection methods based on remote sensing data are gaining more and more attention among the scientific community, and many algorithms have been developed for this purpose. In order to assess the location and the characteristics of burned areas, some of them apply a suitable threshold to a multispectral index such as the NBR (Noise Burn Ratio) index or the NDII (Normalized Difference Infrared Index) evaluated on a single image acquired after the fire season. Other methods use a multitemporal approach based on the processing of a couple of images, the former acquired before and the latter after the fire season, and applying a chosen threshold to the differential value of the same, or other multispectral indexes. This paper focuses the problem of assessing the performance of some burnt areas detection methods based on a couple of satellite images acquired both after the fire season. In particular the threshold method applied to the differential form of the NDII and NDVI (Normalized Differential Vegetation Index) are considered as concern their capacity of locating or detecting (not characterizing) burnt areas and the resulting performances are evaluated and compared with the corresponding ones of the same methods applied to a single image only, acquired after the fire season.

In the context of a sustainable agriculture, a controlled and efficient irrigation management is required to avoid negative effects of the increasing water scarcity, especially in arid and semi-arid regions. Within this background, the project 'Participatory multi-Level EO-assisted tools for Irrigation water management and Agricultural Decision-Support' (PLEIADeS: http://www.pleiades.es) addressed the efficient and sustainable use of water for food production in water-scarce environments. Economical, environmental, technical, social and political dimensions are considered by means of a synergy of leading-edge technologies and participatory approaches. Project partners, represented by a set of nine pilot case studies, include a broad range of conditions characteristic for the European, Southern Mediterranean and American regions. PLEIADeS aimed at improving the performance of irrigation schemes by means of a range of measures, made possible through wide space-time coverage of Earth observation (E.O.) data and interactive networking capabilities of Information and Communication Technologies (ICT). Algorithms for a number of basic products to estimate Irrigation Water Requirements (IWR) in an operational context are defined. In this study, the pilot zone at the Nurra site in Sardinia, Italy, is chosen to test, validate and apply these methodologies.

The integration of time series of high-resolution remote sensing images in the FAO crop evapotranspiration (ET) model is receiving growing interest in the last years, specially for operational applications in irrigated areas. In this study, a simplified methodology to estimate actual ET for these areas in large watersheds was developed. Then it was applied to the Guadalquivir river watershed (Southern Spain) in the 2007 and 2008 irrigation seasons. The evolution of vegetation indices, obtained from 10 Landsat and IRS images per season, was used for two purposes. Firstly, it was used for identifying crop types based on a classification algorithm. This algorithm used training data from a screened subset of the information declared by farmers for EU agriculture subsidies purposes. Secondly, the vegetation indices were used to obtain basal crop coefficients (K_cb, the component of the crop coefficient that represents transpiration). The last step was the parameterization of the influence of evaporation from the soil surface, considering the averaged effect of a given rain distribution and irrigation schedule. The results showed only small discrepancies between the crop coefficients calculated using the simplified model and those calculated based on a soil water balance and the dual approach proposed by FAO. Therefore, it was concluded that the simplified method can be applied to large irrigation areas where detailed information about soils and/or water applied by farmers lacks..

This paper aims to highlight the benefits from the integration of wireless sensor network / meteorological data and remote sensing for monitoring and determine irrigation demand in Cyprus. Estimating evapotranspiration in Cyprus will help, in taking measures for an effective irrigation water management in the future in the island. For this purpose both multi-spectral satellite images (Landsat 7 ETM+ and ASTER) and hydro-meteorological data from wireless sensors and automatic meteorological stations have been used. The wireless sensor network, which consist approximately twenty wireless nodes, was placed in our case study. The wireless sensor network acts as a wide area distributed data collection system deployed to collect and reliably transmit soil and air environmental data to a remote base-station hosted at Cyprus University of Technology. Furthermore auxiliary meteorological field data, from an automatic meteorological station, nearby our case study, where used such as solar radiation, air temperature, air humidity and wind speed. These data were used in conjunction with remote sensing results. Satellite images where used in ERDAS Imagine Software after the necessary processing: geometric rectification, radiometric calibration and atmospheric corrections. The satellite images were atmospheric corrected and calibrated using spectro-radiometers and sun-photometers measurements taken in situ, in an agricultural area, south-west of the island of Cyprus. Evapotranspiration is difficult to determine since it combines various meteorological and field parameters while in literature quite many different models for estimating ET are indicated. For estimating evapotranspiration from satellite images and the hydro-meteorological data different methods have been evaluated such as FAO Penman-Monteith, Carlson-Buffum and Granger methods. These results have been compared with E-pan methods. Finally a water management irrigation schedule has been applied. The final results are presented and compared with some conclusion remarks.

A vineyard architecture model coupled with a soil water balance model is used to explore the links between vineyard reflectance in different scenarios of plant water stress, taking into account meteorological data, agricultural data and site specific information to produce virtual 3D scenes describing vineyard canopies. The ability of the model to predict LAI dynamics was evaluated against destructive measurements in four experimental sites producing reasonable results (RMSE=0.30). The model was run in direct mode to simulate vineyard canopies with three different levels of water stress (no irrigation, intermediate irrigation and full irrigation). Canopy reflectance and NDVI vegetation index were calculated for the simulated scenes using ray-tracing techniques. The results show that vineyard water stress can be detected in vineyard canopies through LAI estimation from canopy reflectance and NDVI. However, the links between LAI and reflectance are highly influenced by canopy architecture at zenith viewings: the effect of agricultural practices as trellis system or pruning should be taken into account to interpret correctly reflectance data. The sensitivity of reflectance to vineyard architecture depends on the viewing direction: at zenith the contribution of row dimensions and leaf spatial distribution is maxima, while at viewing zenith angles > 60° and azimuth perpendicular to rows, sensitivity of reflectance to LAI is enhanced, with a small influence of vineyard architecture.

Forest vegetation interaction with climate and anthropogenic stressors is done through a series of complex feedbacks, which are not very well understood. The patterns of forest vegetation are highly determined by temperature, precipitation, solar irradiance, soil conditions and CO2 concentration. Vegetation impacts climate directly through moisture, energy, and momentum exchanges with the atmosphere and indirectly through biogeochemical processes that alter atmospheric CO₂ concentration. Changes in forest vegetation land use/cover alter the surface albedo and radiation fluxes, leading to a local temperature change and eventually a vegetation response. This albedo (energy) feedback is particularly important when forests mask snow cover. Forest vegetation-climate feedback regimes are designated based on the temporal correlations between the vegetation and and the surface temperature and precipitation. The different feedback regimes are linked to the relative importance of vegetation and soil moisture in determining land-atmosphere interactions. The spatio-temporal dynamics are assessed in terms of the NDVI-surface temperature correlations. Observed vegetation feedbacks on temperature and precipitation are assessed based on Landsat TM, ETM, MODIS and IKONOS satellite data across some forested areas placed in North/Eastern part of Bucharest town as well as in Prahova Valley, Romania for 1989 -2007 period.

The coralline reefs in Banco Chinchorro, Mexico, are part of the great reef belt of the western Atlantic. This reef complex is formed by an extensive coralline structure with great biological richness and diversity of species. These colonies are considered highly valuable ecologically, economically, socially and culturally, and they also inherently provide biological services. Fishing and scuba diving have been the main economic activities in this area for decades. However, in recent years, there has been a bleaching process and a decrease of the coral colonies in Quintana Roo, Mexico. This drop is caused mainly by the production activities performed in the oil platforms and the presence of hurricanes among other climatic events. The deterioration of the reef system can be analyzed synoptically using remote sensing. Thanks to this type of analysis, it is possible to have updated information of the reef conditions. In this paper, satellite imagery in Landsat TM and SPOT 5 is applied in the coralline reefs classification in the 1980- 2006 time period. Thus, an integral analysis of the optical components of the water surrounding the coralline reefs, such as on phytoplankton, sediments, yellow substance and even on the same water adjacent to the coral colonies, is performed. The use of a texture algorithm (Markov Random Field) was a key tool for their identification. This algorithm, does not limit itself to image segmentation, but also works on edge detection. In future work the multitemporal analysis of the results will determine the deterioration degree of these habitats and the conservation status of the coralline areas.

The proposed project has as main objective the monitoring of coastal waters using satellite remote sensing and wireless sensor technology employed on a buoy with emphasis firstly in municipal beaches and further to areas that a systematic sampling is required. Satellite remote sensing has the advantage of using remote sensing data to assess the quality of water bodies has proven to be successful not only in inland waters but to coastal water areas as shown by several others conducted studies. Reflectance signature of municipal coastal water is monitored using a GER 1500 field spectroradiometer. Simultaneous measurements of turbidity, temperature have been acquired. Cross-validation of measurements of water quality both from micro-sensor and remote sensing are planned to be undertaken. An overall methodology that integrates both micro-sensor technology and satellite remote sensing is presented.

The framework for the development of a 30 year global record of remotely sensed vegetation optical depth is presented. The vegetation data set is derived from passive microwave observations and spans the period from November 1978 through the end of 2008. Different satellite sensor observations (i.e. Nimbus-7 SMMR, DMSP SSM/I, TRMM TMI, and AQUA AMSR-E). are used in a radiative transfer model to derive vegetation optical depth. Vegetation optical depth can directly be related to vegetation water content and is a function of biomass. The retrieval model is described and the quality of the retrieved vegetation optical depth is discussed. The new dataset will be merged into one consistent global product for the entire period of data record. To explore the potential to use this new product for long term vegetation modeling, the product was compared to total biomass from the biogeochemical model CASA. The results indicate that the vegetation optical depth can be an important contribution to the derivation of biophysical properties like biomass. It can also increase the reliability of optical sensor derived vegetation indices, because the microwave vegetation optical depth can be derived under cloudy conditions. This unique feature could create the possibility to improve the temporal resolution of other biophysical data products. The entire vegetation density dataset will be made available for download by the general science community and could give a significant contribution in climate research.

Spatially distributed soil moisture is required for watershed applications such as drought and flood prediction, crop irrigation scheduling, etc. In particular, an accurate assessment of the spatial and temporal variation of soil moisture is necessary to improve the predictive capability of runoff models, and for improving and validating hydrological processes forecasting. In recent years, several models have been developed in order to retrieve soil moisture using RADAR data. However, these models need precise prior knowledge about surface roughness. Within this framework, the present research aims to investigate the capabilities of multi polarimetric RADAR images to overcome the use of in situ data for surface roughness assessment. The research is carried out on a 24 km² test-site of DEMMIN (Görmin farm), Mecklenburg Vorpommern, in the North-East of Germany approximately 150 km north from Berlin. Data were acquired within ESA-funded project AgriSAR 2006 between April and July 2006. Images used include L-band in HH, VV and HV polarizations acquired from the airborne sensor E-SAR system operated by the German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt - DLR). Two models have been coupled in order to obtain a rms Surface Roughness Index (rSRI) that is related to terrain physical characteristics as well as vegetation surface properties. These are the PSEM (Polarimetric Semi-Empirical Model) published by Oh et al. in 2002 and a semi empirical model developed by Dubois in 1995. A finite difference iterative solution allowed rSRI retrieval without the use of in situ data. Results have been compared both with in situ rms roughness over bare soil and with Normalized Difference Vegetation Index (NDVI) obtained from Airborne Hyperspectral Scanner (AHS) optical images collected over the whole phenological cycle. They show a good agreement with bare soil in situ data, describing its whole range of variability well, and moreover the NDVI vs. rSRI relationship seems similar to that occurring between NDVI and Leaf Area Index (LAI) for most crop types meaning that rSRI can be considered as LAI look like.

Remote sensing of Radar Polarimety has great potential to determine the extent and properties of snow cover. Availability of spaceborne sensor dual polarimetric C-band data of ENVISAT-ASAR can enhance the accuracy in measurement of snow physical parameters as compared to single fixed polarization data measurement. This study shows that the capability of C-band SAR data for estimating dry snow density over snow coverer rugged terrain in Himalayan region. The study area lies in Beas, Chandra and Bhaga catchments of Himachal state (India). For this investigation, the main assumptions are that the snow is dry and at C-band, total backscattering coefficient comes from snowpack and snow ground interface. An algorithm for estimating snow density has been developed based on snow volume scattering and snow-ground scattering components. Snow density estimation algorithm requires HH and VV polarization combination data. The radar backscattering coefficients of both HH and VV polarization and incidence angle are given as input to the developed algorithm. Finally, the algorithm gives the snow dielectric constant which can further be related to snow density using Looyenga's semi empirical formula. Comparison was done between algorithm estimated snow density and field value of snow density in the study region. The mean absolute error between estimated and measured snow density was 21.3 kg/m³.

Spatial and temporal information of soil water content is of essential importance for modelling of land surface processes in hydrological studies and applications for operative systems of irrigation management. In the last decades, several remote sensing domains have been considered in the context of soil water content monitoring, ranging from active and passive microwave to optical and thermal spectral bands. In the framework of an experimental campaign in Southern Italy in 2007, two innovative methodologies to retrieve soil water content information from airborne earth observation (E.O.) data were exploited: a) analyses of the dependence of surface temperature of vegetation with soil water content using thermal infrared radiometer (TIR), and b) estimation of superficial soil moisture content using reflectance in the visible and near infrared regions acquired from optical sensors. The first method (a) is applicable especially at surfaces completely covered with vegetation, whereas the second method is preferably applicable at surfaces without or with sparse vegetation. The synergy of both methods allows the establishment of maps of spatially distributed soil water content. Results of the analyses are presented and discussed, in particular in view of an operative context in irrigation studies.

The triangle/trapezoid method is a well known method for retrieving spatialized soil moisture from remotely sensed temperature and vegetation index (NDVI). We selected three approaches with different requirements for ancillary data (triangle empirical method by Sandholt et al. [7], trapezoid method by Moran et al. [2], SVAT triangle method by Carlson et al. [3]-[6]). The empirical inversion is well suited when no information on meteorological data is available. Otherwise, one can build theoretical isolines in the T-NDVI-moisture space by applying the Penman-Monteith equation or by using a detailed SVAT model. The three approaches were compared by using remote sensing data obtained during the airborne HyEurope 2007 campaign over Camargue, south of France. We showed that the SVAT-triangle method has a potential for separately identifying surface moisture and root zone moisture.

The first rainy season in South China is a unique climate phenomenon. It is hypothesized that the particular topographic characteristics (e.g. mesocale mountains) give rise to such a phenomenon. 10 years' Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) data such as rainfall frequency and vertical profile regarding two different kinds of rainfalls (i.e. stratiform and convective rainfall) are comparatively considered. It is discovered that terrains play an important role in the determination of the occurrence and intensity of rainfall, even these almost neglected mesoscale mountains. The above is also confirmed by 6 years' Atmospheric Infrared Sounder (AIRS) temperature and humidity data.

High-resolution multispectral images in the green (0.57 μm), red (0.65 μm), near-infrared (0.80 μm) and thermal infrared (8-12 μm) bands were acquired using the Utah State University airborne multispectral system over several active geothermal areas in Yellowstone National Park as part of an ongoing monitoring program initiated in the Fall of 2005. The imagery was acquired under clear sky conditions at two different times of the day, early afternoon and midnight, with the objective of studying the geothermal properties of the different active thermal areas in the park as well as providing calibrated thermal imagery for long-term monitoring of changes. The paper will describe the image acquisition and processing methodology, as well as surface emissivity and atmospheric corrections conducted to obtain at-surface temperatures. Examples of the products obtained over different areas will be shown and discussed.

The assessment of the water needs for a specific crop has a fundamental importance in the management of water resources. The application of empirical models able to retrieve estimates of the actual evapotranspiration (ETa) to assess the need for water could give a valid tool for the planning of water supply, avoiding unnecessary water losses. In this context, two independent models for estimating actual evapotranspiration were compared. The first model is based on an energy balance and uses remotely sensed data and ancillary data from weather stations to assess the ETa. The second model also uses remotely sensed data and climatic data on a daily basis from a weather network. Field measurements are needed to calibrate both models. The study was conducted in a commercial vineyard located in Napa Valley (California). The observed range of ETdaily is included within the values measured by other authors. The results retrieved from both models show actual ETdaily values with a different trend over time; after mid-summer (early July) VSIM estimates of ETdaily trend downwards, while SEBAL estimates remain fairly constant. This disagreement illustrate the difficulty in estimating the actual evapotranspiration at the end of season, when soil moisture gets low and vine water stress increases due to reducing stomatal conductance.

An alternative way to map the actual evapotranspiration (ET) spatial distribution at daily scale is the application of residual surface energy balance models to satellite images that are characterised by high temporal frequency and moderate spatial resolution, like those acquired by the MODIS sensors on board of TERRA and AQUA platforms. Within this research the well-known SEBAL model has been applied on an area located in the southern part of Sicily (Imera Meridionale catchment) using four images acquired between the 27th of March and the 11th of April 2007. The catchment extends for about 2000 km² and includes both mountains and hill areas: the first are located in the northern part (the Madonie Mountains), while the latter characterise the southern area. The altitude ranges between 0 and 1900 m a.s.l., with an average altitude of 500 m a.s.l.. In order to validate the outputs of the energy balance model, a flux tower has been installed within a homogeneous field of cereal located in the valley part of the basin (close to Licata town within the Quignones Farm), characterised by semi-arid conditions. The selected images have been acquired before and after a three-rainfall events period (5 mm during the night between the 2nd and the 3rd of April, 17 mm on the 3rd evening, 8 mm on the 4th thoroughly all the day). The study area is mostly cultivated with cereals that, during the study period, were characterised by flag and bolting phenological phases. In the first, the leaf at the stem apex assumes a flag shape, while during the latter, the terminal part of the stem a barrel shape enlargement containing the ear appears. The study aim is the evaluation of the energy partition between sensible and latent heat fluxes, due to the increased water availability in a period characterised by a significant vegetation growth. The comparison between model results and flux tower measurements shows that both the latent heat flux and the other energy balance components are modelled with high accuracy. Moreover, the model well represents the variation in time of energy partition, which increases from 0.5 up to 0.65 in terms of evaporative fraction (Λ). Therefore a greater percent of energy is used to evapotranspiration after the rainfall events.

This research paper focus on how spectral signatures and evapotranspiration (ET) vary during the whole life cycle of specific annual crops cultivated in the area located in Mandria village, in Paphos District area in Cyprus. The spectral signatures of these crops are collected for the whole life cycle in order to examine how ET changes due to morphological changes of the crops. The GER 1500 spectro-radiometer has been used to collect the spectral signatures of the crops for the whole crop season. The spectral signatures of two main crops -potatoes and peas- have been acquired in-situ, from the beginning of March to the end of April 2009. The purpose of this paper is to enlighten irrigation managers how ET fluctuates during the crop season and how irrigation scheduling is related to spectral signature of the crops. The data extracted from this paper will also assist the decision makers on irrigation level to have a general idea how crops need to be irrigated during their whole life cycle. This project has been funded by the Cyprus Research Promotion Foundation and the Regional Development Fund of European Union.

The aim of this research is the early detection of plant diseases based on the combination of vegetation indices. We have seen that an individual index such as the most popular one, namely NDVI, does not discriminate adequately between healthy and diseased plants, e.g. Cercospora beticola, Erysiphe betae, and Uromyces betae. However, by combining vegetation indices, which are usually called features in classification, very reliable results can be achieved. We use Support Vector Machines for classification. By this we receive a classification accuracy of almost 95% for Cercospora beticola and Uromyces betae and still over 92% for Erysiphe betae. Depending on the different plant diseases we have found that different vegetation indices are important, too. Consequently, the question how to find the best index for every plant disease and the choice of the best subset arise. Both questions are not the same, because different indices contain similar information which can already be seen from the formula of the calculation of the vegetation index. These dependencies do not have to be linear. In order to identify optimal subsets of features for the different pathogens already at an early stage of infestation, we have found that entropy and mutual information are adequate concepts. Accordingly we use the minimum redundancy - maximum relevance (mRMR) criterion to evaluate the features. We have found that we need different indices and feature subsets of different sizes for different diseases.

During 2008 an experimental study aimed to investigate the capabilities of a new Airborne Remote sensing platform as an aid in precision viticulture was conducted. The study was carried out on 2 areas located in the town of Velletri, near Rome; the acquisitions were conducted on 07-08-2008 and on 09-09-2008, using ASPIS (Advanced Spectroscopic Imager System) the new airborne multispectral sensor, capable to acquire 12 narrow spectral bands (10 nm) located in the visible and near-infrared region. Several vegetation indices, for a total of 22 independent variables, were tested for the estimation of different oenological parameters. Anova test showed that several oenochemical parameters, such as sugars and acidity, differ according to the variety taken into consideration. The remotely sensed data were significantly correlated with the following oenochemical parameters: Leaf Surface Exposed (SFE) (correlation coefficient R² ~ 0.8), wood pruning (R² ~ 0.8), reducing sugars (R2 ~ 0.6 and Root Mean Square Error ~ 5g/l), total acidity (R² ~ 0.6 and RMSE ~ 0.5 g/l), polyphenols (R²~ 0.9) and anthocyanins content (R² ~ 0.89) in order to provide "prescriptives" thematic maps related to the oenological variables of interest, the relationships previously carried out have been applied to the vegetation indices.

The study presented here investigates the potential of improvement for a physically based model approach, when the static input data is enhanced by dynamic remote sensing information. The land surface model PROMET (Processes of Radiation, Mass and Energy Transfer) was generally applied, while the remote sensing input data was derived from hyperspectral data of the CHRIS (Compact High Resolution Imaging Spectrometer) sensor, which is operated by ESA (European Space Agency). The PROMET model, whose vegetation routine basically applies the Farquhar et al. photosynthesis approach, was set up to a field scale model run (10 x 10m) for a test acre tilled with wheat (Triticum aestivum L.) mapping the crop development of the season 2005. During the model run, information on the absorptive capacity of the leaves for two canopy layers (top, sunlit layer and bottom, shaded layer) was updated from remote sensing measurements, where angular CHRIS images were available. Control data were acquired through an intensive field campaign, which monitored the development of the stand throughout the vegetation period of the year 2005, also accompanying the satellite overflights. While the model without additional dynamic input data was able to reasonably reproduce the average development of the crop and yield, the spatial heterogeneity was severely underestimated. The combination of remote sensing information with the vegetation model led to a significant improvement of both the spatial heterogeneity of the crop development in the model and yield, which again entailed an overall improvement of the model results in comparison to measured reference data.

Rapid urbanization transforms the natural landscape to anthropogenic urban land and changes surface biogeophysical characteristics. Urban growth affects the ecology of cities in a number of ways, such as eliminating and fragmenting native habitats, modifying local climate conditions, and generating anthropogenic pollutants. Urbanization has changed many landscapes throughout the world with serious ecological consequences. To understand the ecology of urban systems, it is necessary to quantify the spatial and temporal patterns of urbanization, which often requires dynamic modeling and spatial analysis. Geospatial information provided by satellite remote sensing sensors and biogeophysical field data are very useful for urban land cover dynamics and impacts analysis. This paper aims to provide a spatiotemporal analysis of urban structure for Bucharest urban area in Romania based on multi-spectral and multi-temporal satellite imagery (LANDSAT TM, ETM; IKONOS) over 1989 - 2007 period. Understanding the structure of urban cover dynamics is very important to urban management for reasons such as runoff control, urban forest planning, air quality improvement, and mitigation of global climate change. Accurate maps of urban land cover/use changes can provide critical information to better understand urban ecosystems and help improve environmental quality and human health in urban areas.

Soil Moisture and Vegetation Growth are the most important and direct index in drought monitoring, and the spectral interpretation of vegetation and soil are serious factors in the judgment of drought degree. Based on the spectral character of water, recently, a new model of Surface Water Capacity Index (SWCI) has been put forward, and the index is more sensitive to the surface water content, and suit for regional drought monitoring. The comparative analysis showed: SWCI is more sensitive than NDVI to monitoring surface soil water content; this is available in real-time soil drought monitoring.

In the present study, a detailed analysis of AVHRR-based Vegetation Health Indices and meteorological data of Huanghuai region has been carried out for the years 1981-2008. Detailed analyses of spatial and temporal drought dynamics have been carried out. The results revealed that the low-grade of droughts are common phenomena in north China's main agricultural area. Moreover, the area coverage of droughts in different seasons and different regions displayed different trends. There is a decrease tendency for soil moisture in recent years.

In this study, the temperature vegetation dryness index (TVDI) which derived from NOAA/AVHRR data was applied to monitor the severe drought in Sichuan Basin in the summer of 2006. The result using TVDI shown : the drought developed rapidly in the last ten days of July, and became most severe at the end of August, then alleviated in the first ten days of September. The change trends of TVDI with low rainfall and high air temperature were basically consistent. The sensitivity of TVDI to air temperature and principle was also analyzed. TVDI had positive correlation with temperature, negative correlation with precipitation. TVDI is an effective method for the monitoring of the regional drought.

The thesis, on the basis of the researches in the past, discusses the researches on agricultural drought monitoring, forecasting and loss assessment evaluation as well as its application status in China. While discussing and comparing different soil moisture monitoring methods, the thesis also introduces Gstar-1 which is an automatic soil moisture observer with independent property right, and CSMI which is the new remote sensing monitoring index for soil moisture on the basis of MODIS data, and gives a comprehensive introduction to the loss assessment of China. Through the real-time monitoring, forecasting and assessment of drought occurrence and development, the thesis is dedicated to reducing the influence of drought to agricultural production to the largest extent. At last, on the basis of the problems in research, the thesis proposes the future research direction.

The method of Normalized Multi-Band Drought Index (NMDI) is constructed by fully considered the channel 2 (860nm) sensitive to leaf water content changes and the difference between two liquid water absorption bands (1640 nm and 2130 nm) as the soil and vegetation water sensitive band. The potential have been confirmed with the application in different time-series MODIS data. The results show: there is a significant correlation between Normalized Multi-Band Drought Index (NMDI) and soil moisture, the index adopted passed the significant F-tests with α = 0. 01. So the method of Normalized Multi-Band Drought Index (NMDI) could be used in Henan drought monitoring. We found that the index of NMDI application to areas with moderate vegetation coverage, however, needs further investigation.

Chlorophyll-a of Chlorella vulgaris had been related with spectrum. Based on hyperspectral measurement for Chlorella vulgaris, the hyperspectral characteristics of Chlorella vulgaris and their optimal hyperspectral quantitative models of chlorophyll-a (Chla) estimation were researched in situ experiment. The results showed that the optimal hyperspectral quantitative model of Chlorella vulgaris was Chla=180.5+1125787(R₇₀₀)'+2.4 *10⁹[(R⁷⁰⁰)']² (P0<.01), and the suitability order of corresponding methods was spectral ratio<single band < reflectance first-derivative. According to hyperspectral characteristics of Chlorella vulgaris, two reflectance crests were around 540 nm and 700 nm and their locations moved right while Chl-a concentration increased. The reflectance of Chlorella vulgaris decreases with Cha concentration increase in 540 nm, but on the contrary in 700nm.

In the present paper, the spectrum response of Cyanobacter (Chlorella ulgaris) to the stress of heavy metal Pb²⁺ pollution was studied in three spectral ranges of the red edge position(REP) (680-740 nm), the visible spectrum (460-680 nm) and the near infrared spectrum (750-1000 nm). The results indicate that the chlorophyll level reduces with the increase of Pb2+ concentration in medium. With increase of the Pb²⁺ content of Chlorella ulgaris, the spectral reflectivity in visible light and the range of red edge shift ascends, the spectral reflectivity in the near infrared light decreases. The visible light, the near infrared light and the range of red edge shift are fitted much linearly with the logarithm of Pb²⁺ content in Chlorella ulgaris with the high squared regression coefficients of 0.8548, 0.9247 and 0.8475 respectively. The regression models are reliable to estimate the Pb²⁺ content in Chlorella ulgaris.

Stellera chamaejasme L.(Stellera) is a poisonous weed that widely distributed in grassland ecosystems of Western China. Field reflectance measurements were performed for the Stellera with varied coverage in Qilian county of China. The spectral characteristics of some main species were analyzed. The result indicated that the reflectance of the corolla of Stellera was greater than that of other species over the full range of wavelengths. The best time to distinguish Stellera is the full bloom period of the Stellera. Three groups of spectral measurements were performed for the Stellera with varied coverage. The first experiment indicated that the reflectance increased with the increased densities in near-infrared wavelengths, but no obvious regular pattern existed in visible bands. On the contrary, in the full bloom period of the Stellera, there is an obviously increasing trend of reflectance both in visible and near-infrared bands with the increased densities. No distinct trend was found in the third experiment that conducted after the full bloom period. A clear linear relationship exited in analyzing the correlation between Stellera with varied density and their spectral characteristics. Thus, the density of Stellera could be quantitatively estimated based on the spectral characteristics from hyperspectral remote sensing images.

A model for the complex transmittivity of tree crowns in the microwave band is proposed. Experimental technique and results of measurements of the tree branches complex transmittivity (module and phase) in the frequency band 0.8-8 GHz are presented. It is noted that an abnormal frequency dispersion of the refractive index is observed in the experiment that is connected with the resonance attenuation by branches.

Process-oriented dynamic vegetation models are effective tools to assess carbon and water exchanges between vegetation and environment for different scales. Lund-Potsdam-Jena Dynamic Global Vegetation Model (LPJ-DGVM) is one of the well-established, process-oriented dynamic vegetation models. It can simulate seasonal trends of EvapoTranspiration (ET) and Net Ecosystem Exchange (NEE) forced by weather data. In this study, LPJ-DGVM was employed to simulate the ET and NEE in Yingke (YK) oasis station and A'Rou (AR) freeze/thaw observation station. The results indicate that LPJ-DGVM could not make good estimations in both YK station and AR station. The simulation results were validated with the water and CO₂ flux observation from Eddy Covariance (EC). The freeze-thaw phenomenon and irrigation have great impacts on soil water content dynamic in arid region, but they are not considered in LPJ-DGVM. In order to improve the simulation accuracy, a soil water content data assimilation scheme was designed. The observed soil water content was assimilated into LPJ-DGVM with Ensemble Kalman Filter (EnKF) algorithm. The simulation accuracy of LPJ-DGVM was improved obviously when soil water content was assimilated into LPJ-DGVM. The EnKF is effective for assimilating in situ observation.

Soil Moisture and Vegetation Growth are the most important and direct index in drought monitoring, and the spectrum interpretation of vegetation and soil are serious factors in the judgment of drought degree. To find a more real-time monitoring index of cropland soil moisture by remote sensing, a Cropland Soil Moisture Index (CSMI) was established in this paper based on the effective reflections of Normalized Difference Vegetation Index (NDVI) on deeper soil moisture and well expressions of Surface Water Content Index (SWCI) on surface soil moisture. By validation with different time-series MODIS data, the Cropland Soil Moisture Index (CSMI) not only overcome the limitation of hysteretic nature and saturated quickly of Normalized Difference Vegetation Index (NDVI), but also take the advantage of the Surface Water Content Index (SWCI) which effectively reduce the atmosphere disturbance and retrieval surface soil water content better. The index passed the significant F-tests with α = 0. 01, and is a true real-time drought monitoring index.

Photosynthetic efficiency is very important, and not yet generally assessable by remote sensing. Much research has proved the possibility of the separation of solar-induced chlorophyll fluorescence (ChlF) from the reflected hyperspectral data. As the 'probe' of plant photosynthesis, it is possible to detect photosynthetic light use efficiency (LUE) by the separated solar-induced ChlF. A diurnal experiment was carried out on winter wheat on Apr. 18, 2008, and the canopy radiance spectra and leaf LUE data were measured synchronously. The solar-induced chlorophyll fluorescence signals at 760nm and 688nm were separated from the reflected radiance spectral based on Fraunhofer lines in two oxygen absorption bands. The result showed that LUE was negatively correlated to the separated chlorophyll signals. The statistical models for LUE based on the solar-induced chlorophyll fluorescence values at 688 nm and 760 nm bands had correlation coefficients (R²) of 0.64 and 0.78, respectively. In addition, photochemical reflectance index (PRI) was also linked to LUE, and a statistical model for LUE based on PRI has a correlation coefficient (R²) of 0.66. The presented method provides a novel solution for monitoring LUE from remote sensing data.

The rate at which the net radiation is transferred to the soil as ground heat flux varies with surface characteristics. Surface energy balance algorithms use empirical relationships taking into account the effects of the canopy cover to insulate the soil through vegetation indexes, the soil capacity to absorb incoming net radiation via the albedo, and the surface temperature promoting the energy transfer. However empirical relationships are often dependent on local conditions, such as the soil humidity and vegetation type. Ground heat flux assumes a minimum value in case of full canopy cover and a maximum value for dry bare soil. Aim of the present research is the critical analysis of some ground heat flux equations on a homogeneous field of cereal using measured data acquired between February and May 2008. The study period covers almost a full phenological cycle, including phases characterised by a significant change in both reflected radiation and vegetation cover. The dataset begins with the emergence phase, in November, within which shoots emerge from the ground and finishes with the flowering phase, in May, when tiny white stems begin to come-out; moreover the dataset includes a bare soil period (from September up to November). The daily evapotranspiration is calculated in energy balance models under the hypotheses of negligible daily ground heat flux and constant daily evaporative fraction. Actually micrometeorological data show that daily average ground heat flux is not null but characterised by an increasing or decreasing transient. As a consequence, it is particular important to assess the effects of neglecting the daily ground heat flux on daily evapotranspiration estimation.

Information of crop phenological stages is essential for evaluating crop productivity and crop management. We used MODIS EVI time-series to monitoring winter-wheat phenology in North China. The phenological estimations from MODIS EVI measurements were compared with situ data. Results indicate that winter-wheat phenological stages derived from MODIS EVI time series data is feasible. The spatial pattern of winter-wheat shows obvious latitudinal trends in this region. Green up, tassel, and maturity onset dates in more southern zone begin earlier progressively than the northern zone.

Depending on environmental factors fungal diseases of crops are often distributed heterogeneously in fields. Precision agriculture in plant protection implies a targeted fungicide application adjusted these field heterogeneities. Therefore an understanding of the spatial and temporal occurrence of pathogens is elementary. As shown in previous studies, remote sensing techniques can be used to detect and observe spectral anomalies in the field. In 2008, a sugar beet field site was observed at different growth stages of the crop using different remote sensing techniques. The experimental field site consisted of two treatments. One plot was sprayed with a fungicide to avoid fungal infections. In order to obtain sugar beet plants infected with foliar diseases the other plot was not sprayed. Remote sensing data were acquired from the high-resolution airborne hyperspectral imaging ROSIS in July 2008 at sugar beet growth stage 39 and from the HyMap sensor systems in August 2008 at sugar beet growth stage 45, respectively. Additionally hyperspectral signatures of diseased and non-diseased sugar beet plants were measured with a non-imaging hand held spectroradiometer at growth stage 49 in September. Ground truth data, in particular disease severity were collected at 50 sampling points in the field. Changes of reflection rates were related to disease severity increasing with time. Erysiphe betae causing powdery mildew was the most frequent leaf pathogen. A classification of healthy and diseased sugar beets in the field was possible by using hyperspectral vegetation indices calculated from canopy reflectance.

Mapping grain crop land productivity that associated soil quality and crop field management are needed over intensively cropped regions such as the North China Plain to support science and policy application focused on understanding the current and potential capacity of regional food support. In this study, the crop growth dynamic presenting by time series field Greenness derived from MODIS 250 m data and soil moisture condition assessing by Normalized Difference Water Index (NDWI) derived by MODIS 250 m and 500 m data were combined to detect the temporal and spatial variability of productivity of winter wheat-summer maize field in the period 2000 to 2008 in Hebei and Shandong Province in North China Plain. Annual average NDVI levels, average levels of nine years and coefficients of variation of levels in the main growing season indicated corresponding crop growth condition and clearly presented spatial distribution of crop growth. Both the levels of NDWI and the coefficients of variation of the levels have almost same pattern of spatial distribution and correlations between two indexes levels were very high. The results of analysis of levels and coefficients of variation of levels of NDVI and NDWI shows the combination analysis of two indexes can be used to assess the levels of land productivity with a high spatial or temporal resolution .

Overuse of chemical fertilizers raises the risk of nitrate pollution of groundwater in the North China Plain. To preserve the groundwater and reduce the economic losses, an efficiently and quickly assessment of nitrate leaching risk on regional farmland is crucial. In this research we developed a GIS-based model named 'Arc-NLEAP' based on NLEAP model, combined the statistical and Remote Sensing data, to estimate applied fertilizer rates and crop yields, which are two key variables indicating amount of input and output nitrogen in crop land, since crop greenness derived by MODIS may reflect the content of chlorophyll of canopy which is closely related to nitrogen content, and NDVI values of crop crucial growing periods determine crop production. The simulated results showed that the value for parameter NAL (Nitrate Available for Leaching) was between 8 kg / ha and 474 kg / ha and the average was 117 kg / ha, for NL (amount of Nitrate Leached) 18kg / ha (Low) , 59 kg / ha (Average) and 222 kg / ha(High).Percentages of parameter MRI(Movement Risk Index) accounted for 8%,77% and 15% for low risk, medium risk and high risk respectively. Taking water leaching index, nitrogen available for leaching, amount of Nitrate Leached, ammonia volatilization and denitrification into consideration, we defined the N hazard class to evaluate the nitrogen leaching risk and the result indicated that lager 74% of the study area was labeled as low N hazard class. Despite the spatial patterns for parameters NAL and NL were similar, the values for MRI was determined by site-specific soil type and the capacity of water movement principally, demonstrating that measures of controlling nitrate leaching should be based on the spatial pattern of MRI, along with decreasing the amount of application rate simultaneity.