India has a very well developed system for collection of crop statistics covering more than 50 crops at village level and aggregating it at different administrative levels. However, need for early and in-season crop production forecasting has been strongly felt. Remote sensing for crop assessment has been explored since very beginning of space applications in India. A nation-wide project called Crop Acreage and Production Estimation (CAPE) was launched at the behest of Ministry of Agriculture, Government of India in 1988. Major growing regions in the country for wheat, rice, cotton, groundnut, rapeseed/mustard and Rabi (winter) sorghum were covered. Production forecasts were made about a month before the harvesting using multi-band remote sensing data acquired at optimum bio-window and weather data. Ministry of Agriculture, satisfied with the performance of CAPE, came out with a request to target multiple crop production forecasts starting with crop sowing to end of season. Crop identification with remote sensing data requires using the data when crop has sufficiently grown. However, forecasting of crop at sowing stage would require use of weather data and information on economic factors controlling the farmer's response. Considering these things "Forecasting Agricultural output using Space, Agrometeorological and Land based observations (FASAL)" concept was devised. FASAL aims at using econometric models to forecast the area and production before the crop sowing operations. In unirrigated areas, information on amount and distribution of rainfall is being used for forecasting the crop acreage as well as yield. Remote sensing data, both optical and microwave form the core of crop area enumeration, crop condition assessment and production forecasting. Temporal remote sensing data is being used to monitor the crop through its growing period. Vegetation indices and weather parameter derived from surface and satellite observations will be used to develop the crop growth monitoring system. Components of FASAL concept have been developed, tested and implemented through a series of exercise and these are i) National wheat and winter potato production forecasting using IRS AWiFS data, ii) National Kharif rice production forecasting using Radarsat SAR data, and iii) District level FASAL implementation in Orissa state. Typically three in-season forecasts are being made. With this the FASAL concept of using the multi source data and techniques has been successfully demonstrated. FASAL implementation has been taken up to make national level multiple forecast of crops like rice, wheat, cotton, sugarcane, rapeseed/mustard, rabi-sorghum, winter-potato and jute. Procedure development for use of remote sensing, weather data - surface measurements as well as derived from satellite data, field and ancillary data to run the crop growth simulation models has been taken up. The programme is sponsored by Ministry of Agriculture, Government of India. Space Applications Centre of Indian Space Research Organisation has provided the scientific leadership to the project. A large team drawn form a number of institutions such as ISRO/Department of Space, State Remote Sensing Applications Centres, State Agricultural Universities and many other institutions are working for the project.

This paper describes the methodology adopted and results obtained during forecasting of national level wheat production in India using multi-date medium resolution Advanced Wide Field Sensor (AWiFS) data. Multidate, geometrically registered and radiometrically normalized Resourcesat-1 AWiFS data were classified using hierarchical decision rules, which exploited differential crop spectral profiles of various crops in winter season. Wheat acreage estimates were arrived by aggregation of stratified samples. Wheat yields were predicted for meteorological sub-divisions by correlation weighted weather regression models developed using fortnightly temperatures. Multiple preharvest wheat acreage and production forecasts were made with additional information on spatio-temporal crop growth performance in comparison to previous / normal season.

Rice crop grown during the monsoon (wet) season is the most important food grain in India. The crop is grown under varied cultural and management practices. The present paper highlights the results of rice monitoring being carried out for the past five years (2001-02 to 2005-06) using multi-date RADARSAT ScanSAR Narrow-B data. 30 ScanSAR scenes covering thirteen states account for 95 percent of national crop area. 90 scenes are analysed to assess the national wet season rice crop. A stratified sampling plan is used to analyse 5*5 km segments accounting for 15 per cent of the crop area in each of the study states. A decision-rule classifier has been developed based on a Radiative Transfer (RT) model developed and calibrated using large number of rice sites in India and controlled field experiments. This procedure accounts for change in backscatter as a result of transplanting of rice and crop growth in multi-date data to classify rice areas. Results indicate more than 93 per cent accuracy of area estimation at state level and 97 per cent at national level. It is feasible to assess deviations in crop planting operation (late or early) for a given area.

Remote Sensing Laboratory, Field Science Center, Graduate School of Agriculture Science, Tohoku University starts at April 2004. For studies and education at the laboratory we are now developing the system of remote sensing and GIS. Earth Remote Sensing Data Analysis Center (ERSDAC) made the Home Pages of Terra/ASTER Image Web Library 3 "The Major Airport of the World." http://www.Ersdac.or.jp/ASTERimage3/library_E.html. First, we check the Airport Data to use agricultural understanding for the world. Almost major airport is located in rural area and surrounded with agriculture field. To survey the agriculture field adjacent to the major airport has almost the same condition of human activities. The images are same size and display about 18km X 14km. We can easily understand field size and surrounding conditions. We study seven airports as follows, 1. Tokyo Narita Airport (NRT), Japan, 2. Taipei Chiang kai Shek International Airport (TPE), Taiwan, 3. Bangkok International Airport (BKK), Thailand, 4. Riyadh King Khalid International Airport (RUH), Saudi Arabia, 5. Charles de Gaulle Airport (CDG), Paris, France, 6. Vienna International Airport (VIE), Austria, 7. Denver International Airport (DEN), CO, USA. At the area of Tokyo Narita Airport, there are many golf courses, big urban area and small size of agricultural fields. At Taipei Airport area are almost same as Tokyo Narita Airport area and there are many ponds for irrigations. Bangkok Airport area also has golf courses and many ponds for irrigation water. Riyadh Airport area is quite different from others, and there are large bare soils and small agriculture fields with irrigation and circle shape. Paris Airport area and Vienna Airport area are almost agricultural fields and there are vegetated field and bare soil fields because of crop rotation. Denver Airport area consists of almost agriculture fields and each field size is very large. The advantages of ASTER data are as follows, 1. High-resolution and large swath, 2. Large wavelength and many bands, 3. High-revel of geographical location, 4. Stereo pair images, 5. High performance data searching system, 6. High speed data delivery system, 7. Cheap price, 8. Seven years observation and large volume archive. A kind of project "Determination of Local Characteristics at Global Agriculture Using Archive ASTER Data" was started at middle of November 2005. We establish data processing system and get some results. Paddy rice fields analysis was started at first, we analyze 1) the Shonai Plains in Japan, 2) the Yangtze River delta in Middle-East China, 3) Mekong Delta in South Vietnam, 4) North-east Thai Plaines, Thailand, 5) Sacrament Valley, California, USA. The results of this studies are as follows, 1) Using ASTER images, we can easily understand agricultural characteristics of each local area. 2) ASTER data are high accuracy for location, and the accuracy is suitable for global study without the fine topographical maps, 3) By five years observation of ASTER, there is huge numbers of ASTER scenes, but not enough volumes for cloud free data for seasonal analysis. It means that follow-on program of ASTER is necessary, 4) We need not only paddy field, but also all crop fields and all area, 5) The studies are necessary to international corroboration.

Wheat is an important food crop of the country. Its productivity lies in a very wide range due to diverse bio-physical and socio-economic conditions in the growing regions. Crop cutting and sample surveys are time consuming as well tedious, and procedure of forecast is delayed. CAPE methodology, which uses remote sensing, ground truth and prevailing weather, has been very successful, but empirical in nature. In a joint IARI-SAC Research Programme, possibility of linking the dynamic wheat growth model with the remote sensing input and other relational database layers was tried. Use of WTGROWS, a wheat growth model developed at IARI, with the remote sensing and relational databases is dynamic and can be updated whenever weather, acreage and fertilizer and other inputs are received. National wheat yield forecast was done for three seasons on meteorological sub-division scale by using WTGROWS, relational database layers and satellite image. WTGROWS was run for historic weather dataset (last 25 years), with the relational database inputs through their associated growth rates and compared with the productivity trends of the met-subdivision. Calibration factor, for each met-subdivision, were obtained to capture the other biotic and abiotic stresses and subsequently used to bring down the yields at each sub-division to realistic scale. The satellite image was used to compute the acreage with wheat in each sub-division. Meteorological data for each-subdivision was obtained from IMD (weekly basis). WTGROWS was run with actual weather data obtained upto a given time, and weather normals use for subsequent period, and the forecast was prepared. This was updated on weekly basis, and the methodology could forecast the wheat yield well in advance with a great accuracy. This procedure shows the pathway for Crop Growth Monitoring System (CGMS) for the country, to be used for land use planning and agri-production estimates, which although looks difficult for diverse agro-ecologies and wide range of bio-physical and socio-economic characters contributing to differential productivity trends.

A study was undertaken to validate the Wheat Growth Simulator (WTGROWS) in the farmers' fields of Alipur Block of Delhi and linking satellite derived vegetation index with the simulation model to estimate the wheat yield. Date of sowing, management practices and cultivars varied widely among the study sites. Leaf area index (LAI), phenological development and agronomic management (fertilizers and irrigation) were monitored at regular intervals for the 25 field sites selected in the study area. Above ground biomass and grain yield were recorded at harvest. Using the parameters derived for these sites, WTGROWS was run for each of the individual 25 sites. Crop phenology, temporal course of LAI and grain yield of each site was compared with the actual observations. The simulated and actual LAI temporal profile matched well for sites with different dates of sowing, excepting larger deviation noticed in the later stages of the crop growth. The simulated pre-anthesis duration and total above ground biomass were also correlated well with the observed values being mostly within ±15%. There were large discrepancies in simulated and observed grain yield. A satellite image near anthesis of IRS 1D LISS-3 was acquired for the study area. The sites were identified on the image and their vegetation indices were derived. Average grey value in Infrared (IR) and Red (R) band, Ratio Vegetation Index (RVI), Soil Adjusted Vegetation Index (SAVI) and Normalized Difference Vegetation Index (NDVI) were giving significant relation with measured LAI of 5th February which corresponded to crop anthesis stage. The relation between vegetation indices and LAI was logarithmic in nature. This logarithmic relation was incorporated into the WTGROWS to force the LAI to the equation-derived value at particular growth stage and model yield was computed and compared with actual observations.

Timely assessment of area under different standing crops and their spatial distribution is essential for irrigation planning and management in command areas. The conventional methods of collecting information on crop acreage are time-consuming and uneconomical, especially when large areas are involved. The modern Remote Sensing technology provides real-time, accurate and cost-effective data on crop acreage due to its multi-spatial, multi-spectral and multi-temporal nature. The attempt has been made in this study to use the multi-date data of RESOURCESAT sensors like AWiFS, LISS-III and LISS-IV as well as multispectral data of IKONOS with suitable digital imaging processing techniques to identify Sugarcane, which is a long duration crop and Onion, which is a short duration crop, in order to plan need based irrigation facilities, to collect the revenue from the farmers in the form of water assessment, better crop management and future crop planning.

Crop acreage estimation in hilly regions is till date a challenge for the remote sensing community due to the problems of undulating topography, inaccessibility to vast areas, smaller field size, practice of shifting cultivation, accounting for area falling under hill shades and valleys. Remote sensing alone may not be able to provide reliable estimate of crop acreage in these areas. In addition to this if these regions are humid or tropical for which it is difficult to get cloud free data then the problem becomes even more complicated. Many studies has been conducted in past for cloud/shadow removal but to estimate the crop area under the cloud cover has not been attempted. In this study at attempt has been made to estimate area under paddy crop in a district of Meghalaya by using sampling approach devised by integrating remote sensing, GIS and ground survey data taking into account the problem of hilly regions mentioned above. In addition to this technique for estimating the area under cloud /shadow using the previous year data and only on the basis of current year data is also proposed.

This study investigated the relationship between sorghum grain yield over range of soil depth with seasonal crop water stress index based on relative evapotranspiration deficits and spectral vegetation indices. A root zone soil moisture model has been used to evaluate the seasonal soil moisture fluctuation and actual evapotranspiration within a toposequence having varying soil depth of 30 to 75 cm as well as different available water capacity ranging from 6.9% to 12.6% (V/V%). The higher r² values between modeled and observed values of soil water (r²> 0.69 significant at <0.001) and runoff (r² = 0.95, significant at P<0.001) indicated good agreement between model output and observed values. The spectral vegetation indices like simple ratio, normalized difference vegetation index (NDVI), green NDVI, perpendicular vegetation index, soil adjusted vegetation index (SAVI) and modified SAVI (MSAVI) was recorded through out the growth period of sorghum. The vegetation indices except perpendicular vegetation index measured during booting to anthesis stages were positively correlated (P<0.05) with leaf area index and yield. The MSAVI measured during booting to milk-grain stage have the highest positive correlation with yield. Variation was noticed when additive and multiplicative forms of water-production functions calculated from water budget model were used to predict crop yield. But the yield estimation was improved when spectral vegetation indices measured during booting to milk-grain is incorporated along with water production functions. The water budget model along with spectral vegetation indices gave satisfactory estimates of sorghum grain yields and appears to be a useful tool to estimate yield as a function of soil depth and available soil water.

The characteristics of canopy spectrum and growth status of winter wheat under different soil moisture levels were studied in the field. Correlations between FMC and EWT of leaf and spectral reflectance of canopy were calculated and analysed quantitatively, and the sensitive bands to leaf water were found. Simple ratio water index(SWI)and normalized difference water index(NDWI) were constructed with the sensitive bands. Simple statistical models at different growth stages were established using spectral indices data and FMC and EWT of leaf. Bands centered at 469, 645, 700 and 710nm of VIS region, bands centered at 760, 815, 855, 930, 1075, 1100nm of NIR region and bands centred 550, 1600, 1640, 1750, 2130nm of SWIR were defined as sensitive bands to estimate leaf water content. These bands centered atmosphere windows had the potential to be applied in monitoring canopy leaf content of crop. The SWI and NDWI constructed with the sensitive bands could estimate leaf content more accurately than single band. The four band MODIS combined index: R (1640,2130) / ND (855,555) showed a good indicator to detect canopy water content of winter wheat.

Directional reflectance measurement has been found to be better and more reliable compared to the conventional statistical approach to retrieve plant biophysical parameters as it takes care of its anisotropic nature. Keeping this in view, a field experiment was conducted with the objectives set as (i) to relate canopy biophysical parameters and geometry to its bidirectional reflectance, (ii) to evaluate a canopy reflectance model to best represent the radiative transfer within the canopy for its inversion and (iii) to retrieve crop biophysical parameters through inversion of the model. Two varieties of the mustard crop (Brassica juncea L) were grown with two nitrogen treatments to generate a wide range of Leaf Area Index (LAI) and biomass. The reflectance data obtained at 5nm interval for a range of 400- 1100nm were integrated to IRS LISS -II sensor's four band values using Newton Cotes Integration technique. Biophysical parameters were estimated synchronizing with the bi-directional reflectance measurements. The radiative transfer model PROSAIL was used for its evaluation and to retrieve biophysical parameters mainly LAI and Average Leaf Angle (ALA) through its inversion. Look Up Table (LUT) of BRDF was prepared simulating through PROSAIL model varying only LAI (0.2 interval from 1.2 to 5.4 ) and ALA (5° interval from 40 to 55°) parameters and inversion was done using a merit function and numerical optimization technique given by Press et al., 1986. The derived LAI and ALA values from inversion were well matched with observed one with RMSE 0.521 and 5.57, respectively.

The method of visible and near infrared reflectance spectroscopy is adopted to analyze relative nitrogen content inside fresh tea leaves because it is fast and non-destructive. The spectroradiometer, FieldSpec 3 is used to acquire the spectral reflectance of fresh tea leaves in the field, and the relative value of nitrogen content inside fresh tea leaves are measured with the chlorophyll meter, SPAD 502. About 150 leaves are sampled, which cover the wide range of relative nitrogen content from 20 to 90. A software NIRSA is used to process the spectra data. The preprocessing of spectra includes the second derivative of reflectance with the gap of 25 points and smoothing with Savitzky-Golay filter for removing spectra noise. And the normalizing operation is not done because the variation of sunlight optical path could be ignored. 122 samples are used to establish the calibration model with the method of PLS regression, and multiple correlation coefficient is 0.7301 and RMSEC is 10.0781. The prediction model is established with the prediction set made up of 13 samples and the correlation coefficient is 0.8992 and RMSEP is 10.698. Spectral reflectance-based detection of nitrogen content or chlorophyll in fresh tea leaves is applicable.

A leafspot susceptible peanut cultivar (cv JL 24) was sown during kharif season of 2005 in rainfed region of Andhra Pradesh, India. Five disease levels (scale 0-4) were created in the field by differential fungicidal spray schedule, and each treatment was replicated four times. Spectral data was recorded at 2 nm intervals using a portable spectroradiometer within a spectral range of 300-1100 nm. The loss of leaf pigments (Chlorophyll a and b) due to diseases was quantified using spectrophotometer. The red and infrared reflectance values between 620- 680 and 770-860 nm, respectively were used for calculating Normalized Difference Vegetation Index (NDVI). Significant reduction in chlorophyll content was observed only when the disease reached a stage of scale 2 and above. The ratio of chlorophyll a to b showed a declining trend as the number of spots per leaf increased. From the spectral reflectance studies typical chlorophyll absorption bands (350 - 500 and 620-690 nm) of healthy and diseased plants could not be differentiated. However, the difference was evident in the chlorophyll reflection bands (522- 600 nm). The infrared spectral region between 700-850 nm was found to be sensitive to canopy disease stress. The reflectance (%) in this part of spectrum was higher for healthy plants compared to diseased plants. But, the low level of disease intensity (scale 1) was not differentiated by the spectral reflectance. The NDVI value for 82 days old healthy crop was in the range of 0.45 to 0.50, while the same for diseased plants was between 0.34 and 0.45. This study finds potential application of remote sensing techniques in detection of plant diseases.

Evapotranspiration, a major component in terrestrial water balance and net primary productivity models, is difficult to measure and predict. Remote sensing cannot provide a direct measurement of evapotranspiration (ET) but it can provide a reasonably good estimate of Evaporative Fraction (EF), defined as the ratio of ET and available energy. Recent studies have successfully estimated EF using a contextual interpretation of radiometric surface temperature (T₀) and normalized difference vegetation index (NDVI) such as from the Advanced Very High Resolution Radiometer (AVHRR) onboard NOAA-14 satellite and Moderate Resolution Imaging Spectroradiometer (MODIS), onboard EOS Terra satellite, sensors to estimate ET over large areas. This study compares AET from MODIS and AVHRR applied to a mountainous river basin in Indian Himalayas. Surface Energy Balance (SEBAL) has been used for estimating Actual Evapotranspiration (AET) in the region. In this paper, remote sensing data are used to evaluate the surface albedo, net radiation, ground heat flux, sensible to estimate evapotranspiration and surface conditions using energy balance approach. Being a mountainous basin, an attempt has been made to consider terrain effects in estimating net radiations. Results showed that AVHRR gives an average value whereas MODIS data gives better results since resolution of MODIS is better than that of AVHRR.

Land degradation is the result of both natural and biotic forces operating on the earth. Natural calamities, over exploitation of land resources, unwise land use and the consequences of high inputs agriculture on soil and water resource are of great concern both at national and international level. It aggravated food insecurity in the world especially in the developing countries that calls for combating land degradation and desertification with scientific means. Development of degraded lands in India is one of the options to enhance food production and to restore the fragile ecosystem. The scientific information and spatial distribution of various kinds of degraded lands is thus essential for formulation of strategic plan to arrest the menace of land degradation. Remote sensing provides an opportunity for rapid inventorying of degraded lands to generate realistic database by virtue of multi-spectral and multi-temporal capabilities in the country. The satellite data provides subtle manifestations of degradation of land due to water and wind erosion, water-logging, salinity and alkalinity, shifting cultivation, etc., that facilitate mapping. All India Soil and Land Use Survey (AISLUS) has undertaken the task of land degradation mapping using remotely sensed data and developed a methodology accordingly. The mapping has been conceptualized as a four-tier approach comprising kind of degradation, severity of degradation, degradation under major landform and major land use. Visual mode of interpretation technique based on image characteristics followed by ground verification has been employed for mapping of degraded lands. Image interpretation key has been formulated based on the spectral signatures of various causative factors of different kinds of degraded lands. The mapping legend has been made systematic and connotative. The extent and spatial distribution of different kinds of degraded lands with degree of severity under major landform and major land use in a district could be derived easily from the report published by the organization. Generation of realistic information on degraded lands of the country is utmost necessary. It should be given due importance and taken up on mission mode in order to check further degradation of the environment and loss of top fertile soils. The data base would enable to develop District Information System using advanced technology for periodic monitoring and development of degraded lands.

The repetitive cultivation of an ordered succession of crops (or crop and fallow) on the same land defined as crop rotation has a significant role on sustainability of agricultural practice. This paper highlights the methodology used to map seasonal cropping pattern and crop rotation of West Bengal state in India. Multi-date, remote sensing data of IRS WiFS and Radarsat SAR were used to map seasonal cropping patterns, which were combined to derive the crop rotation map. Three distinct crop-growing seasons could be identified. The main one coinciding with monsoon from June- October, followed by winter crop season from November- February and the summer one March-June. It was feasible to classify seven major crops using the SAR and WiFS data sets. Rice is the dominant crop in wet season occupying more than 75 per cent of net sown area. Mustard, potato, wheat, gram, rice are the major dry season crops. Rice-rice, ricepotato, rice-wheat, rice-mustard, rice-gram, and jute-rice were the major two crop rotations. Rice-fallow was the dominant practice accounting for 55 per cent of area.

Vegetation in arid community grazinglands shows monsoonal growth. Its matching phenology with crops makes its detection difficult during July to September. While crops are harvested during September-October, using satellite data thereafter for the natural vegetation seems most appropriate but by then it turns dry. An index capable of sensing dry vegetation was needed since conventional NDVI is sensitive to greenness of vegetation. Performance of NDVI vis-à-vis another index, PD54, based on cover was therefore compared in assessing degradation of grazinglands. The PD54 was used to isolate anthropogenic impacts from environmental induced degradation by analyzing satellite images from dry and wet seasons. Substantial absence of appreciable vegetation response indicated poor resilience and severe degradation. Five grazinglands in Shergarh tehsil of Jodhpur district in Rajasthan were studied following above approach. Ground radiometric observations were recorded. Satellite data of IRS 1C/1D/P6 with LISS 3 sensor for both pre and post monsoon season were acquired for three contrasting wet-dry season events. These were geometrically registered and radiometrically calibrated to calculate an index of vegetation cover PD54 as well as NDVI. PD54 is a perpendicular vegetation index based on the green and red spectral band width. The PD54 and NDVI calculated from spectro-radiometer were related to vegetation cover measured on ground in permanent plots. This confirmed that PD54 was superior index for estimating cover in arid dry grasslands. These ground vegetation trends in a good rainfall year (2001) with drought year (2002) were related with satellite data for a protected and four unprotected grazinglands. NDVI failed to detect any vegetation in protected areas supporting excellent grass cover which was succinctly brought out by PD54. Successful validation of PD54 in detecting degradation of 13 additional sites confirmed its efficacy. These findings have implication in forage availability assessments, forage forecasting, drought preparedness, pastoralism and transhumance.

Spatial databases of natural resources are very much essential to ensure enhanced productivity by conserving soil and water and to maintain ecological integrity of any region. Integration of various thematic layers prepared from high resolution data and detailed field survey would be preferred for grass root level planning (Panchayat) aimed to realize the potential of production system on a sustained basis. In this study, a detailed spatial data base was created for part of Kasaragod dist., Kerala, India. Detailed soil survey was carried out using cadastral map and registered over high resolution satellite data (IRS LISS-IV) which helped to identify problems and potentials of the area. Nearly 600 ha of land were found to be at higher erosion risk category out of ten soil series identified in the study area. Remote sensing data was used to prepare land use/land cover map and coconut (53%) followed by mixed vegetation type (16%) were found to be dominant. Soil site suitability assessment for major crops of the area was carried out and crossed with present land use to get the mismatch in land use/land utilization type. Alternate land use plan was prepared considering the potentials and problems of various available resources. Decision Support System (DSS) along with user interface is developed to support decision and extract relevant information. As organic carbon is one of the most important indicators of soil fertility C stock in the present and proposed land use was also estimated to understand the environmental significance.

Backscatter coefficient estimated from ERS-2 SAR sensor can effectively be used to derive soil moisture state of a river catchment which is of great importance from hydrological point of view. However, the backscatter coefficient is highly affected by a number of factors such as topography, vegetation density, and variations in small-scale surface roughness. Analysing the effect of these factors to eliminate their effect on backscatter coefficient for accurately estimating the soil moisture is the main focus of the present study. ERS-2 SAR image of date 28th July 2003 (i.e., autumn season) was utilised for carrying out the study in a typical river catchment in India. Incidence angle based model was used to account the effects due to topography. The effects of vegetation on backscatter coefficient were minimised by using the semi-empirical water cloud model. Four agricultural crops and grassland compose the set of vegetation classes in the study area. A comparative study between three important parameters that describe vegetation in terms of their bulk characteristics (e.g., leaf area index; LAI, plant water content; PWC and crop height 'h') was carried out to identify a vegetation descriptor that had the maximum influence on backscatter coefficient. The effect of three canopy descriptors namely LAI, PWC and h were assessed on individual basis by proposing three separate models used in the water cloud model so as to simplify the model, that could use a single canopy descriptor instead of two or more as used in many other studies. Results indicated that the backscatter coefficient obtained from the model using LAI showed stronger relationship with the observed volumetric soil moisture with high R² values. A nonlinear least square method (LSM) was implemented to estimate volumetric soil moisture. A significantly high correlation was obtained between the retrieved soil moisture and the observed soil moisture with high R² values of the order of 0.95 to 0.97 and low rmse values for almost all the vegetation classes and barren land. Subsequently, soil moisture map of the study area was generated from the SAR image.

Watershed-based development is the strategy for sustainable growth in the vast rain-fed regions of India since 1980s to enhance agricultural production, conservation of natural resources and raising rural livelihood of farming communities. Although soil and water conservation was initially the primary objective of watershed program that saw large public investment since inception, later its focus shifted to principles of equity and enhancing rural livelihood opportunities and more recently to sustainable development since mid-1990s. At present a major emphasis under watershed program is the regeneration of degraded fragile lands in rain-fed regions. Several noteworthy watershed programs have been carried out since inception that have yielded sterling results while many others have yielded little by way of unbalanced development because of improper characterization of watersheds and poor project planning and implementation. Tools of Geomatics like satellite data, GIS and GPS besides conventional ones like field survey, topographical and cadastral maps along with traditional multi-disciplinary methods like PRA, soil and water analysis, socio-economic survey etc. provide insight into characterization of watersheds, project formulation and proper implementation of such development programs. The present paper illustrates the methodology for characterization of watersheds using the tools of Geomatics on one hand, besides exhibiting its utility for scaling-out the program benefits like sustaining higher agricultural productivity, enhancing irrigation efficiency, equity, enhanced rural livelihood opportunities, women empowerment, drought-proofing etc. during Watershed-Plus phase in the coming decades, on the other.

Hydrological parameters have vital role in civil engineering, infrastructure planning and natural resource management. Since the last one and a half-decade their extraction using the High-resolution satellite stereo data has been very helping and less time consuming. In the earlier times, the ground surveys use to take a lot of time to cover a small area. Whereas with the availability of high accuracy Digital Elevation models from satellite stereo data one can extract these parameters with ease and good accuracy. During last one and half-decade vast experience has been gained in techniques of processing stereo data acquired from space. This paper deals with the extraction and validation of hydrological parameters from digital elevation model (DEM) derived using CARTOSAT-1 stereo data. In this paper two sub-watershed, one in Chamoli, Uttaranchal and other in Shimla, Himachal Pradesh were selected, A detailed morphometric analysis of the derived drainage network from the various sources and using various algorithms has been carried out. The parameters like stream order, stream length, bifurcation ratio, length ratio etc., have been used for comparison purpose.

Natural resource management calls for study of natural system prevailing in the country. In India floods and droughts visit regularly, causing extensive damages of natural wealth including agriculture that are crucial for sustenance of economic growth. The Indian Sub-continent drained by many major rivers and their tributaries where watershed, the hydrological unit forms a natural system that allows management and development of land resources following natural harmony. Acquisition of various kinds and levels of soil and land characteristics using both conventional and remote sensing techniques and subsequent development of digital spatial data base are essential to evolve strategy for planning watershed development programmes, their monitoring and impact evaluation. The multi-temporal capability of remote sensing sensors helps to update the existing data base which are of dynamic in nature. The paper outlines the concept of spatial data base development, generation using remote sensing techniques, designing of data structure, standardization and integration with watershed layers and various non spatial attribute data for various applications covering watershed development planning, alternate land use planning, soil and water conservation, diversified agriculture practices, generation of soil health card, soil and land reclamation, etc. The soil and land characteristics are vital to derive various interpretative groupings or master table that helps to generate the desired level of information of various clients using the GIS platform. The digital spatial data base on soils and watersheds generated by All India Soil and Land Use Survey will act as a sub-server of the main GIS based Web Server being hoisted by the planning commission for application of spatial data for planning purposes under G2G domain. It will facilitate e-governance for natural resource management using modern technology.

The present study was undertaken to investigate the effect of cell size variation on watershed characteristics and hydrological simulations of the physically based distributed parameter Areal Non point Source Watershed Environment Response Simulation (ANSWERS) model. The study is carried out in Banha watershed located in Upper Damodar catchment, Jharkhand, India having 16.13 km² area (with average slope of 1.91%.) using Digital Elevation Model (DEM), GIS and remote sensing techniques for automatic extraction of the model input parameters. The spatial resolution (cell size) variation from 30m to 150m with incremental step of 30m influences the accuracy of watershed characteristics extracted from DEM. The flow path length and average watershed slope decreased by 53.71% and 20.94% respectively due to variation in cell size. Important watershed parameters such as drainage area, stream network, slope etc. were extracted most accurately automatically with variations less than 10% using DEM of 30m resolution through EASI/PACE and IDRISI GIS. Land use and land cover information generated from Indian Remote Sensing Satellite (IRS-1B, LISS-II) data at 30 m resolution resulted in overall classification accuracy greater than 88%. The watershed hydrological data from fifteen storms of 1995 and 1996 were used for the ANSWERS model cell size sensitivity study. The runoff, peak flow and sediment yield simulations by the model decrease as cell size increases from 30 m to 150 m. The model simulated peak flow at acceptable accuracy for 30 m cell size. The runoff and sediment yield simulations are not observed to be significantly different from the observed values up to 120 m cell size.

In this study surface runoff resulting from isolated rainfall events has been estimated using spatially distributed unit hydrograph. The digital elevation model (DEM) was prepared from the contour map of watershed and used to extract the cell to cell flow path, flow direction, flow length, and slope. Soil Conservation Service-Curve Number (SCS-CN) method was used to compute the excess rainfall generated from each cells. The surface runoff generated from each cell was routed to the watershed outlet through the stream network. Kinematic wave equation was used to calculate the overland flow travel time of each cell in the watershed. The watershed Time Area isochrone was developed by summing up the time of concentration in each cell. S-hydrograph technique has been used to derive spatially distributed unit hydrographs. The distributed unit hydrograph has been convoluted with one hour rainfall excess hyetograph to get the direct runoff hydrograph for the storm event. The developed model was applied to five storm events and observed that the derived flood hydrograph of this model was closely matching with the flow records of the watershed. Indicating suitability of present modeling approach for predicting the direct runoff for given storm event of rainfall.

Field observations were carried out using Spectroradiometer (350-2500nm) to understand effect of contamination on the snow reflectance and mix of snow, vegetation and soil by different proportions on its reflectance. The experiments were carried out in Beas Basin, India during the winter 2004-05 and 2005-06. The investigation has shown that as contamination increases, snow reflectance decreases in all wavelengths, however highest decrease was observed in visible region. In addition, peak of reflectance shifted to high wavelengths, as amount contamination increases. For snow cover and soil cover mix, as proportion of soil cover increases reflectance increases in SWIR region and decreases in visible region. In snow and vegetation mix, as proportion of vegetation increases, curve pattern of vegetation was observed. This means reflectance and absorption due to vegetation presence in visible and NIR region can be observed. If proportion of vegetation area is equal to snow, then reflectance pattern is similar to vegetation. However, if vegetation proportion is lower, then reflectance curve moves to higher reflectance range in visible and NIR region and beyond that reflectance slightly reduces. This study suggests, if hyperspectral data is available then proportion of snow in mixed pixel can be estimated. This study will help in improving existing Normalized Difference Snow Index based algorithms for snow cover monitoring and developing new algorithm, if hyperspectral data is available.

Himalayas possess one of the largest resources of snow and ice, which act as a huge freshwater reservoir. Monitoring the glaciers is important to assess the overall reservoir health. In this investigation glacial retreat was estimated for 466 glaciers in Chenab, Parbati and Baspa basins from 1962. Expeditions to Chhota Shigri, Patsio and Samudra Tapu glaciers in Chenab basin, Parbati glacier in Parbati basin and Shaune Garang glacier in Baspa basin were organized to identify and map glacial terminus. The investigation has shown, an overall reduction in glacier area from 2077 sq km to 1628 sq km from 1962, an overall deglaciation of 21 percent. However, number of glaciers is increased due to fragmentation. Mean of glacial extent was reduced from 1.4 to 0.32 km² between 1962 and 2001. In addition, number of glaciers with higher areal extent is reduced and lower areal extent has been increased between the periods. Small glaciarates and ice fields have shown extensive deglaciation. For example, 127 glaciarates and ice fields less than 1 km² have shown retreat of 38 percent from 1962, possibly due to small response time. This means combination glacial fragmentation, higher retreat of small glaciers and climate change are influencing sustainability of Himalayan glaciers.

It is a fundamental work to realize intelligent fruit-picking that mature fruits are distinguished from complicated backgrounds and determined their three-dimensional location. Various methods for fruit identification can be found from the literatures. However, surprisingly little attention has been paid to image segmentation of multi-fruits which growth states are separated, connected, overlapped and partially covered by branches and leaves of plant under the natural illumination condition. In this paper we present an automatic segmentation method that comprises of three main steps. Firstly, Red and Green component image are extracted from RGB color image, and Green component subtracted from Red component gives RG of chromatic aberration gray-level image. Gray-level value between objects and background has obviously difference in RG image. By the feature, Ostu's threshold method is applied to do adaptive RG image segmentation. And then, marker-controlled watershed segmentation based on morphological grayscale reconstruction is applied into Red component image to search boundary of connected or overlapped tomatoes. Finally, intersection operation is done by operation results of above two steps to get binary image of final segmentation. The tests show that the automatic segmentation method has satisfactory effect upon multi-tomatoes image of various growth states under the natural illumination condition. Meanwhile, it has very robust for different maturity of multi-tomatoes image.

As an important classifier, fuzzy c-means clustering technique has been widely used in segmentation of image. It is an adaptive segmentation method for plant disease images. However, it has some uncertain factors, when it is used for specific segmentation problem, that are input parameters value. The input parameters include the feature of the date set, the optimal number of cluster, and the degree of fuzziness. These parameters affect the speed and precision of fuzzy clustering segmentation. In this paper, the optimal choice of parameters in a fuzzy c-means algorithm for segmentation of plant disease image was discussed and investigated. Using the pixels gray and means of neighborhood pixels as input feature data; an adapting the FCM algorithm parameters based on fuzzy partition entropy, fuzzy partition coefficient, and compactness measures was used to choose the optimal cluster number; and experiments was used for choosing the degree of fuzziness. The Results show that the optimal clustering number for disease leaf segmentation problem is 4 and the degree of fuzziness is 2.

The Digital Elevation Model (DEM) are terrain elevations at regularly spaced horizontal intervals, i.e., an a grid of regularly spaced elevations. With the development of computing technology, the methods of data acquisition, data storage and data processing speed for DEMs get along well. Heihe river lies in the northwest of China and is a continental river. It roots from snow-ice and disappears in deserts. The drainage area is about 140,000 square kilometers and the river is the main water source for the living. The upper reaches are mountainous and the runoff is very important. The middle reaches are oasis. At present, many hydrological and ecological models are introduced. The catchment basin and stream network data acquired from DEMs are main input data for many surface hydrological models. So the quality and resolution of DEMs are significant. Software ENVI Version 4.2 furnishes with the DEMs Extraction Module. The paper compared several methods for extracting DEMs of the upper reaches. We extracted the elevation data from the ASTER---stereo images with the module and created the DEM. Secondly; we collected the DEM based on the contour map of Heihe river. The comparison of the DEMs quality was carried out between the DEM from the contour map, the DEM extracted by ENVI Version 4.2. To a certain extent, the DEM from Aster imagery can reflect the terrain and be used in hydrological models.

Accurate crop growth monitoring and yield predicting is very important to food security and agricultural sustainable development. Crop models can be forceful tools for monitoring crop growth status and predicting yield over homogeneous areas, however, their application to a larger spatial domains is hampered by lack of sufficient spatial information about model inputs, such as the value of some of their parameters and initial conditions, which may have great difference between regions even fields. The use of remote sensing data helps to overcome this problem. By incorporating remote sensing data into the WOFOST crop model (through LAI), it is possible to incorporate remote sensing variables (vegetation index) for each point of the spatial domain, and it is possible for this point to re-estimate new values of the parameters or initial conditions, to which the model is particularly sensitive. This paper describes the use of such a method on a local scale, for winter wheat, focusing on the parameters describing emergence and early crop growth. These processes vary greatly depending on the soil, climate and seedbed preparation, and affect yield significantly. The WOFOST crop model is calibrated under standard conditions and then evaluated under test conditions to which the emergence and early growth parameters of the WOFOST model are adjusted by incorporating remote sensing data. The inversion of the combined model allows us to accurately monitoring crop growth status and predicting yield on a regional scale.

The first 19 bands of MODIS, covering visible to shortwave infrared spectral wavelength, were simulated by the ground-level reflectance spectra. All Normalized Difference Vegetation Index (NDVI)-like and Ratio Vegetation Index (RVI)-like spectral indices formed by every two bands were calculated to obtain their determinate coefficients with theoretical and real yield. Results showed that combinative NIR-infrared index (b2, b19) and (b16, b19) of MODIS were strongly correlated with rice yield, specially the correlative coefficient exceeded the significant level in maturing stage, but combinative visible light index were strongly correlated with rice yield in early stage and poorly in late stage. The best spectral index were the combination of (b2, b19) and (b16, b19) in predicting rice yield in whole rice growth, if considering the spatial resolution, Enhanced Vegetation Index (EVI) was better than the two band combination of (b2, b19) and (b16, b19) and was best suitable for monitoring rice yield.

Studies on the spectral reflectance of canopy and leaves of various cottons were performed. Spectral reflectance of six variety cotton over the 350-2500 nm range with a spectral resolution of 3 nm were obtained this study. In the early growth the spectral reflectance of cotton canopy increased with time, but in the later growth the spectral reflectance of cotton canopy decreased with time. In the main stem of cotton, the spectral reflectance of leaf decreased with leaf position up. The spectral reflectance of leaf increased with addition of leaves. The curve of spectral reflectance was up with water losing of leaf. In the single leaf, the spectral reflectance in leafstalk was higher than the spectral reflectance in edge of leaf. The action of derivative spectra on eliminating background influence on cotton canopy spectra and that of vegetation indices on determining agricultural parameters were analyzed. The results show that the position and slope of red edge of canopy spectral reflectance of cotton are certainly correlative to fractional vegetation cover.

Before MODIS products will be applied in rice monitoring, it need to be validated. A site-intensive MODIS product validation on rice planting region in southern China was performed. Validation approach involved scaling up independent fine-grained datasets, including ground and high spatial resolution imagery (Thematic Mapper), to the coarser MODIS spatial resolutions. Results showed that The difference between situ rice LAI and MOD15-LAI in different rice growth sage is different. The differences are bigger in booting stage, heading stage and milking stage, but smaller in tiller stage and maturing stage. The 16-day composited MODIS reflectance and VIs matched well with ground measurement reflectance and VIs. Otherwise, results indicate that the MODIS algorithm will underestimate LAI value by about 5-10 % in total from 1-km resolution data over the Situ rice LAI at Southern China, the mean and standard deviation deviated from MOD15-LAI are smaller than that of situ rice LAI.

The present study deals with the works relating to integrated watershed management on sustainable basis for evolving tractable operational package so that nutrient, sediment and runoff losses from catchment could be minimized. Study area lies between latitudes 22°5' and 22°12' and longitudes 77°17' and 77°23' covering an area of 6357.5 hectares. Physically it is divided into two different parts, hills and plains. The height of elevation of study area is in between 518 to 630 meters above m.s.l. The thematic maps were generated using satellite data. The present tropical catchment possessing diverse forest ecosystem and agriculture land characterized by weathered black cotton soil derived from basalt with the slope ranging from nearly level to moderately steep to steep sloping and receiving average annual rainfall 1150 mm. The annual return of carbon and nutrient (N, P, K, Ca, Na and Mg) in non degraded and degraded forest and nutrient concentrations in runoff flow and sediment output (sediment loss) during monsoon period from non-degraded forest, degraded forest and agriculture lands were worked out. The sediment and nutrient losses from the catchment to the tropical water body are alarming particularly from agricultural land. The nutrient losses in both the forms (runoff water plus sediment movement) are in the order of agriculture > degraded forest > non-degraded forest. The loss of soil in the form of sediment loss follows the same pattern. The results were alarming when the value of sediment loss of forest was compared to the agriculture land of the catchment. The soil loss as sediment is 33.5 times greater in agriculture land compared to non-degraded forest and 10.2 times greater in agriculture land compared to degraded forest.

Aerosol presence reduces sunshine hours and the amount of radiation received. The extent of reduction in radiation during this extreme event (January-March 1999) was relatively lower, as the extent of the diffused radiation increases. During this time, the reduction ranged from 5-12%. The differential response of the crops (wheat, rice and sugarcane) under changed proportion of direct and diffused radiation due to haze was seen through using crop simulation models (WTGROWS for wheat, DSSAT for rice and sugarcane). The growing conditions were optimal. Regions chosen for simulation were north-west India for wheat, coastal and southern regions for rice and north-eastern, western and southern regions for sugarcane. Simulation results were obtained in terms of phenology, biomass and economic yield at harvest. There was slight reduction in the yield of these three crops due to reduction in the radiation, but coupled weather changes (lowering of temperature, etc.) due to cloudy condition could benefit the crops through phenology modifications and other crop process activities, which can some times give higher yields of crops under the aerosol layer when compared to no haze layer situation. Diffused radiation is more photo-synthetically active, and this feature has still to be included in most of the existing crop growth models, as the existing crop models do not differentiate between direct and diffused radiation. The scope of using remote sensing for assessing the haze layer (spatial and temporal extent) could be employed in the crop simulation models for regional impact analysis.

The growing concern about land resource management and the associated decline in land quality have led to the attention of land consolidation in many countries. Land consolidation is a tool for improving the effectiveness of land cultivation and may improve land productivity and possibly also the total factor productivity if it induces and enhances technical progress and increases scale economies. Land consolidation can also improve labor productivity to supporting rural development. Consolidation deals with a large number of phenomena, such as fields, roads, and land use, all of which exhibit characteristic forms and patterns which can be analyzed as to their existing spatial organization, or as to their changing spatial organization through time. This Paper put forward some approaches and advices about carrying out the principles of the agro-land consolidation, guiding the ideology, developing the strategy and tidying up the farmland. Firstly, the main conception and methodology of land consolidation are described. Then, the strengths and weaknesses of land consolidation in their process are discussed. Finally, as an example, through analyzing of the present condition and potential of land use and landscape in Beitun Oasis, China, a discussion and conclusions on land consolidation aided by GIS are presented. This will certainly play an exemplary role in the similar areas of north-west arid zone of China.

For the optimal production, proper management and conservation of the available resources is essential. The assessment of various losses and further prioritization of watershed for the conservation of soil and water resources would be a better task for this purpose. While planning the soil and water conservation measures in the watershed, it is not feasible to take whole area at once. This requires dividing the watershed in small units, which are sub-watersheds. As the condition of each sub-watershed may not be similar, they can be prioritized for conservation work. The present study is carried out in the Hire Nadi watershed, which falls under the drought prone zone of Karnataka state. The agricultural activities of the entire area are mostly depending upon the rainfall and hence the conservation of soil and water resources is the major priority. For the priority fixing, the entire watershed area has been delineated into six sub-watersheds having geographical area from 159 ha to 3199 ha. Thornthwaite-Mather (TM) water balance model and the Universal Soil Loss Equation (USLE) in conjunction with digital image processing through GIS software namely Integrated Land and Water Information System (ILWIS) have been utilized for estimating the various soil and water losses from the watershed. Using the weighted index overlay model for ranking of the sub-watersheds, the entire area is prioritized. It is found that the sub-watershed no. 1 is having first ranking and need immediate attention for soil and water conservation activities to prevent further land degradation and its productivity.

Ground truth data have grid cells that consist of single land cover classification (pure grid cell), and they are useful as the training data in classifying global land cover. Previous studies showed many global land cover map, however, they have rarely reported locations and collection method of the training data that they might use. Paddy is indispensable classification for global land cover mapping but there is no established method for collecting paddy ground truth data. In this study we collected paddy ground truth candidate data from the existing 1km-resolution China national land use data, which was produced using 30m-resolution LANDSAT/TM data. Since the land use data recorded classification area ratio in each grid cell, it is efficient to collect paddy's pure grid cells. We collected pure grid cells of paddy and segmented them into small areas, and obtained paddy ground truth candidate sites. After the sites whose area >= nine grid cells (=9 km²) were selected, we examined the selected sites' Normalized Difference Vegetation Index (NDVI) time-series changes in 2003 using TERRA/Moderate Resolution Imaging Spectroradiometer (MODIS) 16-days composite data. Furthermore, we chose the sites whose NDVI standard deviation =< 0.1 through 2003, as a result, the number of the selected sites was 271. The 271 candidate sites were assigned to nine China climatic zones, and the sites that are located near climatic boundaries were eliminated. The site whose area is largest in each climatic zone was tried to be selected. Finally, ten sites of paddy ground truth data were collected.

Torrents are causing vast area submergence and damage to life, property and infrastructure almost every year. This is the most common problem in foothills of Shivaliks, spread over the northern states of India. With the help of modern tools like GIS and remote sensing techniques, a study has been made to assess the extent of problem more accurately.Data from Indian Remote Sensing Satellites has been used for gathering the information about the area under study. The data and imageries were acquired for different northern states under Shivalik region viz. Jammu and Kashmir, Punjab, Haryana, Himachal Pradesh, Uttranchal/Uttar Pradesh and Chandigarh (UT). Data were collected and analyzed with the help of Punjab Remote Sensing Centre Ludhiana and Regional Remote Sensing Service Centre Dehradun. The results shows that the area under torrents and rivers was found to be 323 sq. km in Punjab, 400 sq.km in Himachal Pradesh, 238 sq.km in Haryana, 414 sq.km in Uttar Pradesh/Uttranchal and about 140 sq.km in J and K., which may vary every year depending on the monsoon and other factors in the Shivalik region. The assessment of the magnitude of the problem provides the insight and need of reclamation of the affected land and suitable technology to control the flashy torrents, which do much damage in the rainy season and causes flood in the downstream areas. About 1500-sq. km area lies under torrents and the area affected can be up to 7,500 sq. km in the Shivaliks. This demands a development of economically viable and suitable technology to combat the menace of torrent in the region.

Land degradation has become an important issue in western China recently. Oasis ecosystem is sensitive to environmental disturbances, such as abnormal /extreme events of precipitations, water supply from upper watersheds, fluctuations of temperatures, etc. Satellite remote sensing of terrestrial ecosystems provides temporal dynamics and spatial distributions of landscape green covers over large areas. Seasonal green cover data are normally important in assessing landscape health (ex. desertification, rate of urban sprawl, natural disturbances) in arid and semi-arid regions. In this study, green cover data is derived from vegetation indices retrieved from MODIS sensors onboard Terra. The satellite images during the period April 2000 to December 2005 are analyzed to quantify the spatial distribution and temporal changes of Ejin Oasis. The results will help improving monitoring techniques to evaluate land degradation and to estimate the newest tendency of landscape green cover dynamics in the Ejin Oasis.

The desertification in Northwestern China and Mongolia shows the result of conflicts between economic development and natural conservation. Many researches have proven the desert areas are growing in these regions. The variations of bi-weekly NDVI satellite images are used as one of the parameters to evaluate the vegetation dynamics over large scale studies. In this study, remotely sensed satellite images are conducted to provide multi-temporal vegetated and non-vegetated areas in order to assess the status of desertification in East Asia. Spatial data derived from these satellite images are applied to evaluate vegetation dynamics at regional scale to find out the hot spot areas vulnerable to desertification. The results show that the desert areas are mainly distributed over southern Mongolia, central and western Inner-Mongolia, western China (the Taklimakan desert). The desert areas were expanded from 2000 to 2002, were shrunk in 2003, and were expanded from 2003 to 2005 again. The hot spot areas of desertification are mainly distributed over southeastern Mongolia and eastern Inner-Mongolia. The results will help administrators to refine the planning processes in defining the boundaries of protected areas and will facilitate to take decision of the priority areas for conservation of desertification.