Showing papers by "Sushma Panigrahy published in 2006"

Comparative evaluation of the sensitivity of multi‐polarized multi‐frequency SAR backscatter to plant density

Abstract: Interaction of synthetic aperture radar (SAR) with vegetation is volumetric in nature, hence SAR is sensitive to the variation in vegetation density. At the same time SAR is also sensitive to other target properties such as canopy structure, canopy moisture, soil moisture and surface roughness of the underlying soil. However, the sensitivity of SAR backscatter to the vegetation density depends upon the frequency, polarization and angle of incidence at which the SAR is operated. This paper provides comparative evaluation of the sensitivity of multi‐frequency and multi‐polarized SAR backscatter to the plant density of Prosopis juliflora, a thorny plant. Monitoring of P. juliflora is of importance as the state forest department introduced it to arrest the spread of desert. In carrying out this study, data from the SIR‐C/X‐SAR mission over parts of Gujarat, India, have been used. In the present study, the variation of multi‐frequency (L and C) and multi‐polarized (HH, VV and VH) SAR backscatter with plant den...

Evaluation of hyperspectral indices for LAI estimation and discrimination of potato crop under different irrigation treatments

Abstract: In this study, various hyperspectral indices were evaluated for estimation of leaf area index (LAI) and crop discrimination under different irrigation treatments. The study was conducted for potato crop using the spectral reflectance values measured by a hand‐held spectro‐radiometer. Three categories of hyperspectral indices, such as ratio/difference indices, multivariate indices and derivative based indices were computed. It was found that, among various band combinations for NDVI (normalized difference vegetation index) and SAVI (soil adjusted vegetation index), the band combination of the 780∼680, produced highest correlation coefficient with LAI. Among all the forms of LAI and VI empirical relationships, the power and exponential equations had highest R 2 and F values. Analysis of variance showed that, hyperspectral indices were found to be more efficient than the LAI to detect the differences among crops under different irrigation treatments. The discriminant analysis produced a set of five most opti...

Spatiotemporal modelling of methane flux from the rice fields of India using remote sensing and GIS

Abstract: Rice fields have been accredited as an important source of anthropogenic methane, with estimates of annual emission ranging from 47 to 60 Tg per year, representing 8.5–10.9% of total emission from all sources. In this study, attempts have been made to derive the spatial and temporal pattern of methane emitted from the rice lands of India using an integrated methodology involving satellite remote sensing and geographic information system (GIS) techniques. Multidate SPOT VGT 10‐day Normalized Difference Vegetation Index (NDVI) composite data for a complete year were used to map the rice area, delineate single‐ and double‐cropped rice areas, crop calendar and growth stages. Rainfall, digital elevation and irrigation data were integrated to stratify the rice area into distinct categories related to methane emission. Preliminary analysis of the methane emission pattern was carried out using published values. The results show that around 91% of total methane emission results from wet‐season rice, contributing 4...

Performance of Cropsyst Model in Rice-Wheat Cropping System

Abstract: Simulation models are available for studying the response of individual crops and cropping systems to management practices. However, since the crops growing in a system have influence on each other, there is a need to simulate the cropping system as a whole. Cropsyst (Cropping Systems Simulation Model) is a multi-year, multi-crop, daily time step cropping system simulation model to study the effect of climate, soils, and management on crop productivity and the environment. We have calibrated and validated the Cropsyst model for rice-wheat cropping system at Ludhiana. Model was calibrated using the experimental values of crop parameters, soil profile data and observed daily weather data of experimental site. The model-simulated crop growth parameters, i.e. leaf area index and biomass and yield matched well with observed data, with higher values of R 2 . The simulated effects of transplanting date, irrigation were in accordance with the experimental results observed by previous researchers. The model did not simulate the puddling effect.

Monitoring of wet season rice crop at state and national level in India using multidate synthetic aperture radar data

Abstract: 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.

Estimating Spatial Curve Number for Hydrologic Response analysis of a small Watershed

Abstract: An approach to estimate the curve number (CN) at each pixel unit of a satellite imagery, which is a key parameter in the widely used Soil Conservation Service Curve Number (SCSCN) hydrologic model, is proposed. Instead of mapping land use and its temporal dynamics from satellite imageries, this approach linearly unmixes the multi-spectral radiances into three fractional layers which primarily control the degree of saturation within a watershed occurring due to a 25 cm-depth storm event, i.e., physically interpreted as the CN. The fraction layers used are water, sand and pure vegetation. In order to obtain a relationship between the fractional statistics and CN, a multi-correlationship analysis of known combinations of land use, hydrologic condition and hydrologic soil group is carried out in an agricultural watershed. The obtained relationship is applied onto the fractional layers to compute the spatial distribution of CN. The performance of the SCS-CN model with the spatial CN is found to be 14% more accurate than that of the model results with only land use information from satellite imageries. The spatial difference of two CN layers in which the one represents the condition of the watershed before soil and water conservation measures was taken up and the other for the post conservation period indicates change in the hydrologic response of the watershed spatially.

Analysis of cropping pattern and crop rotation using multidate, multisensor, and multiscale remote sensing data: case study for the state of West Bengal, India

Abstract: 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.