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This study presents a methodology to classify rice cultural types based on water regimes using multi-temporal synthetic aperture radar (SAR) data. The methodology was developed based on the theoretical understanding of radar scattering mechanisms with rice crop canopy, considering crop phenology and variation in water depth in the rice field, emphasizing the sensitivity of SAR to crop geometry and water. The logic used was the characteristic decrease in SAR backscatter that is associated with the puddled or transplanted field due to specular reflection for little exposure of crop, with increase in backscatter as the crop growth progresses due to volume scattering. Besides, the multiple interactions between SAR and vegetation/water also lead to an increase in backscatter as the crop growth progresses. Classification thresholds were established based on the information provided by each pixel in each image, the pixel's typical temporal behaviour due to crop phenology and changing water depth in rice field and their corresponding SAR signature. Based on this logic, the study site (i.e. South 24 Paraganas district, West Bengal) was classified into three major rice cultural types, namely shallow water rice (SWR; 5 cm ≤ water depth ≤ 30 cm), intermediate water rice (IWR; 30 cm ≤ water depth ≤ 50 cm) and deep water rice (DWR; water depth > 50 cm) during the kharif season. These three types represent most of the traditional rice-growing areas of India. The methodology was validated with the field data collected synchronously with the satellite passes. Classification results showed an overall accuracy of 98.5% (95.5% kappa coefficient) compared with a maximum-likelihood classifier (MLC) with an overall accuracy of 95.5% (84.2% of kappa coefficient) with 95% confidence interval. The relationship between field parameters, especially exposed plant height and water depth with SAR backscatter, was explored to design empirical models for each of the three rice classes. Significant relationships were observed in all the rice classes (coefficient of determination, R 2, value more than 0.85) even though they had similar growth profiles but varied with water depth. The two main conclusions drawn from this study are (i) the importance of multi-temporal SAR data for the classification of rice culture types based on water regimes and (ii) the advantages and flexibility of the knowledge-based classifier for classification of RADARSAT-1 data. However, being empirical, the approach needs modification according to the current rainfall pattern and rice-growing practice. [ABSTRACT FROM AUTHOR]

A study was carried out to estimate field-scale Leaf Area Index (LAI) using fine resolution polar orbiting IRS-1D LISS-III sensor data at 23 m spatial resolution. Three cloud-free scenes on 8 January, 2002 (D1), 30 January, 2002 (D2) and 15 January, 2003 (D3) over two sites in Gujarat, India, were acquired. The subscenes encompassing the study area were extracted and geo-registered. Surface reflectances in the red (0.62-0.68 µm) and near-infrared (NIR) (0.77-0.86 µm) bands were generated using 6S atmospheric correction code and coincident ground measurements on aerosol and water vapour. Normalized difference vegetation index (NDVI), simple ratio (SR) and soil-adjusted vegetation index (SAVI) were computed from the reflectances in the red and NIR. A total of 70 mean measured LAI datasets on wheat and tobacco were used for regression analysis and empirical models were developed between LAI and three vegetation indices (VI). Both exponential and power models gave R² between 0.53 and 0.61 except for D2 (R² between 0.04 and 0.11) when wheat was mostly at the post-anthesis stage and the VI-LAI relation seems to be influenced by canopy geometry and angular distribution of leaves. The analysis indicated that with the soil type of the study sites being different, the SAVI-based model had a smaller rms. error (R² = 0.496 and rms. error = 0.685) in estimation of LAI when compared with the SR- (R² = 0.478, rms. error = 0.698) and NDVI- (R² = 0.491, rms. error = 0.689) based models. The LAIs for the study region were estimated by inversion of empirical models and validated against ground-measured data. [ABSTRACT FROM AUTHOR]