Search

The utility of time series polarimetric C-band data for vegetation state monitoring was explored to understand the mechanism of growth and phenology for important winter crops in India. Parameters investigated were HH–VV phase difference (co-polarization phase difference, PPD), amplitude ratio, and polarization indices. Data were acquired during the entire growth phase categorized as early, mid/peak vegetative, and post-vegetative /flowering phase. The trend emerging in this study showed a shift in the phase difference distribution for agricultural areas relating to the growth rate for various crops. The time series data set revealed that the PPD is a function of frequency and was directly affected by crop type (planophile or erectophile), vigour, structure, and crop biophysical parameters, particularly biomass. The behaviour of crop biomass with PPD responded differentially across crop architectures and vigour classes. Co-polarization index was found to be a good measure for discrimination in early growth stages while cross-polarization index suited in advanced vegetative stages where geometrical orientation was uniform. The PPD captured the change in frequency distribution resulting in a peaked distribution at sowing changing to smooth, well-spread frequency distribution as the peak vegetation stage approaches. This histogram nature is observed to be gradual for high-biomass crops and peaked in case of the low-biomass crops. It is indicative of the rate of growth; a low peaked normal curve indicates faster growth rate and resulting in high biomass. The amplitude ratio in the later phase of growth as on the third date is similarly altered as in the VV returns from the crop. Intuition of the rate of growth and plant vigour is obtained from the temporal PPD pattern. The current study shows that while phase differences and amplitude ratio carry little information content on a single resolution cell basis, their spatial distribution over a wider time span can be used to derive quantitative relationships between SAR response and crop condition. The synergy of information involving the above parameters were used to derive useful information on the vegetation. [ABSTRACT FROM PUBLISHER]

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]