Manab Chakraborty

SAR signature investigation of rice crop using RADARSAT data

The current study investigates the potential of multi‐temporal RADARSAT ScanSAR Narrow Beam B (SCNB) data to monitor rice crop growth and condition where special emphasis was given to the signature analysis of the crop. The study area is located in the Baleshwar and Bhadrak districts of Orissa. The temporal variations of radar backscatter of all land‐cover classes were analysed as a function of time. The analysis of the Synthetic Aperture Radar (SAR) backscatter coefficient (σ0) of rice crop shows significant temporal behaviour and a large dynamic range during its growth period, which is due to the interaction of microwave radiation with the crop canopy, increasing from the transplanting stage to the reproductive stage. This temporal variation of SAR backscatter clearly differentiates rice fields from other land‐cover classes. Separability studies among different class pairs carried out using t‐test and Bhattacharya distance show that all the rice classes are separable from each other except the early rice, which was mixed with far sides of hills and shadow. Knowledge‐based decision rule classifier based on the temporal evaluation of SAR backscatter was attempted to classify rice and non‐rice areas, and achieved more than 98% accuracy in the case of rice class. The results are promising and confirm the possibility of operational use of RADARSAT data for rice crop growth monitoring.

Evaluation of RADARSAT Standard Beam data for identification of potato and rice crops in India

Abstract The Canadian satellite RADARSAT launched in November 1995 acquires C-band HH polarisation Synthetic Aperture Radar (SAR) data in various incident angles and spatial resolutions. In this study, the Standard Beam S7 SAR data with 45°–49° incidence angle has been used to discriminate rice and potato crops grown in the Gangetic plains of West Bengal state. Four-date data acquired in the 24-day repeat cycle between January 2 and March 15, 1997 was used to study the temporal backscatter characteristics of these crops in relation to the growth stages. Two, three and four-date data were used to classify the crops. The results show that the backscatter was the lowest during puddling of rice fields and increased as the crop growth progressed. The backscatter during this period changed from −18 dB to −8 dB. This temporal behaviour was similar to that observed in case of ERS-SAR data. The classification accuracy of rice areas was 94% using four-date data. Two-date data, one corresponding to pre-field preparation and the other corresponding to transplantation stage, resulted in 92% accuracy. The last observation is of particular interest as one may estimate the crop area as early as within 20–30 days of transplantation. Such an early estimate is not feasible using optical remote sensing data or ERS-SAR data. The backscatter of potato crop varied from −9 dB to −6 dB during the growth phase and showed large variations during early vegetative stage. Two-date data, one acquired during 40–45 days of planting and another at maturing stage, resulted in 93% classification accuracy for potato. All other combinations of two-date data resulted in less than 90% classification accuracy for potato.

A processing and software system for rice crop inventory using multi-date RADARSAT ScanSAR data

Abstract An operational crop survey program requires standardised procedures and software packages to meet the specified targets of timeliness and accuracy of estimates. Currently, the focus is to include Synthetic Aperture Radar (SAR) data in such a program, as these data are available from a number of sensors. A procedure has been developed to use multi-date SAR data for rice crop inventory. The steps were packaged together for ease-of-use and with minimal user interaction. The package, SARCROPS, is built around the EASI/PACE software. It is, at present, tuned for RADARSAT ScanSAR data. The package was used during the 1998–1999 and 1999–2000 seasons to estimate the rice area at state level in India with the participation of a number of interdisciplinary users. Around 45 and 89 scenes of ScanSAR data were used during these two seasons, respectively. This paper reports the details of the SARCROPS processing chain.

Analysis of temporal backscattering of cotton crops using a semiempirical model

To develop an operational methodology for estimating soil moisture and crop biophysical parameters and to generate a crop cover map, backscattering signatures of vegetation canopies are investigated using multitemporal Radarsat synthetic aperture radar (SAR) data over a predominantly cotton-growing area in India during low to peak crop growth stage. A simple parameterization of the water-cloud model with volumetric soil moisture content (m/sub v/) and leaf area idex (LAI) is used to simulate the microwave backscattering coefficient (/spl sigma//sup 0/), as it is found to be a good candidate for operational purposes as demonstrated by several workers in past. The influence of crop height (H), LAI, and m/sub v/ on /spl sigma//sup 0/ is investigated during peak crop growth stage. A linear relationship between LAI and crop height is derived semiempirically, and a linear zone is chosen for analysis during the peak crop-growing stage. Estimation of average volume fraction of leaves (V~/sub l/) and attenuation factor (L) by two different approaches is discussed: 1) using linear relationship between LAI versus crop height and 2) from the water-cloud model parameter (/spl kappa/) estimation by iterative minimum least square error approach. It is observed that model-estimated parameters agree well with the measured values within an acceptable error limit. At lower soil moisture, m/sub v//spl cong/0.02(cm/sup 3//spl middot/cm/sup -3/), the dynamic range of /spl sigma//sup 0/ is found to be about +5 dB for 0-70 cm of crop height but monotonously decreases to null at a transition point, having m/sub v//spl ap/0.38(cm/sup 3//spl middot/cm/sup -3/). A positive correlation is found between backscattering coefficient and crop height till this transition point but shows a negative correlation beyond that, signifying the predominant attenuation by vegetation over soil. Differential moisture sensitivity (d/spl sigma//sup 0//dm/sub v/) of the backscattering coefficient decreases by half from 20.55 dB/(cm/sup 3//spl middot/cm/sup -3/) for dry and bare-field conditions to 10.68 dB/(cm/sup 3//spl middot/cm/sup -3/) for wet and crop-covered fields (m/sub v/=0.38cm/sup 3//spl middot/cm/sup -3/, H=70cm), whereas differential crop height sensitivity (d/spl sigma//sup 0//dH) varies from 0.22-0.03 dB/cm for bare-field conditions to crop-covered fields with crop height 70 cm. It is found that the percentage of relative error is smallest (2.27%) for LAI and attenuation factor estimation using the value of V~/sub l/, from LAI models, whereas it is 4.25% when estimating from the attenuation coefficient (/spl kappa/) from the model.

The NASA-ISRO SAR mission - An international space partnership for science and societal benefit

The National Aeronautics and Space Administration (NASA) in the United States and the Indian Space Research Organisation (ISRO) have embarked on the formulation of a proposed Earth-orbiting science and applications mission that would exploit synthetic aperture radar to map Earths surface every 12 days. The missions primary objectives would be to study Earth land and ice deformation, and ecosystems, in areas of common interest to the US and Indian science communities. To meet demanding coverage, sampling, and accuracy requirements, the system would require a swath of over 240 km at fine resolution, using full polarimetry where needed. To address the broad range of disciplines and scientific study areas of the mission, a dual-frequency system was conceived, at L-band (24 cm wavelength) and S-band (10 cm wavelength). To achieve these observational characteristics, a reflector-feed system is considered, whereby the feed aperture elements are individually sampled to allow a scan-on-receive (“SweepSAR”) capability at both L-band and S-band. In the partnership, NASA would provide the instrument structure for both L- and S-band electronics, the L-band electronics, the reflector and associated boom, and an avionics payload to interface with the radar that would include a solid state recorder, high-rate Ka-band telecommunication link, and a GPS receiver. ISRO would provide the spacecraft and launch vehicle, and the S-band radar electronics.

ASSESSMENT OF L-BAND SAR DATA AT DIFFERENT POLARIZATION COMBINATIONS FOR CROP AND OTHER LANDUSE CLASSIFICATION

In the present study evaluation of L-band SAR data at difierent polarization combinations in linear, circular as well as hybrid polarimetric imaging modes for crop and other landuse classiflcations has been carried out. Full-polarimetric radar data contains all the scattering information for any arbitrary polarization state, hence data of any combination of transmit and receive polarizations can be synthesized, mathematically from full-polarimetric data. Circular and various modes of hybrid polarimetric data (where the transmitter polarization is either circular or orientated at 45 - , called …=4 and the receivers are at horizontal and vertical polarizations with respect to the radar line of sight) were synthesized (simulated) from ALOS- PALSAR fullpolarimetric data of 14th December 2008 over central state farm central latitude and longitude 29 - 15 0 N/75 - 43 0 E and bounds for northwest corner is 29 - 24 0 N/75 - 37 0 E and southeast corner is 29 - 07 0 N/75 - 48 0 E in Hisar, Haryana (India) Supervised classiflcation was conducted for crops and few other landuse classes based on ground truth measurements using maximum-likelihood distance measures derived from the complex Wishart distribution of SAR data at various polarization combinations. It has been observed that linear full- polarimetric data showed maximum classiflcation accuracy (92%) followed by circular-full (89%) and circular-dual polarimetric data (87%), which was followed by hybrid polarimetric data (73{75%) and then linear dual polarimetric data (63{71%). Among the linear dual polarimetric data, co-polarization complex data showed better

Methodology to classify rice cultural types based on water regimes using multi-temporal RADARSAT-1 data

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 pixels 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.

Analysis of Temporal Polarization Phase Difference for Major Crops in India

A polarimetric radar system measures the complete scattering matrix of a target in the backscattered fleld that includes magnitudes of linearly polarized scattering amplitudes and the co- polarised and cross-polarised phase angles. Apart from backscattering intensity, the co-polarization phase difierence (CPD) calculated from polarimetric synthetic aperture radar (SAR) data produces important information about target physical, geometrical and dielectric properties. In the present work, the distribution of CPD in C-band polarimetric SAR data corresponding to major kharif and rabi crops (denoting the monsoon and the winter season) and other land cover features have been studied over Central State Farm, Hisar, Haryana. The probability density functions (PDF) of CPD have been compared with dominant scattering contributions from these targets as obtained from polarimetric target decompositions. The results show that crops and other land cover features show characteristic CPD distributions, which relates well with crop physical and geometrical properties. An intuition of the rate of growth and plant vigour is indicative from the temporal PDF pattern.

Multiple forecasts of kharif rice in orissa state-four year experience of fasal pilot study

Considering the requirement of multiple pre-harvest crop forecasts, the concept of Forecasting Agricultural output using Space, Agrometeorology and Land based observations (FASAL) has been formulated. Development of procedure and demonstration of this technique for four in-season forecasts for kharif rice has been carried out as a pilot study in Orissa State since 1998. As the availability of cloud-free optical remote sensing data during kharif season is very poor for Orissa state, multi-date RADARSAT SCANSAR data were used for acreage estimation of kharif rice. Meteorological models have been developed for early assessment of acreage and prediction of yield at mid and late crop growth season. Four in-season forecasts were made during four kharif seasons (1998-2001); the first forecast of zone level rice acreage at the beginning of kharif crop season using meteorological models, second forecast of district level acreage at mid growth season using two-date RADARSAT SCANSAR data and yield using meteorological models, third forecast at late growth season of district level acreage using three-date RADARSAT SCANSAR data and yield using meteorological models and revised forecast incorporating field observations at maturity. The results of multiple forecasts have shown rice acreage estimation and yield prediction with deviation up to 14 and 11 per cent respectively. This study has demonstrated the potential of FASAL concept to provide inseason multiple forecasts using data of remote sensing, meteorology and land based observations.

ANALYSIS OF TEMPORAL SAR AND OPTICAL DATA FOR RICE MAPPING

This study investigates the potential of multi-temporal signature analysis of satellite imagery to map rice area in South 24 Paraganas district of West Bengal. Two optical data (IRS ID LISS III) and three RADARSAT SAR data of different dates were acquired during 2001. Multi-temporal SAR backscatter signatures of different landcovers were incorporated into knowledge based decision rules and kharif landcover map was generated. Based on the spectral variation in signature, the optical data acquired during rabi (January) and summer (March) season were classified using supervised maximum likelihood classifier. A co-incidence matrix was generated using logical approach for a combined “rabi-summer” and “kharif-rabi-summer” landcover mapping. The major landcovers obtained in South 24 Paraganas using remote sensing data are rice, water, aquaculture ponds, homestead, mangrove, and urban area. The classification accuracy of rice area was 98.2% using SAR data. However, while generating combined “kharif-rabi-summer” landcovers, the classification accuracy of rice area was improved from 81.6% (optical data) to 96.6% (combined SAR-Optical). The primary aim of the study is to achieve better accuracy in classifying rice area using the synergy between the two kinds of remotely sensed data.