SOYBEAN CROPLAND MAPPING USING MULTI-TEMPORAL SENTINEL-1 DATA
- 1Agricultural Sciences & Applications Group, RS & GIS – Applications Area, National Remote Sensing Centre, Indian Space Research Organization, Hyderabad, India
- 2Madhya Pradesh Council of Science and Technology, Bhopal, Madhya Pradesh, India
Keywords: Soybean, Sentinel-1, SAR, Classification, Phenology, SVM
Abstract. Soybean, a high value oilseed crop, is predominantly grown in the rainfed agro-ecosystem of central and peninsular India. Accurate and up-to-date assessment of the spatial distribution of soybean cultivated area is a key information requirement of all stakeholders including policy makers, soybean farmers and consumers. A methodology for timely assessment with high precision of soybean crop using satellite data is yet not operational in India. In this scenario, synthetic aperture radar (SAR) has been shown to be a reliable form of gathering crop information, especially during monsoon season. In this work, repeat coverage from the C-band Sentinel-1 satellite over Ujjain district, Madhya Pradesh is used for in-season soybean crop mapping along with other agricultural land-cover types. The data were processed through four steps: (a) data preprocessing, (b) constructing smooth time series backscatter data, (c) soybean crop classification using knowledge-based decision rule classifier and support vector machines (SVM) and (d) accuracy assessment. The results indicated that the smooth VH backscatter profiles reflected the temporal characteristics of soybean crop growing in the study region. Phenological characteristics were also derived from the smoothed S-1 VH backscatter time series to segregate early and late sown soybean. This information was used as an input to a decision-rule classifier and SVM in order to classify the input data into soybean and other crops. An overall accuracy of more than 80% using SVM and 75% using rule based classifier, in Ujjain district was achieved. These results demonstrate the scope for using time-series S-1 VH data to develop an operational soybean crop-monitoring framework.