Mapping of crop types at the field-level provides important information for monitoring food production dynamics, predicting market prices, and making decisions for crop insurance claims. The availability of high-resolution satellite remote sensing data opens an unprecedented possibility of field-level crop type mapping at a near-real-time. Murali Krishna Gumma, Head of GIS/RS Lab at ICRISAT, and his colleagues published a new journal article on the mapping of dryland crops in India, including wheat, chickpea, mustard, and lentils) for supporting the crop insurance program. Using the Sentinel-2 Normalized Difference Vegetation Index (NDVI) 15-day time-series data at 10 meter resolution with a Spectral Matching Technique (SMT) approach, the team achieved the overall correlation of 96%, when the district-wise national crop statistics and the remote sensing-based estimates were compared.
Accurate monitoring of croplands helps in making decisions (for insurance claims, crop management and contingency plans) at the macro-level, especially in drylands where variability in cropping is very high owing to erratic weather conditions. Dryland cereals and grain legumes are key to ensuring the food and nutritional security of a large number of vulnerable populations living in the drylands. Reliable information on area cultivated to such crops forms part of the national accounting of food production and supply in many Asian countries, many of which are employing remote sensing tools to improve the accuracy of assessments of cultivated areas. This paper assesses the capabilities and limitations of mapping cultivated areas in the Rabi (winter) season and corresponding cropping patterns in three districts characterized by small-plot agriculture. The study used Sentinel-2 Normalized Difference Vegetation Index (NDVI) 15-day time-series at 10 m resolution by employing a Spectral Matching Technique (SMT) approach. The use of SMT is based on the well-studied relationship between temporal NDVI signatures and crop phenology. The rabi season in India, dominated by non-rainy days, is best suited for the application of this method, as persistent cloud cover will hamper the availability of images necessary to generate clearly differentiating temporal signatures. Our study showed that the temporal signatures of wheat, chickpea and mustard are easily distinguishable, enabling an overall accuracy of 84%, with wheat and mustard achieving 86% and 94% accuracies, respectively. The most significant misclassifications were in irrigated areas for mustard and wheat, in small-plot mustard fields covered by trees and in fragmented chickpea areas. A comparison of district-wise national crop statistics and those obtained from this study revealed a correlation of 96%.
Murali Krishna Gumma , Kimeera Tummala , Sreenath Dixit , Francesco Collivignarelli , Francesco Holecz , Rao N. Kolli & Anthony M. Whitbread (2020): Crop type identification and spatial mapping using Sentinel-2 satellite data with focus on field-level information, Geocarto Internationalhttps://doi.org/10.1080/10106049.2020.1805029