Abstract
Abstract. Large-scale mapping and monitoring of agriculture land use are very important. It helps in forecast crop yields, assesses the factors influencing the crop stress and estimate the damage due to natural hazards. Also, more essentially, aids in calculating the irrigation water demand at the farm level and better water resource management. Recent developments in remote sensing satellite sensors spatial and temporal resolutions, global coverage and open access such as Sentinel-2, created new possibilities in mapping and monitoring land use/land cover features. The present study investigated the performance and applicability of Sen2-Agri system in the heterogeneous cropping system for operational crop type mapping at parcel resolution using time series Sentinel-2 multispectral satellite imagery. The parcel level crop type information was collected in the field by systematic sampling and used to train and validate the random forest (RF) classification in the system. The classification accuracy varied from 57% to 86% for different major crops. The overall classification accuracy was 70% with KAPPA index of 61%. The very small agriculture field size and persistent cloud cover are the major constraint to the improvement of classification accuracy. Combination of the time series imagery from multiple earth observation satellites for the monsoon cropping season and development of a robust system for in-situ data collection will further increase the mapping accuracy. Sen2-Agri system has the potential to handle a large amount of earth observation data and can be scaled up to the entire country, which will help in the efficient monitoring of crops.
Highlights
Climate change affects the global agriculture and food security in complex ways (Schmidhuber and Tubiello, 2007)
The Sentinel-2 (S-2) twin satellites equipped with Multispectral Imager (MSI) has the advantage of lesser revisiting time (5 days), high spatial resolution (10 meters), and a number of bands in the red-edge spectrum
The system evaluated with processing strategies including random forest (RF) and support vector machine (SVM) classifiers for crop mapping in multiple sites and the results were compatible with the operational production of crop type maps at country scale
Summary
Climate change affects the global agriculture and food security in complex ways (Schmidhuber and Tubiello, 2007). The Copernicus open-access hub provided full free access to sentinel series satellite imagery from 2014 onwards and published more than 4.81 pebibyte volume of data (European Space Agency, 2018). This creates a new challenge and opportunity for developing automated systems that handle a large volume of remote sensing data and efficient information extraction. The cloudprocessing facilities along with the application of artificial intelligence and machine learning algorithms getting more attention in remote sensing It benefits the satellite data processing by optimization in handling a large volume of data, automation, and information extraction (Lary et al, 2016). Later the system was named as “Sen2-Agri” operational standalone processing
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