Deep Learning in the Mapping of Agricultural Land Use Using Sentinel-2 Satellite Data

Gurwinder Singh, Sartajvir Singh (), Ganesh Sethi and Vishakha Sood
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Gurwinder Singh: Department of Computer Science, Punjabi University, Patiala 147 002, India
Sartajvir Singh: Chitkara University School of Engineering and Technology, Chitkara University, Solan 174 103, India
Ganesh Sethi: Department of Computer Science, Multani Mal Modi College, Patiala 147 001, India
Vishakha Sood: Aiotronics Automation, Palampur, Palampur 176 061, India

Geographies, 2022, vol. 2, issue 4, 1-10

Abstract: Continuous observation and management of agriculture are essential to estimate crop yield and crop failure. Remote sensing is cost-effective, as well as being an efficient solution to monitor agriculture on a larger scale. With high-resolution satellite datasets, the monitoring and mapping of agricultural land are easier and more effective. Nowadays, the applicability of deep learning is continuously increasing in numerous scientific domains due to the availability of high-end computing facilities. In this study, deep learning (U-Net) has been implemented in the mapping of different agricultural land use types over a part of Punjab, India, using the Sentinel-2 data. As a comparative analysis, a well-known machine learning random forest (RF) has been tested. To assess the agricultural land, the major winter season crop types, i.e., wheat, berseem, mustard, and other vegetation have been considered. In the experimental outcomes, the U-Net deep learning and RF classifiers achieved 97.8% (kappa value: 0.9691) and 96.2% (Kappa value: 0.9469), respectively. Since little information exists on the vegetation cultivated by smallholders in the region, this study is particularly helpful in the assessment of the mustard (Brassica nigra), and berseem (Trifolium alexandrinum) acreage in the region. Deep learning on remote sensing data allows the object-level detection of the earth’s surface imagery.

Keywords: ENVINet5-based deep learning; agriculture land; random forest; Sentinel-2 satellite data (search for similar items in EconPapers)
JEL-codes: Q1 Q15 Q5 Q53 Q54 Q56 Q57 (search for similar items in EconPapers)
Date: 2022
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