Optimised DNN-Based Agricultural Land Mapping Using Sentinel-2 and Landsat-8 with Google Earth Engine

Nisha Sharma, Sartajvir Singh () and Kawaljit Kaur
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Nisha Sharma: University Institute of Computing, Chandigarh University, Mohali 140413, Punjab, India
Sartajvir Singh: University Institute of Engineering, Chandigarh University, Mohali 140413, Punjab, India
Kawaljit Kaur: University Institute of Computing, Chandigarh University, Mohali 140413, Punjab, India

Land, 2025, vol. 14, issue 8, 1-22

Abstract: Agriculture is the backbone of Punjab’s economy, and with much of India’s population dependent on agriculture, the requirement for accurate and timely monitoring of land has become even more crucial. Blending remote sensing with state-of-the-art machine learning algorithms enables the detailed classification of agricultural lands through thematic mapping, which is critical for crop monitoring, land management, and sustainable development. Here, a Hyper-tuned Deep Neural Network (Hy-DNN) model was created and used for land use and land cover (LULC) classification into four classes: agricultural land, vegetation, water bodies, and built-up areas. The technique made use of multispectral data from Sentinel-2 and Landsat-8, processed on the Google Earth Engine (GEE) platform. To measure classification performance, Hy-DNN was contrasted with traditional classifiers—Convolutional Neural Network (CNN), Random Forest (RF), Classification and Regression Tree (CART), Minimum Distance Classifier (MDC), and Naive Bayes (NB)—using performance metrics including producer’s and consumer’s accuracy, Kappa coefficient, and overall accuracy. Hy-DNN performed the best, with overall accuracy being 97.60% using Sentinel-2 and 91.10% using Landsat-8, outperforming all base models. These results further highlight the superiority of the optimised Hy-DNN in agricultural land mapping and its potential use in crop health monitoring, disease diagnosis, and strategic agricultural planning.

Keywords: Deep Neural Network (DNN); remote sensing (RS); Land Use and Land Cover (LULC); Multispectral Satellite Imagery (MSI); machine learning; hyperparameter optimisation; classification accuracy metrics; Convolutional Neural Network (CNN); U-Net (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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