Journal ID : AMA-27-03-2022-11245
[This article belongs to Volume - 53, Issue - 04]
Total View : 471

Title : Satellite-based crop typing at cadastral level using traditional and machine learning methods

Abstract :

Crop typing at cadastral level is considered as an important input for precision farming, farm management, crop water requirement, crop yield assessment, and crop insurance settlement among others. Advances in satellite remote sensing, Geographic Information System (GIS), classification algorithms, and computational infrastructure provided us the opportunity to classify and map crop types at cadastral level. However, it remains a question that, which particular method of classification is best for a given site? especially when it comes to map the crops at cadastral level using satellite data. The current work is an attempt to answer this question using a combination of five data types (including, optical, SAR, merged optical and SAR, time series optical, and time series SAR), and four popular classification algorithms (including Unsupervised k-mean, supervised Maximum likelihood (MXL), Support vector machine (SVM), and Random Forest (RF)). Results reveal that the time series of optical and microwave data performs better with random forest classifiers (over all accuracy ranging between 67 to 73% and Kappa coefficient ranging from 0.54 to 0.60) as compared to other combinations and classifiers, when a 100% accuracy check approach (new approach) was used. The most of the errors occur at the margin pixels due to mixing. The current finding is applicable for a large part of the nation corresponding to heterogeneous cropping, especially during monsoon season.

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