Early identification of crop types using Sentinel-2 satellite images and an incremental multi-feature ensemble method (Case study: Shahriar, Iran)

Thematic crop-type maps provide helpful information for the decision-makers to ensure society’s food security, control market prices, impose new export or import restrictions, and effective disaster management. This article aims to identify the cultivated products (wheat, barley, alfalfa, and rapeseed) in Shahriar’s farmlands before cropping season ends using time-series of Sentinel-2 satellite images and a novel multi-feature ensemble classifier. The proposed classifier consisted of 3 steps: 1-ensemble generation, 2- ensemble pruning, 3- ensemble integration. First, different spectral indices were derived from the input time-series of Sentinel-2 images, and we trained two popular machine learning classifiers, Random Forest (RF) and Support Vector Machine (SVM), for each index. In the pruning step, Q- statistics analysis selected the best candidate classifiers to be ensembled. The final decision was made using weighted average integration of candidate classifiers based on overall accuracy. We used this classifier in an incremental process that determines the shortest time series enabling early identification of different crops. The results indicate the superiority of the proposed method in early crop-type identification as it achieved at least 9.56% and 4.01% higher overall accuracy than the best base classifier and the best classifier trained by all indices, respectively. Ensemble pruning also resulted in an increase of at least 2.58% in overall accuracy. In addition, the proposed classifier could identify different agricultural products more than two months before the end of the cropping season with an overall accuracy of 80.12%.