Reliable Crops Classification Using Limited Number of Sentinel-2 and Sentinel-1 Images

Abstract

The study presents the analysis of the possible use of limited number of the Sentinel-2 and Sentinel-1 to check if crop declarations that the EU farmers submit to receive subsidies are true. The declarations used in the research were randomly divided into two independent sets (training and test). Based on the training set, supervised classification of both single images and their combinations was performed using random forest algorithm in SNAP (ESA) and our own Python scripts. A comparative accuracy analysis was performed on the basis of two forms of confusion matrix (full confusion matrix commonly used in remote sensing and binary confusion matrix used in machine learning) and various accuracy metrics (overall accuracy, accuracy, specificity, sensitivity, etc.). The highest overall accuracy (81%) was obtained in the simultaneous classification of multitemporal images (three Sentinel-2 and one Sentinel-1). An unexpectedly high accuracy (79%) was achieved in the classification of one Sentinel-2 image at the end of May 2018. Noteworthy is the fact that the accuracy of the random forest method trained on the entire training set is equal 80% while using the sampling method ca. 50%. Based on the analysis of various accuracy metrics, it can be concluded that the metrics used in machine learning, for example: specificity and accuracy, are always higher then the overall accuracy. These metrics should be used with caution, because unlike the overall accuracy, to calculate these metrics, not only true positives but also false positives are used as positive results, giving the impression of higher accuracy. Correct calculation of overall accuracy values is essential for comparative analyzes. Reporting the mean accuracy value for the classes as overall accuracy gives a false impression of high accuracy. In our case, the difference was 10–16% for the validation data, and 25–45% for the test data.

An Application of the “Traffic Lights” Idea to Crop Control in Integrated Administration Control System

“…However, we can cite the results of studies for which the accuracy analysis was performed in a manner similar to our study, with AO's obtained in Belgium of 82% [10], Australia 84.2% [11], South Africa 82% [12] or Poland 69% [7], 81% [15]. In this context, the classification accuracy presented in this paper is moderate, but consistent with similar studies on the verification of declarations in Poland [7].…”

“…In both cases, the time series of indices were classified. On the other hand, our later research on all Sentinel-2 channels allowed us to obtain a higher accuracy of 81% [15], similar to the above-cited results of foreign researchers.…”

“…These metrics are designed to evaluate binary tests, e.g., the evaluation of medical tests with only positive-patient sick (TP), positive-patient healthy (FP), negative-patient sick (FN) and negative-patient healthy (TN). These metrics are inadequate for multi-class classification, and in particular, average accuracy (ACC) is not equated to overall accuracy (OA) [15]. The impact of ignored classes on the classification result can be seen in [24].…”

“…The test area was selected in coordination with ARMA for 2 projects in 2018 and 2019 [13][14][15]. Initially, the commune of Brzeżno located in the north-western part of Poland was chosen as a representative test area.…”

Recognition of Maize Phenology in Sentinel Images with Machine Learning

“…In recent years, the use of data extracted from Sentinel 1 and 2 satellite images for the classification of agricultural crops has increased [ 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ]. Hejmanowska et al [ 18 ] used a limited number of Sentinel 1 and 2 images to classify established crops in central Poland, by training a Random Forest (RF) classification model; the precisions reported by the authors were 81% when data extracted from Sentinels 1 and 2 were combined and 79% when only Sentinel 2 data were used. Rao et al [ 19 ] discriminated corn, mustard, tobacco, and wheat crops using a support vector machine model (SVM) trained from Sentinel 1 and 2 data with an accuracy of 85%.…”