Crops identification based on Sentinel-2 data with multi-feature optimization

doi:10.6046/zrzyyg.2022272

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Abstract

Focusing on Quanjiao County in Chuzhou City, this study determined 90 features, including spectral, traditional vegetation index, red-edge vegetation index, and texture features, from Sentinel-2 satellite data on the GEE platform. This study examined the effects of diverse feature optimization algorithms combined with a random forest classifier on identifying crop planting types in the study area. These algorithms included the random forest-recursive feature elimination (RF_RFE) algorithm, the Relief F algorithm based on Relief expansion, and the correlation-based feature selection (CFS) algorithm. On this basis, this study further analyzed the classification effects of the optimal feature optimization algorithm in various machine learning classification approaches. The study demonstrates that: ① Spectral features proved to be the most crucial for crop identification, followed by red-edge index features, and texture features manifested minimal effects; ② RF_RFE-based remote sensing identification results exhibited the highest accuracy, with overall accuracy of 92% and a Kappa coefficient of 0.89; ③ Under the RF_RFE feature optimization method, the RF’s Kappa coefficient was 0.01 and 0.41 higher than that of the support vector machine (SVM) and the minimum distance classification (MDC), respectively. This indicates that the RF_RFE feature optimization method based on multiple features, combined with the RF algorithm, can effectively enhance the accuracy and efficiency of remote sensing identification of crops.

Keywords Google Earth Engine Sentinel-2 crop identification feature optimization random forest

ZTFLH:

TP79

Issue Date: 21 December 2023

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	Jian CHEN
	Hu LI
	Yufeng LIU
	Zhu CHANG
	Weijie HAN
	Saisai LIU

Cite this article:

Jian CHEN,Hu LI,Yufeng LIU, et al. Crops identification based on Sentinel-2 data with multi-feature optimization[J]. Remote Sensing for Natural Resources, 2023, 35(4): 292-300.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022272 OR https://www.gtzyyg.com/EN/Y2023/V35/I4/292

Fig.1 Geographical location of the study area and its sample distribution

Tab.1 The growth cycle of mid-season crops in Quanjiao County

Fig.2 Technical flow chart of crop information extraction in the study area

Tab.2 Crop remote sensing identification feature set

Fig.3 The Kappa coefficient for different numbers of features

Tab.3 Characteristic distribution of 3 optimization results

Fig.4 The importance score for feature names and their correspondings

Tab.4 Accuracy assessment of different feature optimization methods

Tab.5 Local result plots of different feature optimization methods

Fig.5 Spatial distribution of crops in the study area based on different machine learning methods under RF_RFE

Tab.6 RF_RFE characteristics are preferably based on different crop classification accuracy of machine learning algorithms

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