Crop Classification and Growth Monitoring in Coal Mining Subsidence Water Areas Based on Sentinel Satellite

Ruihao Cui; Zhenqi Hu; Peijun Wang; Jiazheng Han; Xi Zhang; Xuyang Jiang; Yingjia Cao

doi:10.3390/rs15215095

Remote Sensing (Oct 2023)

Crop Classification and Growth Monitoring in Coal Mining Subsidence Water Areas Based on Sentinel Satellite

Ruihao Cui,
Zhenqi Hu,
Peijun Wang,
Jiazheng Han,
Xi Zhang,
Xuyang Jiang,
Yingjia Cao

Affiliations

Ruihao Cui: School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
Zhenqi Hu: School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
Peijun Wang: School of Public Policy and Management, China University of Mining and Technology, Xuzhou 221116, China
Jiazheng Han: Spatial Dynamics Lab, School of Architecture, Planning and Environmental Policy, University College Dublin, D04 V1W8 Dublin, Ireland
Xi Zhang: School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
Xuyang Jiang: School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
Yingjia Cao: School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China

DOI: https://doi.org/10.3390/rs15215095
Journal volume & issue: Vol. 15, no. 21
p. 5095

Abstract

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In high groundwater level mining areas, subsidence resulting from mining can lead to waterlogging in farmland, causing damage to crops and affecting their growth and development, thereby affecting regional food security. Therefore, it is necessary to restore agricultural production in the coal mining subsidence water areas in the densely populated eastern plains. This study focuses on the Yongcheng coal mining subsidence water areas. It utilizes Sentinel-1 and Sentinel-2 data from May to October in the years 2019 to 2022 to monitor the growth and development of crops. The results demonstrated that (1) the accuracy of aquatic crops categorization was improved by adjusting the elevation of the study region with Mining Subsidence Prediction Software (MSPS 1.0). The order of accuracy for classifying aquatic crops using different machine learning techniques is Random Forest (RF) > Classification and Regression Trees (CART) ≥ Support Vector Machine (SVM). Using the RF method, the obtained classification results can be used for subsequent crop growth monitoring. (2) During the early stages of crop growth, when vegetation cover is low, the Radar Vegetation Index (RVI) is sensitive to the volume scattering of crops, making it suitable for tracking the early growth processes of crops. The peak RVI values for crops from May to July are ranked in the following order: rice (2.595), euryale (2.590), corn (2.535), and lotus (2.483). (3) The order of crops showing improved growth conditions during the mid-growth stage is as follows: rice (47.4%), euryale (43.4%), lotus (27.6%), and corn (4.01%). This study demonstrates that in the Yongcheng coal subsidence water areas, the agricultural reclamation results for the grain-focused model with rice as the main crop and the medicinal herb-focused model with euryale as the main crop are significant. This study can serve as a reference for agricultural management and land reclamation efforts in other coal subsidence water areas.

Published in Remote Sensing

ISSN: 2072-4292 (Online)
Publisher: MDPI AG
Country of publisher: Switzerland
LCC subjects: Science
Website: http://www.mdpi.com/journal/remotesensing/

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