Remote sensing monitoring method for flooded farmland based on domestic GF-3 radar images

doi:10.6046/zrzyyg.2023141

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Abstract

Against the backdrop of global warming, increasingly frequent floods become a primary agricultural disaster that causes reduced crop production in China. Radar remote sensing technology, possessing all-weather earth observation capabilities, serves as a critical means for rapid monitoring of regional flood information. With the advancement in artificial intelligence, machine learning methods have been extensively applied in the remote sensing-based monitoring of floods. Despite the high accuracy of their algorithms, their training processes often entail extensive field investigations or numerous samples for remote sensing image interpretation. This study aims to overcome sample labeling limitations and improve regional flood monitoring accuracy. Based on the catastrophic flood that occurred in northern Henan on July 20, 2021, this study constructed a flooded crop monitoring method based on the weakly supervised Gaussian mixture model (GMM) using domestic GF-3 HH-HV radar images. Then this method was applied to extract the flooding range of farmland in some areas of northern Henan. Compared to four typical machine learning methods, i.e., random forest (RF), support vector machine (SVM), K-nearest neighbor classification, and parallelepiped classification, the weakly supervised GMM in this study enjoyed the highest accuracy, with overall precision of 0.95 and a Kappa coefficient of 0.90. This study holds great significance for enhancing the accuracy and universality of regional crop flooding monitoring based on remote sensing technology and synthetic aperture radars (SARs).

Keywords GF-3 radar image flood farmland flooding Gaussian mixture model

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TP79

Issue Date: 21 December 2023

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	Chiyi YANG
	Haixiang GUAN
	Wei WU
	Meiyu LIU
	Ying LI
	Wei SU

Cite this article:

Chiyi YANG,Haixiang GUAN,Wei WU, et al. Remote sensing monitoring method for flooded farmland based on domestic GF-3 radar images[J]. Remote Sensing for Natural Resources, 2023, 35(4): 71-80.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2023141 OR https://www.gtzyyg.com/EN/Y2023/V35/I4/71

Fig.1 Study area location,study area elevation,Sentinel-2 image and field investigation of “July 20” rainstorm in Henan Province

Fig.2 Precipitation of some meteorological observation stations in Northern Henan from July 15 to 31

Fig.3 Technique process

Fig.4 Backscatter coefficients of HH and HV images of flood-affected farmland and non-disaster-stricken farmland

Fig.5 Partial plot of Sentinel-2 optical imagery and classification results

Fig.6 Accuracy verification of five machine learning methods

Fig.7 Global view of Sentinel 2 optical imagery and classification results

Tab.1 The area of farmland affected by the flood disaster in each urban,district and county

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