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Tian J, Tian Y, Cao Y, Wan W, Liu K. Research on Rice Fields Extraction by NDVI Difference Method Based on Sentinel Data. SENSORS (BASEL, SWITZERLAND) 2023; 23:5876. [PMID: 37447726 DOI: 10.3390/s23135876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 06/18/2023] [Accepted: 06/20/2023] [Indexed: 07/15/2023]
Abstract
To meet the challenge of food security, it is necessary to obtain information about rice fields accurately, quickly and conveniently. In this study, based on the analysis of existing rice fields extraction methods and the characteristics of intra-annual variation of normalized difference vegetation index (NDVI) in the different types of ground features, the NDVI difference method is used to extract rice fields using Sentinel data based on the unique feature of rice fields having large differences in vegetation between the pre-harvest and post-harvest periods. Firstly, partial correlation analysis is used to study the influencing factors of the rice harvesting period, and a simulation model of the rice harvesting period is constructed by multiple regression analysis with data from 32 sample points. Sentinel data of the pre-harvest and post-harvest periods of rice fields are determined based on the selected rice harvesting period. The NDVI values of the rice fields are calculated for both the pre-harvest and post-harvest periods, and 33 samples of the rice fields are selected from the high-resolution image. The threshold value for rice field extraction is determined through statistical analysis of the NDVI difference in the sample area. This threshold was then utilized to extract the initial extent of rice fields. Secondly, to address the phenomenon of the "water edge effect" in the initial data, the water extraction method based on the normalized difference water index (NDWI) is used to remove the pixels of water edges. Finally, the extraction results are verified and analyzed for accuracy. The study results show that: (1) The rice harvesting period is significantly correlated with altitude and latitude, with coefficients of 0.978 and 0.922, respectively, and the simulation model of the harvesting period can effectively determine the best period of remote sensing images needed to extract rice fields; (2) The NDVI difference method based on sentinel data for rice fields extraction is excellent; (3) The mixed pixels have a large impact on the accuracy of rice fields extraction, due to the water edge effect. Combining NDWI can effectively reduce the water edge effect and significantly improve the accuracy of rice field extraction.
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Affiliation(s)
- Jinglian Tian
- Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing 400715, China
- Chongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing 400715, China
- Daotian Science and Technology Limited Company, Chongqing 400700, China
| | - Yongzhong Tian
- Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing 400715, China
- Chongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing 400715, China
- Daotian Science and Technology Limited Company, Chongqing 400700, China
| | - Yan Cao
- Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing 400715, China
- Chongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing 400715, China
- Daotian Science and Technology Limited Company, Chongqing 400700, China
| | - Wenhao Wan
- Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing 400715, China
- Chongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing 400715, China
- Daotian Science and Technology Limited Company, Chongqing 400700, China
| | - Kangning Liu
- Chongqing Geomatics and Remote Sensing Center, Chongqing 400715, China
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SFRS-Net: A Cloud-Detection Method Based on Deep Convolutional Neural Networks for GF-1 Remote-Sensing Images. REMOTE SENSING 2021. [DOI: 10.3390/rs13152910] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Clouds constitute a major obstacle to the application of optical remote-sensing images as they destroy the continuity of the ground information in the images and reduce their utilization rate. Therefore, cloud detection has become an important preprocessing step for optical remote-sensing image applications. Due to the fact that the features of clouds in current cloud-detection methods are mostly manually interpreted and the information in remote-sensing images is complex, the accuracy and generalization of current cloud-detection methods are unsatisfactory. As cloud detection aims to extract cloud regions from the background, it can be regarded as a semantic segmentation problem. A cloud-detection method based on deep convolutional neural networks (DCNN)—that is, a spatial folding–unfolding remote-sensing network (SFRS-Net)—is introduced in the paper, and the reason for the inaccuracy of DCNN during cloud region segmentation and the concept of space folding/unfolding is presented. The backbone network of the proposed method adopts an encoder–decoder structure, in which the pooling operation in the encoder is replaced by a folding operation, and the upsampling operation in the decoder is replaced by an unfolding operation. As a result, the accuracy of cloud detection is improved, while the generalization is guaranteed. In the experiment, the multispectral data of the GaoFen-1 (GF-1) satellite is collected to form a dataset, and the overall accuracy (OA) of this method reaches 96.98%, which is a satisfactory result. This study aims to develop a method that is suitable for cloud detection and can complement other cloud-detection methods, providing a reference for researchers interested in cloud detection of remote-sensing images.
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