Generating Salt-Affected Irrigated Cropland Map in an Arid and Semi-Arid Region Using Multi-Sensor Remote Sensing Data
Soil salinization is a widespread environmental hazard and a major abiotic constraint affecting global food production and threatening food security. Salt-affected cropland is widely distributed in China, and the problem of salinization in the Hetao Irrigation District (HID) in the Inner Mongolia Au...
| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/23/6010 |
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| author | Deji Wuyun Junwei Bao Luís Guilherme Teixeira Crusiol Tuya Wulan Liang Sun Shangrong Wu Qingqiang Xin Zheng Sun Ruiqing Chen Jingyu Peng Hongtao Xu Nitu Wu Anhong Hou Lan Wu Tingting Ren |
| author_facet | Deji Wuyun Junwei Bao Luís Guilherme Teixeira Crusiol Tuya Wulan Liang Sun Shangrong Wu Qingqiang Xin Zheng Sun Ruiqing Chen Jingyu Peng Hongtao Xu Nitu Wu Anhong Hou Lan Wu Tingting Ren |
| author_sort | Deji Wuyun |
| collection | DOAJ |
| description | Soil salinization is a widespread environmental hazard and a major abiotic constraint affecting global food production and threatening food security. Salt-affected cropland is widely distributed in China, and the problem of salinization in the Hetao Irrigation District (HID) in the Inner Mongolia Autonomous Region is particularly prominent. The salt-affected soil in Inner Mongolia is 1.75 million hectares, accounting for 14.8% of the total land. Therefore, mapping saline cropland in the irrigation district of Inner Mongolia could evaluate the impacts of cropland soil salinization on the environment and food security. This study hypothesized that a reasonably accurate regional map of salt-affected cropland would result from a ground sampling approach based on PlanetScope images and the methodology developed by Sentinel multi-sensor images employing the machine learning algorithm in the cloud computing platform. Thus, a model was developed to create the salt-affected cropland map of HID in 2021 based on the modified cropland base map, valid saline and non-saline samples through consistency testing, and various spectral parameters, such as reflectance bands, published salinity indices, vegetation indices, and texture information. Additionally, multi-sensor data of Sentinel from dry and wet seasons were used to determine the best solution for mapping saline cropland. The results imply that combining the Sentinel-1 and Sentinel-2 data could map the soil salinity in HID during the dry season with reasonable accuracy and close to real time. Then, the indicators derived from the confusion matrix were used to validate the established model. As a result, the combined dataset, which included reflectance bands, spectral indices, vertical transmit–vertical receive (VV) and vertical transmit–horizontal receive (VH) polarization, and texture information, outperformed the highest overall accuracy at 0.8938, while the F1 scores for saline cropland and non-saline cropland are 0.8687 and 0.9109, respectively. According to the analyses conducted for this study, salt-affected cropland can be detected more accurately during the dry season by using just Sentinel images from March to April. The findings of this study provide a clear explanation of the efficiency and standardization of salt-affected cropland mapping in arid and semi-arid regions, with significant potential for applicability outside the current study area. |
| first_indexed | 2024-03-09T17:33:32Z |
| format | Article |
| id | doaj.art-2438de3d88fd4735a9c5ed1f69f77c3a |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T17:33:32Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-2438de3d88fd4735a9c5ed1f69f77c3a2023-11-24T12:04:10ZengMDPI AGRemote Sensing2072-42922022-11-011423601010.3390/rs14236010Generating Salt-Affected Irrigated Cropland Map in an Arid and Semi-Arid Region Using Multi-Sensor Remote Sensing DataDeji Wuyun0Junwei Bao1Luís Guilherme Teixeira Crusiol2Tuya Wulan3Liang Sun4Shangrong Wu5Qingqiang Xin6Zheng Sun7Ruiqing Chen8Jingyu Peng9Hongtao Xu10Nitu Wu11Anhong Hou12Lan Wu13Tingting Ren14Research Center of Agricultural Remote Sensing Engineering Technology in Inner Mongolia Autonomous Region, Institute of Rural Economic and Information, Inner Mongolia Academy of Agricultural & Animal Husbandry Sciences, Hohhot 010031, ChinaResearch Center of Agricultural Remote Sensing Engineering Technology in Inner Mongolia Autonomous Region, Institute of Rural Economic and Information, Inner Mongolia Academy of Agricultural & Animal Husbandry Sciences, Hohhot 010031, ChinaEmbrapa Soja (National Soybean Research Center-Brazilian Agricultural Research Corporation), Londrina 86001-970, BrazilResearch Center of Agricultural Remote Sensing Engineering Technology in Inner Mongolia Autonomous Region, Institute of Rural Economic and Information, Inner Mongolia Academy of Agricultural & Animal Husbandry Sciences, Hohhot 010031, ChinaInstitute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaInstitute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaResearch Center of Agricultural Remote Sensing Engineering Technology in Inner Mongolia Autonomous Region, Institute of Rural Economic and Information, Inner Mongolia Academy of Agricultural & Animal Husbandry Sciences, Hohhot 010031, ChinaInstitute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaInstitute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaInstitute of Resources, Environment, Sustainable Development, Inner Mongolia Academy of Agricultural & Animal Husbandry Sciences, Hohhot 010031, ChinaInstitute of Grassland Research, Chinese Academy of Agricultural Sciences, Hohhot 010010, ChinaKey Laboratory of Grassland Resources of the Ministry of Education, College of Grassland, Resources and Environment, Inner Mongolia Agricultural University, Hohhot 010011, ChinaResearch Center of Agricultural Remote Sensing Engineering Technology in Inner Mongolia Autonomous Region, Institute of Rural Economic and Information, Inner Mongolia Academy of Agricultural & Animal Husbandry Sciences, Hohhot 010031, ChinaCollege of Resources and Environmental Economics, Inner Mongolia University of Finance and Economics, Hohhot 010070, ChinaResearch Center of Agricultural Remote Sensing Engineering Technology in Inner Mongolia Autonomous Region, Institute of Rural Economic and Information, Inner Mongolia Academy of Agricultural & Animal Husbandry Sciences, Hohhot 010031, ChinaSoil salinization is a widespread environmental hazard and a major abiotic constraint affecting global food production and threatening food security. Salt-affected cropland is widely distributed in China, and the problem of salinization in the Hetao Irrigation District (HID) in the Inner Mongolia Autonomous Region is particularly prominent. The salt-affected soil in Inner Mongolia is 1.75 million hectares, accounting for 14.8% of the total land. Therefore, mapping saline cropland in the irrigation district of Inner Mongolia could evaluate the impacts of cropland soil salinization on the environment and food security. This study hypothesized that a reasonably accurate regional map of salt-affected cropland would result from a ground sampling approach based on PlanetScope images and the methodology developed by Sentinel multi-sensor images employing the machine learning algorithm in the cloud computing platform. Thus, a model was developed to create the salt-affected cropland map of HID in 2021 based on the modified cropland base map, valid saline and non-saline samples through consistency testing, and various spectral parameters, such as reflectance bands, published salinity indices, vegetation indices, and texture information. Additionally, multi-sensor data of Sentinel from dry and wet seasons were used to determine the best solution for mapping saline cropland. The results imply that combining the Sentinel-1 and Sentinel-2 data could map the soil salinity in HID during the dry season with reasonable accuracy and close to real time. Then, the indicators derived from the confusion matrix were used to validate the established model. As a result, the combined dataset, which included reflectance bands, spectral indices, vertical transmit–vertical receive (VV) and vertical transmit–horizontal receive (VH) polarization, and texture information, outperformed the highest overall accuracy at 0.8938, while the F1 scores for saline cropland and non-saline cropland are 0.8687 and 0.9109, respectively. According to the analyses conducted for this study, salt-affected cropland can be detected more accurately during the dry season by using just Sentinel images from March to April. The findings of this study provide a clear explanation of the efficiency and standardization of salt-affected cropland mapping in arid and semi-arid regions, with significant potential for applicability outside the current study area.https://www.mdpi.com/2072-4292/14/23/6010irrigation districtcroplandquantile and quantile plots testingdry seasonGoogle Earth Engine |
| spellingShingle | Deji Wuyun Junwei Bao Luís Guilherme Teixeira Crusiol Tuya Wulan Liang Sun Shangrong Wu Qingqiang Xin Zheng Sun Ruiqing Chen Jingyu Peng Hongtao Xu Nitu Wu Anhong Hou Lan Wu Tingting Ren Generating Salt-Affected Irrigated Cropland Map in an Arid and Semi-Arid Region Using Multi-Sensor Remote Sensing Data Remote Sensing irrigation district cropland quantile and quantile plots testing dry season Google Earth Engine |
| title | Generating Salt-Affected Irrigated Cropland Map in an Arid and Semi-Arid Region Using Multi-Sensor Remote Sensing Data |
| title_full | Generating Salt-Affected Irrigated Cropland Map in an Arid and Semi-Arid Region Using Multi-Sensor Remote Sensing Data |
| title_fullStr | Generating Salt-Affected Irrigated Cropland Map in an Arid and Semi-Arid Region Using Multi-Sensor Remote Sensing Data |
| title_full_unstemmed | Generating Salt-Affected Irrigated Cropland Map in an Arid and Semi-Arid Region Using Multi-Sensor Remote Sensing Data |
| title_short | Generating Salt-Affected Irrigated Cropland Map in an Arid and Semi-Arid Region Using Multi-Sensor Remote Sensing Data |
| title_sort | generating salt affected irrigated cropland map in an arid and semi arid region using multi sensor remote sensing data |
| topic | irrigation district cropland quantile and quantile plots testing dry season Google Earth Engine |
| url | https://www.mdpi.com/2072-4292/14/23/6010 |
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