文献类型： 外文期刊

作者： Wuyun, Deji ¹ ; Bao, Junwei ¹ ; Crusiol, Luis Guilherme Teixeira ³ ; Wulan, Tuya ¹ ; Sun, Liang ² ; Wu, Shangrong ² ; Xin, Qingqiang ¹ ; Sun, Zheng ² ; Chen, Ruiqing ² ; Peng, Jingyu ⁴ ; Xu, Hongtao ⁵ ; Wu, Nitu ⁶ ; Hou, Anhong ¹ ; Wu, Lan ⁷ ; Ren, Tingting ¹ ;

作者机构： 1.Inner Mongolia Acad Agr & Anim Husb Sci, Inst Rural Econ & Informat, Res Ctr Agr Remote Sensing Engn Technol Inner Mong, Hohhot 010031, Peoples R China

2.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Beijing 100081, Peoples R China

3.Natl Soybean Res Ctr Brazilian Agr Res Corp, Embrapa Soja, BR-86001970 Londrina, Brazil

4.Inner Mongolia Acad Agr & Anim Husb Sci, Inst Resources Environm Sustainable Dev, Hohhot 010031, Peoples R China

5.Chinese Acad Agr Sci, Inst Grassland Res, Hohhot 010010, Peoples R China

6.Inner Mongolia Agr Univ, Coll Grassland Resources & Environm, Key Lab Grassland Resources, Minist Educ, Hohhot 010011, Peoples R China

7.Inner Mongolia Univ Finance & Econ, Coll Resources & Environm Econ, Hohhot 010070, Peoples R China

8.Nanjing Agr Univ, Asia Hub, Nanjing, Peoples R China

关键词： irrigation district; cropland; quantile and quantile plots testing; dry season; Google Earth Engine

期刊名称：REMOTE SENSING （ 影响因子：5.349； 五年影响因子：5.786 ）

ISSN：

年卷期： 2022 年 14 卷 23 期

页码：

收录情况： SCI

摘要： 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.