Exploring PlanetScope Satellite Capabilities for Soil Salinity Estimation and Mapping in Arid Regions Oases
One reason for soil degradation is salinization in inland dryland, which poses a substantial threat to arable land productivity. Remote-sensing technology provides a rapid and accurate assessment for soil salinity monitoring, but there is a lack of high-resolution remote-sensing spatial salinity est...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/4/1066 |
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| author | Jiao Tan Jianli Ding Lijing Han Xiangyu Ge Xiao Wang Jiao Wang Ruimei Wang Shaofeng Qin Zhe Zhang Yongkang Li |
| author_facet | Jiao Tan Jianli Ding Lijing Han Xiangyu Ge Xiao Wang Jiao Wang Ruimei Wang Shaofeng Qin Zhe Zhang Yongkang Li |
| author_sort | Jiao Tan |
| collection | DOAJ |
| description | One reason for soil degradation is salinization in inland dryland, which poses a substantial threat to arable land productivity. Remote-sensing technology provides a rapid and accurate assessment for soil salinity monitoring, but there is a lack of high-resolution remote-sensing spatial salinity estimations. The PlanetScope satellite array provides high-precision mapping for land surface monitoring through its 3-m spatial resolution and near-daily revisiting frequency. This study’s use of the PlanetScope satellite array is a new attempt to estimate soil salinity in inland drylands. We hypothesized that field observations, PlanetScope data, and spectral indices derived from the PlanetScope data using the partial least-squares regression (PLSR) method would produce reasonably accurate regional salinity maps based on 84 ground-truth soil salinity data and various spectral parameters, like satellite band reflectance, and published satellite salinity indices. The results showed that using the newly constructed red-edge salinity and yellow band salinity indices, we were able to develop several inversion models to produce regional salinity maps. Different algorithms, including Boruta feature preference, Random Forest algorithm (RF), and Extreme Gradient Boosting algorithm (XGBoost), were applied for variable selection. The newly constructed yellow salinity indices (YRNDSI and YRNDVI) had the best Pearson correlations of 0.78 and −0.78. We also found that the proportions of the newly constructed yellow and red-edge bands accounted for a large proportion of the essential strategies of the three algorithms, with Boruta feature preference at 80%, RF at 80%, and XGBoost at 60%, indicating that these two band indices contributed more to the soil salinity estimation results. The best PLSR model estimation for different strategies is the XGBoost-PLSR model with coefficient of determination (R<sup>2</sup>), root mean square error (RMSE), and ratio of performance to deviation (RPD) values of 0.832, 12.050, and 2.442, respectively. These results suggest that PlanetScope data has the potential to significantly advance the field of soil salinity research by providing a wealth of fine-scale salinity information. |
| first_indexed | 2024-03-11T08:12:29Z |
| format | Article |
| id | doaj.art-1112553aeb2e4600ae5c9e64eeed67f5 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T08:12:29Z |
| publishDate | 2023-02-01 |
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| series | Remote Sensing |
| spelling | doaj.art-1112553aeb2e4600ae5c9e64eeed67f52023-11-16T23:03:15ZengMDPI AGRemote Sensing2072-42922023-02-01154106610.3390/rs15041066Exploring PlanetScope Satellite Capabilities for Soil Salinity Estimation and Mapping in Arid Regions OasesJiao Tan0Jianli Ding1Lijing Han2Xiangyu Ge3Xiao Wang4Jiao Wang5Ruimei Wang6Shaofeng Qin7Zhe Zhang8Yongkang Li9College of Geography and Remote Sensing Science, Xinjiang University, Urumqi 800017, ChinaCollege of Geography and Remote Sensing Science, Xinjiang University, Urumqi 800017, ChinaCollege of Geography and Remote Sensing Science, Xinjiang University, Urumqi 800017, ChinaCollege of Geography and Remote Sensing Science, Xinjiang University, Urumqi 800017, ChinaCollege of Geography and Remote Sensing Science, Xinjiang University, Urumqi 800017, ChinaCollege of Geography and Remote Sensing Science, Xinjiang University, Urumqi 800017, ChinaCollege of Geography and Remote Sensing Science, Xinjiang University, Urumqi 800017, ChinaCollege of Geography and Remote Sensing Science, Xinjiang University, Urumqi 800017, ChinaCollege of Geography and Remote Sensing Science, Xinjiang University, Urumqi 800017, ChinaCollege of Geography and Remote Sensing Science, Xinjiang University, Urumqi 800017, ChinaOne reason for soil degradation is salinization in inland dryland, which poses a substantial threat to arable land productivity. Remote-sensing technology provides a rapid and accurate assessment for soil salinity monitoring, but there is a lack of high-resolution remote-sensing spatial salinity estimations. The PlanetScope satellite array provides high-precision mapping for land surface monitoring through its 3-m spatial resolution and near-daily revisiting frequency. This study’s use of the PlanetScope satellite array is a new attempt to estimate soil salinity in inland drylands. We hypothesized that field observations, PlanetScope data, and spectral indices derived from the PlanetScope data using the partial least-squares regression (PLSR) method would produce reasonably accurate regional salinity maps based on 84 ground-truth soil salinity data and various spectral parameters, like satellite band reflectance, and published satellite salinity indices. The results showed that using the newly constructed red-edge salinity and yellow band salinity indices, we were able to develop several inversion models to produce regional salinity maps. Different algorithms, including Boruta feature preference, Random Forest algorithm (RF), and Extreme Gradient Boosting algorithm (XGBoost), were applied for variable selection. The newly constructed yellow salinity indices (YRNDSI and YRNDVI) had the best Pearson correlations of 0.78 and −0.78. We also found that the proportions of the newly constructed yellow and red-edge bands accounted for a large proportion of the essential strategies of the three algorithms, with Boruta feature preference at 80%, RF at 80%, and XGBoost at 60%, indicating that these two band indices contributed more to the soil salinity estimation results. The best PLSR model estimation for different strategies is the XGBoost-PLSR model with coefficient of determination (R<sup>2</sup>), root mean square error (RMSE), and ratio of performance to deviation (RPD) values of 0.832, 12.050, and 2.442, respectively. These results suggest that PlanetScope data has the potential to significantly advance the field of soil salinity research by providing a wealth of fine-scale salinity information.https://www.mdpi.com/2072-4292/15/4/1066PlanetScopesoil salinityspectral indicesremote sensing |
| spellingShingle | Jiao Tan Jianli Ding Lijing Han Xiangyu Ge Xiao Wang Jiao Wang Ruimei Wang Shaofeng Qin Zhe Zhang Yongkang Li Exploring PlanetScope Satellite Capabilities for Soil Salinity Estimation and Mapping in Arid Regions Oases Remote Sensing PlanetScope soil salinity spectral indices remote sensing |
| title | Exploring PlanetScope Satellite Capabilities for Soil Salinity Estimation and Mapping in Arid Regions Oases |
| title_full | Exploring PlanetScope Satellite Capabilities for Soil Salinity Estimation and Mapping in Arid Regions Oases |
| title_fullStr | Exploring PlanetScope Satellite Capabilities for Soil Salinity Estimation and Mapping in Arid Regions Oases |
| title_full_unstemmed | Exploring PlanetScope Satellite Capabilities for Soil Salinity Estimation and Mapping in Arid Regions Oases |
| title_short | Exploring PlanetScope Satellite Capabilities for Soil Salinity Estimation and Mapping in Arid Regions Oases |
| title_sort | exploring planetscope satellite capabilities for soil salinity estimation and mapping in arid regions oases |
| topic | PlanetScope soil salinity spectral indices remote sensing |
| url | https://www.mdpi.com/2072-4292/15/4/1066 |
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