Soil Moisture Mapping with Moisture-Related Indices, OPTRAM, and an Integrated Random Forest-OPTRAM Algorithm from Landsat 8 Images
Remote sensing tools have been extensively used for large-scale soil moisture (SM) mapping in recent years, using Landsat satellite images. Rainfall, soil clay percentage, and the standardized precipitation index play key roles in determining the moisture content of crop fields. The objective of thi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/15/3801 |
| _version_ | 1797432737305460736 |
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| author | Umesh Acharya Aaron L. M. Daigh Peter G. Oduor |
| author_facet | Umesh Acharya Aaron L. M. Daigh Peter G. Oduor |
| author_sort | Umesh Acharya |
| collection | DOAJ |
| description | Remote sensing tools have been extensively used for large-scale soil moisture (SM) mapping in recent years, using Landsat satellite images. Rainfall, soil clay percentage, and the standardized precipitation index play key roles in determining the moisture content of crop fields. The objective of this study was to (i) calculate and determine the effectiveness of moisture-related indices in predicting surface SM, (ii) predict surface SM from satellite images using the Optical Trapezoid Model (OPTRAM), and (iii) evaluate if the OPTRAM predictions can be improved by incorporating weather station, soil, and crop data with a random forest algorithm. The ENVI<sup>®</sup> platform was used to create moisture-related indices maps, and the Google Earth Engine (GEE) was used to prepare OPTRAM maps. The results showed a very weak relationship between the moisture-related indices and surface SM content where r<sup>2</sup> and slopes were ˂0.10 and ˂0.20, respectively. OPTRAM SM, when compared with in situ surface moisture, showed weak relationship with regression values ˂0.2. Surface SM was then predicted using random forest regression using OPTRAM moisture values, rainfall, and the standardized precipitation index (SPI), and percent clay showed high goodness of fit (r<sup>2</sup> = 0.69) and low root mean square error (RMSE = 0.053 m<sup>3</sup> m<sup>−3</sup>). |
| first_indexed | 2024-03-09T10:05:58Z |
| format | Article |
| id | doaj.art-ed496fdb6b4c438fa47ba5bac053b886 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T10:05:58Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-ed496fdb6b4c438fa47ba5bac053b8862023-12-01T23:08:51ZengMDPI AGRemote Sensing2072-42922022-08-011415380110.3390/rs14153801Soil Moisture Mapping with Moisture-Related Indices, OPTRAM, and an Integrated Random Forest-OPTRAM Algorithm from Landsat 8 ImagesUmesh Acharya0Aaron L. M. Daigh1Peter G. Oduor2CFAES Rattan Lal Center for Carbon Management and Sequestration, The Ohio State University, Columbus, OH 43210, USADepartment of Soil Science, School of Natural Resources Sciences, North Dakota State University, Fargo, ND 58102, USADepartment of Geoscience, North Dakota State University, Fargo, ND 58102, USARemote sensing tools have been extensively used for large-scale soil moisture (SM) mapping in recent years, using Landsat satellite images. Rainfall, soil clay percentage, and the standardized precipitation index play key roles in determining the moisture content of crop fields. The objective of this study was to (i) calculate and determine the effectiveness of moisture-related indices in predicting surface SM, (ii) predict surface SM from satellite images using the Optical Trapezoid Model (OPTRAM), and (iii) evaluate if the OPTRAM predictions can be improved by incorporating weather station, soil, and crop data with a random forest algorithm. The ENVI<sup>®</sup> platform was used to create moisture-related indices maps, and the Google Earth Engine (GEE) was used to prepare OPTRAM maps. The results showed a very weak relationship between the moisture-related indices and surface SM content where r<sup>2</sup> and slopes were ˂0.10 and ˂0.20, respectively. OPTRAM SM, when compared with in situ surface moisture, showed weak relationship with regression values ˂0.2. Surface SM was then predicted using random forest regression using OPTRAM moisture values, rainfall, and the standardized precipitation index (SPI), and percent clay showed high goodness of fit (r<sup>2</sup> = 0.69) and low root mean square error (RMSE = 0.053 m<sup>3</sup> m<sup>−3</sup>).https://www.mdpi.com/2072-4292/14/15/3801OPTRAMrandom forestweather stationSPImoisture-related indices |
| spellingShingle | Umesh Acharya Aaron L. M. Daigh Peter G. Oduor Soil Moisture Mapping with Moisture-Related Indices, OPTRAM, and an Integrated Random Forest-OPTRAM Algorithm from Landsat 8 Images Remote Sensing OPTRAM random forest weather station SPI moisture-related indices |
| title | Soil Moisture Mapping with Moisture-Related Indices, OPTRAM, and an Integrated Random Forest-OPTRAM Algorithm from Landsat 8 Images |
| title_full | Soil Moisture Mapping with Moisture-Related Indices, OPTRAM, and an Integrated Random Forest-OPTRAM Algorithm from Landsat 8 Images |
| title_fullStr | Soil Moisture Mapping with Moisture-Related Indices, OPTRAM, and an Integrated Random Forest-OPTRAM Algorithm from Landsat 8 Images |
| title_full_unstemmed | Soil Moisture Mapping with Moisture-Related Indices, OPTRAM, and an Integrated Random Forest-OPTRAM Algorithm from Landsat 8 Images |
| title_short | Soil Moisture Mapping with Moisture-Related Indices, OPTRAM, and an Integrated Random Forest-OPTRAM Algorithm from Landsat 8 Images |
| title_sort | soil moisture mapping with moisture related indices optram and an integrated random forest optram algorithm from landsat 8 images |
| topic | OPTRAM random forest weather station SPI moisture-related indices |
| url | https://www.mdpi.com/2072-4292/14/15/3801 |
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