Soil temperature estimation at different depths, using remotely-sensed data
Soil temperatures at different depths down the soil profile are important agro-meteorological indicators which are necessary for ecological modeling and precision agricultural activities. In this paper, using time series of soil temperature (ST) measured at different depths (0, 5, 10, 20, and 40 cm)...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2020-01-01
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| Series: | Journal of Integrative Agriculture |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2095311919626572 |
| _version_ | 1818647750803193856 |
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| author | Ran HUANG Jian-xi HUANG Chao ZHANG Hong-yuan MA Wen ZHUO Ying-yi CHEN De-hai ZHU Qingling WU Lamin R. MANSARAY |
| author_facet | Ran HUANG Jian-xi HUANG Chao ZHANG Hong-yuan MA Wen ZHUO Ying-yi CHEN De-hai ZHU Qingling WU Lamin R. MANSARAY |
| author_sort | Ran HUANG |
| collection | DOAJ |
| description | Soil temperatures at different depths down the soil profile are important agro-meteorological indicators which are necessary for ecological modeling and precision agricultural activities. In this paper, using time series of soil temperature (ST) measured at different depths (0, 5, 10, 20, and 40 cm) at agro-meteorological stations in northern China as reference data, ST was estimated from land surface temperature (LST) and normalized difference vegetation index (NDVI) derived from AQUA/TERRA MODIS data, and solar declination (Ds) in univariate and multivariate linear regression models. Results showed that when daytime LST is used as predictor, the coefficient of determination (R2) values decrease from the 0 cm layer to the 40 cm layer. Additionally, with the use of nighttime LST as predictor, the R2 values were relatively higher at 5, 10 and 15 cm depths than those at 0, 20 and 40 cm depths. It is further observed that the multiple linear regression models for soil temperature estimation outperform the univariate linear regression models based on the root mean squared errors (RMSEs) and R2. These results have demonstrated the potential of MODIS data in tandem with the Ds parameter for soil temperature estimation at the upper layers of the soil profile where plant roots grow in. To the best of our knowledge, this is the first attempt at the synergistic use of LST, NDVI and Ds for soil temperature estimation at different depths of the upper layers of the soil profile, representing a significant contribution to soil remote sensing. |
| first_indexed | 2024-12-17T01:07:30Z |
| format | Article |
| id | doaj.art-ee2c395debbc4333899f3f38507753eb |
| institution | Directory Open Access Journal |
| issn | 2095-3119 |
| language | English |
| last_indexed | 2024-12-17T01:07:30Z |
| publishDate | 2020-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Integrative Agriculture |
| spelling | doaj.art-ee2c395debbc4333899f3f38507753eb2022-12-21T22:09:14ZengElsevierJournal of Integrative Agriculture2095-31192020-01-01191277290Soil temperature estimation at different depths, using remotely-sensed dataRan HUANG0Jian-xi HUANG1Chao ZHANG2Hong-yuan MA3Wen ZHUO4Ying-yi CHEN5De-hai ZHU6Qingling WU7Lamin R. MANSARAY8College of Land Science and Technology, China Agricultural University/Key Laboratory of Remote Sensing for Agri-hazards, Ministry of Agriculture and Rural Affairs/Key Laboratory for Agricultural Land Quality, Ministry of Natural Resources, Beijing 100083, P.R.ChinaCollege of Land Science and Technology, China Agricultural University/Key Laboratory of Remote Sensing for Agri-hazards, Ministry of Agriculture and Rural Affairs/Key Laboratory for Agricultural Land Quality, Ministry of Natural Resources, Beijing 100083, P.R.China; Correspondence HUANG Jian-xiCollege of Land Science and Technology, China Agricultural University/Key Laboratory of Remote Sensing for Agri-hazards, Ministry of Agriculture and Rural Affairs/Key Laboratory for Agricultural Land Quality, Ministry of Natural Resources, Beijing 100083, P.R.ChinaCollege of Land Science and Technology, China Agricultural University/Key Laboratory of Remote Sensing for Agri-hazards, Ministry of Agriculture and Rural Affairs/Key Laboratory for Agricultural Land Quality, Ministry of Natural Resources, Beijing 100083, P.R.ChinaCollege of Land Science and Technology, China Agricultural University/Key Laboratory of Remote Sensing for Agri-hazards, Ministry of Agriculture and Rural Affairs/Key Laboratory for Agricultural Land Quality, Ministry of Natural Resources, Beijing 100083, P.R.ChinaCollege of Information & Electrical Engineering, China Agricultural University, Beijing 100083, P.R.ChinaCollege of Land Science and Technology, China Agricultural University/Key Laboratory of Remote Sensing for Agri-hazards, Ministry of Agriculture and Rural Affairs/Key Laboratory for Agricultural Land Quality, Ministry of Natural Resources, Beijing 100083, P.R.ChinaDepartment of Geography, University College London, London WC1E 6BT, UKDepartment of Agro-meteorology and Geo-informatics, Magbosi Land, Water and Environment Research Centre (MLWERC), Sierra Leone Agricultural Research Institute (SLARI), Freetown PMB 1313, Sierra LeoneSoil temperatures at different depths down the soil profile are important agro-meteorological indicators which are necessary for ecological modeling and precision agricultural activities. In this paper, using time series of soil temperature (ST) measured at different depths (0, 5, 10, 20, and 40 cm) at agro-meteorological stations in northern China as reference data, ST was estimated from land surface temperature (LST) and normalized difference vegetation index (NDVI) derived from AQUA/TERRA MODIS data, and solar declination (Ds) in univariate and multivariate linear regression models. Results showed that when daytime LST is used as predictor, the coefficient of determination (R2) values decrease from the 0 cm layer to the 40 cm layer. Additionally, with the use of nighttime LST as predictor, the R2 values were relatively higher at 5, 10 and 15 cm depths than those at 0, 20 and 40 cm depths. It is further observed that the multiple linear regression models for soil temperature estimation outperform the univariate linear regression models based on the root mean squared errors (RMSEs) and R2. These results have demonstrated the potential of MODIS data in tandem with the Ds parameter for soil temperature estimation at the upper layers of the soil profile where plant roots grow in. To the best of our knowledge, this is the first attempt at the synergistic use of LST, NDVI and Ds for soil temperature estimation at different depths of the upper layers of the soil profile, representing a significant contribution to soil remote sensing.http://www.sciencedirect.com/science/article/pii/S2095311919626572soil temperatureland surface temperaturenormalized difference vegetation indexsolar declination |
| spellingShingle | Ran HUANG Jian-xi HUANG Chao ZHANG Hong-yuan MA Wen ZHUO Ying-yi CHEN De-hai ZHU Qingling WU Lamin R. MANSARAY Soil temperature estimation at different depths, using remotely-sensed data Journal of Integrative Agriculture soil temperature land surface temperature normalized difference vegetation index solar declination |
| title | Soil temperature estimation at different depths, using remotely-sensed data |
| title_full | Soil temperature estimation at different depths, using remotely-sensed data |
| title_fullStr | Soil temperature estimation at different depths, using remotely-sensed data |
| title_full_unstemmed | Soil temperature estimation at different depths, using remotely-sensed data |
| title_short | Soil temperature estimation at different depths, using remotely-sensed data |
| title_sort | soil temperature estimation at different depths using remotely sensed data |
| topic | soil temperature land surface temperature normalized difference vegetation index solar declination |
| url | http://www.sciencedirect.com/science/article/pii/S2095311919626572 |
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