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Journal of Integrative Agriculture  2020, Vol. 19 Issue (1): 277-290    DOI: 10.1016/S2095-3119(19)62657-2
Special Issue: 农业生态环境-遥感合辑Agro-ecosystem & Environment—Romote sensing
Agro-ecosystem & Environment Advanced Online Publication | Current Issue | Archive | Adv Search |
Soil temperature estimation at different depths, using remotely-sensed data
HUANG Ran1, HUANG Jian-xi1, ZHANG Chao1, MA Hong-yuan1, ZHUO Wen1, CHEN Ying-yi2, ZHU De-hai1, Qingling WU3, Lamin R. MANSARAY4  
1 College 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
2 College of Information & Electrical Engineering, China Agricultural University, Beijing 100083, P.R.China
3 Department of Geography, University College London, London WC1E 6BT, UK
4 Department of Agro-meteorology and Geo-informatics, Magbosi Land, Water and Environment Research Centre (MLWERC), Sierra Leone Agricultural Research Institute (SLARI), Freetown PMB 1313, Sierra Leone
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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
Keywords:  soil temperature        land surface temperature        normalized difference vegetation index        solar declination  
Received: 26 September 2018   Accepted:
Fund: This study was supported by the National Natural Science Foundation of China (41671418 and 41371326), the Science and Technology Facilities Council of UK-Newton Agritech Programme (Sentinels of Wheat), and the Fundamental Research Funds for the Central Universities, China (2019TC117).
Corresponding Authors:  Correspondence HUANG Jian-xi, E-mail:   
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HUANG Ran, HUANG Jian-xi, ZHANG Chao, MA Hong-yuan, ZHUO Wen, CHEN Ying-yi, ZHU De-hai, Qingling WU, Lamin R. MANSARAY. 2020. Soil temperature estimation at different depths, using remotely-sensed data. Journal of Integrative Agriculture, 19(1): 277-290.

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