Investigating the Effects of Snow Cover and Vegetation on Soil Temperature Using Remote Sensing Indicators in the Three River Source Region, China
Soil temperature is an important physical variable that characterises geothermal conditions and influences geophysical, biological and chemical processes in the earth sciences. Soil temperature is not only affected by climatic and geographical factors; it is also modulated by local factors such as s...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-08-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/14/16/4114 |
| _version_ | 1797442188847611904 |
|---|---|
| author | Xiaoqing Tan Siqiong Luo Hongmei Li Xiaohua Hao Jingyuan Wang Qingxue Dong Zihang Chen |
| author_facet | Xiaoqing Tan Siqiong Luo Hongmei Li Xiaohua Hao Jingyuan Wang Qingxue Dong Zihang Chen |
| author_sort | Xiaoqing Tan |
| collection | DOAJ |
| description | Soil temperature is an important physical variable that characterises geothermal conditions and influences geophysical, biological and chemical processes in the earth sciences. Soil temperature is not only affected by climatic and geographical factors; it is also modulated by local factors such as snow cover and vegetation. This paper investigates the relationship between snow cover and vegetation and soil temperature with the help of two classical remote sensing indicators, the Snow Cover Days (SCD) based Advanced Very High Resolution Radiometer and the Normalized Difference Vegetation Index (NDVI)-based Global Inventory Modelling and Mapping Studies, to analyse the influence of local factors on soil temperature in the Three River Source Region (TRSR). Combing multi-layer geothermal observations from 23 stations in the TRSR with meteorological dataset, soil properties datasets, snow cover and vegetation indices, a non-linear model, the Random Forest model, is used to establish a multi-layer soil temperature dataset to analyse the influence of surface cover factors in each depth. The results showed that the annual SCD had a decreasing trend during 1982–2015 and was negatively correlated with the annual mean soil temperature; the annual NDVI had no significant trend, but it was positively correlated with the annual mean soil temperature. Regionally, there was a significant decrease in SCD in the mountainous areas bordering the source areas of the three rivers, and there was a trend of increasing NDVI in the northwest and decreasing vegetation in the southwest in the TRSR. The stronger the correlation with soil temperature in areas with a larger SCD, the more the snow has a cooling effect on the shallower soil temperatures due to the high albedo of the accumulated snow and the repeated melting and heat absorption of the snow in the area. The snow has an insulating effect on the 40 cm soil layer by impeding the cooling effect of the atmosphere in winter. In sparsely vegetated areas, vegetation lowers ground albedo and warms the soil, but in July and August, in areas with more vegetation, NDVI is negatively correlated with soil temperature, with heavy vegetation intercepting summer radiant energy and having a cooling effect on the soil. |
| first_indexed | 2024-03-09T12:38:13Z |
| format | Article |
| id | doaj.art-bf54cdd6c2af483a92259ee1ebb101c2 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T12:38:13Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-bf54cdd6c2af483a92259ee1ebb101c22023-11-30T22:21:40ZengMDPI AGRemote Sensing2072-42922022-08-011416411410.3390/rs14164114Investigating the Effects of Snow Cover and Vegetation on Soil Temperature Using Remote Sensing Indicators in the Three River Source Region, ChinaXiaoqing Tan0Siqiong Luo1Hongmei Li2Xiaohua Hao3Jingyuan Wang4Qingxue Dong5Zihang Chen6Key Laboratory of Land Surface Process and Climate Change in Cold and Arid Regions, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaKey Laboratory of Land Surface Process and Climate Change in Cold and Arid Regions, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaQinghai Climate Center, Xining 810001, ChinaKey Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaKey Laboratory of Land Surface Process and Climate Change in Cold and Arid Regions, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaKey Laboratory of Land Surface Process and Climate Change in Cold and Arid Regions, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaKey Laboratory of Land Surface Process and Climate Change in Cold and Arid Regions, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaSoil temperature is an important physical variable that characterises geothermal conditions and influences geophysical, biological and chemical processes in the earth sciences. Soil temperature is not only affected by climatic and geographical factors; it is also modulated by local factors such as snow cover and vegetation. This paper investigates the relationship between snow cover and vegetation and soil temperature with the help of two classical remote sensing indicators, the Snow Cover Days (SCD) based Advanced Very High Resolution Radiometer and the Normalized Difference Vegetation Index (NDVI)-based Global Inventory Modelling and Mapping Studies, to analyse the influence of local factors on soil temperature in the Three River Source Region (TRSR). Combing multi-layer geothermal observations from 23 stations in the TRSR with meteorological dataset, soil properties datasets, snow cover and vegetation indices, a non-linear model, the Random Forest model, is used to establish a multi-layer soil temperature dataset to analyse the influence of surface cover factors in each depth. The results showed that the annual SCD had a decreasing trend during 1982–2015 and was negatively correlated with the annual mean soil temperature; the annual NDVI had no significant trend, but it was positively correlated with the annual mean soil temperature. Regionally, there was a significant decrease in SCD in the mountainous areas bordering the source areas of the three rivers, and there was a trend of increasing NDVI in the northwest and decreasing vegetation in the southwest in the TRSR. The stronger the correlation with soil temperature in areas with a larger SCD, the more the snow has a cooling effect on the shallower soil temperatures due to the high albedo of the accumulated snow and the repeated melting and heat absorption of the snow in the area. The snow has an insulating effect on the 40 cm soil layer by impeding the cooling effect of the atmosphere in winter. In sparsely vegetated areas, vegetation lowers ground albedo and warms the soil, but in July and August, in areas with more vegetation, NDVI is negatively correlated with soil temperature, with heavy vegetation intercepting summer radiant energy and having a cooling effect on the soil.https://www.mdpi.com/2072-4292/14/16/4114soil temperaturerandom forestnormalized difference vegetation indexsnow cover |
| spellingShingle | Xiaoqing Tan Siqiong Luo Hongmei Li Xiaohua Hao Jingyuan Wang Qingxue Dong Zihang Chen Investigating the Effects of Snow Cover and Vegetation on Soil Temperature Using Remote Sensing Indicators in the Three River Source Region, China Remote Sensing soil temperature random forest normalized difference vegetation index snow cover |
| title | Investigating the Effects of Snow Cover and Vegetation on Soil Temperature Using Remote Sensing Indicators in the Three River Source Region, China |
| title_full | Investigating the Effects of Snow Cover and Vegetation on Soil Temperature Using Remote Sensing Indicators in the Three River Source Region, China |
| title_fullStr | Investigating the Effects of Snow Cover and Vegetation on Soil Temperature Using Remote Sensing Indicators in the Three River Source Region, China |
| title_full_unstemmed | Investigating the Effects of Snow Cover and Vegetation on Soil Temperature Using Remote Sensing Indicators in the Three River Source Region, China |
| title_short | Investigating the Effects of Snow Cover and Vegetation on Soil Temperature Using Remote Sensing Indicators in the Three River Source Region, China |
| title_sort | investigating the effects of snow cover and vegetation on soil temperature using remote sensing indicators in the three river source region china |
| topic | soil temperature random forest normalized difference vegetation index snow cover |
| url | https://www.mdpi.com/2072-4292/14/16/4114 |
| work_keys_str_mv | AT xiaoqingtan investigatingtheeffectsofsnowcoverandvegetationonsoiltemperatureusingremotesensingindicatorsinthethreeriversourceregionchina AT siqiongluo investigatingtheeffectsofsnowcoverandvegetationonsoiltemperatureusingremotesensingindicatorsinthethreeriversourceregionchina AT hongmeili investigatingtheeffectsofsnowcoverandvegetationonsoiltemperatureusingremotesensingindicatorsinthethreeriversourceregionchina AT xiaohuahao investigatingtheeffectsofsnowcoverandvegetationonsoiltemperatureusingremotesensingindicatorsinthethreeriversourceregionchina AT jingyuanwang investigatingtheeffectsofsnowcoverandvegetationonsoiltemperatureusingremotesensingindicatorsinthethreeriversourceregionchina AT qingxuedong investigatingtheeffectsofsnowcoverandvegetationonsoiltemperatureusingremotesensingindicatorsinthethreeriversourceregionchina AT zihangchen investigatingtheeffectsofsnowcoverandvegetationonsoiltemperatureusingremotesensingindicatorsinthethreeriversourceregionchina |