Soil Moisture Index Model for Retrieving Soil Moisture in Semiarid Regions of China
There have been some limitations in acquiring an accurate representation of remotely sensed data-derived soil moisture. Here, we propose a simplified thermal inertia model (STIM). This model requires only the albedo and surface maximum temperature easily obtained from satellite imagery, such as that...
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doaj-158b3eeb201f4f3aa684031e42d27de82021-06-03T23:06:51ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352020-01-01135929593710.1109/JSTARS.2020.30255969204441Soil Moisture Index Model for Retrieving Soil Moisture in Semiarid Regions of ChinaZhenhua Liu0Ziqing Xia1Feixiang Chen2Yueming Hu3Ya Wen4Jianbin Liu5Huiming Liu6Luo Liu7https://orcid.org/0000-0003-1942-931XCollege of Natural Resources and Environment, South China Agricultural University, Guangzhou, ChinaCollege of Natural Resources and Environment, South China Agricultural University, Guangzhou, ChinaCollege of Natural Resources and Environment, South China Agricultural University, Guangzhou, ChinaSouth China Academy of Natural Resources Science and Technology, Guangzhou, ChinaCollege of Natural Resources and Environment, South China Agricultural University, Guangzhou, ChinaCollege of Natural Resources and Environment, South China Agricultural University, Guangzhou, ChinaCollege of Natural Resources and Environment, South China Agricultural University, Guangzhou, ChinaCollege of Natural Resources and Environment, South China Agricultural University, Guangzhou, ChinaThere have been some limitations in acquiring an accurate representation of remotely sensed data-derived soil moisture. Here, we propose a simplified thermal inertia model (STIM). This model requires only the albedo and surface maximum temperature easily obtained from satellite imagery, such as that of the moderate resolution imaging spectroradiometer (MODIS). In this study, we defined a soil moisture index (SMI) using the MODIS imagery to obtain a simplified thermal inertia-ratio vegetation index spectral feature space. SMI results from STIM were validated at several locations in the study area of western Inner Mongolia and compared with those from the apparent thermal inertia (ATI) model. Our results showed that our SMI model could explain 71% of the variance in the surface soil moisture, approximately 5% higher than that of the ATI model. In a comparison of field-measured soil moisture data with data simulated using two methods, the SMI and ATI, the SMI showed better retrieval accuracy by lessening the effective error due to the vegetation by 4.2%-10.8%, whereas soil moisture data simulated with ATI showed an effective error of 4.5%-17.0%. The SMI model was also used to map soil moisture; a relative root-mean-square error of 7.67% was recorded for the region, implying the ability of the model to map soil moisture over large areas. Here, the proposed SMI model was proven to be more suitable for estimating soil moisture in locations in which the vegetation index values ranged from 0 to 3. Thus, the proposed SMI model provides a new approach using remote sensing thermal inertia methods to quantify soil moisture at the regional scale.https://ieeexplore.ieee.org/document/9204441/Albedoapparent thermal inertia (ATI)moderate resolution imaging spectroradiometer (MODIS)simplified thermal inertial model (STIM)soil moisturesimplified thermal inertia (STI) ratio vegetation index (RVI) |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Zhenhua Liu Ziqing Xia Feixiang Chen Yueming Hu Ya Wen Jianbin Liu Huiming Liu Luo Liu |
spellingShingle |
Zhenhua Liu Ziqing Xia Feixiang Chen Yueming Hu Ya Wen Jianbin Liu Huiming Liu Luo Liu Soil Moisture Index Model for Retrieving Soil Moisture in Semiarid Regions of China IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Albedo apparent thermal inertia (ATI) moderate resolution imaging spectroradiometer (MODIS) simplified thermal inertial model (STIM) soil moisture simplified thermal inertia (STI) ratio vegetation index (RVI) |
author_facet |
Zhenhua Liu Ziqing Xia Feixiang Chen Yueming Hu Ya Wen Jianbin Liu Huiming Liu Luo Liu |
author_sort |
Zhenhua Liu |
title |
Soil Moisture Index Model for Retrieving Soil Moisture in Semiarid Regions of China |
title_short |
Soil Moisture Index Model for Retrieving Soil Moisture in Semiarid Regions of China |
title_full |
Soil Moisture Index Model for Retrieving Soil Moisture in Semiarid Regions of China |
title_fullStr |
Soil Moisture Index Model for Retrieving Soil Moisture in Semiarid Regions of China |
title_full_unstemmed |
Soil Moisture Index Model for Retrieving Soil Moisture in Semiarid Regions of China |
title_sort |
soil moisture index model for retrieving soil moisture in semiarid regions of china |
publisher |
IEEE |
series |
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
issn |
2151-1535 |
publishDate |
2020-01-01 |
description |
There have been some limitations in acquiring an accurate representation of remotely sensed data-derived soil moisture. Here, we propose a simplified thermal inertia model (STIM). This model requires only the albedo and surface maximum temperature easily obtained from satellite imagery, such as that of the moderate resolution imaging spectroradiometer (MODIS). In this study, we defined a soil moisture index (SMI) using the MODIS imagery to obtain a simplified thermal inertia-ratio vegetation index spectral feature space. SMI results from STIM were validated at several locations in the study area of western Inner Mongolia and compared with those from the apparent thermal inertia (ATI) model. Our results showed that our SMI model could explain 71% of the variance in the surface soil moisture, approximately 5% higher than that of the ATI model. In a comparison of field-measured soil moisture data with data simulated using two methods, the SMI and ATI, the SMI showed better retrieval accuracy by lessening the effective error due to the vegetation by 4.2%-10.8%, whereas soil moisture data simulated with ATI showed an effective error of 4.5%-17.0%. The SMI model was also used to map soil moisture; a relative root-mean-square error of 7.67% was recorded for the region, implying the ability of the model to map soil moisture over large areas. Here, the proposed SMI model was proven to be more suitable for estimating soil moisture in locations in which the vegetation index values ranged from 0 to 3. Thus, the proposed SMI model provides a new approach using remote sensing thermal inertia methods to quantify soil moisture at the regional scale. |
topic |
Albedo apparent thermal inertia (ATI) moderate resolution imaging spectroradiometer (MODIS) simplified thermal inertial model (STIM) soil moisture simplified thermal inertia (STI) ratio vegetation index (RVI) |
url |
https://ieeexplore.ieee.org/document/9204441/ |
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