Mapping Soil Moisture at a High Resolution over Mountainous Regions by Integrating In Situ Measurements, Topography Data, and MODIS Land Surface Temperatures
Hydro-agricultural applications often require surface soil moisture (SM) information at high spatial resolutions. In this study, daily spatial patterns of SM at a spatial resolution of 1 km over the Babao River Basin in northwestern China were mapped using a Bayesian-based upscaling algorithm, which...
Main Authors: | , , , , , , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2019-03-01
|
Series: | Remote Sensing |
Subjects: | |
Online Access: | http://www.mdpi.com/2072-4292/11/6/656 |
id |
doaj-2b4555d5f0da424cbe52e0b4b50bca52 |
---|---|
record_format |
Article |
spelling |
doaj-2b4555d5f0da424cbe52e0b4b50bca522020-11-25T00:36:59ZengMDPI AGRemote Sensing2072-42922019-03-0111665610.3390/rs11060656rs11060656Mapping Soil Moisture at a High Resolution over Mountainous Regions by Integrating In Situ Measurements, Topography Data, and MODIS Land Surface TemperaturesLei Fan0A. Al-Yaari1Frédéric Frappart2Jennifer J. Swenson3Qing Xiao4Jianguang Wen5Rui Jin6Jian Kang7Xiaojun Li8R. Fernandez-Moran9J.-P. Wigneron10Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disaster, School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaUMR 1391 ISPA, INRA, F-33140 Villenave d’Ornon, FranceLaboratoire d’Etudes en Géophysique et Océanographie Spatiales (LEGOS), 31400 Toulouse, FrenchNicholas School of the Environment, Duke University, Durham, NC 27708, USAState Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, ChinaKey Laboratory of Remote Sensing of Gansu Province, Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaKey Laboratory of Remote Sensing of Gansu Province, Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaUMR 1391 ISPA, INRA, F-33140 Villenave d’Ornon, FranceImage Processing Laboratory (IPL), University of Valencia, 91354 Valencia, SpainUMR 1391 ISPA, INRA, F-33140 Villenave d’Ornon, FranceHydro-agricultural applications often require surface soil moisture (SM) information at high spatial resolutions. In this study, daily spatial patterns of SM at a spatial resolution of 1 km over the Babao River Basin in northwestern China were mapped using a Bayesian-based upscaling algorithm, which upscaled point-scale measurements to the grid-scale (1 km) by retrieving SM information using Moderate Resolution Imaging Spectroradiometer (MODIS)-derived land surface temperature (LST) and topography data (including aspect and elevation data) and in situ measurements from a wireless sensor network (WSN). First, the time series of pixel-scale (1 km) representative SM information was retrieved from in situ measurements of SM, topography data, and LST. Second, Bayesian linear regression was used to calibrate the relationship between the representative SM and the WSN measurements. Last, the calibrated relationship was used to upscale a network of in situ measured SM to map spatially continuous SM at a high resolution. The upscaled SM data were evaluated against ground-based SM measurements with satisfactory accuracy—the overall correlation coefficient (r), slope, and unbiased root mean square difference (ubRMSD) values were 0.82, 0.61, and 0.025 m3/m3, respectively. Moreover, when accounting for topography, the proposed upscaling algorithm outperformed the algorithm based only on SM derived from LST (r = 0.80, slope = 0.31, and ubRMSD = 0.033 m3/m3). Notably, the proposed upscaling algorithm was able to capture the dynamics of SM under extreme dry and wet conditions. In conclusion, the proposed upscaled method can provide accurate high-resolution SM estimates for hydro-agricultural applications.http://www.mdpi.com/2072-4292/11/6/656upscalingsoil moisturehigh resolutionBayesian linear regressionwireless sensor networktopographic effects |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Lei Fan A. Al-Yaari Frédéric Frappart Jennifer J. Swenson Qing Xiao Jianguang Wen Rui Jin Jian Kang Xiaojun Li R. Fernandez-Moran J.-P. Wigneron |
spellingShingle |
Lei Fan A. Al-Yaari Frédéric Frappart Jennifer J. Swenson Qing Xiao Jianguang Wen Rui Jin Jian Kang Xiaojun Li R. Fernandez-Moran J.-P. Wigneron Mapping Soil Moisture at a High Resolution over Mountainous Regions by Integrating In Situ Measurements, Topography Data, and MODIS Land Surface Temperatures Remote Sensing upscaling soil moisture high resolution Bayesian linear regression wireless sensor network topographic effects |
author_facet |
Lei Fan A. Al-Yaari Frédéric Frappart Jennifer J. Swenson Qing Xiao Jianguang Wen Rui Jin Jian Kang Xiaojun Li R. Fernandez-Moran J.-P. Wigneron |
author_sort |
Lei Fan |
title |
Mapping Soil Moisture at a High Resolution over Mountainous Regions by Integrating In Situ Measurements, Topography Data, and MODIS Land Surface Temperatures |
title_short |
Mapping Soil Moisture at a High Resolution over Mountainous Regions by Integrating In Situ Measurements, Topography Data, and MODIS Land Surface Temperatures |
title_full |
Mapping Soil Moisture at a High Resolution over Mountainous Regions by Integrating In Situ Measurements, Topography Data, and MODIS Land Surface Temperatures |
title_fullStr |
Mapping Soil Moisture at a High Resolution over Mountainous Regions by Integrating In Situ Measurements, Topography Data, and MODIS Land Surface Temperatures |
title_full_unstemmed |
Mapping Soil Moisture at a High Resolution over Mountainous Regions by Integrating In Situ Measurements, Topography Data, and MODIS Land Surface Temperatures |
title_sort |
mapping soil moisture at a high resolution over mountainous regions by integrating in situ measurements, topography data, and modis land surface temperatures |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2019-03-01 |
description |
Hydro-agricultural applications often require surface soil moisture (SM) information at high spatial resolutions. In this study, daily spatial patterns of SM at a spatial resolution of 1 km over the Babao River Basin in northwestern China were mapped using a Bayesian-based upscaling algorithm, which upscaled point-scale measurements to the grid-scale (1 km) by retrieving SM information using Moderate Resolution Imaging Spectroradiometer (MODIS)-derived land surface temperature (LST) and topography data (including aspect and elevation data) and in situ measurements from a wireless sensor network (WSN). First, the time series of pixel-scale (1 km) representative SM information was retrieved from in situ measurements of SM, topography data, and LST. Second, Bayesian linear regression was used to calibrate the relationship between the representative SM and the WSN measurements. Last, the calibrated relationship was used to upscale a network of in situ measured SM to map spatially continuous SM at a high resolution. The upscaled SM data were evaluated against ground-based SM measurements with satisfactory accuracy—the overall correlation coefficient (r), slope, and unbiased root mean square difference (ubRMSD) values were 0.82, 0.61, and 0.025 m3/m3, respectively. Moreover, when accounting for topography, the proposed upscaling algorithm outperformed the algorithm based only on SM derived from LST (r = 0.80, slope = 0.31, and ubRMSD = 0.033 m3/m3). Notably, the proposed upscaling algorithm was able to capture the dynamics of SM under extreme dry and wet conditions. In conclusion, the proposed upscaled method can provide accurate high-resolution SM estimates for hydro-agricultural applications. |
topic |
upscaling soil moisture high resolution Bayesian linear regression wireless sensor network topographic effects |
url |
http://www.mdpi.com/2072-4292/11/6/656 |
work_keys_str_mv |
AT leifan mappingsoilmoistureatahighresolutionovermountainousregionsbyintegratinginsitumeasurementstopographydataandmodislandsurfacetemperatures AT aalyaari mappingsoilmoistureatahighresolutionovermountainousregionsbyintegratinginsitumeasurementstopographydataandmodislandsurfacetemperatures AT fredericfrappart mappingsoilmoistureatahighresolutionovermountainousregionsbyintegratinginsitumeasurementstopographydataandmodislandsurfacetemperatures AT jenniferjswenson mappingsoilmoistureatahighresolutionovermountainousregionsbyintegratinginsitumeasurementstopographydataandmodislandsurfacetemperatures AT qingxiao mappingsoilmoistureatahighresolutionovermountainousregionsbyintegratinginsitumeasurementstopographydataandmodislandsurfacetemperatures AT jianguangwen mappingsoilmoistureatahighresolutionovermountainousregionsbyintegratinginsitumeasurementstopographydataandmodislandsurfacetemperatures AT ruijin mappingsoilmoistureatahighresolutionovermountainousregionsbyintegratinginsitumeasurementstopographydataandmodislandsurfacetemperatures AT jiankang mappingsoilmoistureatahighresolutionovermountainousregionsbyintegratinginsitumeasurementstopographydataandmodislandsurfacetemperatures AT xiaojunli mappingsoilmoistureatahighresolutionovermountainousregionsbyintegratinginsitumeasurementstopographydataandmodislandsurfacetemperatures AT rfernandezmoran mappingsoilmoistureatahighresolutionovermountainousregionsbyintegratinginsitumeasurementstopographydataandmodislandsurfacetemperatures AT jpwigneron mappingsoilmoistureatahighresolutionovermountainousregionsbyintegratinginsitumeasurementstopographydataandmodislandsurfacetemperatures |
_version_ |
1725303206716637184 |