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Mapping soil moisture at a high resolution over mountainous regions by integrating in situ measurements, topography data, and MODIS land surface temperatures

Publication ,  Journal Article
Fan, L; Al-Yaari, A; Frappart, F; Swenson, JJ; Xiao, Q; Wen, J; Jin, R; Kang, J; Li, X; Fernandez-Moran, R; Wigneron, JP
Published in: Remote Sensing
March 1, 2019

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.

Duke Scholars

Published In

Remote Sensing

DOI

EISSN

2072-4292

Publication Date

March 1, 2019

Volume

11

Issue

6

Related Subject Headings

  • 4013 Geomatic engineering
  • 3709 Physical geography and environmental geoscience
  • 3701 Atmospheric sciences
  • 0909 Geomatic Engineering
  • 0406 Physical Geography and Environmental Geoscience
  • 0203 Classical Physics
 

Citation

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Fan, L., Al-Yaari, A., Frappart, F., Swenson, J. J., Xiao, Q., Wen, J., … Wigneron, J. P. (2019). Mapping soil moisture at a high resolution over mountainous regions by integrating in situ measurements, topography data, and MODIS land surface temperatures. Remote Sensing, 11(6). https://doi.org/10.3390/rs11060656
Fan, L., A. Al-Yaari, F. Frappart, J. J. Swenson, Q. Xiao, J. Wen, R. Jin, et al. “Mapping soil moisture at a high resolution over mountainous regions by integrating in situ measurements, topography data, and MODIS land surface temperatures.” Remote Sensing 11, no. 6 (March 1, 2019). https://doi.org/10.3390/rs11060656.
Fan L, Al-Yaari A, Frappart F, Swenson JJ, Xiao Q, Wen J, Jin R, Kang J, Li X, Fernandez-Moran R, Wigneron JP. Mapping soil moisture at a high resolution over mountainous regions by integrating in situ measurements, topography data, and MODIS land surface temperatures. Remote Sensing. 2019 Mar 1;11(6).

Published In

Remote Sensing

DOI

EISSN

2072-4292

Publication Date

March 1, 2019

Volume

11

Issue

6

Related Subject Headings

  • 4013 Geomatic engineering
  • 3709 Physical geography and environmental geoscience
  • 3701 Atmospheric sciences
  • 0909 Geomatic Engineering
  • 0406 Physical Geography and Environmental Geoscience
  • 0203 Classical Physics