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Spatial Downscaling of Gridded Soil Moisture Products Using Optical and Thermal Satellite Data: Effect of Using Different Vegetation Indices

Halldórsson Alexander, Tómas ; Luan, Haijun ; Jin, Hongxiao LU and Duan, Zheng LU (2025) In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 18. p.7728-7728
Abstract
Satellite remote sensing offers global-scale soil moisture (SM) estimation to assess water and energy cycles. However, the coarse resolution of SM products from microwave remote sensing is unsuitable for fine-scale analysis. This study explored spatial downscaling methods to refine the 0.25° ESA CCI SM product to a 1-km resolution, utilizing optical and thermal remote sensing data, including the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), kernel NDVI (kNDVI), and plant phenology index (PPI), together with land surface temperature from MODIS products over two study areas in Europe. The vegetation temperature condition index based approach was used for downscaling, in which the wet and dry edges of the... (More)
Satellite remote sensing offers global-scale soil moisture (SM) estimation to assess water and energy cycles. However, the coarse resolution of SM products from microwave remote sensing is unsuitable for fine-scale analysis. This study explored spatial downscaling methods to refine the 0.25° ESA CCI SM product to a 1-km resolution, utilizing optical and thermal remote sensing data, including the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), kernel NDVI (kNDVI), and plant phenology index (PPI), together with land surface temperature from MODIS products over two study areas in Europe. The vegetation temperature condition index based approach was used for downscaling, in which the wet and dry edges of the triangular feature space were determined by fitting a line to the maximum and minimum temperatures, respectively, for each vegetation index. The PPI-based downscaling showed consistent results between the two study areas, having a good correlation coefficient and unbiased root-mean-square deviation (ubRMSD) against the in-situ measurements. The NDVI-based downscaling had poor performance overall in terms of ubRMSD and correlation. Results from the EVI- and kNDVI-based methods varied in the two study areas. Compared with the original coarse SM product, spatially downscaled SM products exhibited inferior performance against in-situ SM measurements in terms of evaluation metrics. (Less)
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author
; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Soil Moisture Downscaling, Physical Geography and Ecosystem Analysi, Remote Sensing, Vegetation Temperature Condition Index (VTCI)
in
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
volume
18
pages
7741 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • scopus:105001207167
ISSN
2151-1535
DOI
10.1109/JSTARS.2025.3543012
language
English
LU publication?
yes
id
37e089f5-1feb-462c-8ac9-621eff1815c4
date added to LUP
2025-06-03 09:08:56
date last changed
2025-06-04 04:03:46
@article{37e089f5-1feb-462c-8ac9-621eff1815c4,
  abstract     = {{Satellite remote sensing offers global-scale soil moisture (SM) estimation to assess water and energy cycles. However, the coarse resolution of SM products from microwave remote sensing is unsuitable for fine-scale analysis. This study explored spatial downscaling methods to refine the 0.25° ESA CCI SM product to a 1-km resolution, utilizing optical and thermal remote sensing data, including the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), kernel NDVI (kNDVI), and plant phenology index (PPI), together with land surface temperature from MODIS products over two study areas in Europe. The vegetation temperature condition index based approach was used for downscaling, in which the wet and dry edges of the triangular feature space were determined by fitting a line to the maximum and minimum temperatures, respectively, for each vegetation index. The PPI-based downscaling showed consistent results between the two study areas, having a good correlation coefficient and unbiased root-mean-square deviation (ubRMSD) against the in-situ measurements. The NDVI-based downscaling had poor performance overall in terms of ubRMSD and correlation. Results from the EVI- and kNDVI-based methods varied in the two study areas. Compared with the original coarse SM product, spatially downscaled SM products exhibited inferior performance against in-situ SM measurements in terms of evaluation metrics.}},
  author       = {{Halldórsson Alexander, Tómas and Luan, Haijun and Jin, Hongxiao and Duan, Zheng}},
  issn         = {{2151-1535}},
  keywords     = {{Soil Moisture Downscaling; Physical Geography and Ecosystem Analysi; Remote Sensing; Vegetation Temperature Condition Index (VTCI)}},
  language     = {{eng}},
  month        = {{02}},
  pages        = {{7728--7728}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing}},
  title        = {{Spatial Downscaling of Gridded Soil Moisture Products Using Optical and Thermal Satellite Data: Effect of Using Different Vegetation Indices}},
  url          = {{http://dx.doi.org/10.1109/JSTARS.2025.3543012}},
  doi          = {{10.1109/JSTARS.2025.3543012}},
  volume       = {{18}},
  year         = {{2025}},
}