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Disaggregation of SMOS soil moisture over West Africa using the Temperature and Vegetation Dryness Index based on SEVIRI land surface parameters

Tagesson, T. LU ; Horion, S.; Nieto, H.; Zaldo Fornies, V.; Mendiguren González, G.; Bulgin, C. E.; Ghent, D. and Fensholt, R. (2018) In Remote Sensing of Environment 206. p.424-441
Abstract

The overarching objective of this study was to produce a disaggregated SMOS Soil Moisture (SM) product using land surface parameters from a geostationary satellite in a region covering a diverse range of ecosystem types. SEVIRI data at 15 min temporal resolution were used to derive the Temperature and Vegetation Dryness Index (TVDI) that served as SM proxy within the disaggregation process. West Africa (3°N 26°W; 28°N 26°E) was selected as a case study as it presents both an important North-South climate gradient and a diverse range of ecosystem types. The main challenge was to set up a methodology applicable over a large area that overcomes the constraints of SMOS (low spatial resolution) and TVDI (requires similar atmospheric forcing... (More)

The overarching objective of this study was to produce a disaggregated SMOS Soil Moisture (SM) product using land surface parameters from a geostationary satellite in a region covering a diverse range of ecosystem types. SEVIRI data at 15 min temporal resolution were used to derive the Temperature and Vegetation Dryness Index (TVDI) that served as SM proxy within the disaggregation process. West Africa (3°N 26°W; 28°N 26°E) was selected as a case study as it presents both an important North-South climate gradient and a diverse range of ecosystem types. The main challenge was to set up a methodology applicable over a large area that overcomes the constraints of SMOS (low spatial resolution) and TVDI (requires similar atmospheric forcing and triangular shape formed when plotting morning rise temperature versus fraction of vegetation cover) in order to produce a 0.05° resolution disaggregated SMOS SM product at the sub-continental scale. Consistent cloud cover appeared as one of the main constraints for deriving TVDI, especially during the rainy season and in the southern parts of the region and a large adjustment window (105 × 105 SEVIRI pixels) was therefore deemed necessary. Both the original and the disaggregated SMOS SM products described well the seasonal dynamics observed at six locations of in situ observations. However, there was an overestimation in both products for sites in the humid southern regions; most likely caused by the presence of forest. Both TVDI and the associated disaggregated SM product were found to be highly sensitive to algorithm input parameters; especially for conditions of high fraction of vegetation cover. Additionally, seasonal dynamics in TVDI did not follow the seasonal patterns of SM. Still, its spatial heterogeneity was found to be a good proxy for disaggregating SMOS SM data; main river networks and spatial patterns of SM extremes (i.e. droughts and floods) not seen in the original SMOS SM product were revealed in the disaggregated SM product for a test case of July–September 2012. The disaggregation methodology thereby successfully increased the spatial resolution of SMOS SM, with potential application for local drought/flood monitoring of importance for the livelihood of the population of West Africa.

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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Disaggregation, Downscaling, Sensitivity analysis, SEVIRI, SMOS, Soil moisture, TVDI
in
Remote Sensing of Environment
volume
206
pages
18 pages
publisher
Elsevier
external identifiers
  • scopus:85040231426
ISSN
0034-4257
DOI
10.1016/j.rse.2017.12.036
language
English
LU publication?
yes
id
05fc14ab-dc84-4104-afa0-edf3211ee335
date added to LUP
2018-02-08 07:52:12
date last changed
2018-02-08 10:31:21
@article{05fc14ab-dc84-4104-afa0-edf3211ee335,
  abstract     = {<p>The overarching objective of this study was to produce a disaggregated SMOS Soil Moisture (SM) product using land surface parameters from a geostationary satellite in a region covering a diverse range of ecosystem types. SEVIRI data at 15 min temporal resolution were used to derive the Temperature and Vegetation Dryness Index (TVDI) that served as SM proxy within the disaggregation process. West Africa (3°N 26°W; 28°N 26°E) was selected as a case study as it presents both an important North-South climate gradient and a diverse range of ecosystem types. The main challenge was to set up a methodology applicable over a large area that overcomes the constraints of SMOS (low spatial resolution) and TVDI (requires similar atmospheric forcing and triangular shape formed when plotting morning rise temperature versus fraction of vegetation cover) in order to produce a 0.05° resolution disaggregated SMOS SM product at the sub-continental scale. Consistent cloud cover appeared as one of the main constraints for deriving TVDI, especially during the rainy season and in the southern parts of the region and a large adjustment window (105 × 105 SEVIRI pixels) was therefore deemed necessary. Both the original and the disaggregated SMOS SM products described well the seasonal dynamics observed at six locations of in situ observations. However, there was an overestimation in both products for sites in the humid southern regions; most likely caused by the presence of forest. Both TVDI and the associated disaggregated SM product were found to be highly sensitive to algorithm input parameters; especially for conditions of high fraction of vegetation cover. Additionally, seasonal dynamics in TVDI did not follow the seasonal patterns of SM. Still, its spatial heterogeneity was found to be a good proxy for disaggregating SMOS SM data; main river networks and spatial patterns of SM extremes (i.e. droughts and floods) not seen in the original SMOS SM product were revealed in the disaggregated SM product for a test case of July–September 2012. The disaggregation methodology thereby successfully increased the spatial resolution of SMOS SM, with potential application for local drought/flood monitoring of importance for the livelihood of the population of West Africa.</p>},
  author       = {Tagesson, T. and Horion, S. and Nieto, H. and Zaldo Fornies, V. and Mendiguren González, G. and Bulgin, C. E. and Ghent, D. and Fensholt, R.},
  issn         = {0034-4257},
  keyword      = {Disaggregation,Downscaling,Sensitivity analysis,SEVIRI,SMOS,Soil moisture,TVDI},
  language     = {eng},
  month        = {03},
  pages        = {424--441},
  publisher    = {Elsevier},
  series       = {Remote Sensing of Environment},
  title        = {Disaggregation of SMOS soil moisture over West Africa using the Temperature and Vegetation Dryness Index based on SEVIRI land surface parameters},
  url          = {http://dx.doi.org/10.1016/j.rse.2017.12.036},
  volume       = {206},
  year         = {2018},
}