Relation between Seasonally Detrended Shortwave Infrared Reflectance Data and Land Surface Moisture in Semi‑Arid Sahel

Abstract

Abstract:In the Sudano-Sahelian areas of Africa droughts can have serious impacts on natural resources, and therefore land surface moisture is an important factor. Insufficient conventional sites for monitoring land surface moisture make the use of Earth Observation data for this purpose a key issue. In this study we explored the potential of using reflectance data in the Red, Near Infrared (NIR), and Shortwave Infrared (SWIR) spectral regions for detecting short term variations in land surface moisture in the Sahel, by analyzing data from three test sites and observations from the geostationary Meteosat Second Generation (MSG) satellite. We focused on responses in surface reflectance to soil-and surface moisture for bare soil and early to mid-growing season. A method for implementing detrended time series of the Shortwave Infrared Water Stress Index (SIWSI) is examined for detecting variations in vegetation moisture status, and is compared to detrended time series of the Normalized Difference Vegetation Index (NDVI). It was found OPEN ACCESSRemote Sens. 2013, 5 2899 that when plant available water is low, the SIWSI anomalies increase over time, while the NDVI anomalies decrease over time, but less systematically. Therefore SIWSI may carry important complementary information to NDVI in terms of vegetation water status, and can provide this information with the unique combination of temporal and spatial resolution from optical geostationary observations over Sahel. However, the relation between SIWSI anomalies and periods of water stress were not found to be sufficiently robust to be used for water stress detection.

Evaluation of MODIS Spectral Indices for Monitoring Hydrological Dynamics of a Small, Seasonally-Flooded Wetland in Southern Spain

“…For dry years, LSWI B6 and MNDWI had a stronger relation with water level than NDVI and NDWI. This could be partly attributed to the smaller sensitivity of NIR reflection to variations in soil moisture, vegetation water content (Eitel et al 2006;Olsen et al 2013) and open water (Campbell 2002;Li et al 2003) as compared to SWIR reflection. Low correlation between LSWI B6 /MNDWI and water level for wet years may again be explained by the accumulation of salt at the soil surface (as for TCWI).…”

Satellite Detection of Water Stress Effects on Terrestrial Latent Heat Flux With MODIS Shortwave Infrared Reflectance Data

“…f (cm)_swir was highly sensitive to plant structural and photosynthetic changes (indicated by f APAR ) at most of the biome sites ( R 2 > 0.54). Other studies also demonstrated the potential utility of alternative MODIS‐derived SWIRs as environmental stress indicators for simulating ET, GPP, and agricultural drought (Daniela & Virginia, ; Olsen et al, ; X. Xiao et al, ; Yebra et al, ; Y. Zhou et al, ). Thus, using alternative MODIS‐derived SWIRs across multiple biomes may enhance the performance of the PT algorithm when estimating the regional LE under different environmental stress conditions.…”

“…The traditional SWIR‐based surface moisture indices are the most useful indicators for understanding short‐term changes in terrestrial water availability and LE , whereas VNIR‐based indices do not reflect them well because the SWIR spectra (1,605–2,105 nm) are sensitive to liquid water in the soil and the vegetation canopy (Ceccato et al, ; B. Gao, ; Olsen et al, ). Previous studies that employed a SWIR‐based water stress index or normalized difference water index ( NDWI ) as a VNIR‐based NDVI proxy for estimating LE obtained good agreement with ground‐measured LE and improved detection of intraseasonal stress (Lu & Zhuang, ; Olsen et al, , ). MODIS‐derived SWIRs coupled with a modified PT algorithm also obtained good agreement with ground‐measured LE under a wide variety of conditions (Daniela & Virginia, ).…”

Optimizing Satellite-Based Precipitation Estimation for Nowcasting of Rainfall and Flash Flood Events over the South African Domain

“…More information on these MPEF products can be found at [9]. Various other applications have recently been developed which makes use of MSG data, including sun shine duration over Europe [10]; surface moisture in the Sahel region [11] as well as dust detection [12] and volcanic eruptions over the Italian region [13], to name but a few.…”