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Soil moisture retrieval using thermal inertia, determined with visible and thermal spaceborne data, validated for European forests [An article from: Remote Sensing of Environment]
Book Details
PublisherElsevier
ISBN / ASINB000RR8GXO
ISBN-13978B000RR8GX8
AvailabilityAvailable for download now
Sales Rank99,999,999
MarketplaceUnited States 🇺🇸
Description
This digital document is a journal article from Remote Sensing of Environment, published by Elsevier in 2006. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.
Description:
Variations in soil moisture strongly affect surface energy balances, regional runoff, land erosion and vegetation productivity (potential crop yield). Hence, the detection of soil moisture content (SMC) is very valuable in the social, economic, humanitarian (food security) and environmental segments of society. A method to estimate SMC from optical and thermal spectral information of METEOSAT imagery based on thermal inertia (TI) is presented. Minimum and maximum TI values from time series are combined in the Soil Moisture Saturation Index (SMSI). To convert surface to soil profile values, a Markov type filter is used, based on a simple two layer water balance equation (the surface layer and the reservoir below) and an autocorrelation function. Ten-daily SMC values are compared with up-scaled (using AVHRR/NDVI) observations on 10 EUROFLUX sites in Europe for the 1997 growing season (March-October). Moreover, the thermal inertia approach is compared for 1997, with ERS Scatterometer data for eight EUROFLUX sites. METEOSAT pixels are up-scaled to accommodate the ERS Scatterometer spatial resolution. The regression coefficients (slope, intercept and R^2) of the thermal inertia approach versus the up-scaled soil moisture observations from EUROFLUX sites vary between 0.811-1.148, -0.0029-0.66 and 0.544-0.877, respectively, with a RRMSE range of 3.9% to 35.7%. The regression coefficients of the comparison of ERS Scatterometer derived Soil Water Index (SWI) versus the up-scaled Soil Moisture Saturation Index for the pooled case (binning the eight EUROFLUX sites) are 0.587, 0.105 and 0.441, respectively, with a RRMSE of 38%. A simple error propagation model applied for the thermal inertia approach reveals that the absolute and relative errors of the obtained soil moisture content is at least 0.010 m^3 m^-^3 or 2.0% with a SMC of 0.203 m^3 m^-^3. Recommendations are made to test and implement the TI methodology using NOAA/AVHRR imagery.
Description:
Variations in soil moisture strongly affect surface energy balances, regional runoff, land erosion and vegetation productivity (potential crop yield). Hence, the detection of soil moisture content (SMC) is very valuable in the social, economic, humanitarian (food security) and environmental segments of society. A method to estimate SMC from optical and thermal spectral information of METEOSAT imagery based on thermal inertia (TI) is presented. Minimum and maximum TI values from time series are combined in the Soil Moisture Saturation Index (SMSI). To convert surface to soil profile values, a Markov type filter is used, based on a simple two layer water balance equation (the surface layer and the reservoir below) and an autocorrelation function. Ten-daily SMC values are compared with up-scaled (using AVHRR/NDVI) observations on 10 EUROFLUX sites in Europe for the 1997 growing season (March-October). Moreover, the thermal inertia approach is compared for 1997, with ERS Scatterometer data for eight EUROFLUX sites. METEOSAT pixels are up-scaled to accommodate the ERS Scatterometer spatial resolution. The regression coefficients (slope, intercept and R^2) of the thermal inertia approach versus the up-scaled soil moisture observations from EUROFLUX sites vary between 0.811-1.148, -0.0029-0.66 and 0.544-0.877, respectively, with a RRMSE range of 3.9% to 35.7%. The regression coefficients of the comparison of ERS Scatterometer derived Soil Water Index (SWI) versus the up-scaled Soil Moisture Saturation Index for the pooled case (binning the eight EUROFLUX sites) are 0.587, 0.105 and 0.441, respectively, with a RRMSE of 38%. A simple error propagation model applied for the thermal inertia approach reveals that the absolute and relative errors of the obtained soil moisture content is at least 0.010 m^3 m^-^3 or 2.0% with a SMC of 0.203 m^3 m^-^3. Recommendations are made to test and implement the TI methodology using NOAA/AVHRR imagery.
