Observation uncertainty of satellite soil moisture products determined with physically-based modeling
Wanders, N. ; Karssenberg, D. ; Bierkens, M.F.P. ; Parinussa, R. ; Jeu, R. de; Dam, J.C. van; Jong, S. de - \ 2012
Remote Sensing of Environment 127 (2012). - ISSN 0034-4257 - p. 341 - 356.
passive microwave measurements - improving runoff prediction - vegetation optical depth - ers scatterometer - amsr-e - retrieval - assimilation - validation - algorithm - index
Accurate estimates of soil moisture as initial conditions to hydrological models are expected to greatly increase the accuracy of flood and drought predictions. As in-situ soil moisture observations are scarce, satellite-based estimates are a suitable alternative. The validation of remotely sensed soil moisture products is generally hampered by the difference in spatial support of in-situ observations and satellite footprints. Unsaturated zone modeling may serve as a valuable validation tool because it could bridge the gap of different spatial supports. A stochastic, distributed unsaturated zone model (SWAP) was used in which the spatial support was matched to these of the satellite soil moisture retrievals. A comparison between point observations and the SWAP model was performed to enhance understanding of the model and to assure that the SWAP model could be used with confidence for other locations in Spain. A timeseries analysis was performed to compare surface soil moisture from the SWAP model to surface soil moisture retrievals from three different microwave sensors, including AMSR-E, SMOS and ASCAT. Results suggest that temporal dynamics are best captured by AMSR-E and ASCAT resulting in an averaged correlation coefficient of 0.68 and 0.71, respectively. SMOS shows the capability of capturing the long-term trends, however on short timescales the soil moisture signal was not captured as well as by the other sensors, resulting in an averaged correlation coefficient of 0.42. Root mean square errors for the three sensors were found to be very similar (± 0.05 m3m- 3). The satellite uncertainty is spatially correlated and distinct spatial patterns are found over Spain.
Retrieval of soil moisture and vegetation water content using SSM/I data over a corn and soybean region
Jiang, W. ; Jackson, T.J. ; Bindlish, R. ; Hsu, A.Y. ; Su, Z. - \ 2005
Journal of Hydrometeorology 6 (2005)6. - ISSN 1525-755X - p. 854 - 863.
southern great-plains - sensor microwave imager - hydrology experiment - amsr-e - index - radiometers - emission - freeze/thaw - algorithm - salinity
The potential for soil moisture and vegetation water content retrieval using Special Sensor Microwave Imager (SSM/I) brightness temperature over a corn and soybean field region was analyzed and assessed using datasets from the Soil Moisture Experiment 2002 (SMEX02). Soil moisture retrieval was performed using a dual-polarization 19.4-GHz data algorithm that requires the specification of two vegetation parameters¿single scattering albedo and vegetation water content. Single scattering albedo was estimated using published values. A method for estimating the vegetation water content from the microwave polarization index using SSM/I 37.0-GHz data was developed for the region using extensive datasets developed as part of SMEX02. Analyses indicated that the sensitivity of the brightness temperature to soil moisture decreased as vegetation water content increased. However, there was evidence that SSM/I brightness temperatures changed in response to soil moisture increases resulting from rainfall during the later stages of crop growth. This was partly attributed to the lower soil and vegetation thermal temperatures that typically followed a rainfall. Comparisons between experimentally measured volumetric soil moisture and SSM/I-retrieved soil moisture indicated that soil moisture retrieval was feasible using SSM/I data, but the accuracy highly depended upon the levels of vegetation and atmospheric precipitable water; the standard error of estimate over the 3-week study period was 5.49%. The potential for using this approach on a larger scale was demonstrated by mapping the state of Iowa. Results of this investigation provide new insights on how one might operationally correct for vegetation effects using high-frequency microwave observations