Vegetation water content (VWC) is an important land surface parameter that is used in retrieving surface soil moisture from microwave satellite platforms. Operational approaches utilize relationships between VWC and satellite vegetation indices for broad categories of vegetation, i.e., “agricultural crops,” based on climatological databases. Determining crop type–specific equations for water content could lead to improvements in the soil moisture retrievals. Data to address this issue are lacking, and as a part of the calibration and validation program for NASA’s Soil Moisture Active Passive (SMAP) Mission, field experiments are conducted in northern central Iowa and southern Manitoba to investigate the performance of the SMAP soil moisture products for these intensive agricultural regions. Both sites are monitored for soil moisture, and the calibration and validation assessments had indicated performance issues in both domains. One possible source could be the characterization of the vegetation. In this investigation, Landsat 8 data are used to compute a normalized difference water index for the entire summer of 2016 that is then integrated with extensive VWC sampling to determine how to best characterize daily estimates of VWC for improved algorithm implementation. In Iowa, regression equations for corn and soybean are developed that provided VWC with root mean square error (RMSE) values of 1.37 and 1.10 kg / m2, respectively. In Manitoba, corn and soybean equations are developed with RMSE values of 0.55 and 0.25 kg / m2. Additional crop-specific equations are developed for winter wheat (RMSE of 0.07 kg / m2), canola (RMSE of 0.90 kg / m2), oats (RMSE of 0.74 kg / m2), and black beans (RMSE of 0.31 kg / m2). Overall, the conditions are judged to be typical with the exception of soybeans, which had an exceptionally high biomass as a result of significant rainfall as compared to previous studies in this region. Future implementation of these equations into algorithm development for satellite and airborne radiative transfer modeling will improve the overall performance in agricultural domains.
Microwave remote sensing can provide reliable measurements of surface soil moisture. However, some land surface conditions can have a perturbing influence on soil moisture retrievals. In the soil moisture experiments in 2005 (SMEX05), we attempted to contribute to the understanding of the effect of dew using concurrent ground and aircraft observations. Early morning flights were conducted with an airborne microwave radiometer from June 19 to July 2, 2005, in Iowa, USA over an agricultural domain. Results of the experiment indicated that dew had a small but measurable effect on the observed 10.7-GHz brightness temperatures. The results indicate that the H-pol emissivity increased 0.015 to 0.04 for the corn sites, 0.014 to 0.02 for soybean, and 0.01 for forest sites as dew evaporated. These results suggest that the presence of dew decreases X-band land surface emissivity slightly and the effect of dew varies with vegetation types. Our findings are consistent with other works in the literature that has found that the effect of dew depends on both the type of vegetation and the wavelength of observation, but further studies should be conducted to verify this hypothesis.
Mapping land cover and vegetation characteristics on a regional scale is critical to soil moisture retrieval using microwave remote sensing. In aircraft-based experiments such as the National Airborne Field Experiment 2006 (NAFE'06), it is challenging to provide accurate high resolution vegetation information, especially on a daily basis. A technique proposed in previous studies was adapted here to the heterogenous conditions encountered in NAFE'06, which included a hydrologically complex landscape consisting of both irrigated and dryland agriculture. Using field vegetation sampling and ground-based reflectance measurements, the knowledge base for relating the Normalized Difference Water Index (NDWI) and the vegetation water content was extended to a greater diversity of agricultural crops, which included dryland and irrigated wheat, alfalfa, and canola. Critical to the generation of vegetation water content maps, the land cover for this region was determined from satellite visible/infrared imagery and ground surveys with an accuracy of 95.5% and a kappa coefficient of 0.95. The vegetation water content was estimated with a root mean square error of 0.33 kg/m<sup>2</sup>. The results of this investigation contribute to a more robust database of global vegetation water content observations and demonstrate that the approach can be applied with high accuracy.
A recent study established the theoretical basis for a new type of index based on passive microwave vegetation indices
(MVIs). The approach was then calibrated for use with data from the Advanced Microwave Scanning Radiometer
(AMSR-E) on the Aqua satellite under the assumption that there is no significant polarization dependence of the
vegetation emission and attenuation properties. To demonstrate the potential of the new microwave vegetation indices,
these were compared with the Normalized Difference of Vegetation Index (NDVI) derived using MODIS at continental
and global scales. These results verified that the microwave vegetation indices can provide new and complementary
information on vegetation to NDVI for the global monitoring of vegetation and ecosystem properties from space. The
next phase of analysis has focused on quantifiable vegetation parameters, specifically vegetation water content that is a
valuable parameter in soil moisture retrievals using microwave data. Data sets collected in several recent large scale field
campaigns included vegetation water content over domains in addition to conventional indices. Comparisons to date
indicate that the MVI does provide vegetation water content information, however, further analysis of vegetation type
effects are needed.
WindSat is a spaceborne multi-frequency polarimetric microwave radiometer and has the potential of contributing to the
retrieval of land variables and complementing efforts directed at the Aqua AMSR-E. In this study, a previously
established algorithm was applied to WindSat data to estimate global soil moisture. Comprehensive validation was
performed by comparing the retrievals with in situ soil moisture observations from networks located at four soil moisture
validation sites. The overall standard error of estimate for surface soil moisture was 0.038 m3/m3. This analysis shows
that the WindSat soil moisture retrievals are reasonable and fall within the generally accepted error bounds of 0.04
m3/m3. Larger scale qualitative assessments were performed by analysis of the spatial distribution of soil moisture,
which were found to be consistent with the known global climatology. There are other soil moisture algorithms under
investigation, however, these result show the potential of the WindSat sensor for soil moisture as well as future
operational satellite instruments.