The Evaluation of the Additive Information Contained in New Ecohydrological Measurements

The development of ecohydrological frameworks and theories under the ongoing global climate crisis depends on the development of new and advanced ecohydrological measurements. Currently, numerous of datasets have been collected at plot and ecosystem levels to understand the complex interactions of along the soil, plant, and atmosphere continuum. The development of new measurements costs considerable effort, time, and funding. Therefore, it is important to quantify if new measurements contain useful information for ecohydrological studies, and how the information encoded in the new measurements are transferred to understand and predict key environmental variables. In this dissertation, we show that predictions in vegetation dynamics can be improved by adding observations from advanced satellite and in situ sensor systems. First, we evaluated new satellite soil moisture and vegetation optical depth measurements by Soil Moisture Passive Active (SMAP). We find that there is more opportunity for SMAP soil moisture and vegetation observations to be useful in locations where the daily vegetation climatology cannot adequately reflect observed vegetation dynamics. We also find that the uncertainties SMAP dual channel algorithm (DCA) are largely contributed by uncertainty of the algorithm’s inputs (horizontal and vertical polarized brightness temperature), while the informational uncertainty of the SMAP DCA model itself is more related to the retrieval quality of soil moisture. The informational redundancy and synergy of the two brightness temperature measurements from SMAP are tightly related to the SMAP soil moisture retrieval quality. Finally, we apply a similar informational analysis to new isotopic measurements at the National Ecological Observation Network (NEON). We find that majority of the information from the isotope measurements collected by NEON is unique, which cannot be obtained by other meteorological variables. Carbon isotope (δ13C) provides more additional information about LH in arid locations, while the water isotope (δ2H) provides more additional information about LH at locations with higher aridity, lower mean annual temperature, and lower mean site elevation. These studies show that informational analysis is useful to evaluated how additional information is encoded in new ecohydrological measurements

Improved SMAP Dual-Channel Algorithm for the Retrieval of Soil Moisture

The soil moisture active passive (SMAP) mission was designed to acquire L-band radiometer measurements for the estimation of soil moisture (SM) with an average ubRMSD of not more than 0.04 m3 m-3 volumetric accuracy in the top 5 cm for vegetation with a water content of less than 5 kg m 2. Single-channel algorithm (SCA) and dual-channel algorithm (DCA) are implemented for the processing of SMAP radiometer data. The SCA using the vertically polarized brightness temperature (SCA-V) has been providing satisfactory SM retrievals. However, the DCA using prelaunch design and algorithm parameters for vertical and horizontal polarization data has a marginal performance. In this article, we show that with the updates of the roughness parameter $h$ and the polarization mixing parameters Q, a modified DCA (MDCA) can achieve improved accuracy over DCA; it also allows for the retrieval of vegetation optical depth (VOD or τ). The retrieval performance of MDCA is assessed and compared with SCA-V and DCA using four years (April 1, 2015 to March 31, 2019) of in situ data from core validation sites (CVSs) and sparse networks. The assessment shows that SCA-V still outperforms all the implemented algorithms