A Quasi-Global Approach to Improve Day-Time Satellite Surface Soil Moisture Anomalies through the Land Surface Temperature Input

Abstract

Passive microwave observations from various spaceborne sensors have been linked to the soil moisture of the Earth's surface layer. A new generation of passive microwave sensors are dedicated to retrieving this variable and make observations in the single theoretically optimal L-band frequency (1-2 GHz). Previous generations of passive microwave sensors made observations in a range of higher frequencies, allowing for simultaneous estimation of additional variables required for solving the radiative transfer equation. One of these additional variables is land surface temperature, which plays a unique role in the radiative transfer equation and has an influence on the final quality of retrieved soil moisture anomalies. This study presents an optimization procedure for soil moisture retrievals through a quasi-global precipitation-based verification technique, the so-called R value metric. Various land surface temperature scenarios were evaluated in which biases were added to an existing linear regression, specifically focusing on improving the skills to capture the temporal variability of soil moisture. We focus on the relative quality of the day-time (01:30 pm) observations from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E), as these are theoretically most challenging due to the thermal equilibrium theory, and existing studies indicate that larger improvements are possible for these observations compared to their night-time (01:30 am) equivalent. Soil moisture data used in this study were retrieved through the Land Parameter Retrieval Model (LPRM), and in line with theory, both satellite paths show a unique and distinct degradation as a function of vegetation density. Both the ascending (01:30 pm) and descending (01:30 am) paths of the publicly available and widely used AMSR-E LPRM soil moisture products were used for benchmarking purposes. Several scenarios were employed in which the land surface temperature input for the radiative transfer was varied by imposing a bias on an existing regression. These scenarios were evaluated through the R value technique, resulting in optimal bias values on top of this regression. In a next step, these optimal bias values were incorporated in order to re-calibrate the existing linear regression, resulting in a quasi-global uniform LST relation for day-time observations. In a final step, day-time soil moisture retrievals using the re-calibrated land surface temperature relation were again validated through the R value technique. Results indicate an average increasing R value of 16.5%, which indicates a better performance obtained through the re-calibration. This number was confirmed through an independent Triple Collocation verification over the same domain, demonstrating an average root mean square error reduction of 15.3%. Furthermore, a comparison against an extensive in situ database (679 stations) also indicates a generally higher quality for the re-calibrated dataset. Besides the improved day-time dataset, this study furthermore pr...

The Evaluation of Single-Sensor Surface Soil Moisture Anomalies over the Mainland of the People’s Republic of China

“…It has been continuously developed and is also extensively documented in the literature (e.g., [11,15,[22][23][24][25][26][27]). LPRM soil moisture products also play a key role in the existing multi-decadal ECV-SM dataset (e.g., [9,28]) due to their applicability to a wide range of low microwave frequencies and the complementarity with soil moisture data from active microwave observations [29].…”

“…The secondary verification technique that was used in this study is the so-called R value technique (e.g., [27,37,38]). This technique is based on the assimilation of remotely sensed soil moisture into a Remote Sens.…”

The Effect of Three Different Data Fusion Approaches on the Quality of Soil Moisture Retrievals from Multiple Passive Microwave Sensors

“…2018, 10, 107 9 of 23 refer for the complete mathematical framework of the technique as used in this study. One modification was introduced following Parinussa et al [45,60], who applied an artificial deterioration to the gauge-corrected TRMM 3B42 precipitation product in order to create the low quality precipitation dataset, instead of relying on the near-real-time (3B40RT) precipitation product. This is advantageous for the R value technique as it creates a contrast between the two precipitation datasets that is uniform in space.…”

“…There are well-known limitations [35,36,45,60] to the precipitation-based R value verification technique over extremely arid climate regimes, as this technique requires a sufficient number of precipitation events. This requirement cannot be met under extremely arid conditions, as indicated in Figures 2-4 by "No sensitivity-not enough precipitation".…”

“…In our case, the active microwave source was chosen to be the ASCAT soil moisture (see Section 2.3), the reanalysis model was MERRA-Land (see Section 2.4) while the passive microwave source was swapped to include the various soil moisture products retrieved from SMOS and AMSR-E observations. Within this study, we follow the conventional TCA that is extensively documented in the literature [24,36,37,45,55,60,62]. Of specific relevance for the Triple Collocation analysis is the number of collocated samples that was set to at least 100, which is a relatively standard number that was adopted from Stoffelen [37] and Parinussa et al [36].…”