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Information about the spatiotemporal variability of soil moisture is critical for many purposes, including monitoring of hydrologic extremes, irrigation scheduling, and prediction of agricultural yields. We evaluated the temporal dynamics of 18 state-of-the-art (quasi-)global near-surface soil moisture products, including six based on satellite retrievals, six based on models without satellite data assimilation (referred to hereafter as “open-loop” models), and six based on models that assimilate satellite soil moisture or brightness temperature data. Seven of the products are introduced for the first time in this study: one multi-sensor merged satellite product called MeMo (Merged soil Moisture) and six estimates from the HBV (Hydrologiska Byråns Vattenbalansavdelning) model with three precipitation inputs (ERA5, IMERG, and MSWEP) with and without assimilation of SMAPL3E satellite retrievals, respectively. As reference, we used in situ soil moisture measurements between 2015 and 2019 at 5 cm depth from 826 sensors, located primarily in the USA and Europe. The 3-hourly Pearson correlation (R) was chosen as the primary performance metric. We found that application of the Soil Wetness Index (SWI) smoothing filter resulted in improved performance for all satellite products. The best-to-worst performance ranking of the four single-sensor satellite products was SMAPL3ESWI, SMOSSWI, AMSR2SWI, and ASCATSWI, with the L-band-based SMAPL3ESWI (median R of 0.72) outperforming the others at 50 % of the sites. Among the two multi-sensor satellite products (MeMo and ESA-CCISWI), MeMo performed better on average (median R of 0.72 versus 0.67), probably due to the inclusion of SMAPL3ESWI. The best-to-worst performance ranking of the six open-loop models was HBV-MSWEP, HBV-ERA5, ERA5-Land, HBV-IMERG, VIC-PGF, and GLDAS-Noah. This ranking largely reflects the quality of the precipitation forcing. HBV-MSWEP (median R of 0.78) performed best not just among the open-loop models but among all products. The calibration of HBV improved the median R by +0.12 on average compared to random parameters, highlighting the importance of model calibration. The best-to-worst performance ranking of the six models with satellite data assimilation was HBV-MSWEP+SMAPL3E, HBV-ERA5+SMAPL3E, GLEAM, SMAPL4, HBV-IMERG+SMAPL3E, and ERA5. The assimilation of SMAPL3E retrievals into HBV-IMERG improved the median R by +0.06, suggesting that data assimilation yields significant benefits at the global scale.

We investigated the use of a recently developed satellite-based vegetation optical depth (VOD) data set to estimate fire severity and carbon emission over Australian tropical savannahs. VOD is sensitive to the dynamics of all aboveground vegetation and available nearly every two days. For areas burned during 2003–2010, we calculated the VOD change (ΔVOD) pre- and post-fire and the associated loss in the above ground biomass carbon. ΔVOD agreed well with the Normalized Burn Ratio change (ΔNBR) which is the metric used to estimate fire severity and carbon loss compared well with modelled emissions from the Global Fire Emissions Database (GFED). We found that the ΔVOD and ΔNBR are generally linearly related. The Pearson correlation coefficients (r) between VOD- and GFED-based fire carbon emissions for monthly and annual total estimates are very high, 0.92 and 0.96, respectively. A key feature of fire carbon emissions is the strong inter-annual variation, ranging from 21.1 Mt in 2010 to 84.3 Mt in 2004. This study demonstrates that a reasonable estimate of fire severity and carbon emissions can be achieved in a timely manner based on multiple satellite observations over Australian tropical savannahs, which can be complementary to the currently used approaches.

In the recent years, the FengYun-3B passive microwave observations (FY3B) have been used to study global and regional soil moisture estimates, and land surface temperature (LST) has been an important input in the retrieval model. Previous studies proposed a linear model to derive LST from Ka-band brightness temperatures (H09 approach) which were routinely masked based on open water data. This study aimed to further understand microwave emission under such challenging open water conditions and developed the first data-driven approach to correct for such open water conditions. The H09 was first applied to the FY3B observations, and then inter-compared against MODIS (Moderate-resolution Imaging Spectro-radiometer), MERRA (Modern Era Retrospective analysis for Research and Applications) and ground observations over the Jiangsu province in the People’s Republic of China. Several freshwater bodies are located within this region, and the Pacific Ocean bounds at the east, this makes it an excellent region for the study of open water impact on the passive microwave observations. As in previous studies, the quality of the FY3B LST based on the H09 approach dropped over the regions with high open water. While the MERRA and MODIS LST products maintained an average correlation of about 0.95, the FY3B LST product had an average correlation of 0.85 with drops to about 0.65 over the open water regions. Additional comparisons also revealed biases over the open water regions within the passive microwave product. An open water metric, which is based on the percentage of a pixel and its surrounding pixels covered by water was developed to quantify the impact of open water. Based on a pixel-wise linear relationship between the MERRA LST and FY3B Ka-band brightness temperature, spatially variable slopes and intercepts were obtained as a function of open water to produce a correction for open water in the H09 which leads to the HG18 approach. Validations with both the MODIS LST product and the ground observations showed that there was a significant reduction in both the absolute bias (MODIS: 2 K, in situ: 10 K) and the standard errors (MODIS: 0.8 K, in situ: 1 K) using the HG18 approach. This opens up a new window towards further improving and understanding LST retrievals from passive microwave products.

An existing methodology to derive surface soil moisture from passive microwave satellite observations is applied to the WindSat multifrequency polarimetric microwave radiometer. The methodology is a radiative-transfer-based model that has successfully been applied to a series of (historical) satellite sensors, including the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). Brightness temperature observations from the WindSat and AMSR-E radiometers were compared, and the WindSat observations were adjusted to overcome small sensor differences (e.g., frequency, bandwidth, incidence angle, and original sensor calibration procedure). The method to relate Ka-band brightness temperature observations to land surface temperature was adapted to the overpass times of WindSat. Statistical analysis with both satellite-observed and in situ soil moistures indicates that the quality of the newly derived WindSat soil moisture product is similar to that obtained with AMSR-E after the adjustment of the WindSat brightness temperature observations. The average correlation coefficients (R) between satellite soil moisture and in situ observations are similar for the two satellites with average values of R = 0.60 for WindSat and R = 0.62 for AMSR-E as calculated from 33 sites. On a global scale, the average correlation coefficient between the two satellite soil moisture products is high with a value of R = 0.83. The results of this study demonstrate that soil moisture from WindSat is consistent with existing soil moisture products derived from AMSR-E using the land parameter retrieval model. Therefore, the soil moisture retrievals from these two satellites could easily be combined to increase the temporal resolution of satellite-derived soil moisture observations. © 2011 IEEE.