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Soil moisture and vegetation optical depth (VOD; related to vegetation water content) retrieved from SMAP and SMOS satellites are widely used for a range of hydrosphere and biosphere applications. However, while soil moisture has been globally well-validated, VOD validation has been sparse. Furthermore, simultaneously retrieval of these parameters results in uncertainties both individually in soil moisture and VOD retrievals as well as in compensation between the parameters. Here, we show global locations where soil moisture and VOD retrievals will have lower uncertainty, based on complementary brightness temperature information content and signal-to-noise ratio metrics. In these same locations, we show that error still propagates more into VOD. However, using VOD regularization algorithms, this error is greatly reduced, especially at sub-weekly timescales where algorithmic error can be most apparent. Despite these regularization approaches that reduce errors, there are yet vast differences in available global regularized retrievals originating from different algorithmic choices. © 2022 IEEE.

European Space Agency’s 2019 Living Planet Symposium, 13-17 May 2019, Milan, Italy, Soil moisture is an essential climate variable which plays a crucial role linking the Earth’s water, energy and carbon cycles between the land and the atmosphere. Although it represents a small percentage of all the water on Earth, it provides key information about evaporation, transpiration, infiltration and runoff. As a result, accurate knowledge of soil moisture is crucial for both global and local applications. Currently there are two operating missions, SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Active and Passive), launched by ESA and NASA, which are specifically devoted to measure the (surface) soil moisture. Their passive microwave instruments operate at L-band, which is favorable for measuring soil moisture due to the high sensitivity of the microwave signal to the soil dielectric constant at this frequency. The Copernicus Imaging Microwave Radiometer (CIMR) high priority candidate mission is planned to have an L-band radiometer on-board and could provide continuation of the observational data stream. Accurate high-resolution soil moisture maps are needed to fulfill a growing number of application tasks e.g., prevention of wildfires [1][2], early-detection of forest decline [3], monitoring the evolution of insect pests [4]. To downscale soil moisture from the coarse-scale radiometer resolution to a finer one, downscaling algorithms typically use additional observations at fine scales. A wide variety of downscaling algorithms exists [5]. They may use observations from different sensors, use different ancillary data or rely on different physical assumptions. The performance of these algorithms depends on the physics they are based on, and on the information at fine scale that they use, which may also depend on season, climate and land cover. This makes a direct comparison difficult, since their performance is intrinsically time and region dependent, and the algorithm set-up will therefore reflect into the results. The baseline downscaling approach for the SMAP mission uses active and passive microwaves. SMAP was designed to have a radar and a radiometer on-board, but the radar stopped operating on July 7, 2015. An alternative active-passive soil moisture product is now available merging SMAP and Sentinel-1 data [6]. Recently a spatially-consistent downscaling algorithm has been developed to obtain high-resolution soil moisture maps at 1 km from the ~30-40 km native resolution of current passive microwave instruments using optical data. This algorithm is based on the semi-empirical downscaling approach implemented at the Barcelona Expert Center (BEC) to blend SMOS and MODIS data into 1 km soil moisture estimates [7]. The novelty is that it introduces the concept of a shape adaptive window, improving its spatial consistency and thereby allowing for its global implementation [8]. The objective of this study is to provide insight into the physical mechanisms that can influence the production (downscaling) of high-resolution soil moisture maps with a special focus on soil moisture anomalies. We will investigate soil moisture maps produced by the BEC (microwave-optical) and the SMAP-Sentinel (active-passive microwave) algorithms presented above and understand the characteristics of the downscaled soil moisture maps with a focus on spatial and temporal anomalies., [1] D. Chaparro, M. Piles, M. Vall-Llossera, A. Camps, “Surface moisture and temperature trends anticipate drought conditions linked to wildfire activity in the Iberian Peninsula,” European Journal of Remote Sensing, vol. 49, issue 1, pp. 955-971, 2016. [2] D. Chaparro, M. Vall-llossera, M. Piles, A. Camps, C. Rüdiger and R. Riera-Tatché, "Predicting the Extent of Wildfires Using Remotely Sensed Soil Moisture and Temperature Trends," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 9, no. 6, pp. 2818-2829, June 2016. [3] D. Chaparro, J. Vayreda, M. Vall-llossera, M. Banqué, M. Piles, A. Camps, J. Martínez-Vilalta, “The role of climatic anomalies and soil moisture in the decline of drought-prone forests,”. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 10, issue 2, pp. 503-514, 2017. [4] M. J. Escorihuela, O. Merlin, V. Stefan, G. Moyano, O. A. Eweys, M. Zribi, ... & S. Ghaout. “SMOS based high resolution soil moisture estimates for Desert locust preventive. [5] J. Peng, A. Loew, O. Merlin, N. Verhoest, “A review of spatial downscaling of satellite remotely sensed soil moisture,” Reviews of Geophysics, 10.1002/2016RG000543, March 2017. [6] N. Das, R.S. Dunbar, “Level 2 SMAP/Sentinel Active/Passive Soil Moisture Product Specification Document,” Jet Propulsion Laboratory, D-56548, August 2017. [7] M. Piles, N. Sánchez, M. Vall-llossera, A. Camps, J. Martínez-Fernández, J. Martínez, V. González-Gambau, “A Downscaling Approach for SMOS Land Observations: Evaluation of High-Resolution Soil Moisture Maps Over the Iberian Peninsula,” IEEE Journal of Sel. Topics in Applied Earth Obs. and Remote Sens., vol. 7, no. 9, pp. 3845-3857, 2014. [8] G. Portal et al., "A Spatially Consistent Downscaling Approach for SMOS Using an Adaptive Moving Window," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 11, no. 6, pp. 1883-1894, June 2018

Soil moisture is considered a key variable in drought analysis. The soil moisture dynamics given by the change in soil moisture between two time periods can provide information on the intensification or improvement of drought conditions. The aim of this work is to analyze how the soil moisture dynamics respond to changes in drought conditions over multiple time intervals. The change in soil moisture estimated from the Soil Moisture Active Passive (SMAP) satellite observations was compared with the United States Drought Monitor (USDM) and the Standardized Precipitation Index (SPI) over the contiguous United States (CONUS). The results indicated that the soil moisture change over 13-week and 26-week intervals is able to capture the changes in drought intensity levels in the USDM, and the change over a four-week interval correlated well with the one-month SPI values. This suggested that a short-term negative soil moisture change may indicate a lack of precipitation, whereas a persistent long-term negative soil moisture change may indicate severe drought conditions. The results further indicate that the inclusion of soil moisture change will add more value to the existing drought-monitoring products. Keywords: soil moisture; SMAP; drought; United States Drought Monitor; SPI

Several passive microwave satellites orbit the Earth and measure rainfall. These measurements have the advantage of almost full global coverage when compared to surface rain gauges. However, these satellites have low temporal revisit and missing data over some regions. Image fusion is a useful technique to fill in the gaps of one image (one satellite measurement) using another one. The proposed algorithm uses an iterative fusion scheme to integrate information from two satellite measurements. The algorithm is implemented on two datasets for 7 years of half-hourly data. The results show significant improvements in rain detection and rain intensity in the merged measurements., Comment: 6 pages, 10 Figures, WCRP Open Science Conference, 2011

Distributed and continuous catchment models are used to simulate water and energy balance and fluxes across varied topography and landscape. The landscape is discretized into computational plan elements at resolutions of 10[superscript 1]–10[superscript 3] m, and soil moisture is the hydrologic state variable. At the local scale, the vertical soil moisture dynamics link hydrologic fluxes and provide continuity in time. In catchment models these local-scale processes are modeled using 1-D soil columns that are discretized into layers that are usually 10[superscript −3]–10[superscript −1] m in thickness. This creates a mismatch between the horizontal and vertical scales. For applications across large domains and in ensemble mode, this treatment can be a limiting factor due to its high computational demand. This study compares continuous multi-year simulations of soil moisture at the local scale using (i) a 1-pixel version of a distributed catchment hydrologic model and (ii) a benchmark detailed soil water physics solver. The distributed model uses a single soil layer with a novel dual-pore structure and employs linear parameterization of infiltration and some other fluxes. The detailed solver uses multiple soil layers and employs nonlinear soil physics relations to model flow in unsaturated soils. Using two sites with different climates (semiarid and sub-humid), it is shown that the efficient parameterization in the distributed model captures the essential dynamics of the detailed solver., Singapore-MIT Alliance for Research and Technology (Singapore. National Research Foundation)

NASA's Soil Moisture Active Passive (SMAP) mission launched on January 31, 2015 into a sun-synchronous 6 am/6 pm orbit with an objective to produce global mapping of high-resolution soil moisture and freeze-thaw state every 2–3 days. The SMAP radiometer began acquiring routine science data on March 31, 2015 and continues to operate nominally. SMAP's radiometer-derived standard soil moisture product (L2SMP) provides soil moisture estimates posted on a 36-km fixed Earth grid using brightness temperature observations and ancillary data. A beta quality version of L2SMP was released to the public in October, 2015, Version 3 validated L2SMP soil moisture data were released in May, 2016, and Version 4 L2SMP data were released in December, 2016. Version 4 data are processed using the same soil moisture retrieval algorithms as previous versions, but now include retrieved soil moisture from both the 6 am descending orbits and the 6 pm ascending orbits. Validation of 19 months of the standard L2SMP product was done for both AM and PM retrievals using in situ measurements from global core cal/val sites. Accuracy of the soil moisture retrievals averaged over the core sites showed that SMAP accuracy requirements are being met.

The Soil Moisture Active Passive (SMAP) observatory was launched January 31, 2015, and its L-band radiometer and radar instruments became operational during April 2015. This paper provides a summary of the quality assessment of its baseline soil moisture and freeze/thaw products as well as an overview of new products. The first new product explores the Backus Gilbert optimum interpolation based on the oversampling characteristics of the SMAP radiometer. The second one investigates the disaggregation of the SMAP radiometer data using the European Space Agency's Sentinel-1 C-band synthetic aperture radar (SAR) data to obtain soil moisture products at about 1 to 3 km resolution. In addition, SMAP's L-band data have been found useful for many scientific applications, including depictions of water cycles, vegetation opacity, ocean surface salinity and hurricane ocean surface wind mapping. Highlights of these new applications will be provided., United States. National Aeronautics and Space Administration.

Over land the vegetation canopy affects the microwave brightness temperature by emission, scattering and attenuation of surface soil emission. Attenuation, as represented by vegetation optical depth (VOD), is a potentially useful ecological indicator. The NASA Soil Moisture Active Passive (SMAP) mission carries significant potential for VOD estimates because of its radio frequency interference mitigation efforts and because the L-band signal penetrates deeper into the vegetation canopy than the higher frequency bands used for many previous VOD retrievals. In this study, we apply the multi-temporal dual-channel retrieval algorithm (MT-DCA) to derive global VOD, soil moisture, and effective scattering albedo estimates from SMAP Backus-Gilbert enhanced brightness temperatures posted on a 9 km grid and with three day revisit time. SMAP VOD values from the MT-DCA follow expected global distributions and are shown to be highly correlated with canopy height. They are also broadly similar in magnitude (though not always in seasonal amplitude) to European Space Agency Soil Moisture and Ocean Salinity (SMOS) VOD. The SMOS VOD values are based on angular brightness temperature information while the SMAP measurements are at a constant incidence angle, requiring an alternate approach to VOD retrieval presented in this study. Globally, albedo values tend to be high over regions with heterogeneous land cover types. The estimated effective scattering albedo values are generally higher than those used in previous soil moisture estimation algorithms and linked to biome classifications. MT-DCA retrievals of soil moisture show only small random differences with soil moisture retrievals from the Baseline SMAP algorithm, which uses a prior estimate of VOD based on land cover and optical data. However, significant biases exist between the two datasets. The soil moisture biases follow the pattern of differences between the MT-DCA retrieved and Baseline-assigned VOD values.

A validated large-eddy simulation model was employed to study the effect of the aspect ratio and ground heating on the flow and pollutant dispersion in urban street canyons. Three ground-heating intensities (neutral, weak and strong) were imposed in street canyons of aspect ratio 1, 2, and 0.5. The detailed patterns of flow, turbulence, temperature and pollutant transport were analyzed and compared. Significant changes of flow and scalar patterns were caused by ground heating in the street canyon of aspect ratio 2 and 0.5, while only the street canyon of aspect ratio 0.5 showed a change in flow regime (from wake interference flow to skimming flow). The street canyon of aspect ratio 1 does not show any significant change in the flow field. Ground heating generated strong mixing of heat and pollutant; the normalized temperature inside street canyons was approximately spatially uniform and somewhat insensitive to the aspect ratio and heating intensity. This study helps elucidate the combined effects of urban geometry and thermal stratification on the urban canyon flow and pollutant dispersion., Singapore National Research Foundation (Singapore-MIT Alliance for Research and Technology (SMART))

A valid tool for the retrieving of the turbulent fluxes that characterize the surface energy budget is constituted by the remote sensing of land surface states. In this study sequences of satellite-derived observations (from SEVIRI sensors aboard the Meteosat Second Generation) of Land Surface Temperature have been used as input in a data assimilation scheme in order to retrieve parameters that describe energy balance at the ground surface in the Tuscany region, in central Italy, during summer 2005. A parsimonious 1-D multiscale variational assimilation procedure has been followed, that requires also near surface meteorological observations. A simplified model of the surface energy balance that includes such assimilation scheme has been coupled with the limited area atmospheric model RAMS, in order to improve in the latter the accuracy of the energy budget at the surface. The coupling has been realized replacing the assimilation scheme products, in terms of surface turbulent fluxes and temperature and humidity states during the meteorological simulation. Comparisons between meteorological model results with and without coupling with the assimilation scheme are discussed, both in terms of reconstruction of surface variables and of vertical characterization of the lower atmosphere. In particular, the effects of the coupling on the moisture feedback between surface and atmosphere are considered and estimates of the precipitation recycling ratio are provided. The results of the coupling experiment showed improvements in the reconstruction of the surface states by the atmospheric model and considerable influence on the atmospheric dynamics.