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The Soil Moisture Active–Passive (SMAP) mission has greatly contributed to the use of remote sensing technologies for monitoring the Earth’s land surface and estimating geophysical parameters that influence the climate system. Since the SMAP mission switched its radar receiver to allow the reception of Global Positioning System (GPS) signals, Global Navigation Satellite System Reflectometry (GNSS-R) configuration has been enabled, providing full polarimetric forward scattering measurements of the Earth’s surface, also known as SMAP Reflectometry or SMAP-R. Polarimetric GNSS-R is beneficial for sensing land surface properties, especially for more accurate estimations of soil moisture (SM) in densely vegetated areas. In this study, we explore the opportunity to enhance SMAP mission soil moisture estimates using reflected GNSS signals. We achieve this by interpolating the sparse reflectivity data with terrain information to disaggregate radiometer brightness temperatures. Our main objective is to present a novel algorithm based on Graph Signal Processing (GSP) that uses reflectometry data to enhance SMAP radiometer observations and ultimately improve SM retrievals. By implementing methods from the GSP field, we formulate the reflectivity interpolation problem as a signal reconstruction on a graph, where the weights of the edges between the nodes are chosen as a function of geophysical information. Subsequently, using the retrieved reflectivity maps, we increase the resolution of the brightness temperature data, leading to an improvement in the SM estimates. Initial findings indicate that our GSP method presents a promising alternative for analyzing sparse remote sensing observations, leveraging Earth’s surface geophysical information. This approach results in a notable improvement, with a reduced Root Mean Square Error (RMSE) of 11.8% compared to SMAP data and a reduction in unbiased RMSE (uRMSE) by 14.7% over vegetated areas.

A good knowledge of the quality of the satellite soil moisture products is of great importance for their application and improvement. This paper examines the performance of eight satellite-based soil moisture products, including the Soil Moisture Active Passive (SMAP) passive Level 3 (L3), the Soil Moisture and Ocean Salinity (SMOS) Centre Aval de Traitement des Données SMOS (CATDS) L3, the Japan Aerospace Exploration Agency (JAXA) Advanced Microwave Scanning Radiometer 2 (AMSR2) L3, the Land Parameter Retrieval Model (LPRM) AMSR2 L3, the European Space Agency (ESA) Climate Change Initiative (CCI) L3, the Chinese Fengyun-3B (FY3B) L2 soil moisture products at a coarse resolution of ~0.25°, and the newly released SMAP enhanced passive L3 and JAXA AMSR2 L3 soil moisture products at a medium resolution of ~0.1°. The ground soil moisture used for validation were collected from two well-calibrated and dense networks, including the Little Washita Watershed (LWW) network in the United States and the REMEDHUS network in Spain, each with different land cover. The results show that the SMAP passive soil moisture product outperformed the other products in the LWW network region, with an unbiased root mean square (ubRMSE) of 0.027 m3 m−3, whereas the FY3B soil moisture performed the best in the REMEDHUS network region, with an ubRMSE of 0.025 m3 m−3. The JAXA product performed much better at 0.25° than at 0.1°, but at both resolutions it underestimated soil moisture most of the time (bias < −0.05 m3 m−3). The SMAP-enhanced passive soil moisture product captured the temporal variation of ground measurements well, with a correlation coefficient larger than 0.8, and was generally superior to the JAXA product. The LPRM showed much larger amplitude and temporal variation than the ground soil moisture, with a wet bias larger than 0.09 m3 m−3. The underestimation of surface temperature may have contributed to the general dry bias found in the SMAP (−0.018 m3 m−3 for LWW and 0.016 m3 m−3 for REMEDHUS) and SMOS (−0.004 m3 m−3 for LWW and −0.012 m3 m−3 for REMEDHUS) soil moisture products. The ESA CCI product showed satisfactory performance with acceptable error metrics (ubRMSE < 0.045 m3 m−3), revealing the effectiveness of merging active and passive soil moisture products. The good performance of SMAP and FY3B demonstrates the potential in integrating them into the existing long-term ESA CCI product, in order to form a more reliable and useful product.

The Aquarius end-of-mission (Version 5) salinity data set was released in December 2017. This article gives a comprehensive overview of the main steps of the Level 2 salinity retrieval algorithm. In particular, we will discuss the corrections for wind induced surface roughness, atmospheric oxygen absorption, reflected galactic radiation and side-lobe intrusion from land surfaces. Most of these corrections have undergone major updates from previous versions, which has helped mitigating temporal and zonal biases. Our article also discusses the ocean target calibration for Aquarius Version 5. We show how formal error estimates for the Aquarius retrievals can be obtained by perturbing the input to the algorithm. The performance of the Aquarius Version 5 salinity retrievals is evaluated against salinity measurements from the ARGO network and the HYCOM model. When stratified as function of sea surface temperature or sea surface wind speed, the difference between Aquarius Version 5 and ARGO is within +/-0.1 psu. The estimated global RMS uncertainty for monthly 100 km averages is 0.128 psu for the Aquarius Version 5 retrievals. Finally, we show how the Aquarius Version 5 salinity retrieval algorithm is adapted to retrieve salinity from the Soil-Moisture Active Passion (SMAP) mission.

SMAP/Sentinel-1 soil moisture is the latest SMAP (Soil Moisture Active Passive) product derived from synergistic utilization of the radiometry observations of SMAP and radar backscattering data of Sentinel-1. This product is the first and only global soil moisture (SM) map at 1 km and 3 km spatial resolutions. In this paper, we evaluated the SMAP/Sentinel-1 SM product from different viewpoints to better understand its quality, advantages, and likely limitations. A comparative analysis of this product and in situ measurements, for the time period March 2015 to January 2022, from 35 dense and sparse SM networks and 561 stations distributed around the world was carried out. We examined the effects of land cover, vegetation fraction, water bodies, urban areas, soil characteristics, and seasonal climatic conditions on the performance of active–passive SMAP/Sentinel-1 in estimating the SM. We also compared the performance metrics of enhanced SMAP (9 km) and SMAP/Sentinel-1 products (3 km) to analyze the effects of the active–passive disaggregation algorithm on various features of the SMAP SM maps. Results showed satisfactory agreement between SMAP/Sentinel-1 and in situ SM measurements for most sites (r values between 0.19 and 0.95 and ub-RMSE between 0.03 and 0.17), especially for dense sites without representativeness errors. Thanks to the vegetation effect correction applied in the active–passive algorithm, the SMAP/Sentinel-1 product had the highest correlation with the reference data in grasslands and croplands. Results also showed that the accuracy of the SMAP/Sentinel-1 SM product in different networks is independent of the presence of water bodies, urban areas, and soil types.

Soil moisture (SM) is an important biophysical parameter by which to evaluate water resource potential, especially for agricultural activities under the pressure of global warming. The recent advancements in different types of satellite imagery coupled with deep learning-based frameworks have opened the door for large-scale SM estimation. In this research, high spatial resolution Sentinel-1 (S1) backscatter data and high temporal resolution soil moisture active passive (SMAP) SM data were combined to create short-term SM predictions that can accommodate agricultural activities in the field scale. We created a deep learning model to forecast the daily SM values by using time series of climate and radar satellite data along with the soil type and topographic data. The model was trained with static and dynamic features that influence SM retrieval. Although the topography and soil texture data were taken as stationary, SMAP SM data and Sentinel-1 (S1) backscatter coefficients, including their ratios, and climate data were fed to the model as dynamic features. As a target data to train the model, we used in situ measurements acquired from the International Soil Moisture Network (ISMN). We employed a deep learning framework based on long short-term memory (LSTM) architecture with two hidden layers that have 32 unit sizes and a fully connected layer. The accuracy of the optimized LSTM model was found to be effective for SM prediction with the coefficient of determination (R2) of 0.87, root mean square error (RMSE) of 0.046, unbiased root mean square error (ubRMSE) of 0.045, and mean absolute error (MAE) of 0.033. The model’s performance was also evaluated concerning above-ground biomass, land cover classes, soil texture variations, and climate classes. The model prediction ability was lower in areas with high normalized difference vegetation index (NDVI) values. Moreover, the model can better predict in dry climate areas, such as arid and semi-arid climates, where precipitation is relatively low. The daily prediction of SM values based on microwave remote sensing data and geophysical features was successfully achieved by using an LSTM framework to assist various studies, such as hydrology and agriculture.

Cytotoxic T cells (CTLs) are the main cellular mediators of the adaptive immune defenses against intracellular pathogens and malignant cells. Upon recognition of specific antigen on their cellular target, CTLs assemble an immunological synapse where they mobilise their killing machinery that is released into the synaptic cleft to orchestrate the demise of their cell target. The arsenal of CTLs is stored in lysosome-like organelles that undergo exocytosis in response to signals triggered by the T cell antigen receptor following antigen recognition. These organelles include lytic granules carrying a cargo of cytotoxic proteins packed on a proteoglycan scaffold, multivesicular bodies carrying the death receptor ligand FasL, and the recently discovered supramolecular attack particles that carry a core of cytotoxic proteins encased in a non-membranous glycoprotein shell. Here we will briefly review the main features of these killing entities and discuss their interrelationship and interplay in CTL-mediated killing.

An effective soil moisture retrieval method for FY-3E (Fengyun-3E) GNOS-R (GNSS occultation sounder II-reflectometry) is developed in this paper. Here, the LAGRS model, which is totally oriented for GNOS-R, is employed to estimate vegetation and surface roughness effects on surface reflectivity. Since the LAGRS (land surface GNSS reflection simulator) model is a space-borne GNSS-R (GNSS reflectometry) simulator based on the microwave radiative transfer equation model, the method presented in this paper takes more consideration on the physical scattering properties for retrieval. Ancillary information from SMAP (soil moisture active passive) such as the vegetation water content and the roughness coefficient are investigated for the final algorithm’s development. At first, the SR (surface reflectivity) data calculated from GNOS-R is calculated and then calibrated, and then the vegetation roughness factor is achieved and used to eliminate the effects on both factors. After receiving the Fresnel reflectivity, the corresponding soil moisture estimated from this method is retrieved. The results demonstrate good consistency between soil moisture derived from GNOS-R data and SMAP soil moisture, with a correlation coefficient of 0.9599 and a root mean square error of 0.0483 cm3/cm3. This method succeeds in providing soil moisture on a global scale and is based on the previously developed physical LAGRS model. In this way, the great potential of GNOS-R for soil moisture estimation is presented.

Flash droughts– the rapid drying of land and intensification of drought conditions—have devasting impacts to natural resources, food supplies, and the economy. Less is currently known about the drivers of flash droughts and their impact on landscape carbon losses. We leverage carbon and water cycle data from NASA OCO‐2 and Soil Moisture Active and Passive missions to quantify flash drought impacts on U.S. carbon exchange. On average, pre‐onset carbon uptake fully offsets post‐onset losses, creating a carbon neutral biosphere over a ±3 month period surrounding flash drought onset. This contrasts with ecosystem models, which underestimate pre‐onset uptake and overestimate post‐onset loss. Furthermore, spaceborne observations of solar induced fluorescence (SIF) provide a reliable indicator of flash droughts at lead times of 2–3 months, due to feedbacks between vegetation growth and soil water loss. This study is expected to improve understanding of flash drought impacts on carbon exchange, and facilitate flash drought early warning. Plain Language Summary Flash droughts have devasting impacts to the environment, natural resources, and society, and are difficult to predict. Here, we use NASA models and satellite observations to determine (a) the impact of flash drought on carbon exchange in land ecosystems, and (b) the extent to which satellite remote sensing can improve flash drought early warning. We find that beneficial environmental conditions occurring prior to onset of flash drought leads to increases in carbon uptake in ecosystems compared to normal conditions. This anomalous uptake of carbon in ecosystems is, on average, sufficient to fully offset inevitable decreases in carbon uptake associated with hot dry conditions following onset of flash drought, leading to a net zero impact of flash drought on carbon exchange over the 6‐month period surrounding drought onset. Moreover, we find the satellite observations of solar induced chlorophyll fluorescence (SIF), representing a re‐emission of radiation by plants following absorption of sunlight for growth, are extremely well correlated to soil moisture losses associated with flash drought at lead times of 6–12 weeks across diverse landscapes and ecoregions in North America. Satellite SIF thus shows promise as a reliable early warning indicator of flash drought, at sufficient lead time conducive to decision making. Key Points Solar induced fluorescence offers early warning (∼2–3 months) for stealth drought events Pre‐drought carbon gains fully offset post‐drought carbon loss Terrestrial biosphere models overestimate total carbon loss

A lack of high‐density rootzone soil moisture (θRZ) observations limits the estimation of continental‐scale, space‐time contiguous θRZ dynamics. We derive a proxy of daily θRZ dynamics — active rootzone degree of saturation (SRZ) — by recursive low‐pass (LP) filtering of surface soil moisture (θS) within a terrestrial water‐energy coupling (WEC) framework. We estimate the LP filter parameters and WEC thresholds for the piecewise‐linear coupling between SRZ and evaporative fraction (EF) at remote sensing and field scale over the Contiguous U.S. We use θS from the Soil Moisture Active‐Passive (SMAP) satellite and 218 in‐situ stations, with EF from the Moderate Resolution Imaging Spectroradiometer. The estimated SRZ compares well against SMAP Level‐4 estimates and in‐situ θRZ, at the corresponding scale. The instantaneous hydrologic state (SRZ) vis‐à‐vis the WEC thresholds is proposed as a rootzone soil moisture stress index (SMSRZ) for near‐real‐time operational agricultural drought monitoring and agrees well with established drought metrics. Plain Language Summary Rootzone soil moisture plays a vital role in agricultural, hydrological, and ecosystem processes. The available spaceborne satellites for monitoring soil moisture can only capture variability in a shallow soil layer at the surface, typically limited to the top 5 cm. Hence, spatiotemporally continuous estimation of rootzone soil moisture dynamics typically relies on soil moisture estimates from land‐surface models, which are subject to errors in the surface meteorological forcing data, process formulations, and model parameters. Some studies suggest that the rootzone soil moisture dynamics can be estimated by filtering the high‐frequency variability in the surface soil moisture. However, such “filters” require observed rootzone data (often unavailable at high spatial density) for calibration. This study uses the relationship between surface soil moisture and evaporative fraction derived using spaceborne observations from the Soil Moisture Active Passive mission and the Moderate Resolution Imaging Spectroradiometer to estimate rootzone soil moisture dynamics for the Contiguous U.S. at 9 km grid resolution. We further demonstrate that this approach can be extended into a near‐real‐time agricultural drought monitor to assess drought impacts on vegetation using surface soil moisture observations. Key Points Terrestrial water‐energy coupling is used to parameterize low‐pass filter to estimate rootzone dynamics from surface soil moisture Rootzone degree of saturation and water‐energy coupling thresholds are estimated using evaporative fraction and surface soil moisture SMAP‐based rootzone degree of saturation can used for operational, near‐real‐time agricultural drought monitoring over Contiguous U.S

NASA’s Soil Moisture Active Passive (SMAP) was originally designed to combine high-resolution active (radar) and coarse-resolution but highly sensitive passive (radiometer) L-band observations to achieve unprecedented spatial resolution and accuracy for soil moisture retrievals. However, shortly after SMAP was put into orbit, the radar component failed, and the high-resolution capability was lost. In this paper, the integration of an alternative radar sensor with the SMAP radiometer is proposed to enhance soil moisture retrieval capabilities over vegetated areas in the absence of the original high-resolution radar in the SMAP mission. ESA’s Sentinel-1A C-band radar was used in this study to enhance the spatial resolution of the SMAP L-band radiometer and to improve soil moisture retrieval accuracy. To achieve this purpose, we downscaled the 9 km radiometer data of the SMAP to 1 km utilizing the Smoothing Filter-based Intensity Modulation (SFIM) method. An Artificial Neural Network (ANN) was then trained to exploit the synergy between the Sentinel-1A radar, SMAP radiometer, and the in situ-measured soil moisture. An analysis of the data obtained for a plant growing season over the Mississippi Delta showed that the VH-polarized Sentinel-1A radar data can yield a coefficient of correlation of 0.81 and serve as a complimentary source to the SMAP radiometer for more accurate and enhanced soil moisture prediction over agricultural fields.