Soil Moisture Active Passive Level Research Articles

Deducing land–atmosphere coupling regimes from SMAP soil moisture

Abstract. In recent years, there has been a growing recognition of the significance of land–atmosphere (L–A) interactions and feedback mechanisms in understanding and predicting Earth's water and energy cycles. Soil moisture plays a critical role in mediating the strength of L–A interactions and is important for understanding the complex and governing processes across this interface. This study aims to identify the significance of soil moisture in identifying L–A coupling strength within the convective triggering potential (CTP) and humidity index (HI) framework. To address this, a consistent and reliable dataset of atmospheric profiles is created by merging CTP and HI using triple collocation (TC) with three reanalysis datasets. The merged CTP and HI product demonstrates enhanced performance globally compared to the individual datasets when validated with radiosonde and satellite observations. This merged product of CTP and HI is then used to compare the L–A coupling strength based on Soil Moisture Active Passive Level 3 (SMAPL3) and SMAP Level 4 (SMAPL4) over 2 decades (2003–2022) where L–A coupling strength is defined as the persistence probability within the dry and wet coupling regimes. Results indicate that the persistency-based coupling strength is related to the ability of soil moisture to predict future atmospheric humidity and dry vs. wet coupling state. The coupling strength in SMAPL4 is consistently stronger than in SMAPL3 and is likely due to its reliance on a land surface model and reduced susceptibility to random noise. The difference in coupling strength based on the same CTP–HI underscores the importance of soil moisture data in estimating coupling strength within the CTP–HI framework. These findings lay the groundwork for understanding the role of L–A interactions and drought evolution due to soil moisture variations by providing insight into the quantification of coupling strength and its role in drought monitoring and forecast efforts.

Significance of Multi-Variable Model Calibration in Hydrological Simulations within Data-Scarce River Basins: A Case Study in the Dry-Zone of Sri Lanka

Traditional hydrological model calibration using limitedly available streamflow data often becomes inadequate, particularly in dry climates, as the flow regimes may abruptly vary from arid conditions to devastating floods. Newly available remote-sensing-based datasets can be supplemented to overcome such inadequacies in hydrological simulations. To address this shortcoming, we use multi-variable-based calibration by setting up and calibrating a lumped-hydrological model using observed streamflow and remote-sensing-based soil moisture data from Soil Moisture Active Passive Level 4. The proposed method was piloted at the Maduru Oya River Basin, Sri Lanka, as a proof of concept. The relative contributions from streamflow and soil moisture were assessed and optimised via the Kling–Gupta Efficiency (KGE). The Generalized Reduced Gradient non-linear solver function was used to optimise the Tank Model parameters. The findings revealed satisfactory performance in streamflow simulations under single-variable model validation (KGE of 0.85). Model performances were enhanced by incorporating soil moisture data (KGE of 0.89), highlighting the capability of the proposed multi-variable calibration technique for improving the overall model performance. Further, the findings of this study highlighted the instrumental role of remote sensing data in representing the soil moisture dynamics of the study area and the importance of using multi-variable calibration to ensure robust hydrological simulations of river basins in dry climates.

Deep learning-based gap filling for near real-time seamless daily global sea surface salinity using satellite observations

Sea surface salinity (SSS) provides crucial information about ocean environments, influencing global hydrological cycles, thermohaline circulation, and climate change. Although L-band passive microwave radiometers have provided satellite-based SSS data, there are gaps due to the limited daily coverage of the sensors. This study proposes a U-Net-based spatial gap-filling model for global SSS using Soil Moisture Active Passive (SMAP) satellite data. The proposed model utilizes SSS swath data from the target and past days to generate daily global SSS maps with full coverage, incorporating only past temporal information. Additionally, bias-corrected data using gradient-boosted regression trees (GBRT) are employed to reduce inherent errors in the SMAP SSS data. We designed 24 schemes using data from the past 3, 5, and 7 days for both GBRT-corrected and original SMAP SSS data, with one to four times oversampling for low-salinity water, where the number of samples is significantly small. Validation results using masked-out pixels indicate that the gap-filling models with GBRT-corrected SSS data from the past 3 and 5 days and four times oversampling yielded the best performance, with root mean square errors (RMSEs) of 0.388 and 0.413 psu, respectively. Compared with the in situ Argo data for 2020, the RMSEs were 0.237 and 0.241 psu for the two models, respectively, significantly outperforming the SMAP Level 3 8-day SSS, which requires future data (RMSE of 0.456 psu). Notably, these models successfully filled gaps over coastal areas where low SSS (<30 psu) fluctuated due to freshwater discharge from inland areas. This study proposes an effective, novel gap-filling method for generating bias-corrected global daily SSS without delay, meeting operational needs. Moreover, the proposed gap-filling framework can be applied to other environmental swaths or even grid datasets.

Exploring the utility of the downscaled SMAP soil moisture products in improving streamflow simulation

Study regionThe Susquehanna and upper Susquehanna watersheds in the Northeastern of the United States of America (USA) Study focusThis study explored the utility of the Soil Moisture Active Passive (SMAP) soil moisture downscaled to a range of spatial resolutions for improving ensemble streamflow simulations. The SMAP level 3 soil moisture product with spatial resolution of roughly 40 km was downscaled to a range of spatial resolutions including 1, 3 and 9 km over the Susquehanna and upper Susquehanna watersheds. A set of experiments was conducted through direct insertion of the downscaled SMAP soil moisture into a physically-based distributed hydrological model. New hydrological insights for the regionThe updating of the model with the original and downscaled SMAP surface soil moisture markedly improved the accuracy of the ensemble streamflow simulations with the CRPSS and NRMSE values in the range of 0.10–0.17 and 0.79–0.85, respectively when compared to the non-updated model for the Susquehanna watershed. In addition, the ensemble spread was reduced, and the ensemble mean compares well with the observed streamflow. The 1 km downscaled SMAP soil moisture showed the highest accuracy in improving streamflow simulation with the CRPSS and NRMSE value of 0.21 and 0.72, respectively for the Upper Susquehanna watershed, whereas for the Susquehanna watershed downscaled SMAP at 9 km adequately improved the accuracy of the ensemble streamflow simulations with the CRPSS and NRMSE value of 0.17 and 0.80, respectively. Besides the top layer of the model, updating the second layer of the model with the vertically extrapolated SMAP soil moisture only slightly further improved the accuracy of the model.

Long-term reconstruction of satellite-based precipitation, soil moisture, and snow water equivalent in China

Abstract. A long-term high-resolution national dataset of precipitation (P), soil moisture (SM), and snow water equivalent (SWE) is necessary for predicting floods and droughts and assessing the impacts of climate change on streamflow in China. Current long-term daily or sub-daily datasets of P, SM, and SWE are limited by a coarse spatial resolution or the lack of local correction. Although SM and SWE data derived from hydrological simulations at a national scale have fine spatial resolutions and take advantage of local forcing data, hydrological models are not directly calibrated with SM and SWE data. In this study, we produced a daily 0.1∘ dataset of P, SM, and SWE in 1981–2017 across China, using global background data and local on-site data as forcing input and satellite-based data as reconstruction benchmarks. Global 0.1∘ and local 0.25∘P data in 1981–2017 are merged to reconstruct the historical P of the 0.1∘ China Merged Precipitation Analysis (CMPA) available in 2008–2017 using a stacking machine learning model. The reconstructed P data are used to drive the HBV hydrological model to simulate SM and SWE data in 1981–2017. The SM simulation is calibrated by Soil Moisture Active Passive Level 4 (SMAP-L4) data. The SWE simulation is calibrated by the national satellite-based snow depth dataset in China (Che and Dai, 2015) and the Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover data. Cross-validated by the spatial and temporal splitting of the CMPA data, the median Kling–Gupta efficiency (KGE) of the reconstructed P is 0.68 for all grids at a daily scale. The median KGE of SM in calibration is 0.61 for all grids at a daily scale. For grids in two snow-rich regions, the median KGEs of SWE in calibration are 0.55 and −2.41 in the Songhua and Liaohe basins and the northwest continental basin respectively at a daily scale. Generally, the reconstruction dataset performs better in southern and eastern China than in northern and western China for P and SM and performs better in northeast China than in other regions for SWE. As the first long-term 0.1∘ daily dataset of P, SM, and SWE that combines information from local observations and satellite-based data benchmarks, this reconstruction product is valuable for future national investigations of hydrological processes.

Leveraging Pre‐Storm Soil Moisture Estimates for Enhanced Land Surface Model Calibration in Ungauged Hydrologic Basins

AbstractDespite long‐standing efforts, hydrologists still lack robust tools for calibrating land surface model (LSM) streamflow estimates within ungauged basins. Using surface soil moisture estimates from the Soil Moisture Active Passive Level 4 Soil Moisture (L4_SM) product, precipitation observations, and streamflow gauge measurements for 617 medium‐scale (200–10,000 km2) basins in the contiguous United States, we measure the temporal (Spearman) rank correlation between antecedent (i.e., pre‐storm) surface soil moisture (ASM) and the storm‐scale runoff coefficient (RC; the fraction of storm‐scale precipitation accumulation converted into streamflow). In humid and semi‐humid basins, this rank correlation is shown to be sufficiently strong to allow for the substitution of storm‐scale RC observations (available only in basins that are both lightly regulated and gauged) with high‐quality ASM values (available quasi‐globally from L4_SM) in streamflow calibration procedures. Using this principle, we define a new, basin‐wise LSM streamflow calibration approach based on L4_SM alone and successfully apply it to identify LSM configurations that produce a high rank correlation with observed RC. However, since the approach cannot detect RC bias, it is less successful in identifying LSM configurations with low mean‐absolute error.

Multi-Scale Assessment of SMAP Level 3 and Level 4 Soil Moisture Products over the Soil Moisture Network within the ShanDian River (SMN-SDR) Basin, China

The Soil Moisture Active Passive (SMAP) mission with high-precision soil moisture (SM) retrieval products provides global daily composites of SM at 3, 9, and 36 km earth grids measured by L-band active and passive microwave sensors. The capability of passive microwave remote sensing has been recognized for the estimation of SM variations. The purpose of this work was to establish an interaction between the highly variable SM spatial distribution on the ground and the SMAP’s coarse resolution radiometer-based SM retrievals. In this work, SMAP Level 3 (L3) and Level 4 (L4) SM products are validated with in situ datasets observed from the different locations of the Soil Moisture Network within the ShanDian River (SMN-SDR) Basin over the period of January 2018 to December 2019. The values of the unbiased root mean square error (ubRMSE) for L3 (SPL3SMP_E) SM retrievals are close to the standard SMAP mission SM accuracy requirement of 0.04 m3/m3 at the 9-km scale, with an averaged ubRMSE value of 0.041 m3/m3 (0.050 m3/m3) for descending (ascending) SM with the correlation (R) values of 0.62 (0.42) against the sparse network sites. The L4 (SPL4SMGP) Surface and Root-zone SM (RZSM) estimates show less error (ubRMSE &lt; 0.04) and high correlation (R &gt; 0.60) values, and are consistent with the previous SMAP-based SM estimations. The SMAP L4 SM products (SPL4SMGP) performed well compared to the L3 SM retrieval products (SPL3SMP_E). In vegetated land, the variability and compatibility of the SMAP SM estimates with the evaluation metrics for both products (L3 and L4) showed a good performance in the grassland, then in the farmland, and worst in the woodlands. Finally, SMAP algorithm parameters sensitivity analysis of the satellite products was conducted to produce time-series and highly precise SM datasets in China.

Using Saildrones to Assess the SMAP Sea Surface Salinity Retrieval in the Coastal Regions

Remote sensing of sea surface salinity (SSS) near land is difficult due to land contamination. In this study, we assess SSS retrieved from the Soil Moisture Active Passive (SMAP) mission in coastal region. SMAP SSS products from the Jet Propulsion Laboratory (JPL), and from the Remote Sensing Systems (RSS) are collocated with in situ data collected by saildrones during the North American West Coast Survey. Satellite and saildrone salinity measurements reveal consistent large-scale features: the fresh water (low SSS) assocciated with the Columbia River discharge, and the relatively salty water (high SSS) near Baja California associated with regional upwelling. The standard deviation of the difference (stdD) for collocations with SMAP Level 3 (8 days average) between 40 and 100km from land is 0.51 (0.56) psu for JPL V5 (RSS V4 70km). This is encouraging for the potential application of SMAP SSS in monitoring coastal zone freshwater particularly where there exists large freshwater variance. We analyze the different land correction approaches independently developed at JPL and RSS using SMAP Level 2 matchups. We found that JPL&#x0027;s land correction method is more promising in pushing SMAP SSS retrieval towards land. For future improvement, we suggest implementing dynamic land correction versus the current climatology based static land correction to reduce uncertainty in estimating land contribution. In Level 2 to Level 3 processing, a more rigorous quality control may help to eliminate outliers and deliver reliable Level 3 products without over-smoothing, which is important in resolving coastal processes such as fronts or upwelling.

Validation of Soil Moisture Data Products From the NASA SMAP Mission

The National Aeronautics and Space Administration Soil Moisture Active Passive (SMAP) mission has been validating its soil moisture (SM) products since the start of data production on March 31, 2015. Prior to launch, the mission defined a set of criteria for core validation sites (CVS) that enable the testing of the key mission SM accuracy requirement (unbiased root-mean-square error &lt;0.04 m³&#x002F;m³). The validation approach also includes other (&#x201C;sparse network&#x201D;) <i>in situ</i> SM measurements, satellite SM products, model-based SM products, and field experiments. Over the past six years, the SMAP SM products have been analyzed with respect to these reference data, and the analysis approaches themselves have been scrutinized in an effort to best understand the products&#x2019; performance. Validation of the most recent SMAP Level 2 and 3 SM retrieval products (R17000) shows that the <i>L</i>-band (1.4 GHz) radiometer-based SM record continues to meet mission requirements. The products are generally consistent with SM retrievals from the European Space Agency Soil Moisture Ocean Salinity mission, although there are differences in some regions. The high-resolution (3-km) SM retrieval product, generated by combining Copernicus Sentinel-1 data with SMAP observations, performs within expectations. Currently, however, there is limited availability of 3-km CVS data to support extensive validation at this spatial scale. The most recent (version 5) SMAP Level 4 SM data assimilation product providing surface and root-zone SM with complete spatio&#x2013;temporal coverage at 9-km resolution also meets performance requirements. The SMAP SM validation program will continue throughout the mission life; future plans include expanding it to forested and high-latitude regions.

Estimating the spatial distribution of possible livestock production level using a mathematical model and the SMAP soil moisture data: The case of Botswana

In a traditional livestock farming, the livelihood of animals depends highly on existing plant biomass, which is affected by the level and intensity of temperature, rainfall, humidity and other meteorological variables. Understanding the interaction of such meteorological factors and agricultural production in general is an important aspect in planning at the macro and micro levels. Particularly livestock agriculture is heavily affected by the changing climate, and hence the variation in major meteorological variables. However, there is still limited research regarding the impacts of meteorological variables on livestock production in each particular region. Soil moisture is one of the main factors in agricultural production and hydrological cycles with better memory of previous weather conditions. It also involves complex structural characteristics and meteorological factors. In this study, a soil moisture dependent mathematical model for the interaction of plants and herbivores is developed and analysed. The Soil Moisture Active Passive level 4 satellite soil moisture data is used in the model to simulate the possible spatial distribution of plants and the corresponding potential livestock production level for Botswana. A global dynamic sensitivity analysis is employed to study the sensitivity of the solution of the model with a variation in the involved parameter values. The results of the simulations of the model show that estimated livestock harvest in wet regions is more than triple as compared to what is estimated for dry regions. If some important parameters are properly estimated and the soil moisture data is available for each region, it is possible to estimate the livestock production level for each spatial region with better accuracy using the proposed model.

Root Zone Soil Moisture Comparisons: AirMOSS, SMERGE, and SMAP

A long-standing goal in the terrestrial remote sensing community has been the development of a root-zone soil moisture (RZSM) product based on microwave radar or radiometry observations. For example, the National Aeronautics and Space Administration's (NASA's) Airborne Microwave Observatory of Subcanopy and Surface (AirMOSS) mission between 2012 and 2015 served as an airborne testbed for the development of RZSM estimates acquired from a 420-440 MHz (P-band) radar. Results from the AirMOSS mission suggest that P-band backscatter can be inverted to provide a direct measurement of 0- to 40-cm RZSM. However, whether AirMOSS RZSM is inherently more robust than indirect RZSM estimates including soil MERGE (SMERGE) and Soil Moisture Active Passive Level 4 (SMAP L4) is not clear. SMERGE and SMAP L4 leverage surface (2-5 cm depth), higher-frequency microwave observations (C- and L-bands) via data fusion (DFus) and data assimilation (DA) techniques, respectively. Therefore, the comparisons of these products are warranted and were based on bias, root mean square error (RMSE), and unbiased root mean standard deviation (ubRMSD) metrics. Comparisons also were made against in situ observations providing additional context. The seven AirMOSS sites within the contiguous United States (CONUS) were examined. Overall, direct AirMOSS RZSM measurements, based on the original retrieval algorithms, did not outperform indirect SMERGE and SMAP L4 RZSM estimates. These results provide insight into the viability of achieving enhanced RZSM accuracy via deployment of a spaceborne P-band radar.

Radio Frequency Interference Detection for SMAP Radiometer Using Convolutional Neural Networks

Passive remote sensing is a crucial technology for climate studies and Earth science. NASA's soil moisture active passive (SMAP) is a remote sensing observatory that uses passive microwave radiometer measurements to estimate soil moisture and detect the freeze or thaw state. Despite operating in the protected band of the radio spectrum (1400-1427 MHz), the radiometer's measurements are nonetheless tainted by radio frequency interference (RFI). An increasing number of radio-frequency transmissions such as those from air surveillance radars, 5G wireless communications, and unmanned aerial vehicles are contributing to RFI through either out-of-band emissions or operating in-band illegally. Physical modeling to detect RFI globally might prove to be challenging as RFI can be generated from single as well as multiple sources and these can be divided as pulsed or continuous wave RFI. In this study, a deep learning (DL) based RFI detection method is proposed with a novel convolutional neural network framework that can detect different types of RFI on a global scale. This is a data-driven approach where the detection framework learns directly from the SMAP data products to make a decision whether a certain footprint is RFI contaminated or not. SMAP's level 1A data products containing antenna counts of different raw moments along with Stokes parameters are used in this study to produce spectrograms and level 1B data products containing the quality flags are used to dynamically label those spectrograms. This study's robust DL framework provided the highest accuracy with the raw moments of horizontal polarization (99.99%) to detect RFI globally.

The role of geomorphology, rainfall and soil moisture in the occurrence of landslides triggered by 2018 Typhoon Mangkhut in the Philippines

Abstract. In 2018 Typhoon Mangkhut (locally known as Typhoon Ompong) triggered thousands of landslides in the Itogon region of the Philippines. A landslide inventory of the affected region is compiled for the first time, comprising 1101 landslides over a 570 km2 area. The inventory is used to study the geomorphological characteristics and land cover more prone to landsliding as well as the hydrometeorological conditions that led to widespread failure. The results showed that landslides mostly occurred on grassland and wooded slopes of clay superficial geology, predominantly facing east-southeast. Rainfall (Integrated Multi-satellitE Retrievals for Global Precipitation Measurement, IMERG GPM) associated with Typhoon Mangkhut is compared with 33 high-intensity rainfall events that did not trigger regional landslide events in 2018. Results show that landslides occurred during high-intensity rainfall that coincided with the highest soil moisture values (estimated clays saturation point), according to Soil Moisture Active Passive level 4 (SMAP-L4) data. Our results demonstrate the potential of SMAP-L4 and GPM IMERG data for landslide hazard assessment and early warning where ground-based data are scarce. However, other rainfall events in the months leading up to Typhoon Mangkhut that had similar or higher rainfall intensities and also occurred when soils were saturated did not trigger widespread landsliding, highlighting the need for further research into the conditions that trigger landslides in typhoons.

Global Soil Water Estimates as Landslide Predictor: The Effectiveness of SMOS, SMAP, and GRACE Observations, Land Surface Simulations, and Data Assimilation

AbstractThis global feasibility study assesses the potential of coarse-scale, gridded soil water estimates for the probabilistic modeling of hydrologically triggered landslides, using Soil Moisture Ocean Salinity (SMOS), Soil Moisture Active Passive (SMAP), and Gravity Recovery and Climate Experiment (GRACE) remote sensing data; Catchment Land Surface Model (CLSM) simulations; and six data products based on the assimilation of SMOS, SMAP, and/or GRACE observations into CLSM. SMOS or SMAP observations (~40-km resolution) are only available for less than 20% of the globally reported landslide events, because they are intermittent and uncertain in regions with complex terrain. GRACE terrestrial water storage estimates include 75% of the reported landslides but have coarse spatial and temporal resolutions (monthly, ~300 km). CLSM soil water simulations have the added advantage of complete spatial and temporal coverage, and are found to be able to distinguish between “stable slope” (no landslide) conditions and landslide-inducing conditions in a probabilistic way. Assimilating SMOS and/or GRACE data increases the landslide probability estimates based on soil water percentiles for the reported landslides, relative to model-only estimates at 36-km resolution for the period 2011–16, unless the CLSM model-only soil water content is already high (≥50th percentile). The SMAP Level 4 data assimilation product (at 9-km resolution, period 2015–19) more generally updates the soil water conditions toward higher landslide probabilities for the reported landslides, but is similar to model-only estimates for the majority of landslides where SMAP data cannot easily be converted to soil moisture owing to complex terrain.

Characteristics of the Global Radio Frequency Interference in the Protected Portion of L-Band

The National Aeronautics and Space Administration’s (NASA’s) Soil Moisture Active–Passive (SMAP) radiometer has been providing geolocated power moments measured within a 24 MHz band in the protected portion of L-band, i.e., 1400–1424 MHz, with 1.2 ms and 1.5 MHz time and frequency resolutions, as its Level 1A data. This paper presents important spectral and temporal properties of the radio frequency interference (RFI) in the protected portion of L-band using SMAP Level 1A data. Maximum and average bandwidth and duration of RFI signals, average RFI-free spectrum availability, and variations in such properties between ascending and descending satellite orbits have been reported across the world. The average bandwidth and duration of individual RFI sources have been found to be usually less than 4.5 MHz and 4.8 ms; and the average RFI-free spectrum is larger than 20 MHz in most regions with exceptions over the Middle East and Central and Eastern Asia. It has also been shown that, the bandwidth and duration of RFI signals can vary as much as 10 MHz and 10 ms, respectively, between ascending and descending orbits over certain locations. Furthermore, to identify frequencies susceptible to RFI contamination in the protected portion of L-band, observed RFI signals have been assigned to individual 1.5 MHz SMAP channels according to their frequencies. It has been demonstrated that, contrary to common perception, the center of the protected portion can be as RFI contaminated as its edges. Finally, there have been no significant correlations noted among different RFI properties such as amplitude, bandwidth, and duration within the 1400–1424 MHz band.

Evaluation of SMAP, SMOS, and AMSR2 Soil Moisture Products Based on Distributed Ground Observation Network in Cold and Arid Regions of China

Long-term surface soil moisture (SM) data are increasingly needed in water budget and energy balance analysis of watersheds. The performance of nine remotely sensed SM products from Advanced Microwave Scanning Radiometer 2 (AMSR2), Soil Moisture and Ocean Salinity (SMOS), and Soil Moisture Active Passive (SMAP) missions are evaluated based on observations collected from distributed observation networks in the Heihe River Basin (HRB) of China during 2013 to 2017. Results show that the SMAP Level 3 dual channel algorithm SM retrievals reflect the seasonal SM variations well with high temporal correlations of ∼0.7 and high accuracy within 0.04 m3/m3 in terms of unbiased root mean squared error (ubRMSE) over the grassland in the HRB. The SMOS level 3 SM retrievals present increased underestimation and ubRMSE of ∼0.10 m3/m3 as the vegetation increases. The newly published SMOS Institut National de la Recherche Agronomique–Centre d'Etudes Spatiales de la BIOsphere product in version 2 outperforms the SMOS level 3 product with improved temporal correlation coefficient above 0.4 and lower ubRMSE of ∼0.05 m3/m3. AMSR2 Land Parameter Retrieval Algorithm SM products show extremely large overestimation over the vegetated regions in HRB, especially the C-band products. Drastically high underestimation biases are observed in the Japan Aerospace Exploration Agency AMSR2 SM product. Parameter uncertainty analyses indicate that the different parameterization schemes of vegetation optical depth inputs could be one of the main reasons resulting in the systematic overestimation/underestimation biases in the AMSR2/SMOS/SMAP SM retrievals. The findings aim to provide insights into studies on algorithms refinements and data fusions of SM products in HRB.

Prediction of Large-Scale Wildfires With the Canopy Stress Index Derived from Soil Moisture Active Passive

Land surface variables such as surface soil moisture are recognized as important wildfire indicators. However, quantifying wildfire fuel combustibility through only satellite-retrieved soil moisture remains challenging, because soil moisture does not provide information about vegetation (fuel) moisture and water availability to plants is complicated by another factor of soil properties. Thus, to enhance the wildfire prediction ability of the soil moisture active passive (SMAP) mission, this study examines a canopy stress index (CSI) retrieved from 1 km SMAP level 2 products. The strong relationship between prefire CSI and fire severity is demonstrated over two large-scale (greater than 1000 ha) wildfires in Gang-won Province, South Korea. CSI can effectively predict the severity of large-scale wildfires one week before fire events, differentiating dry soils from wildfire hazards. SMAP L2 data with a temporal resolution of 5–7 d over the study sites are suitable for supporting aerial firefighting activities and reducing false fire warnings.

Satellite Monitoring of Global Surface Soil Organic Carbon Dynamics Using the SMAP Level 4 Carbon Product

AbstractSoil organic carbon (SOC) is an important metric of soil health and the terrestrial carbon balance. Short‐term climate variations affect SOC through changes in temperature and moisture, which control vegetation growth and soil decomposition. We evaluated a satellite data‐driven carbon model, operating under the NASA Soil Moisture Active‐Passive (SMAP) mission, as a means of monitoring global surface SOC dynamics. The SMAP Level 4 Carbon (L4C) product estimates a daily global carbon budget including surface (0‐ to 5‐cm depth) SOC. We found that the L4C mean latitudinal SOC distribution is generally consistent with alternative assessments from static soil inventory records and dynamic global vegetation models (r ≥ 0.89). Within forest systems, based on inventory data, L4C SOC is most similar in magnitude to litterfall but is correlated with coarse woody debris ( ) and total SOC ( ). L4C SOC is sensitive to seasonal and annual climate variability, with mean residence times that range from 1.5 years in the wet tropics to 17 years in the cold tundra. Incorporating soil moisture retrievals from the SMAP L‐band (1.4 GHz) microwave radiometer within the L4C algorithm provides enhanced soil moisture sensitivity under low‐to‐moderate vegetation cover (&lt;5 kg m−2 vegetation water content). The L‐band soil moisture had the greatest impact on the L4C carbon budget in semiarid regions, which span almost 60% of the globe and account for substantial variability in the terrestrial carbon sink. The L4C operational product enables prognostic investigations into effects of recent climate trends and anomalies (e.g., droughts and pluvials) on shallow soil carbon dynamics.

Evaluation of SMAP Level 2, 3, and 4 Soil Moisture Datasets over the Great Lakes Region

Satellite sensor systems for soil moisture measurements have been continuously evolving. The Soil Moisture Active Passive (SMAP) mission represents one of the latest advances in this regard. Thus far, much of our knowledge of the accuracy of SMAP soil moisture over the Great Lakes region of North America has originated from evaluation studies using in situ data from the U.S. Department of Agriculture (USDA) Natural Resources Conservation Service Soil Climate Analysis Network and/or the U.S. Climate Reference Network, which provide only several in situ sensor stations for this region. As such, these results typically underrepresent the accuracy of SMAP soil moisture in this region, which is characterized by a relatively large soil moisture variability and is one of the least studied regions. In this work, SMAP Level 2‒4 soil moisture products: SMAP/Sentinel-1 L2 Radiometer/Radar Soil Moisture (SPL2SMAP_S), SMAP Enhanced L3 Radiometer Soil Moisture (SPL3SMP_E), and SMAP L4 Surface and Root-Zone Soil Moisture Analysis Update (SPL4SMAU) are evaluated over the southern portion of the Great Lakes region using in situ measurements from Michigan State University’s Enviro-weather Automated Weather Station Network. The unbiased root-mean-square error (ubRMSE) values for both SPL4SMAU surface and root zone soil moisture estimates are below 0.04 m3 m−3 at the 36-km scale, with an average ubRMSE of 0.045 m3 m−3 (0.037 m3 m−3) for the surface (root-zone) soil moisture against the sparse network. The ubRMSE values for SPL3SMP_E a.m. (i.e., descending overpasses) soil moisture retrievals are close to or below 0.04 m3 m−3 at the 36-km scale, with an average ubRMSE of ~0.06 m3 m−3 against the sparse network. The average ubRMSE values are ~0.05‒0.06 m3 m−3 for high-resolution SPL2SMAP_S soil moisture retrievals against the sparse network, with the skill of the baseline algorithm-based soil moisture retrievals exceeding that of the optional algorithm-based counterparts. Clearly, the skill of SPL4SMAU surface soil moisture exceeds that of the SPL3SMP_E and SPL2SMAP_S soil moisture retrievals.

A Global Assessment of Added Value in the SMAP Level 4 Soil Moisture Product Relative to Its Baseline Land Surface Model

AbstractThe Soil Moisture Active Passive (SMAP) level 4 product provides enhanced soil moisture estimates by assimilating SMAP brightness temperature observations into a land surface model. Here, a quantitative estimate of the relative skill of SMAP Level‐4 and model‐only surface soil moisture (vs. true soil moisture) is derived using only one additional noisy (but independent) soil moisture product. The method is applied globally and verified using high‐quality, ground‐based measurements where available. Results demonstrate that assimilating SMAP brightness temperature has relatively little impact in data‐rich areas like the United States and Europe. In contrast, much larger improvement is observed in data‐sparse regions, including much of Africa and central Australia, where model‐only simulations are disproportionately impacted by low‐quality model forcing. Therefore, ground validation conducted in data‐rich areas does not adequately sample the added value of SMAP data assimilation for data‐sparse regions and substantially underestimates the added skill provided by the SMAP level 4 system.