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The NASA Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides global, 3-hourly, 9-km resolution estimates of surface (0-5 cm) and root-zone (0-100 cm) soil moisture with a mean latency of ~2.5 days. The underlying L4_SM algorithm assimilates SMAP radiometer brightness temperature (Tb) observations into the NASA Catchment land surface model using a spatially-distributed ensemble Kalman filter. Version 4 of the L4_SM modeling system includes a reduction in the upward recharge of surface soil moisture from below under non-equilibrium conditions, resulting in reduced bias and improved dynamic range of L4_SM surface soil moisture compared to earlier versions. This change and additional technical modifications to the system reduce the mean and standard deviation of the observation-minus-forecast Tb residuals and overall soil moisture analysis increments while maintaining the skill of the L4_SM soil moisture estimates versus independent in situ measurements; the average, bias-adjusted RMSE in Version 4 is 0.039 m(exp 3) m(exp -3) for surface and 0.026 m(exp 3) m(exp -3) for root-zone soil moisture. Moreover, the coverage of assimilated SMAP observations in Version 4 is near-global owing to the use of additional satellite Tb records for algorithm calibration. L4_SM soil moisture uncertainty estimates are biased low (by 0.01-0.02 m(exp 3) m(exp -3)) against actual errors (computed versus in situ measurements). L4_SM runoff estimates, an additional product of the L4_SM algorithm, are biased low (by 35 mm year (exp -1)) against streamflow measurements. Compared to Version 3, bias in Version 4 is reduced by 46% for surface soil moisture uncertainty estimates and by 33% for runoff estimates.

Regional‐scale above‐ground biomass (AGB) estimates of tropical savannas and woodlands are highly uncertain, despite their global importance for ecosystems services and as carbon stores. In response, we collated field inventory data from 253 plots at four study sites in Cameroon, Uganda and Mozambique, and examined the relationships between field‐measured AGB and cross‐polarized radar backscatter values derived from ALOS PALSAR, an L‐band satellite sensor. The relationships were highly significant, similar among sites, and displayed high prediction accuracies up to 150 Mg ha−1 (±∼20%). AGB predictions for any given site obtained using equations derived from data from only the other three sites generated only small increases in error. The results suggest that a widely applicable general relationship exists between AGB and L‐band backscatter for lower‐biomass tropical woody vegetation. This relationship allows regional‐scale AGB estimation, required for example by planned REDD (Reducing Emissions from Deforestation and Degradation) schemes.

Mangroves are a globally important ecosystem that provides a wide range of ecosystem system services, such as carbon capture and storage, coastal protection and fisheries enhancement. Mangroves have significantly reduced in global extent over the last 50 years, primarily as a result of deforestation caused by the expansion of agriculture and aquaculture in coastal environments. However, a limited number of studies have attempted to estimate changes in global mangrove extent, particularly into the 1990s, despite much of the loss in mangrove extent occurring pre-2000. This study has used L-band Synthetic Aperture Radar (SAR) global mosaic datasets from the Japan Aerospace Exploration Agency (JAXA) for 11 epochs from 1996 to 2020 to develop a long-term time-series of global mangrove extent and change. The study used a map-to-image approach to change detection where the baseline map (GMW v2.5) was updated using thresholding and a contextual mangrove change mask. This approach was applied between all image-date pairs producing 10 maps for each epoch, which were summarised to produce the global mangrove time-series. The resulting mangrove extent maps had an estimated accuracy of 87.4% (95th conf. int.: 86.2–88.6%), although the accuracies of the individual gain and loss change classes were lower at 58.1% (52.4–63.9%) and 60.6% (56.1–64.8%), respectively. Sources of error included misregistration in the SAR mosaic datasets, which could only be partially corrected for, but also confusion in fragmented areas of mangroves, such as around aquaculture ponds. Overall, 152,604 km2 (133,996–176,910) of mangroves were identified for 1996, with this decreasing by −5245 km2 (−13,587–1444) resulting in a total extent of 147,359 km2 (127,925–168,895) in 2020, and representing an estimated loss of 3.4% over the 24-year time period. The Global Mangrove Watch Version 3.0 represents the most comprehensive record of global mangrove change achieved to date and is expected to support a wide range of activities, including the ongoing monitoring of the global coastal environment, defining and assessments of progress toward conservation targets, protected area planning and risk assessments of mangrove ecosystems worldwide.

The Soil Moisture and Ocean Salinity (SMOS) mission currently provides multiangular L‐band (1.4 GHz) brightness temperature images of the Earth. Because upwelling radiation at 1.4 GHz is significantly less affected by rain and atmospheric effects than at higher microwave frequencies, these new SMOS measurements offer unique opportunities to complement existing ocean satellite high wind observations that are often contaminated by heavy rain and clouds. To illustrate this new capability, we present SMOS data over hurricane Igor, a tropical storm that developed to a Saffir‐Simpson category 4 hurricane from 11 to 19 September 2010. Thanks to its large spatial swath and frequent revisit time, SMOS observations intercepted the hurricane 9 times during this period. Without correcting for rain effects, L‐band wind‐induced ocean surface brightness temperatures (TB) were co‐located and compared to H*Wind analysis. We find the L‐band ocean emissivity dependence with wind speed appears less sensitive to roughness and foam changes than at the higher C‐band microwave frequencies. The first Stokes parameter on a ∼50 km spatial scale nevertheless increases quasi‐linearly with increasing surface wind speed at a rate of 0.3 K/m s−1 and 0.7 K/m s−1 below and above the hurricane‐force wind speed threshold (∼32 m s−1), respectively. Surface wind speeds estimated from SMOS brightness temperature images agree well with the observed and modeled surface wind speed features. In particular, the evolution of the maximum surface wind speed and the radii of 34, 50 and 64 knots surface wind speeds are consistent with GFDL hurricane model solutions and H*Wind analyses. The SMOS sensor is thus closer to a true all‐weather satellite ocean wind sensor with the capability to provide quantitative and complementary surface wind information of interest for operational Hurricane intensity forecasts. Key Points Presentation of a new capability for hurricane surface winds remote sensing The method based on L‐band radiometers is weakly affected by rain Estimates of wind maxima and meso‐scale structure in hurricanes

Iran, as a semi-arid and arid country, has a water challenge in the recent decades and underground water extraction has been increased because of improper developments in the agricultural sector. Thus, detection and measurement of ground subsidence in major plains is of great importance for hazard mitigation purposes. In this study, we carried out a time series small baseline subset (SBAS) interferometric synthetic aperture radar (InSAR) analysis of 15 L-band PALSAR-2 images acquired from ascending orbits of the ALOS-2 satellite between 2015 and 2020 to investigate long-term ground displacements in East Azerbaijan Province, Iran. We found that two major parts of the study area (Tabriz and Shabestar plains) are subsiding, where the mean and maximum vertical subsidence rates are −10 and −98 mm/year, respectively. The results revealed that the visible subsidence patterns in the study area are associated with either anthropogenic activities (e.g., underground water usage) or presence of compressible soils along the Tabriz–Shabestar and Tabriz–Azarshahr railways. This implies that infrastructure such as railways and roads is vulnerable if progressive ground subsidence takes over the whole area. The SBAS results deduced from L-band PALSAR-2 data were validated with field observations and compared with C-band Sentinel-1 results for the same period. The C-band Sentinel-1 results showed good agreement with the L-band PALSAR-2 dataset, in which the mean and maximum vertical subsidence rates are −13 and −120 mm/year, respectively. For better visualization of the results, the SBAS InSAR velocity map was down-sampled and principal component analysis (PCA) was performed on ~3600 randomly selected time series of the study area, and the results are presented by two principal components (PC1 and PC2).

Interferograms pertaining to large earthquakes typically reveal the occurrence of elastic deformations caused by the earthquake along with several complex surface displacements. In this study, we identified displacement lineaments from interferograms; some of which occur on the shallow section of seismogenic faults. However, such displacements are typically located away from the hypocenter, and they are considered as triggered shallow slips. We found that the triggered shallow slips had a varied nature, as follows. (1) No evidence has yet been obtained regarding the generation of large seismic motion via triggered shallow slips; thus, their occurrence is seldom considered a cause of major earthquakes. However, we found that a movement of triggered shallow slip associated with an M 6-class earthquake occurred on an active fault that has previously caused an M 7-class earthquake. (2) At certain locations, several parallel displacement lineaments have been discovered. During the Kumamoto earthquake sequence in 2016, the strain created by the main shock and the motion of triggered shallow slips coincided at a specific location, resulting in the creation of parallel triggered shallow slips by the main shock. Conversely, at another location, the movements of the main shock and triggered shallow slips did not match, since the main shock was simply a trigger, whereas the latter parallel triggered shallow slips are likely a means for releasing previously accumulated strain. (3) The fault scaling law—which states that the length and displacement of a fault are proportional—does not hold true for triggered shallow slips. However, the parallel triggered shallow slips show a relationship between horizontal spacing and its displacement. This may be attributed to immobility in deep locations. These results lead to the following conclusions. Large earthquakes tend to trigger shallow slips on the pre-existing faults. Subsequently, these triggered shallow slips release accumulated strain by causing fault motions, which in turn result in displacement lineaments. The occurrence of such passive faulting creates weak, mobile fault planes that repeatedly move at the same location. These triggered shallow slips cause the diversity among active faults as a result.

Forest biomass is a crucial component of the global carbon budget in climate change studies. Therefore, it is essential to develop a credible way to estimate forest biomass as carbon stock. Our study used PALSAR-2 (ALOS-2) and Sentinel-2 images to drive the Random Forest regression model, which we trained with airborne lidar data. We used the model to estimate forest aboveground biomass (AGB) of two significant coniferous trees, Japanese cedar and Japanese cypress, in Ibaraki Prefecture, Japan. We used 48 variables derived from the two remote sensing datasets to predict forest AGB under the Random Forest algorithm, and found that the model that combined the two datasets performed better than models based on only one dataset, with R2 = 0.31, root-mean-square error (RMSE) = 54.38 Mg ha−1, mean absolute error (MAE) = 40.98 Mg ha−1, and relative RMSE (rRMSE) of 0.35 for Japanese cedar, and R2 = 0.37, RMSE = 98.63 Mg ha−1, MAE = 76.97 Mg ha−1, and rRMSE of 0.33 for Japanese cypress, over the whole AGB range. In the satellite AGB map, the total AGB of Japanese cedar in 17 targeted cities in Ibaraki Prefecture was 5.27 Pg, with a mean of 146.50 Mg ha−1 and a standard deviation of 44.37 Mg ha−1. The total AGB of Japanese cypress was 3.56 Pg, with a mean of 293.12 Mg ha−1 and a standard deviation of 78.48 Mg ha−1. We also found a strong linear relationship with between the model estimates and Japanese government data, with R2 = 0.99 for both species and found the government information underestimates the AGB for cypress but overestimates it for cedar. Our results reveal that combining information from multiple sensors can predict forest AGB with increased accuracy and robustness.

The main goal of the Soil Moisture and Ocean Salinity (SMOS) mission over land surfaces is the production of global maps of soil moisture (SM) and vegetation optical depth (τ) based on multi-angular brightness temperature (TB) measurements at L-band. The operational SMOS Level 2 and Level 3 soil moisture algorithms account for different surface effects, such as vegetation opacity and soil roughness at 4 km resolution, in order to produce global retrievals of SM and τ. In this study, we present an alternative SMOS product that was developed by INRA (Institut National de la Recherche Agronomique) and CESBIO (Centre d’Etudes Spatiales de la BIOsphère). One of the main goals of this SMOS-INRA-CESBIO (SMOS-IC) product is to be as independent as possible from auxiliary data. The SMOS-IC product provides daily SM and τ at the global scale and differs from the operational SMOS Level 3 (SMOSL3) product in the treatment of retrievals over heterogeneous pixels. Specifically, SMOS-IC is much simpler and does not account for corrections associated with the antenna pattern and the complex SMOS viewing angle geometry. It considers pixels as homogeneous to avoid uncertainties and errors linked to inconsistent auxiliary datasets which are used to characterize the pixel heterogeneity in the SMOS L3 algorithm. SMOS-IC also differs from the current SMOSL3 product (Version 300, V300) in the values of the effective vegetation scattering albedo (ω) and soil roughness parameters. An inter-comparison is presented in this study based on the use of ECMWF (European Center for Medium range Weather Forecasting) SM outputs and NDVI (Normalized Difference Vegetation Index) from MODIS (Moderate-Resolution Imaging Spectroradiometer). A six-year (2010–2015) inter-comparison of the SMOS products SMOS-IC and SMOSL3 SM (V300) with ECMWF SM yielded higher correlations and lower ubRMSD (unbiased root mean square difference) for SMOS-IC over most of the pixels. In terms of τ, SMOS-IC τ was found to be better correlated to MODIS NDVI in most regions of the globe, with the exception of the Amazonian basin and the northern mid-latitudes.

The soil moisture at the medium spatial scale is strongly desired in the context of satellite remote sensing data validation. The use of a ground-installed passive L-band radiometer ELBARA at the Bubnów-Sęków test site in the east of Poland gave a possibility to provide reference soil moisture data from the area with a radius of 100 m. In addition, the test site comprised three different land cover types that could be investigated continuously with one day resolution. The studies were focused on the evaluation of the ω-τ model coefficients for three types of land cover, including meadow, wetland, and cropland, to allow for the assessment of the soil moisture retrievals at a medium scale. Consequently, a set of reference time-dependent coefficients of effective scattering albedo, optical depth, and constant-in-time roughness parameters were estimated. The mean annual values of the effective scattering albedo including two polarisations were 0.45, 0.26, 0.14, and 0.54 for the meadow with lower organic matter, the meadow with higher organic matter, the wetland, and the cropland, respectively. The values of optical depth were in the range from 0.30 to 0.80 for the cropland, from 0.40 to 0.52 for the meadows (including the two investigated meadows), and from 0.60 to 0.70 for the wetland. Time-constant values of roughness parameters at the level of 0.45 were obtained.