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Soil organic matter (SOM) plays an extremely important role in soil formation, soil fertility, environmental protection and the sustainable development of agriculture and forestry, especially in areas with fertile black soil. Remote sensing technology has unique advantages in SOM mapping and can quickly and accurately obtain SOM spatial distribution maps at a low cost. However, the current methods available for fusing radar data, optical data and environmental covariates for use in SOM mapping are far from sufficient. The study area is Youyi Farm, a typical black-soil area in Northeast China. Multiyear monthly composites of the bare-soil periods (April and May) from 2019 to 2022 in the study area were created in the Google Earth Engine (GEE), including VH-polarized and VV-polarized Sentinel-1 radar data, 10 spectral bands of Sentinel-2 data, elevation and slope terms representing terrain covariates, and multiyear average precipitation and temperature data representing climate covariates. SOM was mapped in the study area based on 188 cultivated-layer (0–20 cm) sampling points combined with a random forest regression algorithm, and the SOM mapping accuracies of different strategies were evaluated. The results show that 1) the SOM mapping accuracy obtained using Sentinel-1 data combined with Sentinel-2 data was improved slightly compared to that obtained using Sentinel-2 data only, with improvements observed mainly in the prediction accuracies of areas with SOM contents below 5 %; 2) the highest accuracy was obtained by using the May composite image, while the April and May composite images reduced the accuracy; 3) adding environmental covariates greatly improved the SOM mapping accuracy, with the highest R2 being 0.696 and the lowest RMSE being 0.712 %; and 4) the importance of climate covariates was found to be higher than that of terrain covariates. This study expands the existing SOM mapping research using radar and optical data and evaluates the effects of different environmental covariates on the SOM mapping results. • We propose a SOM prediction model combining different data sources. • An appropriate time window is critical to SOM prediction. • Adding Sentinel-1 can slightly improve the accuracy of SOM prediction. • Climate covariates have proved more useful for SOM mapping. [ABSTRACT FROM AUTHOR]

Systematic Sentinel-1 acquisitions provide an unprecedented stream of SAR data which allows to describe forest temporal dynamics in detail, a powerful tool for phenological studies and forest type classification. Several studies have explored the temporal variation of backscatter intensity in this context, but none considered that scattering directionality of canopies may vary. Said directionality is related to target-sensor geometry (incidence angle), forest height, and optical depth, associated with leaf dynamics. This study explicitly models backscatter dependance on incidence angle by fitting a regression model for each Sentinel-1 image and forest type. Residuals are accumulated across the time series and used to classify pixels into the most likely forest type using the smallest accumulated residual. This modelling and classification strategy has been applied over a North-South transect across the Carpathian Mountains, including forests with different physiognomies, from deciduous broadleaf forest, to mixed broadleaf-needleleaf and pure perennial needleleaf forests. These forests were classified with increasing detail, assessing the results against in-situ forest stand data and satellite-based land cover classification products (Copernicus Forest type layer). The accuracy of our classification was K > 0.8, OA > 90% when separating broadleaf from needleleaf forest types. The accuracy decreased (K > 0.6, OA > 79%) when also separating mixed forest types. Our results suggest that incorporating directional effects into classification models can improve SAR-based forest classification of temperate forest over mountainous terrain. Furthermore, models fitted between backscatter and incidence angle provide an estimate of n , a parameter related to optical depth that has been shown to vary with leaf dynamics. n could be used to improve image normalization in studies aiming at the estimation of biomass, or to aid the estimation of fast-changing parameters such as leaf area index or leaf moisture content. • Backscatter/incidence angle relationship depends on the season. • Common γ 0 normalization may be inadequate for images acquired during the winter. • Forest types have a characteristic temporal signature for backscatter and opacity. • Temporal signatures separated broadleaf and needleleaf forest with 90% accuracy. • When separating mixed forest, the class had 47% omission and 72% commission error. [ABSTRACT FROM AUTHOR]

Detailed maps of forest structure attributes are crucial for sustainable forest management, conservation, and forest ecosystem science at the landscape level. Mapping the structure of broad heterogeneous forests is challenging, but the integration of extensive field inventory plots with wall-to-wall metrics derived from synthetic aperture radar (SAR) and optical remote sensing offers a potential solution. Our goal was to map forest structure attributes (diameter at breast height, basal area, mean height, dominant height, wood volume and canopy cover) at 30-m resolution across the diverse 463,000 km2 of native forests of Argentina based on SAR Sentinel-1, vegetation metrics from Sentinel-2 and geographic coordinates. We modelled the forest structure attributes based on the latest national forest inventory, generated uncertainty maps, quantified the contribution of the predictors, and compared our height predictions with those from GEDI (Global Ecosystem Dynamics Investigation) and GFCH (Global Forest Canopy Height). We analyzed 3788 forest inventory plots (1000 m2 each) from Argentina's Second Native Forest Inventory (2015–2020) to develop predictive random forest regression models. From Sentinel-1, we included both VV (vertical transmitted and received) and VH (vertical transmitted and horizontal received) polarizations and calculated 1st and 2nd order textures within 3 × 3 pixels to match the size of the inventory plots. For Sentinel-2, we derived EVI (enhanced vegetation index), calculated DHIs (dynamic habitat indices (annual cumulative, minimum and variation) and the EVI median, then generated 1st and 2nd order textures within 3 × 3 pixels of these variables. Our models including metrics from Sentinel-1 and 2, plus latitude and longitude predicted forest structure attributes well with root mean square errors (RMSE) ranging from 23.8% to 70.3%. Mean and dominant height models had notably good performance presenting relatively low RMSE (24.5% and 23.8%, respectively). Metrics from VH polarization and longitude were overall the most important predictors, but optimal predictors differed among the different forest structure attributes. Height predictions (r = 0.89 and 0.85) outperformed those from GEDI (r = 0.81) and the GFCH (r = 0.66), suggesting that SAR Sentinel-1, DHIs from Sentinel-2 plus geographic coordinates provide great opportunities to map multiple forest structure attributes for large areas. Based on our models, we generated spatially-explicit maps of multiple forest structure attributes as well as uncertainty maps at 30-m spatial resolution for all Argentina's native forest areas in support of forest management and conservation planning across the country. • Nationwide mapping of multiple forest structure attributes at 30-m resolution. • Uncertainty maps of each forest structure attribute at 30-m resolution. • Best predictors were a combination of Sentinel-1 and 2 data plus coordinates. • Forest height predictions outperformed those from GEDI and the GFCH. • Reliable predictions to support sustainable forest management in Argentina. [ABSTRACT FROM AUTHOR]

[Display omitted] • UAV data accurately predicted SOC concentrations (RMSE < 2.44 %) • Radar data characterized spatial patterns of flood frequency in coastal areas. • The effect of flood frequency on SOC was highest at pioneer and seaward communities. • AGB showed a larger explanatory power than flood frequency in SOC predictions. • UAV-SAR fusion unveils site-specific fine-scale patterns of SOC in coastal meadows. Coastal wetlands are considered important stores of blue carbon, containing some of the largest stores of pedologic and biotic carbon per unit area on the planet. These ecosystems are however highly sensitive to Climate Change and changes in the management practices. It is of utmost importance to address relevant ecosystem scales in order to fully understand carbon dynamics in coastal wetlands. In this regard, Unoccupied Aerial Vehicles (UAVs) can provide spatial scales detailed enough to address the fine scale patters of topsoil organic carbon accumulation in coastal wetlands. This study demonstrates the use of multispectral and photogrammetric data derived from UAVs to accurately map plant communities and topsoil organic carbon concentration in coastal wetlands. The overall accuracies from the classification of plant communities ranged from 88% to 97% whereas RMSE for topsoil organic carbon concentration ranged from 2.44% to 0.74%. By combining both models, site-specific variations in topsoil carbon concentrations among plant communities were unveiled. Open pioneer communities consistently showed the lowest topsoil organic carbon concentration, while the concentrations vary considerably across plant communities characterized by denser vegetation coverage. Furthermore, Sentinel-1 radar data was used to assess the spatial patterns of flood frequency. GAMs were used to combine flood frequency with the plant communities and topsoil organic carbon models, as well as an aboveground biomass (AGB) model from a previous study. GAMs revealed a stronger effect than flood frequency on topsoil organic carbon. Regarding flooding, increased flood frequency generally led decreased topsoil organic concentrations across communities and sites. However, the relative contribution of flood frequency to topsoil organic carbon concentration in Baltic coastal wetlands depends strongly on the location of the wetland and the nature of the floods. Higher flood frequencies could lead to increased topsoil organic carbon in wetlands subject to the input of estuarine sediments. Lastly, the integration of remote sensing platforms constitutes an effective tool for revealing spatial heterogeneity of carbon storage in coastal wetlands. [ABSTRACT FROM AUTHOR]

Remote sensing-based approaches are important for evaluating ecosystem degradation and the efficient planning of ecosystem restoration efforts. However, the large majority of remote sensing-based degradation assessments are trend-based, implying that they can only detect degradation that occurred after medium or high-resolution satellite imagery became available. This makes them less suitable to map long-term degradation in ecosystems that have been under high human pressure since before. The main goal of this study was to develop a robust operational approach to map forest degradation status in heterogeneous landscapes with a long-standing degradation history to inform the planning of restoration interventions. We hereby use the tropical dry forests of Lambayeque, Peru, as a case study. Instead of using a trend-based assessment, we evaluated forest degradation status by comparing current woody cover (WC) and aboveground biomass (AGB) estimates obtained from remote sensing imagery with benchmark values consisting of the 95th percentile WC and AGB values inside environmentally homogenous land capability classes. Using boosted regression tree models and a combination of optical (Sentinel-2) and synthetic aperture radar (Sentinel-1) data of different seasons, we mapped WC and AGB, using training data obtained through very high-resolution imagery and field measurements. Further, we aimed at assessing (i) whether the inclusion of Sentinel-1 data improves mapping accuracy in comparison to using only Sentinel-2 data, and (ii) whether the use of multi-seasonal data improves accuracy in comparison to single-season data. Models combining multi-seasonal Sentinel-1 and Sentinel-2 data resulted in the most accurate WC predictions (mean absolute error (MAE): 16%; MAE normalized by dividing by the inter-quartile range of training data: 26%) and AGB predictions (MAE: 28.6 t/ha; normalized MAE: 65%), but differences in predictive accuracy with single season models or models using only Sentinel-2 data were small. The most accurate models estimated an average WC of 41% and an average AGB of 23.4 t/ha. Average WC and AGB reduction due to degradation was 35% and 36%, respectively, indicating that these forests are highly degraded. The site-specific scaling of WC and AGB allows to efficiently estimate forest degradation status irrespective of the time when this degradation occurred, and to express degradation status against site-specific benchmarks. On the condition that there are still some areas that are sufficiently undegraded to be used as a benchmark, the approach can be used to prioritize forest restoration actions and inform targets for restoration in heterogeneous landscapes suffering the impacts of undocumented long-term degradation. • Mapping woody cover (WC) and aboveground biomass (AGB) in dry forest. • Multi-season models with Sentinel-1 and Sentinel-2 data were most accurate. • Time-series methods can only detect degradation after onset satellite record. • We used site-specific benchmarks to quantify long-term degradation status. • Average WC and AGB reduction relative to benchmarks was 35% and 36%. [ABSTRACT FROM AUTHOR]

• Sentinel-1 L2 radial velocity products suffer from uncorrected biases up to 2 m/s. • Simple land calibration method is described and applied to 78 snapshots. • Corrected Sentinel-1 radial current has a precision of 0.25 m/s against HF radar. • Better results when sea state is taken into account for WASV correction in this coastal area. Direct sensing of total ocean surface currents with microwave Doppler signals is a growing topic of interest for oceanography, with relevance to several new ocean mission concepts proposed in recent years. Since 2014, the spaceborne C-band SAR instruments of the Copernicus Sentinel-1 (S1) mission routinely acquire microwave Doppler data, distributed to users through operational S1 Level-2 ocean radial velocity (L2 OCN RVL) products. S1 L2 RVL data could produce high-resolution maps of ocean surface currents that would benefit ocean observing and modelling, particularly in coastal regions. However, uncorrected platform effects and instrument anomalies continue to impact S1 RVL data and prevent direct exploitation. In this paper, a simple empirical method is proposed to calibrate and correct operational S1 L2 RVL products and retrieve two-dimensional maps of surface currents in the radar line-of-sight. The study focuses on the German Bight where wind, wave and current data from marine stations and an HF radar instrumented site provide comprehensive means to evaluate S1 retrieved currents. Analyses are deliberately limited to Sentinel-1A (S1A) ascending passes to focus on one single instrument and fixed SAR viewing geometry. The final dataset comprises 78 separate S1A acquisitions over 2.5 years, of which 56 are matched with collocated HF radar data. The empirical corrections bring significant improvements to S1A RVL data, producing higher quality estimates and much better agreement with HF radar radial currents. Comparative evaluation of S1A against HF radar currents for different WASV corrections reveal that best results are obtained in this region when computing the WASV with sea state rather than wind vector input. Accounting for sea state produces S1 radial currents with a precision (std of the difference) around 0.3 m/s at ∼1 km resolution. Precision improves to ∼0.24 m/s when averaging over 21 × 27 km2, with correlations with HF radar data reaching up to 0.93. Evidence of wind-current interactions when tides and wind align and short fetch conditions call for further research with more satellite data and other sites to better understand and correct the WASV in coastal regions. Finally, 1 km resolution maps of climatological S1A radial currents obtained over 2.5 years reveal strong coastal jets and fine scale details of the coastal circulation that closely match the known bathymetry and deep-water coastal channels in this region. The wealth of oceanographic information in corrected S1 RVL data is encouraging for Doppler oceanography from space and its application to observing small scale ocean dynamics, atmosphere and ocean vertical exchanges and marine ecosystem response to environmental change. [ABSTRACT FROM AUTHOR]

Around 350 million hectares of land are affected by wildfires every year influencing the health of ecosystems and leaving a trail of destruction. Accurate information over burned areas (BA) is essential for governments and communities to prioritize recovery actions. Prior research over the past decades has established the potentials and limitations of space-borne earth observation for mapping BA over large geographic areas at various scales. The operational deployment of Sentinel-1 and Sentinel-2 constellations significantly improved the quality and quantity of the imagery from the microwave (C-band) and optical regions on the spectrum. Based on that, this study set to investigate whether the existing coarse BA products can be further improved by the synergy of optical surface reflectance (SR), radar backscatter coefficient (BS), and/or radar interferometric coherence (COR) data with higher spatial resolutions. A Siamese Self-Attention (SSA) classification strategy is proposed for the multi-sensor BA mapping and a multi-source dataset is constructed at the object level for the training and testing. Results are analyzed by test sites, feature sources, and classification strategies to appraise the improvements achieved by the proposed method. • Sentinel-1 and Sentinel-2 data are synergistically used for burned area mapping. • A small object dataset is constructed over 12 California fires in 2018–2019. • A deep-learning-based method is applied to discriminate burns and unburns. • Performance is assessed over different data sources, test sites, and classifiers. • The flexibility of the trained classifier is evaluated over independent sites. [ABSTRACT FROM AUTHOR]