Mapping the spatial distribution of soil organic carbon (SOC) in lands covered by tropical forests is important to understand the relationship and dynamics of SOC in this type of ecosystem. In this study, the Random Forest (RF) algorithm was used to map SOC stocks of topsoil (0–15 cm) in forest lands of the Dominican Republic. The methodology was developed using geospatial datasets available in the Google Earth Engine (GEE) platform combined with a set of 268 soil samples. Twenty environmental covariates were analyzed, including climate, topography, and vegetation. The results indicate that Model A (combining all 20 covariates) was only marginally better than Model B (combining topographic and climatic covariates), and Model C (only combining multispectral remote sensing data derived from Landsat 8 OLI images). Model A and Model B yielded SOC mean values of 110.35 and 110.87 Mg C ha⁻¹, respectively. Model A reported the lowest prediction error and uncertainty with an R² of 0.83, an RMSE of 35.02 Mg C ha⁻¹. There was a strong dependence of SOC stocks on multispectral remote sensing data. Therefore, multispectral remote sensing proved accurate to map SOC stocks in forest ecosystems in the region. GRAPHICAL ABSTRACT

Soil moisture (SM) plays a crucial role in land–atmosphere interaction systems, directly influencing evapotranspiration, photosynthesis, and the water dynamics of the soil surface. Invariably, SM is negatively impacted by disturbances such as fires, which are becoming more frequent across semi-arid ecosystems. Different ecological restoration activities have been implemented to mitigate the impacts of disturbance that, when left untreated, can worsen the effects of recurrent droughts and accelerate desertification and land degradation processes. To measure and monitor the dynamics of SM, advanced techniques and tools have been developed that integrate remote sensing and in situ measurement. This review encompasses various themes on the application of remote sensing for measuring and monitoring SM dynamics in semi-arid ecosystems at different scales. We focused our analysis on the western United States region and thus have developed a review on the following topics: (a) the different data sources (e.g., satellite, unmanned aerial vehicles), (b) approaches to measure field-based SM, and (c) algorithms and techniques to model SM at different scales. We summarize these topics by emphasizing repeatable approaches for the transparent estimation of this variable, identifying current data gaps, and highlighting future trends to fulfill the expanding demand for SM monitoring strategies.

Over the last decades, Chile has experienced a long-term drought with significant consequences for water availability, forest productivity, and soil degradation, ultimately dramatically increasing the surface of burned area. Here, we quantify the Palmer Drought Severity Index (PDSI) to ascertain the extent of “moisture deficiency” across the central-southern region of Chile from 2000 to 2023 to assess the drought’s relationship with the frequency of wildfires focusing on the impact of native forests. Our methodology quantifies the PDSI from the burned area data using MODIS MCD64A1 satellite imagery, validated by in situ wildfire occurrence records. The findings indicate that 85.2% of fires occurred under moderate to severe drought conditions. We identified 407,561 ha showing varying degrees of degradation due to wildfires, highlighting the critical areas for targeted conservation efforts. A significant increase in both the frequency of wildfires and the extent of the affected area in native forests was observed with the intensification of drought conditions in the 21st century within mesic to humid Mediterranean climatic zones where drought explains up to 41% of the variability in the burned area (r² = 0.41; p < 0.05). This study highlights the relationship between drought conditions and wildfire frequency, showing the paramount need to adopt comprehensive wildfire mitigation management in native forests.

HIGHLIGHTS. Significant contrast in aridity in response to decreased precipitation. Consecutive droughts lead to a moderate runoff deficit and a higher aridity

Current estimates of CO₂ emissions from forest degradation are generally based on insufficient information and are characterized by high uncertainty, while a global definition of ‘forest degradation’ is currently being discussed in the scientific arena. This study proposes an automated approach to monitor degradation using a Landsat time series. The methodology was developed using the Google Earth Engine (GEE) and applied in a pine forest area of the Dominican Republic. Land cover change mapping was conducted using the random forest (RF) algorithm and resulted in a cumulative overall accuracy of 92.8%. Forest degradation was mapped with a 70.7% user accuracy and a 91.3% producer accuracy. Estimates of the degraded area had a margin of error of 10.8%. A number of 344 Landsat collections, corresponding to the period from 1990 to 2018, were used in the analysis. Additionally, 51 sample plots from a forest inventory were used. The carbon stocks and emissions from forest degradation were estimated using the RF algorithm with an R² of 0.78. GEE proved to be an appropriate tool to monitor the degradation of tropical forests, and the methodology developed herein is a robust, reliable, and replicable tool that could be used to estimate forest degradation and improve monitoring, reporting, and verification (MRV) systems under the reducing emissions from deforestation and forest degradation (REDD+) mechanism.

We analysed a large dataset consisting of 457 soil profiles of Andisols and Ultisols of volcanic origin compared to 60 non-volcanic soils. We hypothesised that soil pH has a greater impact on the development of Al-organomineral complexes in volcanic soils compared to non-volcanic soils, in the latter, the silt and clay fractions play a crucial role. Soil pH >4.5 strongly influenced the formation of Al-organomineral complexes in volcanic soils, while an increase in allophane content led to a decrease in SOC. Ultisols with more crystalline clays, such as halloysite and disordered kaolinite, the pH had a weaker impact and there was no effect on non-volcanic soils. Instead, a positive correlation (R² = 0.63, p < 0.01) was found between silt and clay and SOC in non-volcanic soils, supporting our second hypothesis. Soil pH played a significant role in the interplay between Al-organomineral complexes and allophane formation, while crystalline mineralogy has a direct effect on SOC levels in non-volcanic soils.