The largest and most dynamic agricultural frontier in Brazil is known as MATOPIBA, an area that covers part of the Cerrado biome. Within this region, Western Bahia stands out as a large producer of soy and cotton. There are no studies that quantify carbon stocks for different land uses and land cover types in Western Bahia, which hinders comprehension of the role of agricultural expansion in carbon dynamics and the development of sustainable agriculture policies. Here, we evaluate how the land use changes in this region have affected the carbon balance in the aboveground biomass (AGB), belowground biomass (BGB), and soil reservoirs. We collected soil samples for areas with different land uses and land cover types to estimate soil carbon stocks (SCS) and combined remote sensing results and modeling techniques to develop a historical reconstruction of spatial patterns of SCS, AGB, and BGB during the period 1990–2018. The replacement of areas from the forest formations class with pasture and rainfed agriculture reduced the 100 cm depth SCS (SCS₁₀₀) by 37.3% (p = 0.031) and 30.3% (p = 0.053), respectively. By contrast, the conversion of pasture and rainfed agriculture to irrigated agriculture increased SCS₁₀₀ by 34% (p = 0.034) and 26.5% (p = 0.022), respectively. Spatial changes in historical carbon stocks are strongly associated with land use changes that occurred between 1990 and 2018. We estimated a non-significant loss of 61.9 Tg-C (p = 0.726) from the total carbon stocks (calculated as the sum of AGB, BGB, and SCS) of which 80% of the losses came from soil stocks, 11% from BGB, and 8% from AGB. These findings reveal the need to monitor carbon stocks in sandy soils to reduce the uncertainties of estimates and support the development of effective sustainable agriculture policies. The best alternatives for reducing carbon losses in the Cerrado are to maintain natural forest cover and to recover soils through sustainable soil management, especially in pasturelands where soil carbon stocks are lowest.

Western Bahia is one of the most active agricultural frontiers in the world, which raises concern about its natural resources conservation, especially regarding water availability. This study evaluated the influence of five different land uses and land covers on physical and hydraulic soil properties, and developed pedotransfer functions to derive regional hydraulic properties. Significant changes between physical and hydraulic soil properties under agricultural areas and under natural vegetation cover were found, reinforcing that agricultural activity may influence the soil water balance. Cerrado and Forest formation areas have higher infiltration rates ( $K_{sat}$ ) compared to managed areas, with average values of 16.29 cm h⁻¹, and 14.47 cm h⁻¹, while irrigated croplands, rainfed croplands and pasture areas have much smaller infiltration rates, with $K_{sat}$ equal to 3.01 cm h⁻¹, 6.22 cm h⁻¹ and 5.01 cm h⁻¹, respectively. Our results suggest that the agriculture practices do not directly affect the vertical nature of hydrological flowpath, except in the case of intensive irrigated agriculture areas, where $K_{sat}$ reduction can lead to erosive processes favoring organic matter losses, and decreases in productivity and soil quality. Impacts of land use change on hydraulic and physical soil properties are a reality in the Cerrado agriculture frontier and there is an urgent need to monitor how these changes occur over time to develop effective mitigation strategies of soil and water conservation.

Western Bahia is a critical region in Brazil’s recent expansion of agricultural output. Its outstanding increase in production is associated with strong growth in cropland area and irrigation. Here we present analyses of Western Bahian historical changes in land use, including irrigated area, and suitability for future agricultural expansion that respects permanent protection areas and the limits established by the Brazilian Forest Code in the Cerrado biome. For this purpose, we developed a land use and land cover classification database using a random forest classifier and Landsat images. A spatial multicriteria decision analysis to evaluate land suitability was performed by combining this database with precipitation and slope data. We demonstrate that between 1990 and 2020, the region’s total agricultural area increased by 3.17 Mha and the irrigated area increased by 193,480 ha. Throughout the region, the transition between the different classes of land use and land cover followed different pathways and was strongly influenced by land suitability and also appears to be influenced by Brazil’s new Forest Code of 2012. We conclude that even if conservation restrictions are considered, agricultural area could nearly double in the region, with expansion possible mostly in areas we classify as moderately suitable for agriculture, which are subject to climate hazards when used for rainfed crops but are otherwise fine for pastures and irrigated croplands.

Despite optical remote sensing (and the spectral vegetation indices) contributions to digital soil-mapping studies of soil organic carbon (SOC), few studies have used active radar remote sensing mission data like that from synthetic aperture radar (SAR) sensors to predict SOC. Bearing in mind the importance of SOC mapping for agricultural, ecological, and climate interests and also the recently developed methods for vegetation monitoring using Sentinel-1 SAR data, in this work, we aimed to take advantage of the high operationality of Sentinel-1 imaging to test the accuracy of SOC prediction at different soil depths using machine learning systems. Using linear, nonlinear, and tree regression-based methods, it was possible to predict the SOC content of soils from western Bahia, Brazil, a region with predominantly sandy soils, using as explanatory variables the SAR vegetation indices. The models fed with SAR sensor polarizations and vegetation indices produced more accurate results for the topsoil layers (0–5 cm and 5–10 cm in depth). In these superficial layers, the models achieved an RMSE in the order of 5.0 g kg⁻¹ and an R² ranging from 0.16 to 0.24, therefore explaining about 20% of SOC variability using only Sentinel-1 predictors.

Climate, fire and soil nutrient limitation are important elements that affect vegetation dynamics in areas of the forest–savanna transition. In this paper, we use the dynamic vegetation model INLAND to evaluate the influence of interannual climate variability, fire and phosphorus (P) limitation on Amazon–Cerrado transitional vegetation structure and dynamics. We assess how each environmental factor affects net primary production, leaf area index and aboveground biomass (AGB), and compare the AGB simulations to an observed AGB map. We used two climate data sets (monthly average climate for 1961–1990 and interannual climate variability for 1948–2008), two data sets of total soil P content (one based on regional field measurements and one based on global data), and the INLAND fire module. Our results show that the inclusion of interannual climate variability, P limitation and fire occurrence each contribute to simulating vegetation types that more closely match observations. These effects are spatially heterogeneous and synergistic. In terms of magnitude, the effect of fire is strongest and is the main driver of vegetation changes along the transition. Phosphorus limitation, in turn, has a stronger effect on transitional ecosystem dynamics than interannual climate variability does. Overall, INLAND typically simulates more than 80 % of the AGB variability in the transition zone. However, the AGB in many places is clearly not well simulated, indicating that important soil and physiological factors in the Amazon–Cerrado border region, such as lithology, water table depth, carbon allocation strategies and mortality rates, still need to be included in the model.

Protected areas worldwide have been recognized as crucial entities in preserving and conserving forest ecosystem services. These areas play an important role in mitigating climate change due to their carbon stocks. In this study, we characterized the aboveground carbon stocks (AGC) across different forest types for the Protected Area Carajás National Forest in the Eastern Amazon, based on data from 387 forest inventory plots and two distinct AGC maps from remote sensing and LiDAR (Light Detection and Ranging) data fusion—GEDI and EBA. The estimates of AGC from field data showed higher amounts of AGC in the open forest formations (187.20 ± 46.83 MgC ha⁻¹), followed by dense (105.90 ± 42.56 ha⁻¹ MgC ha⁻¹) and transitional forests (48.86 ± 31.72 MgC ha⁻¹) with lower amounts under seasonal forests (38.65 ± 9.48 MgC ha⁻¹) and secondary forest areas (>23 MgCha⁻¹). However, the dense, seasonal, and transitional forests exhibited higher carbon density than the open and secondary areas in both AGC maps analyzed. We emphasize the significant importance of monitoring open and seasonal forest areas, due to the gap in field data for these forest types in Amazonia. Here, we underscore the importance of more standardized forest field data and better plot distribution in non-disturbed areas to reduce the uncertainty of AGC estimates and improve the estimation of greenhouse gas emissions from deforestation and forest fires.