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SOC is the most important indicator of soil fertility and monitoring its space-time changes is a prerequisite to establish strategies to reduce soil loss and preserve its quality. Here we modelled the topsoil (0–0.3 m) SOC concentration of the cultivated area of Sicily in 1993 and 2008. Sicily is an extremely variable region with a high number of ecosystems, soils, and microclimates. We studied the role of time and land use in the modelling of SOC, and assessed the role of remote sensing (RS) covariates in the boosted regression trees modelling. The models obtained showed a high pseudo-R 2 (0.63–0.69) and low uncertainty (s.d. < 0.76 g C kg − 1 with RS, and < 1.25 g C kg − 1 without RS). These outputs allowed depicting a time variation of SOC at 1 arcsec. SOC estimation strongly depended on the soil texture, land use, rainfall and topographic indices related to erosion and deposition. RS indices captured one fifth of the total variance explained, slightly changed the ranking of variance explained by the non-RS predictors, and reduced the variability of the model replicates. During the study period, SOC decreased in the areas with relatively high initial SOC, and increased in the area with high temperature and low rainfall, dominated by arables. This was likely due to the compulsory application of some Good Agricultural and Environmental practices. These results confirm that the importance of texture and land use in short-term SOC variation is comparable to climate. The present results call for agronomic and policy intervention at the district level to maintain fertility and yield potential. In addition, the present results suggest that the application of RS covariates enhanced the modelling performance. [ABSTRACT FROM AUTHOR]

Soil organic carbon (SOC) is the most important parameter influencing soil health, global climate change, crop productivity, and various ecosystem services. Therefore, estimating SOC at larger scales is important. The present study was conducted to estimate the SOC pool at regional scale using the historical database gathered by the National Soil Survey Staff. Specific objectives of the study were to upscale the SOC density (kg C m −2 ) and total SOC pool (Pg C) across the Midwestern United States using the geographically weighted regression kriging (GWRK), and compare the results with those obtained from the geographically weighted regression (GWR) using the data for 3485 georeferenced profiles. Results from this study support the conclusion that the GWRK produced satisfactory predictions with lower root mean square error (5.60 kg m −2 ), mean estimation error (0.01 kg m −2 ) and mean absolute estimation error (4.30 kg m −2 ), and higher R 2 (0.58) and goodness-of-prediction statistic ( G = 0.59) values. The superiority of this approach is evident through a substantial increase in R 2 (0.45) compared to that for the global regression ( R 2 = 0.28). Croplands of the region store 16.8 Pg SOC followed by shrubs (5.85 Pg) and forests (4.45 Pg). Total SOC pool for the Midwestern region ranges from 31.5 to 31.6 Pg. This study illustrates that the GWRK approach explicitly addresses the spatial dependency and spatial non-stationarity issues for interpolating SOC density across the regional scale. [ABSTRACT FROM AUTHOR]

The main objectives of this paper were 1) to estimate soil organic carbon (SOC) using remote sensing covariates, soil properties, and topographic factors , and 2) to evaluate the interaction and the relative influence of the selected factors on the spatial variation of SOC. Thirteen factors were considered for digital mapping of SOC in the west Urmia Lake in Iran. To quantify multicollinearity among the predictor variables, Variance Inflation Factor (VIF) was calculated. Among them, nine independent factors were remained including silt, sand, slope, enhanced vegetation index (EVI), brightness, wetness, land cover, and latitude and longitude. A machine learning algorithm called Gradient Boosting Machine (GBM) was calibrated for understanding the spatial dynamic and prediction of SOC. Model performance showed that GBM explained 43.5% (R2) of the SOC variation, and root mean square error (RMSE) was 0.23%. Results showed that EVI and sand were the most influential factors of the SOC variation while slope and land cover were the least important ones. Furthermore, significant interaction among EVI-wetness-SOC and EVI-sand-SOC was detected. On the other hand, 45.2% of SOC variation was estimated by remote sensing covariates. These results suggested that GBM was a promising approach for an in-depth understanding of the SOC variation over space. • Remote sensing covariates and field data were integrated for SOC mapping. • A Gradient Boosting Machine (GBM) was calibrated for SOC mapping. • The results showed that EVI and sand were the most influential factors. • 45.2% of SOC variation was estimated by remote sensing covariates. [ABSTRACT FROM AUTHOR]