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• Wet year forage growth clearly associated with 0–30 cm soil organic carbon stocks. • Terrain attributes help explain half of 0–30 cm soil organic carbon variability. • 0–10 cm soil organic carbon stocks are noisier: variability is 75% unexplainable. • Complex rangeland terrain presents a challenge for soil organic carbon inventories. Accurate assessments of soil organic carbon (SOC) stocks are needed at multiple scales given their importance to both local soil health and global C cycles. Rangelands cover 54% of California, representing a large stock of SOC, but existing SOC estimates are uncertain. To improve understanding of fine-resolution SOC stocks in complex terrain and provide guidance to rangeland SOC inventories, we grid-sampled 105 locations (21-m grid cells) at two depths (0–10 and 10–30 cm) in a 10-ha annual grassland catchment in California's Central Coast Range. Soils were analyzed for bulk density, coarse fragments, SOC and texture. Monthly aerial imagery was acquired by an unmanned aerial vehicle to compare surface reflectance during two contrasting years (wet vs. dry) to SOC stocks. We found that the 0–30 cm soil thickness held 3.64 ± 0.71 kg SOC m−2 (mean ± SD) with a range of 1.97–5.49 kg SOC m−2. The 0–10 cm soil thickness stored 47% of the 0–30 cm SOC stock with SOC concentrations twice as high in the 0–10 cm layer (1.40 ± 0.38%) as in the 10–30 cm layer (0.71 ± 0.15% SOC). Multiple linear regression (MLR) models explained 50–57% of SOC variability at 0–30 and 10–30 cm, but only 25% of variability at 0–10 cm. Based on cross-validation tests, MLR outperformed spatial interpolation methods and Random Forest models, best explaining SOC stocks with five environmental covariates: wet-year greenness, mean curvature, elevation, insolation, and slope. Lower hillslope positions, concave landforms, and enhanced wet-year greenness were associated with more SOC, and explained 11%, 24%, and 31% of variability in 0–30 cm SOC stocks, respectively. This study demonstrates that the accuracy of regional-scale SOC mapping of California rangelands benefits from considering microclimatic and topographic controls at the catchment-scale, in addition to broader scale mineralogical and macroclimatic controls identified in previous SOC studies. [ABSTRACT FROM AUTHOR]

• Soil workability threshold was mapped using PTFs and predicted maps of SOC and clay. • Random Forest outperformed Generalized Boosted Regression for predicting SOC and clay. • Workability map can be used for effective on-farm and regional agricultural management. Climate change is presenting sizeable challenges for agricultural production around the world. In some regions, shifting precipitation patterns in the spring and fall are negatively impacting farm operation by reducing the number of "workable days" or the days fields can be worked with heavy equipment without damaging soil structure. This can be particularly problematic for farms on clay soils and/or poor drainage. Approximating a water content threshold at which a soil is not workable due to soil structure destruction can be helpful for planning effective farm operations. In this study, we applied advanced remote sensing and machine learning tools to produce digital maps of soil organic carbon (SOC) and clay (CL) content and used them in existing pedotransfer functions (PTFs) to predict a workability threshold (WT) across a study area in Delta, British Columbia, Canada. We combined field data, soil and vegetation indices derived from multiple Landsat satellite images, topographic indices, and soil survey information to digitally map SOC and CL of the agricultural lands in Delta using random forest (RF) and generalized boosted regression model (GBM). When validated against an independent field dataset, the RF model outperformed GBM for all accuracy measures (coefficient of determination – R2, concordance correlation coefficient – CCC, and normalized root mean square error – nRMSE). We then spatially applied several PTFs using our digital maps to estimate the plasticity limits of the soil and produce WT map. The WT map was then tested against independent field samples of the soil water content at −10 kPa and we achieved R2 of 0.59, CCC of 0.70, and nRMSE of 0.15. Our analysis showed that 40% of the fields in the study area had WT < 30%, a threshold that is already being impacted by reduced workable days. This WT map could be used to improve spatial prioritizations of investments for climate change adaptation at farm to regional scales. [ABSTRACT FROM AUTHOR]