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Abstract: The Kyoto Protocol entering into force on 16 February 2005 continues to spur interest in development of carbon trading mechanisms internationally and domestically. Critical to the development of a carbon trading effort is verification that carbon has been sequestered, and field level measurement of C change is likely cost prohibitive. Estimating C change based on agricultural management practices related to carbon sequestration seems more realistic, and analysis of satellite imagery could be used to monitor and verify these practices over large areas. We examined using Landsat imagery to verify crop rotations and quantify crop residue biomass in north central Montana. Field data were collected using a survey of farms. Standard classification tree analysis (CTA) and boosted classification and regression tree analysis (BCTA) were used to classify crop types. Linear regression (LM), regression tree analysis (RTA), and stochastic gradient boosting (SGB) were used to estimate crop residue. Six crop types were classified with 97% accuracy (BCTA) with class accuracies of 88–99%. Paired t-tests were used to compare the difference between known and predicted mean crop residue biomass. The difference between known and predicted mean residues using SGB was not different than 0 (p-value=0.99); however root mean square error (RMSE) was large (1981kgha−1), implying that SGB accurately predicted regional crop residue biomass but not local predictions (i.e., field or farm level). The results of this study, and previous research classifying tillage practices and estimating soil disturbance, supports using satellite imagery as an effective tool for monitoring and verifying agricultural management practices related to carbon sequestration over large areas. [Copyright &y& Elsevier]

Abstract: Management of agricultural soils, most notably tillage, influences wind, and water erosion, which in turn has implications for non-point source pollution of pesticides, fertilizer, and sediment in agro-ecosystems. No-till (NT) practices improve soil, water, and aquatic ecosystem quality by reducing soil erosion and chemical runoff. The ability of cropland soils to sequester C from the atmosphere might help mitigate global warming. Classification of Landsat ETM+ satellite images has the potential to identify tillage practices and soil disturbance over large areas, enabling efficient monitoring of these agricultural practices. Previous studies predicting tillage management had relatively small study areas (located in a single county), relatively low numbers of fields (6–51), and were temporally focused on non-planted fields to reduce the potential effects of crop canopy interference and/or field patterning. Our objectives were to predict in the presence of crop canopy and over a spatially large, management diverse study area (1) tillage systems (NT versus tilled) and (2) soil disturbance. A farm survey of the study area, north central Montana, was used to as a means to obtain extensive field-level farm management data. We compared logistic regression (LR), traditional classification tree analysis (CTA), and boosted classification tree analysis (BCTA) for identifying NT fields. Logistic regression had an overall accuracy of 94%, BCTA 89%, and CTA 87%, but tillage was not well distinguished. Soil disturbance was estimated using linear regression (LM), regression tree analysis (RTA), and stochastic gradient boosting (SGB), an RTA variant. Classification of soil disturbance was best achieved using RTA (predicted mean soil disturbance not significantly different than known soil disturbance, p-value=0.08). Classification of Landsat ETM+ imagery showed promise for predicting tillage and agricultural soil disturbance over large, heterogeneous areas. [Copyright &y& Elsevier]