Soil organic carbon simulation in cropland: A combined remote sensing, GIS, and modeling approach

Most soil organic carbon (SOC) simulation models emphasize details in soil biogeochemical processes, but pay little attention to aboveground biomass accumulation, which represents a major component of the SOC pool in cropland. In addition, fine spatial scale and lack of supporting data often limit a model's application. Therefore, the overall goal of this study is to develop a protocol that integrates remote sensing, GIS (geographical information system), and modeling techniques to improve regional crop biomass, and thereby SOC simulations in cropland.; This study developed a remote sensing (RS) driven SOC model (rsDNDC) from an existing DeNitrification and DeComposition (DNDC) biogeochemical model to utilize the rich data source of RS. A framework was developed to link RS and the rsDNDC model with a GIS (RS-GIS-Modeling) to extend the model application to regional levels. The rsDNDC model was evaluated against field biomass observations using ground truth LAI data due to the lack of remotely sensed crop LAIs and measured SOC data. Results for winter wheat and corn experiments in China showed that the rsDNDC improved the accuracy of biomass simulations by 4--20%, when compared to the original DNDC model. The rsDNDC also showed a significant improvement on simulating biomass growth patterns. The RS-GIS-Modeling framework was applied to a soybean field in the U.S. to mimic a regional study and the results suggested that the rsDNDC model was able to fairly accurately simulate SOC spatial variability/patterns with either generalized or detailed regional soil datasets.; The rsDNDC model is unique because it not only uses a static RS product of landuse/landcover classification as is commonly used in other studies, but also incorporates a series of real time remotely sensed crop growth information. Zonal approach within the RS-GIS-Modeling framework also allows us to run the model at fine-resolution with a reasonable computing time, which is important for spatial variability analysis.; This approach provides a reliable and effective tool that can be used to monitor large area cropland SOC changes, determine carbon credits for croplands, and help with precision carbon management. Future studies for further improvements and incorporation of online soil and RS databases were also discussed.