Representing the Relationships Between Field Collected Carbon Exchanges and Surface Reflectance Using Geospatial and Satellite-Based Techniques

Carbon exchanges between the atmosphere and the land surface vary in space and time, and are highly dependent on land cover type. It is important to quantify these exchanges to understand how landscapes affect the carbon budget, which will have a significant impact on future climate change and will inform climate change projections. However, how do you represent regional carbon exchanges from a single meteorological station? A single observing station will represent a limited area around the station, but each individual observation will sample a different physical land area in time due to varying wind speeds, wind direction, and atmospheric stability. The methods and techniques presented address the challenges, limitations, and future work that is needed to properly scale and model carbon exchanges in four dimensions for varying agricultural and transitioning ecotones. Seasonal variability of carbon exchanges can be modeled in agricultural land covers using satellite-based techniques, but due to physiological differences in crop types the values must be modeled by crop species. The spatially varying atmospheric conditions must also be considered when modeling carbon exchanges from a single point in the spatial realm because of the dependency of carbon exchange on temperature and humidity conditions. In summary, field-based carbon exchange observations are used to quantify whether a specific land cover in a region is a carbon source to carbon sink to the atmosphere, however, it is important to consider the spatially varying variables that limit the ability of a single point measurement to represent carbon exchanges of an entire region

Carbon Flux Phenology from the Sky: Evaluation for Maize and Soybean

Carbon flux phenology is widely used to understand carbon flux dynamics and surface exchange processes. Vegetation phenology has been widely evaluated by remote sensors; however, very few studies have evaluated the use of vegetation phenology for identifying carbon flux phenology. Currently available techniques to derive net ecosystem exchange (NEE) from a satellite image use a single generic modeling subgroup for agricultural crops. But, carbon flux phenological processes vary highly with crop types and land management practices; this paper reexamines this assumption. Presented here are an evaluation of ground-truth remotely sensed vegetation indices with in situ NEE measurements and an identification of vegetation indices for estimating carbon flux phenology metrics by crop type. Results show that the performance of different vegetation indices as an indicator of phenology varies with crop type, particularly when identifying the start of a season and the peak of a season. Maize fields require vegetation indices that make use of the near-infrared and red reflectance bands, while soybean fields require those making use of the shortwave infrared (IR) and near-IR bands. In summary, the study identifies how to best utilize remote sensing technology as a crop-specific measurement tool

Carbon Flux Phenology from the Sky: Evaluation for Maize and Soybean

Carbon flux phenology is widely used to understand carbon flux dynamics and surface exchange processes. Vegetation phenology has been widely evaluated by remote sensors; however, very few studies have evaluated the use of vegetation phenology for identifying carbon flux phenology. Currently available techniques to derive net ecosystem exchange (NEE) from a satellite image use a single generic modeling subgroup for agricultural crops. But, carbon flux phenological processes vary highly with crop types and land management practices; this paper reexamines this assumption. Presented here are an evaluation of ground-truth remotely sensed vegetation indices with in situ NEE measurements and an identification of vegetation indices for estimating carbon flux phenology metrics by crop type. Results show that the performance of different vegetation indices as an indicator of phenology varies with crop type, particularly when identifying the start of a season and the peak of a season. Maize fields require vegetation indices that make use of the near-infrared and red reflectance bands, while soybean fields require those making use of the shortwave infrared (IR) and near-IR bands. In summary, the study identifies how to best utilize remote sensing technology as a crop-specific measurement tool