Using the collocated Moderate Resolution Imaging Spectroradiometer (MODIS)-derived Bidirectional Reflectance Distribution Function (BRDF) with the U.S. Department of Agriculture’s National Agricultural Statistics Service’s Cropland Data Layer (CDL), the daily albedo of homogenous agricultural fields was derived for 51 common United States field crops by wavelength, sky-type, day of year, crop, and hardiness zone from 2015–2018. This study suggests that crop growth can result in changes in reflectivity up to a factor of 2 at most wavelengths and is unique per crop type in timing and range. Additionally, broadband impacts were studied and found to be less conspicuous than the individual wavelengths, but still significant. The results were used to evaluate a common method of cropland albedo estimation, normalized difference vegetation index (NDVI) as a proxy for albedo, and this method was found to have some significant limitations dependent on wavelength and date. Finally, a database of surface albedo variations as a function of growing period is constructed for common field crops to the United States (as well as additional land-cover types). This database can be used to aid both satellite remote-sensing applications and long-term weather modeling efforts by providing a method for parameter adjustments based on crop driven albedo changes, including changes in cropland composition related to commodity markets and other external factors.
Core Ideas A linked Economics land‐use model with the ALMANAC model has been constructed for crop simulation.The linked crop and economics modeling system can be used for estimating dynamic crop acreages.The impacts of policy and market changes on crop simulations can be studied with the linked system. Using crop models to simulate crop growth and productivity at a regional scale is a complex process designed to represent the observed impact of individual farmer decision‐making on the agricultural landscape. Typically, during agricultural simulation efforts, the planting acreages have largely been based on a set of predetermined, static scenarios. In this study, we developed a system to dynamically enhance the Agricultural Land Management Alternative with Numerical Assessment Criteria (ALMANAC) crop simulation model through a two‐way linkage with an economics land‐use model. This coupled model approach integrated farmers’ land‐use choices based on relative economic returns and produced dynamic land‐use probabilities for ALMANAC simulations through a feedback loop. The coupled model approach was intercompared with static crop modeling through a historic acreage approach, and comparable accuracies were found from both modeling efforts for the 2014 growing season. Furthermore, as a proof‐of‐concept effort, the method was applied to evaluate the impact of two scenarios on crop simulations: major crops (maize, soybean, and wheat) intensification through price increases (e.g., market change) and incentivized grassland conservation (e.g., policy change). The results of this sensitivity study suggest that the coupled system has the capability to integrate economic factors into traditional crop simulation, allowing for insight into the impacts of changes in markets and policies on agricultural landscapes and crop yields.
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