Land Surface Phenologies and Seasonalities in the US Prairie Pothole Region Coupling AMSR Passive Microwave Data with the USDA Cropland Data Layer

Abstract

Land surface phenologies and seasonalities in the US Prairie Pothole Region (PPR) were characterized using land surface variables derived from the coarse spatial resolution (25 km) Advanced Microwave Scanning Radiometer (AMSR) blended data for 2003 to 2016 linked with the optically based USDA NASS Crop Data Layer (CDL) at a much finer spatial resolution. Two transects of AMSR pixels—one in east-central North Dakota and the other in eastern South Dakota—were selected for analysis. The AMSR data were grouped earlier (2003–2005, 2007) and later (2013–2016) to emphasize temporal change and to avoid data discontinuity in 2011–2012 and a major drought in 2006. The nonparametric Mann-Kendall trend test on the CDL data revealed that area in grasslands and wetlands strongly decreased in both transects, while corn and soybean coverage strongly increased. In crop-dominated sites, the AMSR vegetation optical depth (VOD) time series caught the early spring growth, ploughing, and crop growth and senescence. In contrast, the VOD time series at grassland dominated sites exhibited a lower peak but extended growth period. Crop-dominated sites had significantly higher amplitude VODs in both periods and transects. Based on the paired two-sample t-test, neither the peak VOD amplitude nor the peak VOD timing measured in accumulated growing degree-days was significantly different between temporal groups in the North Dakota transect. In contrast, in South Dakota, both the peak VOD amplitude and its timing were significantly different with shifts to later peak timing during the 2013–2016 period. In addition, in South Dakota but not North Dakota, there were significantly earlier shifts in the timing of peak growing degree-days and peak precipitation water vapor. Both spatial and temporal changes in AMSR land surface variables are linked to shifts in land cover along the South Dakota transect as revealed in the CDL data. More research is required to understand the dynamics evident in the passive microwave time series.