Real-time monitoring of crop phenology is critical for assisting farmers managing crop growth and yield estimation. In this study, we presented an approach to monitor in real time crop phenology using timely available daily Visible Infrared Imaging Radiometer Suite (VIIRS) observations and historical Moderate Resolution Imaging Spectroradiometer (MODIS) datasets in the Midwestern United States. MODIS data at a spatial resolution of 500 m from 2003 to 2012 were used to generate the climatology of vegetation phenology. By integrating climatological phenology and timely available VIIRS observations in 2014 and 2015, a set of temporal trajectories of crop growth development at a given time for each pixel were then simulated using a logistic model. The simulated temporal trajectories were used to identify spring green leaf development and predict the occurrences of greenup onset, mid-greenup phase, and maximum greenness onset using curvature change rate. Finally, the accuracy of real-time monitoring from VIIRS observations was evaluated by comparing with summary crop progress (CP) reports of ground observations from the National Agricultural Statistics Service (NASS) of the United States Department of Agriculture (USDA). The results suggest that real-time monitoring of crop phenology from VIIRS observations is a robust tool in tracing the crop progress across regional areas. In particular, the date of mid-greenup phase from VIIRS was significantly correlated to the planting dates reported in NASS CP for both corn and soybean with a consistent lag of 37 days and 27 days on average (p < 0.01), as well as the emergence dates in CP with a lag of 24 days and 16 days on average (p < 0.01), respectively. The real-time monitoring of maximum greenness onset from VIIRS was able to predict the corn silking dates with an advance of 9 days (p < 0.01) and the soybean blooming dates with a lag of 7 days on average (p < 0.01). These findings demonstrate the capability of VIIRS observations to effectively monitor temporal dynamics of crop progress in real time at a regional scale.

In this paper, the accuracy of Suomi National Polar‐orbiting Partnership Visible Infrared Imaging Radiometer Suite (VIIRS) land surface albedo, which is derived from the direct estimation algorithm, was assessed using ground‐based albedo observations from a wireless sensor network over a heterogeneous cropland in the Huailai station, northern China. Data from six nodes spanning 2013–2014 over vegetation, bare soil, and mixed terrain surfaces were utilized to provide ground reference at VIIRS pixel scale. The performance of VIIRS albedo was also compared with Global LAnd Surface Satellite (GLASS) and Moderate Resolution Imaging Spectroradiometer (MODIS) albedos (Collection 5 and 6). The results indicate that the current granular VIIRS albedo has a high accuracy with a root‐mean‐square error of 0.02 for typical land covers. They are significantly correlated with ground references indicated by a correlation coefficient (R) of 0.73. The VIIRS albedo shows distinct advantages to GLASS and MODIS albedos over bare soil and mixed‐cover surfaces, while it is inferior to the other two products over vegetated surfaces. Furthermore, its time continuity and the ability to capture the abrupt change of surface albedo are better than that of GLASS and MODIS albedo.

Land surface albedo, which is defined as the ratio of the reflected shortwave radiation to the incoming solar radiation, represents the integrated surface hemispheric reflectivity over the solar spectrum (Liang et al., 2010;Lucht et al., 2000). It is a critical geographical parameter that directly controls the surface radiation budget between the surface and atmosphere, significantly affecting the energy budget, and atmospheric and water circulations (Boussetta et al., 2015;Liang et al., 2010). Therefore, it is listed as an Essential Climate Variable (GCOS, 2004) and basic climate forcing used in hydrological, and biogeochemical models, as well as in weather forecasting (