Mapping corn and soybean phenometrics at field scales over the United States Corn Belt by fusing time series of Landsat 8 and Sentinel-2 data with VIIRS data

Abstract

Accurate and timely crop phenology is essential for efficient crop management and precise modelling of crop yield and productivity. It has been commonly mapped from moderate or coarse resolution (≥500 m) satellite data at a national or global scale, which generally represents the mixture of multiple crops or between crops and natural vegetation. Crop phenological progress at field scales (≤30 m) is critical for monitoring the growth of specie-specific crop types, but it is very challenging at large scales because of limited cloud-free observations in the satellite time series. To map corn and soybean phenometrics at a field scale (30 m) over entire Corn Belt in the United States, this study for the first time develops a new approach that fuses high temporal resolution Visible Infrared Imaging Radiometer Suite (VIIRS) observations (500 m) and high spatial resolution Harmonized Landsat and Sentinel-2 (HLS) data (30 m). The fused HLS-VIIRS time series (daily 30 m) is applied to map crop phenometrics of greenup onset, mid-greenup phase, maturity onset, senescence onset, mid-senescence phase, and dormancy onset. These six phenometrics are correlated to crop progress (CP) of seven corn and six soybean growth stages from planting to harvest that are observed by the National Agricultural Statistics Service (NASS) of the United States Department of Agriculture (USDA) and are further validated using PhenoCam near-surface time series imagery. The result indicates that the HLS-VIIRS crop phenometrics are capable of tracking the CP very well. Although their biophysical definitions of CP stages and sample coverage were not the same in our comparisons, the HLS-VIIRS crop phenometrics and NASS CP at a state level are closely comparable with a difference<4 days for corn and soybean emergence, 1 day for soybean dropping leaves, and 6–10 days for other corn and soybean growth stages. Their significant correlations (R2 ranging from 0.81 to 0.99, p < 0.01) suggest that the HLS-VIIRS crop phenometrics are robust proxies for predicting NASS CP with a high accuracy. Moreover, the HLS-VIIRS phenometrics aligns closely with PhenoCam phenological timings at various crop growth stages with an absolute average difference from 3 days to 5.3 days and bias from 1.5 to 3.5 days. The result demonstrates that the proposed approach could be implemented for monitoring field scale crop progress at a national or global scale.