Reconstructing daily 30 m NDVI over complex agricultural landscapes using a crop reference curve approach

Abstract Multi-sensor remote sensing data fusion technologies have been developed and widely applied in recent years, providing a feasible and economical solution to increase the availability of high spatial and temporal resolution data. These methods, however, have been challenging to apply in highly heterogeneous areas, especially in complex agricultural landscapes where there are rapid changes at small scales, while features at larger scales change more slowly. In this study, we developed a novel method to reconstruct daily 30 m Normalized Difference Vegetation Index (NDVI) using imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat and Landsat-like platforms, and the Cropland Data Layer (CDL). This method utilizes a crop reference curve (CRC) approach, in which a set of NDVI time series are extracted from pure MODIS pixels (250 m resolution) identified using the CDL, and then used to fit Landsat-like observations (30 m). The CRC based method was applied over a complex agricultural landscape in the Choptank River watershed on the Eastern Shore of Maryland. Landsat data from 2013 and 2014 and Harmonized Landsat and Sentinel-2 (HLS) data from 2018 were used to reconstruct 30 m daily NDVI maps for major crop types. Results show that the relative error (RE) in reconstructed NDVI is around 6–8% during periods of rapid crop growth, and 3–5% during peak periods when growth is slow. The accuracy of the CRC method outperforms a standard image pair-based data fusion algorithm (Spatial and Temporal Adaptive Reflectance Fusion Model; STARFM), which yields RE of 4–9% in slow-growth periods and 10–16% in fast-growth periods when clear Landsat images are scarce. The CRC method was also compared with time-series data fusion methods, including a harmonic fitting model and the SaTellite dAta IntegRation (STAIR) model. The results show that CRC gives similar results when the Landsat-like image availability is high (around 27 images per year), but outperforms other methods when availability is limited (less than 15 images per year). The reconstructed NDVI time series for corn, soybean, winter wheat/soybean and forest at 30-m resolution show clear phenological patterns at the sub-field scale. The resulting 30-m NDVI timeseries data provide useful information for mapping crop phenology and monitoring crop condition in complex agricultural landscapes, especially for complex double-cropping areas. However, the input requirement of an accurate 30-m crop classification map constrains its application to areas and periods where classifications are available.