Despite the potential implications of a cropland canopy water content (CCWC) thematic product, no global remotely sensed CCWC product is currently generated. The successful launch of the Landsat-8 Operational Land Imager (OLI) in 2012, Sentinel-2A Multispectral Instrument (MSI) in 2015, followed by Sentinel-2B in 2017, make possible the opportunity for CCWC estimation at a spatial and temporal scale that can meet the demands of potential operational users. In this study, we designed and tested a novel radiative transfer model (RTM) inversion technique to combine multiple sources of a priori data in a look-up table (LUT) for inverting the NASA Harmonized Landsat Sentinel-2 (HLS) product for CCWC estimation. This study directly builds on previous research for testing the constraint of the leaf parameter (Ns) in PROSPECT, by applying those constraints in PRO4SAIL in an agricultural setting where the variability of canopy parameters are relatively minimal. In total, 225 independent leaf measurements were used to train the LUTs, and 102 field data points were collected over the 2015–2017 growing seasons for validating the inversions. The results confirm increasing a priori information and regularization yielded the best performance for CCWC estimation. Despite the relatively low variable canopy conditions, the inclusion of Ns constraints did not improve the LUT inversion. Finally, the inversion of Sentinel-2 data outperformed the inversion of Landsat-8 in the HLS product. The method demonstrated ability for HLS inversion for CCWC estimation, resulting in the first HLS-based CCWC product generated through RTM inversion.
Machine learning methods’ performance in radiative transfer model inversion to retrieve plant traits from Sentinel-2 data of a mixed mountain forest