A daily, 250 m, and real-time gross primary productivity product(2000–present) covering the Contiguous United States

Abstract. Gross primary productivity (GPP) quantifies the amount of carbon dioxide (CO2) fixed by plants through photosynthesis. Although as a key quantity of terrestrial ecosystems, there is a lack of high-spatial-and-temporal-resolution, real-time, and observation-based GPP products. To address this critical gap, here we leverage a state-of-the-art vegetation index, near‐infrared reflectance of vegetation (NIRV), along with accurate photosynthetically active radiation (PAR), to produce a SatelLite Only Photosynthesis Estimation (SLOPE) GPP product in the Contiguous United States (CONUS). Compared to existing GPP products, the proposed SLOPE product is advanced in its spatial resolution (250 m versus > 500 m), temporal resolution (daily versus 8-day), instantaneity (1 day latency versus > 2 weeks latency), and quantitative uncertainty (on a per-pixel and daily basis versus no uncertainty information available). These characteristics are achieved because of several technical innovations employed in this study: (1) SLOPE couples machine learning models with MODIS atmospheric and land products to accurately estimate PAR. (2) SLOPE couples highly efficient and pragmatic gap-filling and filtering algorithms with surface reflectance acquired by both Terra and Aqua MODIS satellites to derive a soil-adjusted NIRV (SANIRV) dataset. (3) SLOPE couples a temporal pattern recognition approach with a long-term Crop Data Layer (CDL) product to predict dynamic C4 crop fraction. Through developing a parsimonious model with only two slope parameters, the proposed SLOPE product explains 84 % of the spatial and temporal variations in GPP acquired from 50 AmeriFlux eddy covariance sites (332 site-years), with a root-mean-square error (RMSE) of 1.65 gC m−2 d−1. With such a satisfactory performance and its distinct characteristics in spatiotemporal resolution and instantaneity, the proposed SLOPE GPP product is promising for regional carbon cycle research and a broad range of real-time applications. The archived dataset is available at https://doi.org/10.3334/ORNLDAAC/1786 (Download page: https://daac.ornl.gov/daacdata/cms/SLOPE_GPP_CONUS/data/ ) (Jiang and Guan, 2020), and the real-time dataset is available upon request.