Preprints
https://doi.org/10.5194/essd-2020-36
https://doi.org/10.5194/essd-2020-36

  25 May 2020

25 May 2020

Review status: a revised version of this preprint was accepted for the journal ESSD and is expected to appear here in due course.

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

Chongya Jiang1,2, Kaiyu Guan1,2,3, Genghong Wu1,2, Bin Peng1,3, and Sheng Wang1,2 Chongya Jiang et al.
  • 1College of Agricultural, Consumer and Environmental Sciences, University of Illinois at Urbana Champaign, Urbana, IL 61801, USA
  • 2Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana Champaign, Urbana, IL 61801, USA
  • 3National Center of Supercomputing Applications, University of Illinois at Urbana Champaign, Urbana, IL 61801, USA

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.

Chongya Jiang et al.

 
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Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Chongya Jiang et al.

Data sets

SLOPE daily and 250 m gross primary productivity (GPP) for the CONUS, 2000-2019 C. Jiang and K. Guan https://doi.org/10.3334/ORNLDAAC/1786

Chongya Jiang et al.

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Short summary
Photosynthesis, quantified by gross primary production (GPP), is a key process of earth system. To date, there is a lack of high-spatiotemporal-resolution, real-time, and observation-based GPP dataset. This work addresses this gap by developing a SatelLite Only Photosynthesis Estimation (SLOPE) model and generating a new GPP product, which is advanced in spatial and temporal resolutions, instantaneity, and quantitative uncertainty. The dataset will benefit a range of research and applications.