Journal cover Journal topic
Earth System Science Data The data publishing journal
Journal topic

Journal metrics

IF value: 9.197
IF9.197
IF 5-year value: 9.612
IF 5-year
9.612
CiteScore value: 12.5
CiteScore
12.5
SNIP value: 3.137
SNIP3.137
IPP value: 9.49
IPP9.49
SJR value: 4.532
SJR4.532
Scimago H <br class='widget-line-break'>index value: 48
Scimago H
index
48
h5-index value: 35
h5-index35
Preprints
https://doi.org/10.5194/essd-2020-36
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-2020-36
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  25 May 2020

25 May 2020

Review status
This preprint is currently under review for the journal ESSD.

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.

Interactive discussion

Status: final response (author comments only)
Status: final response (author comments only)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
[Login for Authors/Topical Editors] [Subscribe to comment alert] 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.

Viewed

Total article views: 489 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
308 146 35 489 27 40 29
  • HTML: 308
  • PDF: 146
  • XML: 35
  • Total: 489
  • Supplement: 27
  • BibTeX: 40
  • EndNote: 29
Views and downloads (calculated since 25 May 2020)
Cumulative views and downloads (calculated since 25 May 2020)

Viewed (geographical distribution)

Total article views: 467 (including HTML, PDF, and XML) Thereof 467 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved

No saved metrics found.

Discussed

No discussed metrics found.
Latest update: 29 Sep 2020
Download
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.
Photosynthesis, quantified by gross primary production (GPP), is a key process of earth system....
Citation