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

Abstract

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 for 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 d), instantaneity (latency of 1 d versus >2 weeks) 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 atmosphere 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 Cropland 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 85 % of the spatial and temporal variations in GPP acquired from 49 AmeriFlux eddy-covariance sites (324 site years), with a root-mean-square error (RMSE) of 1.63 gC m−2 d−1. The median R2 over C3 and C4 crop sites reaches 0.87 and 0.94, respectively, indicating great potentials for monitoring crops, in particular bioenergy crops, at the field level. With such a satisfactory performance and its distinct characteristics in spatiotemporal resolution and instantaneity, the proposed SLOPE GPP product is promising for biological and environmental research, carbon cycle research, and a broad range of real-time applications at the regional scale. 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/, last access: 20 January 2021) (Jiang and Guan, 2020), and the real-time dataset is available upon request.

Matching high resolution satellite data and flux tower footprints improves their agreement in photosynthesis estimates

“…Consequently, NIRv ECfootprint PF × daily PAR BESS performed similarly to daily NIRvP ECfootprint PF in estimating daily GPP (Figure 10 and 11). Accordingly, Planet Fusion -derived NIRv combined with daily PAR maps enables us to generate daily GPP maps with a high degree of fidelity (Figure 10), which is consistent with the finding of previous studies at different scales (Jiang et al, 2021).…”

“…As PAR is low during those times, NIRvP has a somewhat dampened nonlinearity compared to NIRv. Assuming that background effects are indeed partly causing the nonlinearities, using soil-adjusted NIRv may further improve the results (Jiang et al, 2021). Overall, the better performance of NIRvP ECfootprint PF for estimating GPP (Figure 6; Table A6) is consistent with both a mechanistic understanding of GPP drivers and the previous literature on the matter.…”

“…The results of our study suggest that CubeSat imagery can be used to obtain daily canopy photosynthesis data at fine spatial and temporal resolution, thus overcoming limitations inherent in using only observations from individual sun-synchronous satellites such as MODIS and Landsat. Moreover, potential improvements of Planet Fusion -derived NIRv for NIRvP mapping have already been suggested (Jiang et al, 2021). In particular, Jiang et al (2021) and Zeng et al (2019) suggested methods to account for the non-zero NIRv values for bare soil or dead vegetation, which could lead to improvements in GPP estimation especially outside the main growing season.…”

“…Moreover, potential improvements of Planet Fusion -derived NIRv for NIRvP mapping have already been suggested (Jiang et al, 2021). In particular, Jiang et al (2021) and Zeng et al (2019) suggested methods to account for the non-zero NIRv values for bare soil or dead vegetation, which could lead to improvements in GPP estimation especially outside the main growing season.…”

“…However, reflectance does not include information regarding the amount of incoming photosynthetically active radiation (PAR), which is a dominant factor driving daily GPP variation. Therefore, NIR radiation reflected from vegetation (NIRvP), which is the product of NIRv and PAR, has been proposed as a structural proxy for GPP estimation (Dechant et al, 2022;Dechant et al, 2020;Wu et al, 2020) and has been widely applied at hourly to daily timescales for site-level and larger scale of GPP estimation since then (Baldocchi et al, 2020(Baldocchi et al, , 2022Dechant et al, 2020;Jiang et al, 2021;Liu et al, 2020). Baldocchi et al (2020) found that NIRvP from in situ spectral sensors tracked diurnal to seasonal variations of tower-based GPP well for individual sites, but that the slopes in the NIRvP-GPP regressions varied considerably across sites and years.…”