Evaluating the benefits of chlorophyll fluorescence for in-season crop productivity forecasting

REMOTE SENSING OF ENVIRONMENT

卷:260

Article

Sloat, Lindsey L.^1,2  Lin, Marena³  Butler, Ethan E.⁴  Johnson, Dave⁵  Holbrook, N. Michele⁶  Huybers, Peter J.⁷  Lee, Jung-Eun⁸  Mueller, Nathaniel D.^1,2 

[1] Colorado State Univ, Dept Ecosyst Sci & Sustainabil, Ft Collins, CO 80523 USA

[2] Colorado State Univ, Dept Soil & Crop Sci, Ft Collins, CO 80523 USA

[3] Univ Calif San Diego, Sch Global Policy & Strategy, San Diego, CA 92103 USA

[4] Univ Minnesota, Dept Forest Resources, St Paul, MN USA

[5] Natl Agr Stat Serv, USDA, Washington, DC USA

[6] Harvard Univ, Dept Organism & Evolutionary Biol, Cambridge, MA 02138 USA

[7] Harvard Univ, Dept Earth & Planetary Sci, 20 Oxford St, Cambridge, MA 02138 USA

[8] Brown Univ, Dept Environm & Planetary Sci, Providence, RI 02912 USA

关键词: Solar-induced fluorescence;    SIF;    NDVI;    GOME-2;    Crop productivity;    Crop forecasting;   

DOI  :  10.1016/j.rse.2021.112478

来源: Elsevier

【 摘 要 】

Remote sensing of solar-induced chlorophyll fluorescence (SIF) shows promise for monitoring the productivity of global agricultural systems. SIF-based primary productivity metrics have demonstrated higher fidelity to largescale patterns of crop productivity than reflectance-based vegetation indices when averaged across the growing season. In-season crop yield forecasting typically relies upon reflectance-based vegetation indices, raising the question of whether in-season monitoring could be improved by utilizing SIF. Here, we analyze patterns of US agricultural productivity from USDA surveys and their in-season relationships with coarse-resolution GOME-2 SIF, high-resolution downscaled SIF, SIF-based primary productivity metrics, MODIS NDVI, and MODIS GPP. We find that coarse-resolution SIF-based metrics and NDVI exhibit similar out-of-sample in-season (April-July and April-August) predictive ability, even when spatially filtering higher-resolution NDVI data to cropland areas. The downscaled SIF product performed more poorly than the coarse-resolution SIF, and MODIS GPP performed more poorly than MODIS NDVI. All forecasts are improved by incorporating county fixed effects to control for crosssectional differences between counties. NDVI-based metrics allow for significantly better yield predictions during drought conditions than SIF-based metrics, suggesting limited added value of SIF for early warning of drought impacts. The benefits of SIF for crop monitoring should be continually evaluated as the frequency and quality of SIF measurements continue to improve.

【 授权许可】

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