TY - JOUR
T1 - Evaluating the benefits of chlorophyll fluorescence for in-season crop productivity forecasting
AU - Sloat, Lindsey L.
AU - Lin, Marena
AU - Butler, Ethan E.
AU - Johnson, Dave
AU - Holbrook, N. Michele
AU - Huybers, Peter J.
AU - Lee, Jung Eun
AU - Mueller, Nathaniel D.
N1 - Funding Information:
This research was supported by USDA NIFA2016-67012-27434 to NDM.
Funding Information:
This research was supported by USDA NIFA 2016-67012-27434 to NDM.
Publisher Copyright:
© 2021 Elsevier Inc.
PY - 2021/7
Y1 - 2021/7
N2 - 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 large-scale 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 cross-sectional 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.
AB - 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 large-scale 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 cross-sectional 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.
KW - Crop forecasting
KW - Crop productivity
KW - GOME-2
KW - NDVI
KW - SIF
KW - Solar-induced fluorescence
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U2 - 10.1016/j.rse.2021.112478
DO - 10.1016/j.rse.2021.112478
M3 - Article
AN - SCOPUS:85105026582
SN - 0034-4257
VL - 260
JO - Remote Sensing of Environment
JF - Remote Sensing of Environment
M1 - 112478
ER -