Estimating Crop and Grass Productivity over the United States Using Satellite Solar-Induced Chlorophyll Fluorescence, Precipitation and Soil Moisture Data

Zong-Liang Yang,Maryia Halubok

doi:10.3390/rs12203434

Zong-Liang Yang, Maryia Halubok

Open Access

https://doi.org/10.3390/rs12203434

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Journal: Remote Sensing	Publication Date: Oct 19, 2020
Citations: 6	License type: CC BY 4.0

Affiliation: The University of Texas at Austin

Abstract

This study investigates how gross primary production (GPP) estimates can be improved with the use of solar-induced chlorophyll fluorescence (SIF) based on the interdependence between SIF, precipitation, soil moisture and GPP itself. We have used multi-year datasets from Global Ozone Monitoring Experiment-2 (GOME-2), Tropical Rainfall Measuring Mission (TRMM), European Space Agency Climate Change Initiative Soil Moisture (ESA CCI SM), and FLUXNET observations from ten stations in the continental United States. We have employed a GPP quantification framework that makes use of two factors whose influence on the SIF–GPP relationship was not evaluated previously—namely, differential plant sensitivity to water supply at different stages of its lifecycle and spatial variability patterns in SIF that are in contrast to those of GPP, precipitation, and soil moisture. It was found that over the Great Plains and Texas, fluorescence emission levels lag behind precipitation events from about two weeks for grasses to four weeks for crops. The spatial variability of SIF and GPP is shown to be characterized by different patterns: SIF demonstrates less variation over the same spatial extent as compared to GPP, precipitation and soil moisture. Thus, using newly introduced SIF–precipitation lead–lag relationships, we estimate GPP using SIF, precipitation and soil moisture data for grasses and crops over the US by applying the multiple linear regression technique. Our GPP estimates capture the drought impact over the US better than those from Moderate Resolution Imaging Spectroradiometer (MODIS). During the drought year of 2011 over Texas, our GPP values show a decrease by 50–75 gC/m2/month, as opposed to the normal yielding year of 2007. In 2012, a drought year over the Great Plains, we observe a significant reduction in GPP, as compared to 2007. Hence, estimating GPP using specific SIF–GPP relationships, and information on different plant functional types (PFTs) and their interactions with precipitation and soil moisture over the Great Plains and Texas regions can help produce more reasonable GPP estimates.

Highlights

Knowledge of how global vegetation takes up atmospheric carbon dioxide is crucial for understanding the Earth’s carbon cycle processes
It is to be noted that while changes in soil moisture and precipitation are already reflected in corresponding solar-induced chlorophyll fluorescence (SIF) signal fluctuations, it is possible that these parameters might not be well delineated by SIF due to its comparatively lower spatial resolution
We have modeled gross primary production with the use of solar-induced chlorophyll fluorescence, precipitation and soil moisture, while considering differences in grass and crop plant functional types over the contiguous US

Summary

Introduction

Knowledge of how global vegetation takes up atmospheric carbon dioxide is crucial for understanding the Earth’s carbon cycle processes. Gross primary production (GPP), which is equivalent to the amount of carbon fixed during photosynthesis, constitutes the largest global land carbon flux that maintains ecosystem functions such as growth and respiration [1,2,3]. As drought events are projected to increase over both the US and other regions [8,9,10,11] in future, it is important to quantify their effects on plant productivity [12,13]. Challenges remain due to the complexity of plants’ biophysical and physiological processes associated with droughts [14]

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