Exploring the Potential of Spatially Downscaled Solar-Induced Chlorophyll Fluorescence to Monitor Drought Effects on Gross Primary Production in Winter Wheat

Abstract

The impacts of drought on the terrestrial gross primary production (GPP) are the most intense and widespread in all extreme climate events. Solar-induced chlorophyll fluorescence (SIF) is considered as a direct representative of actual vegetation photosynthesis and has better performance in monitoring vegetation conditions than greenness-based vegetation indices (VIs) during drought events. Based on the spatially downscaled SIF (SIFds), VIs and GPP products, we explored the potential of SIFds to monitor drought effects on GPP in winter wheat. Firstly, the spatiotemporal dynamics of hydrometeorological factors and vegetation variables in winter wheat during drought events were observed. Then, the SIFds-GPP relationships in different phenological stages were examined in the rainfed area. Finally, the drought-induced GPP losses in different phenological stages were evaluated by scaling SIFds to GPP based on the linear SIFds-GPP relationship in the rainfed area. Results showed that SIFds could capture the spatiotemporal dynamics of drought-induced GPP variations in winter wheat during drought events, and it could quantify accurately the drought-induced GPP losses, with higher sensitivities to GPP changes during the vigorous growing periods. Our study reveals the applicability of SIFds to achieve regional agricultural drought detection and drought-induced GPP loss assessment, which can provide some helps for crop adaptation management.

The Relationship of Gross Primary Productivity with NDVI Rather than Solar-Induced Chlorophyll Fluorescence Is Weakened under the Stress of Drought

“…In contrast, downscaled SIF products have a better spatial resolution (0.05 • ), allowing for better differentiation between grassland and other land use types. Downscaled SIF also has a shorter temporal resolution (8 days), capturing the changes in vegetation due to drought better than other SIF data [32]. Our study area is mainly grassland in the Xilingol League, which is relatively homogeneous and meets the research requirements.…”

“…Chen et al ( 2019) investigated the potential of SIF in monitoring summer maize GPP under drought conditions and found that SIF was able to track spatiotemporal variations in GPP due to drought more effectively than NDVI [31]. Shen et al (2022) explored the capability of SIF in estimating the impact of drought on winter wheat GPP, demonstrating that SIF can accurately quantify GPP losses caused by drought [32]. However, research on the potential of SIF monitoring for grassland ecosystem GPP changes induced by drought is still relatively scarce.…”

A new two-decade (2001–2019) high-resolution agricultural primary productivity dataset for India

“…Several models are developed to estimate GPP and mainly they can be grouped as (1) Enzyme kinetic (EK) models 16 , 17 , (2) Empirical models 18 , (3) Solar-induced chlorophyll fluorescence (SIF) models 8 , 19 – 21 and (4) Light use efficiency (LUE) models 22 , 23 . LUE models are the most preferred approach for use of RS data and have undergone extensive improvements and modifications which add to their accuracy and adaptability to a wide range of conditions 24 .…”

Downscaling Solar-Induced Chlorophyll Fluorescence to a 0.05° Monthly Product Using AVHRR Data in East Asia (1995–2003)

“…Li and Xiao [25] OCO Various downscaled satellite SIF products have been developed, including SIF* [15], RSIF [16], SIF ______ 005 [17], SIF*new [18], SIFHuMo [19], SIFnet [20], TSIF [21], SDSIF [22], DSIF [23], and others [24]- [32], with applications in examining GPP responses to climate anomalies [33], [34], crop productivity forecasting [28], [35], poverty prediction [36], photosynthesis phenology [37], and vegetation zonality [38]. The existing SIF downscaling studies are summarized in Table I, showcasing the variety of approaches employed for downscaling satellite SIF products.…”