Investigating the Response of Vegetation to Flash Droughts by Using Cross-Spectral Analysis and an Evapotranspiration-Based Drought Index

Investigating the Response of Vegetation to Flash Droughts by Using Cross-Spectral Analysis and an Evapotranspiration-Based Drought Index

Li, Peng (Chinese Academy of Sciences; International Research Center for Big Data for Sustainable Development Goals; University of Chinese Academy of Sciences)
Jia, Li (Chinese Academy of Sciences; International Research Center for Big Data for Sustainable Development Goals)
Lu, Jing (Chinese Academy of Sciences)
Jiang, Min (Chinese Academy of Sciences)
Zheng, C. (Chinese Academy of Sciences)
Menenti, M. (TU Delft Optical and Laser Remote Sensing; Chinese Academy of Sciences)

2024

Flash droughts tend to cause severe damage to agriculture due to their characteristics of sudden onset and rapid intensification. Early detection of the response of vegetation to flash droughts is of utmost importance in mitigating the effects of flash droughts, as it can provide a scientific basis for establishing an early warning system. The commonly used method of determining the response time of vegetation to flash drought, based on the response time index or the correlation between the precipitation anomaly and vegetation growth anomaly, leads to the late detection of irreversible drought effects on vegetation, which may not be sufficient for use in analyzing the response of vegetation to flash drought for early earning. The evapotranspiration-based (ET-based) drought indices are an effective indicator for identifying and monitoring flash drought. This study proposes a novel approach that applies cross-spectral analysis to an ET-based drought index, i.e., Evaporative Stress Anomaly Index (ESAI), as the forcing and a vegetation-based drought index, i.e., Normalized Vegetation Anomaly Index (NVAI), as the response, both from medium-resolution remote sensing data, to estimate the time lag of the response of vegetation vitality status to flash drought. An experiment on the novel method was carried out in North China during March–September for the period of 2001–2020 using remote sensing products at 1 km spatial resolution. The results show that the average time lag of the response of vegetation to water availability during flash droughts estimated by the cross-spectral analysis over North China in 2001–2020 was 5.9 days, which is shorter than the results measured by the widely used response time index (26.5 days). The main difference between the phase lag from the cross-spectral analysis method and the response time from the response time index method lies in the fundamental processes behind the definitions of the vegetation response in the two methods, i.e., a subtle and dynamic fluctuation signature in the response signal (vegetation-based drought index) that correlates with the fluctuation in the forcing signal (ET-based drought index) versus an irreversible impact indicated by a negative NDVI anomaly. The time lag of the response of vegetation to flash droughts varied with vegetation types and irrigation conditions. The average time lag for rainfed cropland, irrigated cropland, grassland, and forest in North China was 5.4, 5.8, 6.1, and 6.9 days, respectively. Forests have a longer response time to flash droughts than grasses and crops due to their deeper root systems, and irrigation can mitigate the impacts of flash droughts. Our method, based on cross-spectral analysis and the ET-based drought index, is innovative and can provide an earlier warning of impending drought impacts, rather than waiting for the irreversible impacts to occur. The information detected at an earlier stage of flash droughts can help decision makers in developing more effective and timely strategies to mitigate the impact of flash droughts on ecosystems.

cross-spectral analysis
ET-based drought index
flash drought
time lag
vegetation response

http://resolver.tudelft.nl/uuid:3046983c-9eb4-4320-8d7b-2cce24defc0b

https://doi.org/10.3390/rs16091564

2072-4292

Remote Sensing, 16 (9)

Institutional Repository

journal article

© 2024 Peng Li, Li Jia, Jing Lu, Min Jiang, C. Zheng, M. Menenti