TY - JOUR
T1 - Multi-sensor remote sensing for drought characterization: current status, opportunities and a roadmap for the future
AU - Jiao, Wenzhe
AU - Wang, Lixin
AU - McCabe, Matthew
N1 - KAUST Repository Item: Exported on 2021-02-15
Acknowledgements: We thank Genevieve Staats (www.createvedesign.com) for the conceptualization and design of Figure 1. We also thank Bruno Aragon (KAUST) for the comparison of Landsat 8, Sentinel-2A and Planet images of an agricultural area along the Nile River near Banha, Egypt (Figure 2). We acknowledge support from the Division of Earth Sciences of the National Science Foundation (NSF EAR-1554894) and from the Agriculture and Food Research Initiative program (201767013-26191) of the USDA National Institute of Food and Agriculture. Matthew McCabe is supported by funding from KAUST. We would like to thank the editors and three anonymous reviewers for their constructive comments and suggestions, which we believe significantly strengthened our manuscript.
PY - 2021/2/6
Y1 - 2021/2/6
N2 - Satellite based remote sensing offers one of the few approaches able to monitor the spatial and temporal development of regional to continental scale droughts. A unique element of remote sensing platforms is their multi-sensor capability, which enhances the capacity for characterizing drought from a variety of perspectives. Such aspects include monitoring drought influences on vegetation and hydrological responses, as well as assessing sectoral impacts (e.g., agriculture). With advances in remote sensing systems along with an increasing range of platforms available for analysis, this contribution provides a timely and systematic review of multi-sensor remote sensing drought studies, with a particular focus on drought related datasets, drought related phenomena and mechanisms, and drought modeling. To explore this topic, we first present a comprehensive summary of large-scale remote sensing datasets that can be used for multi-sensor drought studies. We then review the role of multi-sensor remote sensing for exploring key drought related phenomena and mechanisms, including vegetation responses to drought, land-atmospheric feedbacks during drought, drought-induced tree mortality, drought-related ecosystem fires, post-drought recovery and legacy effects, flash drought, as well as drought trends under climate change. A summary of recent modeling advances towards developing integrated multi-sensor remote sensing drought indices is also provided. We conclude that leveraging multi-sensor remote sensing provides unique benefits for regional to global drought studies, particularly in: 1) revealing the complex drought impact mechanisms on ecosystem components; 2) providing continuous long-term drought related information at large scales; 3) presenting real-time drought information with high spatiotemporal resolution; 4) providing multiple lines of evidence of drought monitoring to improve modeling and prediction robustness; and 5) improving the accuracy of drought monitoring and assessment efforts. We specifically highlight that more mechanism-oriented drought studies that leverage a combination of sensors and techniques (e.g., optical, microwave, hyperspectral, LiDAR, and constellations) across a range of spatiotemporal scales are needed in order to progress and advance our understanding, characterization and description of drought in the future.
AB - Satellite based remote sensing offers one of the few approaches able to monitor the spatial and temporal development of regional to continental scale droughts. A unique element of remote sensing platforms is their multi-sensor capability, which enhances the capacity for characterizing drought from a variety of perspectives. Such aspects include monitoring drought influences on vegetation and hydrological responses, as well as assessing sectoral impacts (e.g., agriculture). With advances in remote sensing systems along with an increasing range of platforms available for analysis, this contribution provides a timely and systematic review of multi-sensor remote sensing drought studies, with a particular focus on drought related datasets, drought related phenomena and mechanisms, and drought modeling. To explore this topic, we first present a comprehensive summary of large-scale remote sensing datasets that can be used for multi-sensor drought studies. We then review the role of multi-sensor remote sensing for exploring key drought related phenomena and mechanisms, including vegetation responses to drought, land-atmospheric feedbacks during drought, drought-induced tree mortality, drought-related ecosystem fires, post-drought recovery and legacy effects, flash drought, as well as drought trends under climate change. A summary of recent modeling advances towards developing integrated multi-sensor remote sensing drought indices is also provided. We conclude that leveraging multi-sensor remote sensing provides unique benefits for regional to global drought studies, particularly in: 1) revealing the complex drought impact mechanisms on ecosystem components; 2) providing continuous long-term drought related information at large scales; 3) presenting real-time drought information with high spatiotemporal resolution; 4) providing multiple lines of evidence of drought monitoring to improve modeling and prediction robustness; and 5) improving the accuracy of drought monitoring and assessment efforts. We specifically highlight that more mechanism-oriented drought studies that leverage a combination of sensors and techniques (e.g., optical, microwave, hyperspectral, LiDAR, and constellations) across a range of spatiotemporal scales are needed in order to progress and advance our understanding, characterization and description of drought in the future.
UR - http://hdl.handle.net/10754/667386
UR - https://linkinghub.elsevier.com/retrieve/pii/S0034425721000316
UR - http://www.scopus.com/inward/record.url?scp=85100388624&partnerID=8YFLogxK
U2 - 10.1016/j.rse.2021.112313
DO - 10.1016/j.rse.2021.112313
M3 - Article
SN - 0034-4257
VL - 256
SP - 112313
JO - Remote Sensing of Environment
JF - Remote Sensing of Environment
ER -
