Literature DB >> 31217640

Global relationships among traditional reflectance vegetation indices (NDVI and NDII), evapotranspiration (ET), and soil moisture variability on weekly timescales.

Joanna Joiner1, Yasuko Yoshida2, Martha Anderson3, Thomas Holmes1, Christopher Hain4, Rolf Reichle1, Randal Koster1, Elizabeth Middleton1, Fan-Wei Zeng2.   

Abstract

Monitoring the effects of water availability on vegetation globally using satellites is important for applications such as drought early warning, precision agriculture, and food security as well as for more broadly understanding relationships between water and carbon cycles. In this global study, we examine how quickly several satellite-based indicators, assumed to have relationships with water availability, respond, on timescales of days to weeks, in comparison with variations in root-zone soil moisture (RZM) that extends to about 1 m depth. The satellite indicators considered are the normalized difference vegetation and infrared indices (NDVI and NDII, respectively) derived from reflectances obtained with moderately wide (20-40 nm) spectral bands in the visible and near-infrared (NIR) and evapotranspiration (ET) estimated from thermal infrared observations and normalized by a reference ET. NDVI is primarily sensitive to chlorophyll contributions and vegetation structure while NDII may contain additional information on water content in leaves and canopy. ET includes both the loss of root zone soil water through transpiration (modulated by stomatal conductance) as well as evaporation from bare soil. We find that variations of these satellite-based drought indicators on time scales of days to weeks have significant correlations with those of RZM in the same water-limited geographical locations that are dominated by grasslands, shrublands, and savannas whose root systems are generally contained within the 1 m RZM layer. Normalized ET interannual variations show generally a faster response to water deficits and enhancements as compared with those of NDVI and NDII, particularly in sparsely vegetated regions.

Entities:  

Keywords:  Drought; Evapotranspiration; Root zone soil moisture; Vegetation; Vegetation index

Year:  2018        PMID: 31217640      PMCID: PMC6582971          DOI: 10.1016/j.rse.2018.10.020

Source DB:  PubMed          Journal:  Remote Sens Environ        ISSN: 0034-4257            Impact factor:   10.164


  3 in total

1.  Data Assimilation of High-Resolution Thermal and Radar Remote Sensing Retrievals for Soil Moisture Monitoring in a Drip-Irrigated Vineyard.

Authors:  Fangni Lei; Wade T Crow; William P Kustas; Jianzhi Dong; Yun Yang; Kyle R Knipper; Martha C Anderson; Feng Gao; Claudia Notarnicola; Felix Greifeneder; Lynn M McKee; Joseph G Alfieri; Christopher Hain; Nick Dokoozlian
Journal:  Remote Sens Environ       Date:  2020-03-15       Impact factor: 10.164

2.  A Deep Learning Method for Fully Automatic Stomatal Morphometry and Maximal Conductance Estimation.

Authors:  Jonathon A Gibbs; Lorna Mcausland; Carlos A Robles-Zazueta; Erik H Murchie; Alexandra J Burgess
Journal:  Front Plant Sci       Date:  2021-12-02       Impact factor: 5.753

3.  Leaf water potential of coffee estimated by landsat-8 images.

Authors:  Daniel Andrade Maciel; Vânia Aparecida Silva; Helena Maria Ramos Alves; Margarete Marin Lordelo Volpato; João Paulo Rodrigues Alves de Barbosa; Vanessa Cristina Oliveira de Souza; Meline Oliveira Santos; Helbert Rezende de Oliveira Silveira; Mayara Fontes Dantas; Ana Flávia de Freitas; Gladyston Rodrigues Carvalho; Jacqueline Oliveira Dos Santos
Journal:  PLoS One       Date:  2020-03-18       Impact factor: 3.240

  3 in total

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