Vegetation Optical Depth: its potential as an agricultural drought indicator

A case study on pepper fields in Indonesia

Master thesis (2018)

Authors

A.L. Hemshorn de Sanchez Civil Engineering & Geosciences

Contributors

S.C. Steele-Dunne (supervisor 1)
E. Abraham (supervisor 2)
P.J. Vardon (supervisor 2)
Richard De Jeu (supervisor 1)
Faculty
Civil Engineering & Geosciences
Copyright: © 2018 Anna Luisa Hemshorn de Sanchez
Agriculture Water Resources Management Drought Drought monitoring Vegetation Optical Depth Remote Sensing
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Published Date

26-10-2018

Language

English

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Faculty

Civil Engineering & Geosciences

Abstract

Droughts are a serious threat to the planet. Especially in agriculture, the consequences are devastating leading to reduced yields or crop failure with further implications for economics, politics and society. Being able to monitor and predict agricultural droughts is an important step to reduce agricultural vulnerability and secure livelihoods. However, few drought indicators measure the actual crop response towards the hydrological conditions. Remote sensing bears great potential for this challenge due to its worldwide continuous coverage and relatively low costs. Vegetation Optical Depth (VOD), a remote sensing method, is a measure of above-ground vegetation water content based on passive microwaves, which are not affected by atmospheric distortion. The present research aims at analysing the potential of VOD as an agricultural drought indicator. A case study on pepper fields in Indonesia was conducted using daily VOD data between 2012 and 2018 at a downscaled spatial resolution of 100 x 100 m. Correlations between VOD anomalies and anomalies of other meteorological drought indicators were calculated. VOD was found to be correlated with the Standardized Precipitation Evaporation Index (SPEI) by 0.47, with the El Niño Southern Oscillation (ENSO) by -0.46 and with the Indian Ocean Dipole (IOD) by -0.46. These correlations indicate that other factors influence the VOD, which should be considered as an independent dataset. This study provides evidence that VOD is capable of capturing agricultural droughts. A cross-comparison was conducted with the Normalized Difference Vegetation Index (NDVI), which is often compared to VOD as an alternative measure. Results in the investigated area suggest that, although VOD has a lower spatial resolution, it performs better than NDVI. Finally, a roadmap was proposed towards developing a drought indicator based on the VOD anomalies. This roadmap aims at modelling future VOD based on the relation between SPEI and VOD by using the added value of VOD that this study reveals. This study is especially relevant for other tropical areas with a high cloud density.