Improved food-insecurity prediction in smallholder-dominated landscapes using MODIS Enhanced Vegetation Index and Google Earth Engine: a case study in South Central Ethiopia

Recent droughts and food insecurity underline the need for objective, timely, spatially explicit food aid prediction in Ethiopia. We developed a generic user-friendly method to detect greening of agricultural areas and derive predictions of agricultural production for potentially food-insecure areas...

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Autores principales:	Kefyalew Sahle Kibret, Carsten Marohn, Georg Cadisch
Formato:	article
Lenguaje:	EN
Publicado:	Taylor & Francis Group 2021
Materias:	emergency need assessment south central ethiopia drought risk yield variability agricultural production monitoring modis terra/aqua evi Oceanography GC1-1581 Geology QE1-996.5
Acceso en línea:	https://doaj.org/article/05cbc85eca0e40e681f8a75a568b1b44
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spelling	oai:doaj.org-article:05cbc85eca0e40e681f8a75a568b1b442021-11-17T14:22:00ZImproved food-insecurity prediction in smallholder-dominated landscapes using MODIS Enhanced Vegetation Index and Google Earth Engine: a case study in South Central Ethiopia2279-725410.1080/22797254.2021.1999176https://doaj.org/article/05cbc85eca0e40e681f8a75a568b1b442021-01-01T00:00:00Zhttp://dx.doi.org/10.1080/22797254.2021.1999176https://doaj.org/toc/2279-7254Recent droughts and food insecurity underline the need for objective, timely, spatially explicit food aid prediction in Ethiopia. We developed a generic user-friendly method to detect greening of agricultural areas and derive predictions of agricultural production for potentially food-insecure areas. We used the Enhanced Vegetation Index (EVI) from combined Terra/Aqua MODIS (Moderate Resolution Imaging Spectroradiometer) images to generate EVI time series over multiple growing seasons. Maximum seasonal greening (EVImax), as proxy for biomass and expected crop yield, was related to rainfall variability and to indicate areas of risk for crop failure due to drought within the necessary reaction time for emergency aid. Four agroecological zones were covered from 2003 to 2019. Vegetation periods per 250m pixel were calculated back from EVImax. EVImax was validated against measured yields on large-scale farms. Interannual means and variability of EVImax served to assess production and drought risk. Yield predictions corresponded well with wheat production (r2≅0.5 p≤0.05). High temporal variability and low absolute EVI indicated drought-prone areas. EVI was positively correlated with rainfall data in cropped drought-prone areas (r2≅0.4, p≤0.05), but negatively in temporally water-logged highlands (r2≅0.3, p≤0.05). Our user-friendly approach on Google Earth Engine can accurately detect imminent food insecurity and facilitate timely interventions.Kefyalew Sahle KibretCarsten MarohnGeorg CadischTaylor & Francis Grouparticleemergency need assessmentsouth central ethiopiadrought riskyield variabilityagricultural production monitoringmodis terra/aquaeviOceanographyGC1-1581GeologyQE1-996.5ENEuropean Journal of Remote Sensing, Vol 54, Iss 1, Pp 624-640 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	emergency need assessment south central ethiopia drought risk yield variability agricultural production monitoring modis terra/aqua evi Oceanography GC1-1581 Geology QE1-996.5
spellingShingle	emergency need assessment south central ethiopia drought risk yield variability agricultural production monitoring modis terra/aqua evi Oceanography GC1-1581 Geology QE1-996.5 Kefyalew Sahle Kibret Carsten Marohn Georg Cadisch Improved food-insecurity prediction in smallholder-dominated landscapes using MODIS Enhanced Vegetation Index and Google Earth Engine: a case study in South Central Ethiopia
description	Recent droughts and food insecurity underline the need for objective, timely, spatially explicit food aid prediction in Ethiopia. We developed a generic user-friendly method to detect greening of agricultural areas and derive predictions of agricultural production for potentially food-insecure areas. We used the Enhanced Vegetation Index (EVI) from combined Terra/Aqua MODIS (Moderate Resolution Imaging Spectroradiometer) images to generate EVI time series over multiple growing seasons. Maximum seasonal greening (EVImax), as proxy for biomass and expected crop yield, was related to rainfall variability and to indicate areas of risk for crop failure due to drought within the necessary reaction time for emergency aid. Four agroecological zones were covered from 2003 to 2019. Vegetation periods per 250m pixel were calculated back from EVImax. EVImax was validated against measured yields on large-scale farms. Interannual means and variability of EVImax served to assess production and drought risk. Yield predictions corresponded well with wheat production (r2≅0.5 p≤0.05). High temporal variability and low absolute EVI indicated drought-prone areas. EVI was positively correlated with rainfall data in cropped drought-prone areas (r2≅0.4, p≤0.05), but negatively in temporally water-logged highlands (r2≅0.3, p≤0.05). Our user-friendly approach on Google Earth Engine can accurately detect imminent food insecurity and facilitate timely interventions.
format	article
author	Kefyalew Sahle Kibret Carsten Marohn Georg Cadisch
author_facet	Kefyalew Sahle Kibret Carsten Marohn Georg Cadisch
author_sort	Kefyalew Sahle Kibret
title	Improved food-insecurity prediction in smallholder-dominated landscapes using MODIS Enhanced Vegetation Index and Google Earth Engine: a case study in South Central Ethiopia
title_short	Improved food-insecurity prediction in smallholder-dominated landscapes using MODIS Enhanced Vegetation Index and Google Earth Engine: a case study in South Central Ethiopia
title_full	Improved food-insecurity prediction in smallholder-dominated landscapes using MODIS Enhanced Vegetation Index and Google Earth Engine: a case study in South Central Ethiopia
title_fullStr	Improved food-insecurity prediction in smallholder-dominated landscapes using MODIS Enhanced Vegetation Index and Google Earth Engine: a case study in South Central Ethiopia
title_full_unstemmed	Improved food-insecurity prediction in smallholder-dominated landscapes using MODIS Enhanced Vegetation Index and Google Earth Engine: a case study in South Central Ethiopia
title_sort	improved food-insecurity prediction in smallholder-dominated landscapes using modis enhanced vegetation index and google earth engine: a case study in south central ethiopia
publisher	Taylor & Francis Group
publishDate	2021
url	https://doaj.org/article/05cbc85eca0e40e681f8a75a568b1b44
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Improved food-insecurity prediction in smallholder-dominated landscapes using MODIS Enhanced Vegetation Index and Google Earth Engine: a case study in South Central Ethiopia

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