Assessing the spatio-temporal variability of NDVI and VCI as indices of crops productivity in Ethiopia: a remote sensing approach

Abstract

This study aims at characterizing agricultural drought in Ethiopia and understanding the effects of drought on crop yield. Monthly, seasonal and annual Normalized Difference Vegetation Index (NDVI) and Vegetation Condition Index (VCI) values were calculated using MODIS (MOD13Q1) from the year 2003 to 2017. The relationships between NDVI, VCI, and crop yield were examined to predict the possibility of drought impacts on crop productivity. We found that VCI and NDVI data provides consistent and spatially explicit information for operational drought monitoring in Ethiopia. Results also indicated that the most extreme agricultural drought in recent years occurred in 2003, 2004, 2008, 2009, and 2015. These findings also show that mild to severe droughts have a great chance of occurrence in Ethiopia. However, only severe drought has significant impacts on crops. The food crops yield data used in this study include cereals, legumes, and tubers. It was observed that cereals such as (Zea mays), teff (Eragrostis tef), haricot beans (Phaseolus vulgaris) are more sensitive to agricultural drought when compared to the tubers such as sweet potato (Ipomoea batatas) and taro (Colocasia esculenta). Thus, drought preparedness programs need to pay more attention to the cultivation of these crops under severe drought conditions. HIGHLIGHTSNDVI and VCI patterns easily discriminate cereals and legumes when compared to tuber crops. 45% and 43% yield variability of respectively teff and maize is explained by the NDVI patterns. The studied crops (Teff, Maize, Sweet potato and Taro) are less discriminable to seasonal VCI variation.

Monitoring impacts of soil bund on spatial variation of teff and finger millet yield with Sentinel-2 and spectroradiometric data in Ethiopia

“…However, as biomass density increases, NDVI becomes saturated [ 66 , 67 ]. NDVI data accurately explained the variation of sorghum yield [ 68 , 69 ] (maize GY [ 70 , 71 ] and teff GY [ 72 ] in previous researches.…”

“…EVI is a high-sensitivity VI designed for measuring biomass in dense vegetation as a result [ 61 ]. According to earlier research [ 62 ], the variance of teff GY was accurately described by NDVI data. When crops reach their peak growth, a strong relationship between VIs and GY is expected [ 5 ].…”

Machine learning and Global Vegetation: Random Forests for Downscaling and Gapfilling

“…Second, the features used in this study may not incorporate processes critical to vegetation status equally among landcover types (Moussa Kourouma et al, 2021). Forests, grasslands, and other natural ecosystems are closely coupled to natural weather processes.…”

“…It follows that formulating appropriate responses to drought and alleviating the negative effects of ecosystem disruption during these periods requires accurate predictions. Vegetation indices derived from satellite-based remote sensors, such as the Enhanced Vegetation Index (evi), have proven to be an indispensable tool for monitoring vegetation at multiple scales, from the fine scale, such as crop patches (Moussa Kourouma et al, 2021;Sharifi, 2021) to the global scale (Huang et al, 2021;Vicente-Serrano et al, 2010). In recent decades, numerous satellite-based vegetation indices have been developed (Li et al, 2021a).…”

Spatiotemporal monitoring of droughts in Iran using remote-sensing indices

“…A deep understanding of vegetation health in response to annual and seasonal precipitation changes is essential for drought impact mitigation (Kourouma et al, 2021). Drought surveillance is critical drought risk assessment and can improve food security (Kourouma et al, 2021).…”