DOI: 10.1016/j.jag.2014.04.021
Scopus记录号: 2-s2.0-84904768083
论文题名: Predicting maize yield in Zimbabwe using dry dekads derived fromremotely sensed vegetation condition index
作者: Kuri F ; , Murwira A ; , Murwira K ; S ; , Masocha M
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2014
卷: 33, 期: 1 起始页码: 39
结束页码: 46
语种: 英语
英文关键词: Dry dekads
; Index
; Maize yield
; SPOT normalized difference vegetation
; Vegetation condition index
Scopus关键词: crop production
; crop yield
; food security
; maize
; NDVI
; prediction
; regression analysis
; remote sensing
; SPOT
; time series
; Zimbabwe
英文摘要: Maize is a key crop contributing to food security in Southern Africa yet accurate estimates of maizeyield prior to harvesting are scarce. Timely and accurate estimates of maize production are essentialfor ensuring food security by enabling actionable mitigation strategies and policies for prevention offood shortages. In this study, we regressed the number of dry dekads derived from VCI against officialground-based maize yield estimates to generate simple linear regression models for predicting maizeyield throughout Zimbabwe over four seasons (2009-10, 2010-11, 2011-12, and 2012-13). The VCIwas computed using Normalized Difference Vegetation Index (NDVI) time series dataset from the SPOTVEGETATION sensor for the period 1998-2013. A significant negative linear relationship between numberof dry dekads and maize yield was observed in each season. The variation in yield explained by the modelsranged from 75% to 90%. The models were evaluated with official ground-based yield data that was notused to generate the models. There is a close match between the predicted yield and the official yieldstatistics with an error of 33%. The observed consistency in the negative relationship between numberof dry dekads and ground-based estimates of maize yield as well as the high explanatory power of theregression models suggest that VCI-derived dry dekads could be used to predict maize yield before theend of the season thereby making it possible to plan strategies for dealing with food deficits or surpluseson time. © 2014 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79790
Appears in Collections: 气候变化事实与影响
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作者单位: Scientific and Industrial Research and Development Centre, Geo-information and Remote Sensing Institute, 1574 Alpes Road, Hatcliffe, Harare, Zimbabwe; University of Zimbabwe, Department of Geography and Environmental Science, P.O. Box MP167, Mount Pleasant, Harare, Zimbabwe
Recommended Citation:
Kuri F,, Murwira A,, Murwira K,et al. Predicting maize yield in Zimbabwe using dry dekads derived fromremotely sensed vegetation condition index[J]. International Journal of Applied Earth Observation and Geoinformation,2014-01-01,33(1)