Articles
Assessing the performance of vegetation index based single crop coefficient modelling approaches to predict tomato crop water use in a semi-arid climate
Article number
1468_29
Pages
221 – 228
Language
English
Abstract
Vegetation index-based single crop coefficient (Kc) models utilize remote sensing-derived indices to adjust Kc values according to actual vegetation cover conditions.
This results in more precise crop evapotranspiration predictions.
Compared to traditional methods, these strategies provide several advantages, including spatially distributed information on vegetation cover and crop evapotranspiration at a relatively short time, low cost and reduced input requirements.
Despite their simplicity in estimating crop evapotranspiration, these techniques can potentially provide fairly accurate estimates similar to or surpassing those acquired from more complex physically-based remote sensing approaches such as those based upon the shortened surface energy balance.
This study assessed the performance of five vegetation index-based single Kc modelling approaches for estimating tomato evapotranspiration under a semi-arid climate.
These methods included: 1) scaled normalized difference vegetation index (NDVI); 2) scaled enhanced vegetation index (EVI); 3) scaled EVI-2; 4) adjusted Kc using EVI-2 and 5) adjusted Kc using NDVI. The performance assessment for model development and testing was conducted using daily in-situ measurements of tomato evapotranspiration obtained with an open-path eddy covariance system in the semi-arid area of Mopani, Limpopo.
Crop evapotranspiration data were collected for two independent growing periods, which were respectively used for model development and testing: 1) summer to winter (23 February to 27 June 2023) and 2) winter to spring (30 June to 22 October 2023). Adjustment of tomato Kc values using NDVI and EVI-2 vegetation indices significantly enhanced the accuracy in predicting daily tomato evapotranspiration, with coefficient of determination (R2=0.79-0.81), root mean square error (RMSE = 0.54-0.63 mm day‑1), mean absolute percent difference (MAPD=23.38-23.92%) and index of agreement (d=0.99). Simple vegetation index-based single Kc models were successfully developed and tested in this study to predict daily tomato evapotranspiration.
These models can be used as tools for optimizing irrigation strategies in tomato farms situated in semi-arid regions.
This results in more precise crop evapotranspiration predictions.
Compared to traditional methods, these strategies provide several advantages, including spatially distributed information on vegetation cover and crop evapotranspiration at a relatively short time, low cost and reduced input requirements.
Despite their simplicity in estimating crop evapotranspiration, these techniques can potentially provide fairly accurate estimates similar to or surpassing those acquired from more complex physically-based remote sensing approaches such as those based upon the shortened surface energy balance.
This study assessed the performance of five vegetation index-based single Kc modelling approaches for estimating tomato evapotranspiration under a semi-arid climate.
These methods included: 1) scaled normalized difference vegetation index (NDVI); 2) scaled enhanced vegetation index (EVI); 3) scaled EVI-2; 4) adjusted Kc using EVI-2 and 5) adjusted Kc using NDVI. The performance assessment for model development and testing was conducted using daily in-situ measurements of tomato evapotranspiration obtained with an open-path eddy covariance system in the semi-arid area of Mopani, Limpopo.
Crop evapotranspiration data were collected for two independent growing periods, which were respectively used for model development and testing: 1) summer to winter (23 February to 27 June 2023) and 2) winter to spring (30 June to 22 October 2023). Adjustment of tomato Kc values using NDVI and EVI-2 vegetation indices significantly enhanced the accuracy in predicting daily tomato evapotranspiration, with coefficient of determination (R2=0.79-0.81), root mean square error (RMSE = 0.54-0.63 mm day‑1), mean absolute percent difference (MAPD=23.38-23.92%) and index of agreement (d=0.99). Simple vegetation index-based single Kc models were successfully developed and tested in this study to predict daily tomato evapotranspiration.
These models can be used as tools for optimizing irrigation strategies in tomato farms situated in semi-arid regions.
Authors
N. Araya, N.T. Mobe, K. Matsunyane, K. Ayisi, S. Gokool
Keywords
eddy covariance, evapotranspiration, EVI, EVI-2, NDVI, remote sensing
Groups involved
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