Precision Estimation of Crop Coefficient for Maize Cultivation Using High-Resolution Satellite Imagery to Enhance Evapotranspiration Assessment in Agriculture

Attila Nagy; Nikolett Éva Kiss; Erika Buday-Bódi; Tamás Magyar; Francesco Cavazza; Salvatore Luca Gentile; Haidi Abdullah; János Tamás; Zsolt Zoltán Fehér

doi:10.3390/plants13091212

Precision Estimation of Crop Coefficient for Maize Cultivation Using High-Resolution Satellite Imagery to Enhance Evapotranspiration Assessment in Agriculture

Plants (Basel). 2024 Apr 27;13(9):1212. doi: 10.3390/plants13091212.

Authors

Attila Nagy¹, Nikolett Éva Kiss¹, Erika Buday-Bódi¹, Tamás Magyar¹, Francesco Cavazza², Salvatore Luca Gentile², Haidi Abdullah³, János Tamás¹, Zsolt Zoltán Fehér¹

Affiliations

¹ Faculty of Agricultural and Food Sciences and Environmental Management, Institute of Water and Environmental Management, University of Debrecen, H-4032 Debrecen, Hungary.
² Consorzio di Bonifica Canale Emiliano Romagnolo, Via E. Masi 8, 40137 Bologna, Italy.
³ Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Drienerlolaan 5, P.O. Box 217, 7500 AE Enschede, The Netherlands.

Abstract

The estimation of crop evapotranspiration (ETc) is crucial for irrigation water management, especially in arid regions. This can be particularly relevant in the Po Valley (Italy), where arable lands suffer from drought damages on an annual basis, causing drastic crop yield losses. This study presents a novel approach for vegetation-based estimation of crop evapotranspiration (ETc) for maize. Three years of high-resolution multispectral satellite (Sentinel-2)-based Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Red Edge Index (NDRE), and Leaf Area Index (LAI) time series data were used to derive crop coefficients of maize in nine plots at the Acqua Campus experimental farm of Irrigation Consortium for the Emilia Romagna Canal (CER), Italy. Since certain vegetation indices (VIs) (such as NDVI) have an exponential nature compared to the other indices, both linear and power regression models were evaluated to estimate the crop coefficient (K_c). In the context of linear regression, the correlations between Food and Agriculture Organization (FAO)-based K_c and NDWI, NDRE, NDVI, and LAI-based K_c were 0.833, 0.870, 0.886, and 0.771, respectively. Strong correlation values in the case of power regression (NDWI: 0.876, NDRE: 0.872, NDVI: 0.888, LAI: 0.746) indicated an alternative approach to provide crop coefficients for the vegetation period. The VI-based ETc values were calculated using reference evapotranspiration (ET₀) and VI-based K_c. The weather station data of CER were used to calculate ET₀ based on Penman-Monteith estimation. Out of the Vis, NDWI and NDVI-based ETc performed the best both in the cases of linear (NDWI RMSE: 0.43 ± 0.12; NDVI RMSE: 0.43 ± 0.095) and power (NDWI RMSE: 0.44 ± 0.116; NDVI RMSE: 0.44 ± 0.103) approaches. The findings affirm the efficacy of the developed methodology in accurately assessing the evapotranspiration rate. Consequently, it offers a more refined temporal estimation of water requirements for maize cultivation in the region.

Keywords: Sentinel-2; maize water demand; vegetation index-based Kc; vegetation index-based crop evapotranspiration.

Grants and funding

Grant Agreement No. 858375/European Union's Horizon 2020 "WATERAGRI Water retention and nutrient recycling in soils and streams for improved agricultural production"