fatemeh jafari sayadi; Mohammad Ali Gholami Sefidkouhi; mirkahlegh ziyaeetabar ahmadi
Abstract
Water demand is one of the most effective factors in irrigation scheduling. In evapotranspiration formulas, crop coefficient (Kc), as a representative of different plants characteristics, is of great importance. Calculating this coefficient using the existing methods and formulas is costly and time-consuming, ...
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Water demand is one of the most effective factors in irrigation scheduling. In evapotranspiration formulas, crop coefficient (Kc), as a representative of different plants characteristics, is of great importance. Calculating this coefficient using the existing methods and formulas is costly and time-consuming, and results are point-specific. However, nowadays, calculation methods that provide large- scale Kc values are of interest. The methods based on remote sensing have been welcomed by many researchers. The objective of the present study was calculating crop coefficient (Kc) and leaf area index (LAI) of rice in different growing stages, using OLI sensor. In this regard, data LAI of two rice fields (areas of 15 and 65 hectares) located in north part of Sari, Iran, were used in two growing seasons (2014-2015 and 2015-2016). The average Kc at transplantation, tillering, heading, and maturity stages was, respectively, 0.92, 1.24, 1.19, and 1.12, showing that Kc had a good correlation with NDVI at different stages (r>0.97). According to the results, NDVI is a good estimator for rice Kc. In addition, Rice Growth Vegetation Index (RGVI) in all growing stages had a correlation coefficient r>0.93. RGVI is considered as a good estimator of LAI. Approximately at all growing stages, except heading, more than 93% of LAI changes were predicted by RGVI. Generally, it can be concluded that the most suitable indices for estimating Kc and LAI of rice are NDVI and RGVI, respectively.