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• W4313202036 startingPage "100165" @default.
• W4313202036 abstract "Early prediction of crop yield is essential for agricultural planning and policy decisions. Most countries use conventional techniques for vegetation monitoring and yield estimation, such as crop growth models by simulating agricultural applications. Most countries use conventional techniques for vegetation monitoring and yield estimation, such as crop growth models that simulate agricultural applications. Other approaches are related to spatio-temporal monitoring of vegetation conditions. This study aimed to evaluate silage maize yield estimation by using vegetation indices like the Normalized Difference Vegetation Index (NDVI), Soil-Adjustment Vegetation Index (SAVI) and Simple Ratio (SR) using PlanetScope, Sentinel-2A, and Landsat 8 OLI satellite images. Linear regression was used to relate silage maize yield and calculated spectral vegetation indices (SVI) at multiple dates. Significant correlations were found between remotely sensed SVI and silage maize yield values of four large-scale plots. The results indicated that silage maize yield could be estimated most accurately with SR between Days After Sowing (DAS) 73 and DAS 76 at p<0.01. Yield could be estimated successfully using these three satellite images and indices between DAS 33 and DAS 69 at the level of p<0.05." @default.
• W4313202036 created "2023-01-06" @default.
• W4313202036 creator A5034470079 @default.
• W4313202036 creator A5055052817 @default.
• W4313202036 creator A5082682338 @default.
• W4313202036 date "2023-08-01" @default.
• W4313202036 modified "2023-09-30" @default.
• W4313202036 title "Silage maize yield estimation by using planetscope, sentinel-2A and landsat 8 OLI satellite images" @default.
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• W4313202036 doi "https://doi.org/10.1016/j.atech.2022.100165" @default.
• W4313202036 hasPublicationYear "2023" @default.
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