7b989bb708cb2a451c8f8309837397fa remsen_a2023m12v15n23p5476.pdf f8940737c685523b6f1935d9cf2b808ddefa10f5 remsen_a2023m12v15n23p5476.pdf 28dd45b8f42ab4640f0ea7f0f65d4d959ad8b93f97c46be298c97a19a9f611a8 remsen_a2023m12v15n23p5476.pdf Title: Modelling Two Sugarcane Agro-Industrial Yields Using Sentinel/Landsat Time-Series Data and Their Spatial Validation at Different Scales in Costa Rica Subject: Sugarcane production is a relevant socioeconomic activity in Costa Rica that requires tools to improve decision-making, particularly with the advancement of agronomic management using remote sensing (RS) techniques. Some contributions have evaluated sugarcane yield with RS methods, but some gaps remain, such as the lack of operational models for predicting yields and joint estimation with sugar content. Our study is a contribution to this topic that aims to apply an empirical, operational, and robust method to estimate sugarcane yield (SCY) and sugar content (SC) through the combination of field variables, climatic data, and RS vegetation indices (VIs) extracted from Sentinel-2 and Landsat-8 imagery in a cooperative in Costa Rica for four sugarcane harvest cycles (2017–2018 to 2020–2021). Based on linear regression models, four approaches using different VIs were evaluated to obtain the best models to improve the RMSE results and to validate them (using the harvest cycle of 2021–2022) at two management scales: farm and plot. Our results show that the historical yield average, the maximum historical yield, and the growing cycle start were essential factors in estimating SCY and the former variable for SC. For SCY, the most explicative VI was the Simple Ratio (SR), whereas, for SC, it was the Ratio Vegetation Index (RVI). Adding VIs from different months was essential to obtain the phenological variability of sugarcane, being the most common results September, December and January. In SC estimation, precipitation (in May and December) was a clear explicatory variable combined mainly with RVI, whereas in SCY, it was less explanatory. In SCY, RMSE showed values around 8.0 tha-1, a clear improvement from 12.9 tha-1, which is the average obtained in previous works, whereas in SC, it displayed values below 4.0 kgt-1. Finally, in SCY, the best validation result was obtained at the plot scale (RMSE of 7.7 tha-1), but this outcome was not verified in the case of SC validation because the RMSE was above 4.0 kgt-1. In conclusion, our operational models try to represent a step forward in using RS techniques to improve sugarcane management at the farm and plot scales in Costa Rica. Keywords: sugarcane yield; sugar content estimation; Sentinel-2 and Landsat-8; vegetation indices; multivariate linear regression; spatial variability; farm and plot validation Author: Bryan Alemán-Montes, Alaitz Zabala, Carlos Henríquez and Pere Serra Creator: LaTeX with hyperref Producer: pdfTeX-1.40.21 CreationDate: Thu Nov 23 12:17:46 2023 CET ModDate: Thu Nov 23 12:32:49 2023 CET Custom Metadata: no Metadata Stream: no Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 24 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 13250082 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- QMTXQY+VnURWPalladioL Type 1 Custom yes yes no 10 0 ANRBWF+URWPalladioL-Bold Type 1 Custom yes yes no 15 0 VWKMRV+URWPalladioL-Roma Type 1 Custom yes yes no 20 0 OIBKKJ+URWPalladioL-Ital Type 1 Custom yes yes no 24 0 XFIZIB+CMSY10 Type 1 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MIMEtype: application/pdf PDFMetadata: Objects: 877 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: section.1 Item: Title: Materials and Methods Destination: section.2 Children: Item: Title: Study Area Destination: subsection.2.1 Item: Title: Sugarcane Crop Cycle in Costa Rica Destination: subsection.2.2 Item: Title: Available Data Destination: subsection.2.3 Children: Item: Title: Yield and Climatic Data Destination: subsubsection.2.3.1 Item: Title: Satellite Images and Vegetation Indexes Destination: subsubsection.2.3.2 Item: Title: Multivariate Statistical Analysis and Spatio-Temporal Validation Destination: subsection.2.4 Item: Title: Results Destination: section.3 Children: Item: Title: Harmonization of Vegetation Indexes and Temporal Phenological Signatures Destination: subsection.3.1 Item: Title: Sugarcane Yield Models Destination: subsection.3.2 Children: Item: Title: Modelling Sugarcane Yield at the Farm Scale Destination: subsubsection.3.2.1 Item: Title: Spatial Validation of Sugarcane Yield at Farm and Plot Scales Destination: subsubsection.3.2.2 Item: Title: Sugar Content Models Destination: subsection.3.3 Children: Item: Title: Modelling Sugar Content at Farm Scale Destination: subsubsection.3.3.1 Item: Title: Spatial Validation of Sugar Content at Farm and Plot Scales Destination: subsubsection.3.3.2 Item: Title: Discussion Destination: section.4 Item: Title: Conclusions Destination: section.5 Item: Title: References Destination: section.6 Info: Title: Modelling Two Sugarcane Agro-Industrial Yields Using Sentinel/Landsat Time-Series Data and Their Spatial Validation at Different Scales in Costa Rica Author: Bryan Alemán-Montes, Alaitz Zabala, Carlos Henríquez and Pere Serra Subject: Sugarcane production is a relevant socioeconomic activity in Costa Rica that requires tools to improve decision-making, particularly with the advancement of agronomic management using remote sensing (RS) techniques. Some contributions have evaluated sugarcane yield with RS methods, but some gaps remain, such as the lack of operational models for predicting yields and joint estimation with sugar content. Our study is a contribution to this topic that aims to apply an empirical, operational, and robust method to estimate sugarcane yield (SCY) and sugar content (SC) through the combination of field variables, climatic data, and RS vegetation indices (VIs) extracted from Sentinel-2 and Landsat-8 imagery in a cooperative in Costa Rica for four sugarcane harvest cycles (2017 2018 to 2020 2021). Based on linear regression models, four approaches using different VIs were evaluated to obtain the best models to improve the RMSE results and to validate them (using the harvest cycle of 2021 2022) at two management scales: farm and plot. Our results show that the historical yield average, the maximum historical yield, and the growing cycle start were essential factors in estimating SCY and the former variable for SC. For SCY, the most explicative VI was the Simple Ratio (SR), whereas, for SC, it was the Ratio Vegetation Index (RVI). Adding VIs from different months was essential to obtain the phenological variability of sugarcane, being the most common results September, December and January. In SC estimation, precipitation (in May and December) was a clear explicatory variable combined mainly with RVI, whereas in SCY, it was less explanatory. In SCY, RMSE showed values around 8.0 tha-1, a clear improvement from 12.9 tha-1, which is the average obtained in previous works, whereas in SC, it displayed values below 4.0 kgt-1. Finally, in SCY, the best validation result was obtained at the plot scale (RMSE of 7.7 tha-1), but this outcome was not verified in the case of SC validation because the RMSE was above 4.0 kgt-1. In conclusion, our operational models try to represent a step forward in using RS techniques to improve sugarcane management at the farm and plot scales in Costa Rica. 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