Remote Sensor Images and Vegetation Indices to Optimize Rice Yield Analysis for Specific Growth Stages Within Extensive Data
The crop yield in commercial fields is a very important parameter for farmers. The use of Precision Agriculture tools has been shown to improve rice crop yields. One of these tools is remote sensing on satellite platforms. Sentinel-2 provides free data on reflectance at different wavelengths. Focusi...
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2025-04-01
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| author | David Fita Constanza Rubio Antonio Uris Sergio Castiñeira-Ibáñez Belén Franch Daniel Tarrazó-Serrano Alberto San Bautista |
| author_facet | David Fita Constanza Rubio Antonio Uris Sergio Castiñeira-Ibáñez Belén Franch Daniel Tarrazó-Serrano Alberto San Bautista |
| author_sort | David Fita |
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| description | The crop yield in commercial fields is a very important parameter for farmers. The use of Precision Agriculture tools has been shown to improve rice crop yields. One of these tools is remote sensing on satellite platforms. Sentinel-2 provides free data on reflectance at different wavelengths. Focusing on commercial farms, correlations between the yield and satellite reflectance were studied over several years and locations for ‘JSendra’ rice crops. Four years of yield maps for 706 ha composed the database. Mid tillering-MT, panicle initiation-PI and grain filling-GF reflectance values and Vegetation Indices (VIs) were used. At MT, correlations with the yield were variable (0.23–0.70). At PI, correlations with the yield increased in NIR (0.39–0.85), but the other regions and VIs experienced a decrease. Visible bands and B05 Red Edge were significantly correlated with each other; similarly, B08 NIR was highly correlated with B06, B07, and B8A; SWIR bands were correlated with each other but not with the yield. At GF, the previous pattern was similar. Substantial limitations in estimating yield variability directly from reflectance or VIs were discussed. Two periods were established. The first is designing strategies to increase NIR and decrease red reflectance from MT to PI. The second is avoiding the relationship between crop greenness and NIR from PI to harvest. NIR was a better variable than VIs, but the single use of this band is challenging. Future recommendations focus on the visible–NIR collinearities to interpret differences between years or locations. |
| format | Article |
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| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-04-01 |
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| series | Applied Sciences |
| spelling | doaj-art-7ac720ad77e24dc8b046fb71df7201942025-08-20T02:09:15ZengMDPI AGApplied Sciences2076-34172025-04-01157387010.3390/app15073870Remote Sensor Images and Vegetation Indices to Optimize Rice Yield Analysis for Specific Growth Stages Within Extensive DataDavid Fita0Constanza Rubio1Antonio Uris2Sergio Castiñeira-Ibáñez3Belén Franch4Daniel Tarrazó-Serrano5Alberto San Bautista6Departamento de Producción Vegetal, Universitat Politècnica de València, 46022 Valencia, SpainCentro de Tecnologías Físicas, Universitat Politècnica de València, 46022 Valencia, SpainCentro de Tecnologías Físicas, Universitat Politècnica de València, 46022 Valencia, SpainCentro de Tecnologías Físicas, Universitat Politècnica de València, 46022 Valencia, SpainGlobal Change Unit, Image Processing Laboratory, Universitat de València, 46980 Valencia, SpainCentro de Tecnologías Físicas, Universitat Politècnica de València, 46022 Valencia, SpainDepartamento de Producción Vegetal, Universitat Politècnica de València, 46022 Valencia, SpainThe crop yield in commercial fields is a very important parameter for farmers. The use of Precision Agriculture tools has been shown to improve rice crop yields. One of these tools is remote sensing on satellite platforms. Sentinel-2 provides free data on reflectance at different wavelengths. Focusing on commercial farms, correlations between the yield and satellite reflectance were studied over several years and locations for ‘JSendra’ rice crops. Four years of yield maps for 706 ha composed the database. Mid tillering-MT, panicle initiation-PI and grain filling-GF reflectance values and Vegetation Indices (VIs) were used. At MT, correlations with the yield were variable (0.23–0.70). At PI, correlations with the yield increased in NIR (0.39–0.85), but the other regions and VIs experienced a decrease. Visible bands and B05 Red Edge were significantly correlated with each other; similarly, B08 NIR was highly correlated with B06, B07, and B8A; SWIR bands were correlated with each other but not with the yield. At GF, the previous pattern was similar. Substantial limitations in estimating yield variability directly from reflectance or VIs were discussed. Two periods were established. The first is designing strategies to increase NIR and decrease red reflectance from MT to PI. The second is avoiding the relationship between crop greenness and NIR from PI to harvest. NIR was a better variable than VIs, but the single use of this band is challenging. Future recommendations focus on the visible–NIR collinearities to interpret differences between years or locations.https://www.mdpi.com/2076-3417/15/7/3870riceyieldPrecision AgricultureSentinel-2Vegetation Indices |
| spellingShingle | David Fita Constanza Rubio Antonio Uris Sergio Castiñeira-Ibáñez Belén Franch Daniel Tarrazó-Serrano Alberto San Bautista Remote Sensor Images and Vegetation Indices to Optimize Rice Yield Analysis for Specific Growth Stages Within Extensive Data Applied Sciences rice yield Precision Agriculture Sentinel-2 Vegetation Indices |
| title | Remote Sensor Images and Vegetation Indices to Optimize Rice Yield Analysis for Specific Growth Stages Within Extensive Data |
| title_full | Remote Sensor Images and Vegetation Indices to Optimize Rice Yield Analysis for Specific Growth Stages Within Extensive Data |
| title_fullStr | Remote Sensor Images and Vegetation Indices to Optimize Rice Yield Analysis for Specific Growth Stages Within Extensive Data |
| title_full_unstemmed | Remote Sensor Images and Vegetation Indices to Optimize Rice Yield Analysis for Specific Growth Stages Within Extensive Data |
| title_short | Remote Sensor Images and Vegetation Indices to Optimize Rice Yield Analysis for Specific Growth Stages Within Extensive Data |
| title_sort | remote sensor images and vegetation indices to optimize rice yield analysis for specific growth stages within extensive data |
| topic | rice yield Precision Agriculture Sentinel-2 Vegetation Indices |
| url | https://www.mdpi.com/2076-3417/15/7/3870 |
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