Multi-Crop Green LAI Estimation with a New Simple Sentinel-2 LAI Index (SeLI)
The spatial quantification of green leaf area index (LAI<sub>green</sub>), the total green photosynthetically active leaf area per ground area, is a crucial biophysical variable for agroecosystem monitoring. The Sentinel-2 mission is with (1) a temporal resolution lower than a week, (2)...
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MDPI AG
2019-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/4/904 |
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| author | Nieves Pasqualotto Jesús Delegido Shari Van Wittenberghe Michele Rinaldi José Moreno |
| author_facet | Nieves Pasqualotto Jesús Delegido Shari Van Wittenberghe Michele Rinaldi José Moreno |
| author_sort | Nieves Pasqualotto |
| collection | DOAJ |
| description | The spatial quantification of green leaf area index (LAI<sub>green</sub>), the total green photosynthetically active leaf area per ground area, is a crucial biophysical variable for agroecosystem monitoring. The Sentinel-2 mission is with (1) a temporal resolution lower than a week, (2) a spatial resolution of up to 10 m, and (3) narrow bands in the red and red-edge region, a highly promising mission for agricultural monitoring. The aim of this work is to define an easy implementable LAI<sub>green</sub> index for the Sentinel-2 mission. Two large and independent multi-crop datasets of in situ collected LAI<sub>green</sub> measurements were used. Commonly used LAI<sub>green</sub> indices applied on the Sentinel-2 10 m × 10 m pixel resulted in a validation R<sup>2</sup> lower than 0.6. By calculating all Sentinel-2 band combinations to identify high correlation and physical basis with LAI<sub>green</sub>, the new Sentinel-2 LAI<sub>green</sub> Index (SeLI) was defined. SeLI is a normalized index that uses the 705 nm and 865 nm centered bands, exploiting the red-edge region for low-saturating absorption sensitivity to photosynthetic vegetation. A R<sup>2</sup> of 0.708 (root mean squared error (RMSE) = 0.67) and a R<sup>2</sup> of 0.732 (RMSE = 0.69) were obtained with a linear fitting for the calibration and validation datasets, respectively, outperforming established indices. Sentinel-2 LAI<sub>green</sub> maps are presented. |
| first_indexed | 2024-04-11T12:33:15Z |
| format | Article |
| id | doaj.art-5506a088d3e54287a1533342df5b4341 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T12:33:15Z |
| publishDate | 2019-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-5506a088d3e54287a1533342df5b43412022-12-22T04:23:42ZengMDPI AGSensors1424-82202019-02-0119490410.3390/s19040904s19040904Multi-Crop Green LAI Estimation with a New Simple Sentinel-2 LAI Index (SeLI)Nieves Pasqualotto0Jesús Delegido1Shari Van Wittenberghe2Michele Rinaldi3José Moreno4Image Processing Laboratory (IPL), University of Valencia, 46980 Valencia, SpainImage Processing Laboratory (IPL), University of Valencia, 46980 Valencia, SpainImage Processing Laboratory (IPL), University of Valencia, 46980 Valencia, SpainCouncil for Agricultural Research and Economics—Research Centre for Cereal and Industrial Crops, S.S. 673 km 25, 200, 71122 Foggia, ItalyImage Processing Laboratory (IPL), University of Valencia, 46980 Valencia, SpainThe spatial quantification of green leaf area index (LAI<sub>green</sub>), the total green photosynthetically active leaf area per ground area, is a crucial biophysical variable for agroecosystem monitoring. The Sentinel-2 mission is with (1) a temporal resolution lower than a week, (2) a spatial resolution of up to 10 m, and (3) narrow bands in the red and red-edge region, a highly promising mission for agricultural monitoring. The aim of this work is to define an easy implementable LAI<sub>green</sub> index for the Sentinel-2 mission. Two large and independent multi-crop datasets of in situ collected LAI<sub>green</sub> measurements were used. Commonly used LAI<sub>green</sub> indices applied on the Sentinel-2 10 m × 10 m pixel resulted in a validation R<sup>2</sup> lower than 0.6. By calculating all Sentinel-2 band combinations to identify high correlation and physical basis with LAI<sub>green</sub>, the new Sentinel-2 LAI<sub>green</sub> Index (SeLI) was defined. SeLI is a normalized index that uses the 705 nm and 865 nm centered bands, exploiting the red-edge region for low-saturating absorption sensitivity to photosynthetic vegetation. A R<sup>2</sup> of 0.708 (root mean squared error (RMSE) = 0.67) and a R<sup>2</sup> of 0.732 (RMSE = 0.69) were obtained with a linear fitting for the calibration and validation datasets, respectively, outperforming established indices. Sentinel-2 LAI<sub>green</sub> maps are presented.https://www.mdpi.com/1424-8220/19/4/904cropsleaf area indexvegetation indicesremote sensingSentinel-2red-edge |
| spellingShingle | Nieves Pasqualotto Jesús Delegido Shari Van Wittenberghe Michele Rinaldi José Moreno Multi-Crop Green LAI Estimation with a New Simple Sentinel-2 LAI Index (SeLI) Sensors crops leaf area index vegetation indices remote sensing Sentinel-2 red-edge |
| title | Multi-Crop Green LAI Estimation with a New Simple Sentinel-2 LAI Index (SeLI) |
| title_full | Multi-Crop Green LAI Estimation with a New Simple Sentinel-2 LAI Index (SeLI) |
| title_fullStr | Multi-Crop Green LAI Estimation with a New Simple Sentinel-2 LAI Index (SeLI) |
| title_full_unstemmed | Multi-Crop Green LAI Estimation with a New Simple Sentinel-2 LAI Index (SeLI) |
| title_short | Multi-Crop Green LAI Estimation with a New Simple Sentinel-2 LAI Index (SeLI) |
| title_sort | multi crop green lai estimation with a new simple sentinel 2 lai index seli |
| topic | crops leaf area index vegetation indices remote sensing Sentinel-2 red-edge |
| url | https://www.mdpi.com/1424-8220/19/4/904 |
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