Multi-Crop Green LAI Estimation with a New Simple Sentinel-2 LAI Index (SeLI)
The spatial quantification of green leaf area index (LAI ), 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...
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Veröffentlicht in: | Sensors (Basel, Switzerland) Switzerland), 2019-02, Vol.19 (4), p.904 |
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Sprache: | eng |
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Zusammenfassung: | The spatial quantification of green leaf area index (LAI
), 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
index for the Sentinel-2 mission. Two large and independent multi-crop datasets of in situ collected LAI
measurements were used. Commonly used LAI
indices applied on the Sentinel-2 10 m × 10 m pixel resulted in a validation R² lower than 0.6. By calculating all Sentinel-2 band combinations to identify high correlation and physical basis with LAI
, the new Sentinel-2 LAI
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 R² of 0.708 (root mean squared error (RMSE) = 0.67) and a R² 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
maps are presented. |
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ISSN: | 1424-8220 1424-8220 |
DOI: | 10.3390/s19040904 |