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多作物绿色 LAI 估算的新型简单哨兵-2 LAI 指数(SeLI)。

Multi-Crop Green LAI Estimation with a New Simple Sentinel-2 LAI Index (SeLI).

机构信息

Image Processing Laboratory (IPL), University of Valencia, 46980 Valencia, Spain.

Council for Agricultural Research and Economics-Research Centre for Cereal and Industrial Crops, S.S. 673 km 25, 200, 71122 Foggia, Italy.

出版信息

Sensors (Basel). 2019 Feb 21;19(4):904. doi: 10.3390/s19040904.

DOI:10.3390/s19040904
PMID:30795571
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6412664/
Abstract

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.

摘要

绿色叶面积指数(LAI)的空间量化是农业生态系统监测的关键生物物理变量,它表示每单位地面面积的总绿色光合作用活跃叶片面积。Sentinel-2 任务具有以下特点:(1)时间分辨率低于一周;(2)空间分辨率高达 10 米;(3)在红色和红色边缘区域具有较窄的波段,是农业监测极具前景的任务。本工作旨在为 Sentinel-2 任务定义一个易于实现的 LAI 指数。使用了两个大型独立的实地采集 LAI 测量多作物数据集。在 Sentinel-2 的 10 m×10 m 像素上应用常用的 LAI 指数导致验证 R²低于 0.6。通过计算所有 Sentinel-2 波段组合,以确定与 LAI 的高度相关性和物理基础,定义了新的 Sentinel-2 LAI 指数(SeLI)。SeLI 是一个归一化指数,使用以 705nm 和 865nm 为中心的波段,利用红色边缘区域对低饱和吸收的光合作用植被具有敏感性。校准和验证数据集的线性拟合分别获得了 0.708(均方根误差(RMSE)=0.67)和 0.732(RMSE=0.69)的 R²,性能优于现有指数。还展示了 Sentinel-2 LAI 图。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cbad/6412664/afe41f625de4/sensors-19-00904-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cbad/6412664/057f4d8a3d03/sensors-19-00904-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cbad/6412664/87cf524f0a2b/sensors-19-00904-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cbad/6412664/4dfb666c5468/sensors-19-00904-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cbad/6412664/3db1daf0e1e0/sensors-19-00904-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cbad/6412664/2992ef3c9513/sensors-19-00904-g005a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cbad/6412664/83af6b68b768/sensors-19-00904-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cbad/6412664/128d4bcd40d6/sensors-19-00904-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cbad/6412664/afe41f625de4/sensors-19-00904-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cbad/6412664/057f4d8a3d03/sensors-19-00904-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cbad/6412664/87cf524f0a2b/sensors-19-00904-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cbad/6412664/4dfb666c5468/sensors-19-00904-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cbad/6412664/3db1daf0e1e0/sensors-19-00904-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cbad/6412664/2992ef3c9513/sensors-19-00904-g005a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cbad/6412664/83af6b68b768/sensors-19-00904-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cbad/6412664/128d4bcd40d6/sensors-19-00904-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cbad/6412664/afe41f625de4/sensors-19-00904-g008.jpg

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