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Effects of spatial, temporal, and spectral resolutions on the estimation of wheat and barley leaf area index using multi- and hyper-spectral data (case study: Karaj, Iran)
Precision Agriculture ( IF 5.4 ) Pub Date : 2020-08-10 , DOI: 10.1007/s11119-020-09749-9
Yasamin Afrasiabian , Hamideh Noory , Ali Mokhtari , Maryam Razavi Nikoo , Farrokh Pourshakouri , Parisa Haghighatmehr

Leaf area index (LAI) is a key parameter for the calculation of crop biophysical and biochemical processes. Therefore, the accurate estimates of LAI has been always of great importance for agricultural researchers. Remote sensing has shown enormous potential in LAI estimation, however, more evaluations are necessary on choosing the best type of data. In this study, the spatial, temporal, and spectral resolutions of different remotely sensed data (Landsat 8, Sentinel-2, MODIS, and also field hyperspectral data) were evaluated for LAI estimation of wheat and barley. First, the 30-m Landsat 8, 10-m Sentinel-2, 250-m MODIS, and field-based point data were taken into account for assessing the goodness of the relationship between field LAI (collected using LAI-2200c) and Vegetation Indices (VIs) to investigate the effect of a difference in spatial resolution. Afterward, to assess the temporal resolution effects, the Sentinel-2 images were resampled to 30 m and were combined with Landsat 8 data. Also, hyperspectral VIs (HNDVI, HDVI, and HSR) were calculated using field data to evaluate the effects of spectral resolution. Results showed that the difference in spatial and temporal resolutions of the data did not have any considerable effect on improving the LAI-VI relationship. Nevertheless, there were some particular portions of the spectrum which had R2 of more than 0.8 which was a great improvement compared to multispectral data with R2 between 0.6 and 0.69. The best HNDVI and HSR were calculated from the 10-nm bands centered at 1 115 nm and 1 135 nm.

中文翻译:

空间、时间和光谱分辨率对使用多光谱和高光谱数据估算小麦和大麦叶面积指数的影响(案例研究:Karaj,伊朗)

叶面积指数(LAI)是计算作物生物物理和生化过程的关键参数。因此,准确估计 LAI 一直是农业研究人员的重要工作。遥感在 LAI 估计中显示出巨大的潜力,但是,在选择最佳数据类型方面还需要更多的评估。在这项研究中,评估了不同遥感数据(Landsat 8、Sentinel-2、MODIS 以及现场高光谱数据)的空间、时间和光谱分辨率,用于小麦和大麦的 LAI 估计。首先,30 米 Landsat 8、10 米 Sentinel-2、250 米 MODIS,考虑到基于现场的点数据来评估现场 LAI(使用 LAI-2200c 收集)和植被指数 (VI) 之间关系的良好程度,以研究空间分辨率差异的影响。之后,为了评估时间分辨率效果,Sentinel-2 图像被重新采样到 30 m,并与 Landsat 8 数据结合。此外,使用现场数据计算高光谱 VI(HNDVI、HDVI 和 HSR)以评估光谱分辨率的影响。结果表明,数据的空间和时间分辨率的差异对改善 LAI-VI 关系没有任何显着影响。尽管如此,光谱的某些特定部分的 R2 大于 0.8,与 R2 介于 0.6 和 0.69 之间的多光谱数据相比,这是一个很大的改进。
更新日期:2020-08-10
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