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Adjusting Spectral Indices for Spectral Response Function Differences of Very High Spatial Resolution Sensors Simulated from Field Spectra

机译:调整光谱指数以从场光谱模拟非常高空间分辨率的传感器的光谱响应函数差异

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摘要

The use of data from multiple sensors is often required to ensure data coverage and continuity, but differences in the spectral characteristics of sensors result in spectral index values being different. This study investigates spectral response function effects on 48 spectral indices for cultivated grasslands using simulated data of 10 very high spatial resolution sensors, convolved from field reflectance spectra of a grass covered dike (with varying vegetation condition). Index values for 48 indices were calculated for original narrow-band spectra and convolved data sets, and then compared. The indices Difference Vegetation Index (DVI), Global Environmental Monitoring Index (GEMI), Enhanced Vegetation Index (EVI), Modified Soil-Adjusted Vegetation Index (MSAVI2) and Soil-Adjusted Vegetation Index (SAVI), which include the difference between the near-infrared and red bands, have values most similar to those of the original spectra across all 10 sensors (1:1 line mean 1:1R2 > 0.960 and linear trend mean ccR2 > 0.997). Additionally, relationships between the indices’ values and two quality indicators for grass covered dikes were compared to those of the original spectra. For the soil moisture indicator, indices that ratio bands performed better across sensors than those that difference bands, while for the dike cover quality indicator, both the choice of bands and their formulation are important.
机译:通常需要使用来自多个传感器的数据以确保数据覆盖范围和连续性,但是传感器光谱特性的差异会导致光谱指数值不同。这项研究使用10个非常高分辨率的传感器的模拟数据调查了耕地草地的光谱响应函数对48个光谱指数的影响,这些数据来自草覆盖的堤坝(具有不同植被条件)的场反射光谱。针对原始窄带光谱和卷积数据集计算了48个索引的索引值,然后进行了比较。植被指数(DVI),全球环境监测指数(GEMI),植被增强指数(EVI),土壤改良植被指数(MSAVI2)和土壤改良植被指数(SAVI)的指数,包括附近的差异-红外波段和红色波段的值与所有10个传感器的原始光谱值最为相似(1:1线均值1:1R 2 2

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