Development and Demonstration of a Method for GEO-to-LEO NDVI Transformation
Development and Demonstration of a Method for GEO-to-LEO NDVI Transformation
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DOI：
10.3390/rs13204085
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发表时间：
2021-10
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影响因子：
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通讯作者：
K. Obata;Kenta Taniguchi;M. Matsuoka;H. Yoshioka
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作者：
K. Obata;Kenta Taniguchi;M. Matsuoka;H. Yoshioka
本研究提出一种新的方法，以减轻之间的偏差归一化差异植被指数（NDVI）从地球静止（GEO）和低地球轨道（LEO）卫星对地观测。该方法从几何和光谱两方面将GEO NDVI转换为与LEO兼容的GEO NDVI，其中GEO的非天底视图被调整为近天底视图。首先，GEO-to-LEO NDVI转换方程推导出使用各向异性植被和非植被端元光谱的线性混合模型。导出的方程的系数是两个传感器的端元光谱的函数。由此产生的方程是用来开发一个NDVI转换方法，其中端元光谱自动计算每个传感器的数据独立，并结合计算系数。重要的是，这种方法不需要使用两个传感器的NDVI数据进行回归分析。利用Himawari 8高级Himawari成像仪（AHI）的离地观测数据和中纬度Aqua中分辨率成像光谱仪（MODIS）的近地观测数据，对该方法进行了验证。结果表明，5个试验点AHI与MODIS的平均NDVI偏差（0.016-0.026）在转换后减小（<0.01）。这些研究结果表明，所提出的方法有利于GEO和LEO NDVI的组合，以提供NDVI具有较小的差异，除了在植被覆盖率（FVC）取决于视角的情况下。应开展进一步的调查，以减少转换中的剩余误差，并探讨使用所提议的方法利用地球同步轨道数据预测近实时和近最低点低地球轨道植被指数时间序列的可行性。
This study presents a new method that mitigates biases between the normalized difference vegetation index (NDVI) from geostationary (GEO) and low Earth orbit (LEO) satellites for Earth observation. The method geometrically and spectrally transforms GEO NDVI into LEO-compatible GEO NDVI, in which GEO’s off-nadir view is adjusted to a near-nadir view. First, a GEO-to-LEO NDVI transformation equation is derived using a linear mixture model of anisotropic vegetation and nonvegetation endmember spectra. The coefficients of the derived equation are a function of the endmember spectra of two sensors. The resultant equation is used to develop an NDVI transformation method in which endmember spectra are automatically computed from each sensor’s data independently and are combined to compute the coefficients. Importantly, this method does not require regression analysis using two-sensor NDVI data. The method is demonstrated using Himawari 8 Advanced Himawari Imager (AHI) data at off-nadir view and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at near-nadir view in middle latitude. The results show that the magnitudes of the averaged NDVI biases between AHI and MODIS for five test sites (0.016–0.026) were reduced after the transformation (<0.01). These findings indicate that the proposed method facilitates the combination of GEO and LEO NDVIs to provide NDVIs with smaller differences, except for cases in which the fraction of vegetation cover (FVC) depends on the view angle. Further investigations should be conducted to reduce the remaining errors in the transformation and to explore the feasibility of using the proposed method to predict near-real-time and near-nadir LEO vegetation index time series using GEO data.