A Simple Algorithm for Deriving an NDVI-Based Index Compatible between GEO and LEO Sensors: Capabilities and Limitations in Japan
A Simple Algorithm for Deriving an NDVI-Based Index Compatible between GEO and LEO Sensors: Capabilities and Limitations in Japan
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DOI：
10.3390/rs12152417
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
K. Obata;H. Yoshioka
中科院分区：
文献类型：
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作者：
K. Obata;H. Yoshioka
对地静止卫星传感器提供的地球观测数据具有很高的时间频率，可以补充低地球轨道传感器对陆地植被的监测。因此，地球同步轨道和低地球轨道观测数据之间的一致性对于传感器的协同使用至关重要;然而，中高纬度地区的太阳目标传感器观测几何结构与传感器特定光谱响应函数之间的不匹配会给标准化差异植被指数等地球同步轨道-低地球轨道产品带来系统误差。如果可以找到一个参数，其中的值受几何条件和SRF的影响较小，这将是非常宝贵的多个传感器的协同使用。本研究试图开发一种算法来获得这样的参数（基于NDVI的指数），这是相当于从NDVI和端元光谱计算的植被覆盖率（FVC）。该算法基于具有参数自动计算的线性混合模型（LMM），即，端元谱该算法是通过使用Himawari 8高级Himawari成像仪（AHI）的非最低点地球静止轨道观测数据和Aqua中分辨率成像光谱仪（MODIS）的近最低点低地轨道观测数据作为日本中纬度陆地表面参考的归一化差异指数之间的相互比较进行评价的。结果表明，基于NDVI的传感器指数之间的场景依赖性偏差为−0.0004±0.018（平均值±标准差）。在植被丰度分数可能对天顶角不太敏感的地区，观察到小的偏差。基于归一化差异植被指数的传感器之间的协议，一般来说，比归一化差异植被指数值之间的协议。重要的是，所开发的算法不需要回归分析来减少指数之间的偏差。该算法应有助于制定算法，以归一化差异指数为参数，对植被指数进行传感器间转换。
Geostationary (GEO) satellite sensors provide earth observation data with a high temporal frequency and can complement low earth orbit (LEO) sensors in monitoring terrestrial vegetation. Consistency between GEO and LEO observation data is thus critical to the synergistic use of the sensors; however, mismatch between the sun–target–sensor viewing geometries in the middle-to-high latitude region and the sensor-specific spectral response functions (SRFs) introduce systematic errors into GEO–LEO products such as the Normalized Difference Vegetation Index (NDVI). If one can find a parameter in which the value is less influenced by geometric conditions and SRFs, it would be invaluable for the synergistic use of the multiple sensors. This study attempts to develop an algorithm to obtain such parameters (NDVI-based indices), which are equivalent to fraction of vegetation cover (FVC) computed from NDVI and endmember spectra. The algorithm was based on a linear mixture model (LMM) with automated computation of the parameters, i.e., endmember spectra. The algorithm was evaluated through inter-comparison between NDVI-based indices using off-nadir GEO observation data from the Himawari 8 Advanced Himawari Imager (AHI) and near-nadir LEO observation data from the Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) as a reference over land surfaces in Japan at middle latitudes. Results showed that scene-dependent biases between the NDVI-based indices of sensors were −0.0004±0.018 (mean ± standard deviation). Small biases were observed in areas in which the fractional abundances of vegetation were likely less sensitive to the view zenith angle. Agreement between the NDVI-based indices of the sensors was, in general, better than the agreement between the NDVI values. Importantly, the developed algorithm does not require regression analysis for reducing biases between the indices. The algorithm should assist in the development of algorithms for performing inter-sensor translations of vegetation indices using the NDVI-based index as a parameter.