Evaluating the capability of the Sentinel 2 data for soil organic carbon prediction in croplands
Evaluating the capability of the Sentinel 2 data for soil organic carbon prediction in croplands
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DOI：
10.1016/j.isprsjprs.2018.11.026
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发表时间：
2019-01-01
影响因子：
12.7
通讯作者：
van Wesemael, Bas
中科院分区：
文献类型：
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作者：
Castaldi, Fabio;Hueni, Andreas;van Wesemael, Bas
由于Sentinel-2 （S2）卫星的重访时间短，需要大量的遥感数据，但S2数据很少用于土壤有机碳（SOC）含量的预测。因此，本研究旨在比较多光谱S2和航空高光谱遥感数据对土壤有机碳的预测能力，同时通过信噪比（SNR）、预测模型的变量重要度（VIP）以及野外和区域尺度上土壤有机碳地图的空间变异性，研究光谱分辨率和空间分辨率的重要性。在德国、卢森堡和比利时三个不同土壤类型和母质的农田中，采用多元统计和当地土壤定标的方法，对S2数据预测土壤有机碳的能力进行了测试。我们根据土壤图将校准数据划分为子区域，并在每个子区域内建立多元回归模型。S2数据的预测精度一般略低于机载高光谱数据的预测精度。在卢森堡（2.6）和德国（2.2）地区，绩效与偏差之比（RPD）高于2，而在比利时地区则为1.1。根据S2波段对航空数据进行光谱重采样后，5个子区域中有4个子区域的预测精度没有变化。S2数据获得的变量重要值与机载VIP值呈现相同的趋势，而根据S2波段重采样的机载数据，SWIR波段的重要性降低。这些VIP值的差异可以解释为与APEX数据相比光谱分辨率的损失，以及SWIR区域与其他光谱区域在信噪比方面的巨大差异。利用S2数据绘制的有机碳空间变异性研究表明，S2的空间分辨率足以描述场内和区域尺度上的有机碳变异性。
The short revisit time of the Sentinel-2 (S2) constellation entails a large availability of remote sensing data, but S2 data have been rarely used to predict soil organic carbon (SOC) content. Thus, this study aims at comparing the capability of multispectral S2 and airborne hyperspectral remote sensing data for SOC prediction, and at the same time, we investigated the importance of spectral and spatial resolution through the signal-to-noise ratio (SNR), the variable importance in the prediction (VIP) models and the spatial variability of the SOC maps at field and regional scales. We tested the capability of the S2 data to predict SOC in croplands with quite different soil types and parent materials in Germany, Luxembourg and Belgium, using multivariate statistics and local ground calibration with soil samples. We split the calibration dataset into sub-regions according to soil maps and built a multivariate regression model within each sub-region. The prediction accuracy obtained by S2 data is generally slightly lower than that retrieved by airborne hyperspectral data. The ratio of performance to deviation (RPD) is higher than 2 in Luxembourg (2.6) and German (2.2) site, while it is 1.1 in the Belgian area. After the spectral resampling of the airborne data according to S2 band, the prediction accuracy did not change for four out of five of the sub-regions. The variable importance values obtained by S2 data showed the same trend as the airborne VIP values, while the importance of SWIR bands decreased using airborne data resampled according the S2 bands. These differences of VIP values can be explained by the loss of spectral resolution as compared to APEX data and the strong difference in terms of SNR between the SWIR region and other spectral regions. The investigation on the spatial variability of the SOC maps derived by S2 data has shown that the spatial resolution of S2 is adequate to describe SOC variability both within field and at regional scale.