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首页> 外文会议>Remote sensing for agriculture, ecosystems, and hydrology XIX >Modeling soil organic matter (SOM) from satellite data using VIS-NIR-SWIR spectroscopy and PLS regression with step-down variable selection algorithm: case study of Campos Amazonicos National Park savanna enclave, Brazil
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Modeling soil organic matter (SOM) from satellite data using VIS-NIR-SWIR spectroscopy and PLS regression with step-down variable selection algorithm: case study of Campos Amazonicos National Park savanna enclave, Brazil

机译：使用VIS-NIR-SWIR光谱和降阶变量选择算法从PLS回归中根据卫星数据对土壤有机质（SOM）进行建模：巴西坎帕斯·亚马逊国家公园大草原飞地的案例研究

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Deforestation in Amazon basin due, among other factors, to frequent wildfires demands continuous post-fire monitoring of soil and vegetation. Thus, the study posed two objectives: (1) evaluate the capacity of Visible - Near InfraRed - ShortWave InfraRed (VIS-NIR-SWIR) spectroscopy to estimate soil organic matter (SOM) in fire-affected soils, and (2) assess the feasibility of SOM mapping from satellite images. For this purpose, 30 soil samples (surface layer) were collected in 2016 in areas of grass and riparian vegetation of Campos Amazonicos National Park, Brazil, repeatedly affected by wildfires. Standard laboratory procedures were applied to determine SOM. Reflectance spectra of soils were obtained in controlled laboratory conditions using Fieldspec4 spectroradiometer (spectral range 350nm-2500nm). Measured spectra were resampled to simulate reflectances for Landsat-8, Sentinel-2 and EnMap spectral bands, used as predictors in SOM models developed using Partial Least Squares regression and step-down variable selection algorithm (PLSR-SD). The best fit was achieved with models based on reflectances simulated for EnMap bands (R~2=0.93; R~2cv=0.82 and NMSE=0.07; NMSEcv=0.19). The model uses only 8 out of 244 predictors (bands) chosen by the step-down variable selection algorithm. The least reliable estimates (R~2=0.55 and R~2cv=0.40 and NMSE=0.43; NMSEcv=0.60) resulted from Landsat model, while Sentinel-2 model showed R~2=0.68 and R~2cv=0.63; NMSE=0.31 and NMSEcv=0.38. The results confirm high potential of VIS-NIR-SWIR spectroscopy for SOM estimation. Application of step-down produces sparser and better-fit models. Finally, SOM can be estimated with an acceptable accuracy (NMSE～0.35) from EnMap and Sentinel-2 data enabling mapping and analysis of impacts of repeated wildfires on soils in the study area.
机译：除其他因素外，亚马逊河流域的森林砍伐还包括经常发生的野火，因此需要对土壤和植被进行持续的火灾后监测。因此，该研究提出了两个目标：（1）评估可见光-近红外-短波红外（VIS-NIR-SWIR）光谱的能力，以评估受火土壤中的土壤有机质（SOM），以及（2）评估从卫星图像进行SOM映射的可行性。为此，2016年在巴西坎普斯·亚马逊国家公园的草丛和河岸植被地区，反复受到野火的影响，收集了30个土壤样本（表层）。应用标准实验室程序确定SOM。土壤的反射光谱是在受控实验室条件下使用Fieldspec4分光辐射计（光谱范围350nm-2500nm）获得的。对测得的光谱进行重新采样，以模拟Landsat-8，Sentinel-2和EnMap光谱带的反射率，这些光谱在使用偏最小二乘回归和降压变量选择算法（PLSR-SD）开发的SOM模型中用作预测指标。使用基于针对EnMap波段模拟的反射率的模型获得最佳拟合（R〜2 = 0.93； R〜2cv = 0.82和NMSE = 0.07； NMSEcv = 0.19）。该模型仅使用由降压变量选择算法选择的244个预测变量（带）中的8个。最不可靠的估计值（R〜2 = 0.55和R〜2cv = 0.40和NMSE = 0.43； NMSEcv = 0.60）来自Landsat模型，而Sentinel-2模型显示R〜2 = 0.68和R〜2cv = 0.63； NMSE = 0.31和NMSEcv = 0.38。结果证实了VIS-NIR-SWIR光谱技术在SOM估计方面的巨大潜力。降压的应用产生了较稀疏和更好拟合的模型。最后，可以从EnMap和Sentinel-2数据以可接受的准确度（NMSE〜0.35）估算SOM，从而可以绘制和分析反复野火对研究区域土壤的影响。

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