植被指数对估算农田(R)USLE c因子的适用性如何?以巴西东南部为例

Filipe Castro Felix , Bernardo M. Cândido , Jener F.L. de Moraes
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引用次数: 1

摘要

通用土壤流失方程(USLE)的覆盖和管理因子(c因子)表示作物覆盖对降雨模式加权后预测土壤侵蚀率的影响。这需要估计作物在不同物候阶段的季节性降雨侵蚀力和土壤保护作用,以土壤流失率(SLR)表示。然而,土壤侵蚀建模者通常依靠基于植被指数的回归来直接从卫星图像中估计USLE的覆盖和管理因子(c因子)。由于这种方法是基于单一或很少的图像,它不能表征作物覆盖的季节性,也不能反映降雨侵蚀力的季节性。在这里,我们评估了五种植被指数(NDVI、NDRE、SFDVI、ViGREEN和MGRVI)在预测巴西东南部甘蔗地块单反和c因子方面的作用。在每个物候阶段的中期,我们使用了Sentinel-2图像和无人机调查获得的正形图。我们比较了基于单反和降雨侵蚀力的c因子估计值与文献中的直接回归。我们的结果证实了c因子与植被指数之间预期的低相关性。另一方面,以NDVI、NDRE和MGRVI为代表的植被指数是预测甘蔗区SLR的可靠方法。特别是,MGRVI准确地预测了单反,并对无人机衍生的正视镜进行了分类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
How suitable are vegetation indices for estimating the (R)USLE C-factor for croplands? A case study from Southeast Brazil

The cover and management factor (C-factor) of the Universal Soil Loss Equation (USLE) represents the effects of crop cover, weighted by rainfall pattern, on predicted soil erosion rates. This requires an estimate of seasonal rainfall erosivity and soil protection afforded by the crop at different phenological stages, expressed by a soil loss ratio (SLR). However, soil erosion modelers often rely on vegetation-index-based regressions to directly estimate the cover and management factor (C-factor) of the USLE from satellite images. Since this approach is based on a single or very few images, it does not characterize the seasonality of the crop cover or reflect the seasonality of the rainfall erosivity. Here, we evaluated five vegetation indices (NDVI, NDRE, SFDVI, ViGREEN, and MGRVI) in predicting SLRs and the C-factor for a sugarcane plot in Southeast Brazil. We used Sentinel-2 images and orthomosaics obtained by UAV surveys performed at the middle of each phenological stage. We compared the estimates of the C-factor based on the SLRs and rainfall erosivity against direct regressions from the literature. Our results confirmed the expected poor correlation between the C-factor and the vegetation indices. On the other hand, using the proposed vegetation indices proved to be a reliable alternative to predict the SLR in sugarcane areas, especially the NDVI, the NDRE, and MGRVI. In particular, the MGRVI accurately predicted the SLR and classified the UAV-derived orthomosaics.

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