Biney James Kobina Mensah, Houška Jakub, Volánek Jiří, Abebrese David Kwesi, Cervenka Jakub
The Silva Tarouca Research Institute for Landscape and Ornamental Gardening, Department of Landscape Ecology, Lidická 25/27, Brno 602 00, Czech Republic; Department of Soil Science, University of Manitoba, 13 Freedman Crescent, Winnipeg, MB R3T 2N2, Canada.
The Silva Tarouca Research Institute for Landscape and Ornamental Gardening, Department of Landscape Ecology, Lidická 25/27, Brno 602 00, Czech Republic.
Sci Total Environ. 2023 Apr 20;870:161973. doi: 10.1016/j.scitotenv.2023.161973. Epub 2023 Feb 3.
Soil organic content (SOC), an indicator of soil fertility, can be estimated quickly and accurately with remote sensing (RS) datasets; however, the issue of vegetation cover on the field still remains a major concern. In order to minimize the effects of vegetation cover, studies relating reflectance spectra to SOC may require bare soil. However, acquiring satellite images devoid of vegetation is still an enormous challenge for RS techniques. This is because the area that may have been accurately predicted at a targeted date is sometimes limited since many pixels are covered by vegetation. The study goal was to assess the impact of using UAV-borne imagery coupled with auxiliary datasets, which include spectral indices (SPIs) and terrain attributes (TAs) (at 20 cm and 30 m resolution), singly or merged, to estimate and map SOC in an erosion-prone agricultural field. Both field samples and UAV imagery were acquired while the fields were bare. Using a grid sampling design, 133 soil surface samples were collected. The models used include partial least square regression (PLSR), extreme gradient boosting (EGB), multivariate adaptive regression splines (MARS), and regularised random forest (RFF). The models were evaluated using the root mean squared error (RMSE), the coefficient of determination (R2), ratio of performance to interquartile distance (RPIQ), and the mean absolute error (MAE). For prediction, the three merged datasets (Rval = 0.86, RMSEval = 0.13, MAEval = 0.11, RPIQval = 4.19) outperformed the best separate dataset (Rval = 0.82, RMSEval = 0.15, MAEval = 0.10, RPIQval = 2.08). Though all datasets detected both low and high estimates of soil SOC, the three merged datasets with EGB showed a less extreme prediction error. This study demonstrated that SOC can be estimated with high accuracy using completely bare soil UAV imagery with other auxiliary data, and it is thus highly recommended.
土壤有机含量（SOC）作为土壤肥力的一个指标，可以通过遥感（RS）数据集快速准确地估算；然而，田间植被覆盖问题仍然是一个主要关注点。为了尽量减少植被覆盖的影响，将反射光谱与SOC相关联的研究可能需要裸土。然而，获取无植被的卫星图像对RS技术来说仍然是一个巨大的挑战。这是因为在目标日期可能被准确预测的区域有时是有限的，因为许多像素被植被覆盖。本研究的目标是评估使用无人机搭载的图像结合辅助数据集（包括光谱指数（SPI）和地形属性（TA），分辨率分别为20厘米和30米）单独或合并使用，以估计和绘制易侵蚀农田中的SOC。在田地裸露时采集了田间样本和无人机图像。采用网格采样设计，收集了133个土壤表层样本。使用的模型包括偏最小二乘回归（PLSR）、极端梯度提升（EGB）、多元自适应回归样条（MARS）和正则化随机森林（RFF）。使用均方根误差（RMSE）、决定系数（R2）、性能与四分位间距之比（RPIQ）和平均绝对误差（MAE）对模型进行评估。对于预测，三个合并数据集（Rval = 0.86，RMSEval = 0.13，MAEval = 0.11，RPIQval = 4.19）优于最佳单独数据集（Rval = 0.82，RMSEval = 0.15，MAEval = 0.10，RPIQval = 2.08）。尽管所有数据集都检测到了土壤SOC的低估和高估，但三个与EGB合并的数据集显示出较小的极端预测误差。本研究表明，使用完全裸露土壤的无人机图像和其他辅助数据可以高精度地估算SOC，因此强烈推荐。
