Predictive soil mapping with limited sample data
Predictive soil mapping with limited sample data
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DOI：
10.1111/ejss.12244
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发表时间：
2015-05
影响因子：
4.2
通讯作者：
A. Zhu;J. Liu;F. Du;S. Zhang;C. Qin;J. Burt;T. Behrens;T. Scholten
中科院分区：
文献类型：
--
作者：
A. Zhu;J. Liu;F. Du;S. Zhang;C. Qin;J. Burt;T. Behrens;T. Scholten
现有的土壤预测制图（PSM）方法往往需要土壤样本数据足以代表整个研究区域的土壤-环境关系。然而，在世界上许多地方，只有有限数量的土壤样本数据来代表研究区域，这仍然是一个问题，PSM应用。本文提出了一种利用有限的土壤样本数据进行土壤制图的方法--“个体预测土壤制图”（iPSM）。假设相似的环境条件具有相似的土壤，iPSM使用每个土壤样本位置的土壤与环境关系来预测未访问位置的土壤性质并估计预测不确定性。具体而言，一个未访问的位置的一组土壤样本位置的环境相似性被用于在一个加权平均的方法，以集成在样本位置的土壤-环境关系的预测和不确定性估计。作为一个案例研究，iPSM应用于地图土壤有机质（SOM）含量（%）在表土层使用两套土壤样品。与多元线性回归（MLR）相比，iPSM能更准确地得到SOM图（均方根误差（RMSE）1.43，平均绝对误差（MAE）1.16）（RMSE 8.54，MAE 7.34）样本集代表研究区域的能力有限，但实现了相当的准确度（RMSE 1.10，MAE 0.69）与MLR（RMSE 1.01，MAE 0.73）的相关系数。此外，预测不确定性估计的iPSM是正相关的预测残差在这两种情况下。这项研究表明，iPSM是一个有效的替代方案时，现有的土壤样品是有限的，在他们的能力，以代表研究区域和预测的不确定性，在iPSM可以作为其预测精度的指标。
Existing predictive soil mapping (PSM) methods often require soil sample data to be sufficient to represent soil–environment relationships throughout the study area. However, in many parts of the world with only a limited quantity of soil sample data to represent the study area, this is still an issue for PSM application. This paper presents a method, named ‘individual predictive soil mapping’ (iPSM), which can make use of limited soil sample data for PSM. With the assumption that similar environmental conditions have similar soils, iPSM uses the soil–environment relationship at each individual soil sample location to predict soil properties at unvisited locations and estimate prediction uncertainty. Specifically, the environmental similarities of an unvisited location to a set of soil sample locations are used in a weighted average method to integrate the soil–environment relationships at sample locations for prediction and uncertainty estimation. As a case study, iPSM was applied to map soil organic matter (SOM) content (%) in the topsoil layer using two sets of soil samples. Compared with multiple linear regression (MLR), iPSM produced a more accurate SOM map (root mean squared error (RMSE) 1.43, mean absolute error (MAE) 1.16) than MLR (RMSE 8.54, MAE 7.34) the ability of the sample set to represent the study area is limited and achieved a comparable accuracy (RMSE 1.10, MAE 0.69) with MLR (RMSE 1.01, MAE 0.73) when the sample set could represent the study area better. In addition, the prediction uncertainty estimated by iPSM was positively related to prediction residuals in both scenarios. This study demonstrates that iPSM is an effective alternative when existing soil samples are limited in their ability to represent the study area and the prediction uncertainty in iPSM can be used as an indicator of its prediction accuracy.