Comparison of conditioned Latin hypercube and feature space coverage sampling for predicting soil classes using simulation from soil maps

Ma，Tianwu1,2,3; Brus，Dick J.1,2,7; Zhu，A. Xing1,2,3,4,5,6; Zhang，Lei1,2,3; Scholten，Thomas8,9

2020-07-01

10.1016/j.geoderma.2020.114366

GEODERMA   影响因子和分区

0016-7061

1872-6259

This study investigates sampling design for mapping soil classes based on multiple environmental features associated with the soil classes. Two types of sampling design for calibrating the prediction models are compared: conditioned Latin hypercube sampling (CLHS) and feature space coverage sampling (FSCS). Simple random sampling (SRS), which does not utilize the environmental features, is added as a reference design. The sample sizes used are 20, 30, 40, 50, 75, and 100 points, and at each sample size 100 sample sets were drawn using each of the three types of design. Each of these sample sets was then used to calibrate three prediction models: random forest (RF), individual predictive soil mapping (iPSM), and multinomial logistic regression (MLR). These sampling designs were compared based on the overall accuracy of predicted soil class maps obtained by these three prediction methods. The comparison was conducted in two study areas: Ammertal (Germany) and Raffelson (USA). For each of these two areas a detailed legacy soil class map is available. These soil class maps were used as references in a simulation study for the comparison. Results of both study areas show that on average FSCS outperforms CLHS and SRS for all three prediction methods. The difference in estimated medians of overall accuracy with CLHS and SRS was marginal. Moreover, the variation in overall accuracy among sample sets of the same size was considerably smaller for FSCS than that for CLHS. These results in the two study areas suggest that FSCS is a more effective sampling design.

Calibration sampling K-means Random forest Similarity-based predictive soil mapping Simulated annealing Soil sampling

SCI ; EI

英语

其他

German Research Foundation (DFG) through the DFG Cluster of Excellence "Machine Learning - New Perspectives for Science"[EXC 2064/1] ; German Research Foundation (DFG) through the DFG Cluster of Excellence "Machine Learning - New Perspectives for Science"[390727645]

Agriculture

Soil Science

WOS:000528270900001

ELSEVIER

20201508401803

Decision trees ; Mapping ; Soils ; Random forests ; Forecasting ; K-means clustering ; Sampling

Surveying:405.3 ; Soils and Soil Mechanics:483.1 ; Heat Treatment Processes:537.1 ; Machine Learning:723.4.2 ; Information Sources and Analysis:903.1 ; Combinatorial Mathematics, Includes Graph Theory, Set Theory:921.4 ; Systems Science:961

AGRICULTURAL SCIENCES

2-s2.0-85082854483

Scopus

1.Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application,Nanjing,210023,China
2.School of Geography,Nanjing Normal University,Nanjing,210023,China
3.Key Laboratory of Virtual Geographic Environment (Nanjing Normal University),Ministry of Education,Nanjing,210023,China
4.State Key Laboratory of Resources and Environmental Information System,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing,China
5.Department of Geography,University of Wisconsin-Madison,Madison,53706,United States
6.Center for Social Sciences,Southern University of Science and Technology,Shenzhen,518055,China
7.Biometris,Wageningen University and Research,Wageningen,PO Box 16,6700 AA,Netherlands
8.Department of Geosciences,Soil Science and Geomorphology,University of Tübingen,Tübingen,Rümelinstr. 19-23,Germany
9.DFG Cluster of Excellence “Machine Learning”,University of Tübingen,AI Research Building,Tübingen,Maria-von-Linden-Str. 6,72076,Germany

Ma，Tianwu,Brus，Dick J.,Zhu，A. Xing,et al. Comparison of conditioned Latin hypercube and feature space coverage sampling for predicting soil classes using simulation from soil maps[J]. GEODERMA,2020,370.

Ma，Tianwu,Brus，Dick J.,Zhu，A. Xing,Zhang，Lei,&Scholten，Thomas.(2020).Comparison of conditioned Latin hypercube and feature space coverage sampling for predicting soil classes using simulation from soil maps.GEODERMA,370.

Ma，Tianwu,et al."Comparison of conditioned Latin hypercube and feature space coverage sampling for predicting soil classes using simulation from soil maps".GEODERMA 370(2020).