An integrated method of selecting environmental covariates for predictive soil depth mapping
Environmental covariates are the basis of predictive soil mapping. Their selection determines the performance of soil mapping to a great extent, especially in cases where the number of soil samples is limited but soil spatial heterogeneity is high. In this study, we proposed an integrated method to...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2019-02-01
|
| Series: | Journal of Integrative Agriculture |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2095311918619367 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849245330766299136 |
|---|---|
| author | Yuan-yuan LU Feng LIU Yu-guo ZHAO Xiao-dong SONG Gan-lin ZHANG |
| author_facet | Yuan-yuan LU Feng LIU Yu-guo ZHAO Xiao-dong SONG Gan-lin ZHANG |
| author_sort | Yuan-yuan LU |
| collection | DOAJ |
| description | Environmental covariates are the basis of predictive soil mapping. Their selection determines the performance of soil mapping to a great extent, especially in cases where the number of soil samples is limited but soil spatial heterogeneity is high. In this study, we proposed an integrated method to select environmental covariates for predictive soil depth mapping. First, candidate variables that may influence the development of soil depth were selected based on pedogenetic knowledge. Second, three conventional methods (Pearson correlation analysis (PsCA), generalized additive models (GAMs), and Random Forest (RF)) were used to generate optimal combinations of environmental covariates. Finally, three optimal combinations were integrated to produce a final combination based on the importance and occurrence frequency of each environmental covariate. We tested this method for soil depth mapping in the upper reaches of the Heihe River Basin in Northwest China. A total of 129 soil sampling sites were collected using a representative sampling strategy, and RF and support vector machine (SVM) models were used to map soil depth. The results showed that compared to the set of environmental covariates selected by the three conventional selection methods, the set of environmental covariates selected by the proposed method achieved higher mapping accuracy. The combination from the proposed method obtained a root mean square error (RMSE) of 11.88 cm, which was 2.25–7.64 cm lower than the other methods, and an R2 value of 0.76, which was 0.08–0.26 higher than the other methods. The results suggest that our method can be used as an alternative to the conventional methods for soil depth mapping and may also be effective for mapping other soil properties. |
| format | Article |
| id | doaj-art-dbf99eca9b344f8bb79484a8f9179acc |
| institution | Kabale University |
| issn | 2095-3119 |
| language | English |
| publishDate | 2019-02-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Journal of Integrative Agriculture |
| spelling | doaj-art-dbf99eca9b344f8bb79484a8f9179acc2025-08-20T03:58:50ZengKeAi Communications Co., Ltd.Journal of Integrative Agriculture2095-31192019-02-0118230131510.1016/S2095-3119(18)61936-7An integrated method of selecting environmental covariates for predictive soil depth mappingYuan-yuan LU0Feng LIU1Yu-guo ZHAO2Xiao-dong SONG3Gan-lin ZHANG4State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, P.R.China; University of Chinese Academy of Sciences, Beijing 100049, P.R.China; LU Yuan-yuanState Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, P.R.ChinaState Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, P.R.China; University of Chinese Academy of Sciences, Beijing 100049, P.R.ChinaState Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, P.R.ChinaState Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, P.R.China; University of Chinese Academy of Sciences, Beijing 100049, P.R.China; Correspondence ZHANG Gan-lin, Tel: +86-25-86881279Environmental covariates are the basis of predictive soil mapping. Their selection determines the performance of soil mapping to a great extent, especially in cases where the number of soil samples is limited but soil spatial heterogeneity is high. In this study, we proposed an integrated method to select environmental covariates for predictive soil depth mapping. First, candidate variables that may influence the development of soil depth were selected based on pedogenetic knowledge. Second, three conventional methods (Pearson correlation analysis (PsCA), generalized additive models (GAMs), and Random Forest (RF)) were used to generate optimal combinations of environmental covariates. Finally, three optimal combinations were integrated to produce a final combination based on the importance and occurrence frequency of each environmental covariate. We tested this method for soil depth mapping in the upper reaches of the Heihe River Basin in Northwest China. A total of 129 soil sampling sites were collected using a representative sampling strategy, and RF and support vector machine (SVM) models were used to map soil depth. The results showed that compared to the set of environmental covariates selected by the three conventional selection methods, the set of environmental covariates selected by the proposed method achieved higher mapping accuracy. The combination from the proposed method obtained a root mean square error (RMSE) of 11.88 cm, which was 2.25–7.64 cm lower than the other methods, and an R2 value of 0.76, which was 0.08–0.26 higher than the other methods. The results suggest that our method can be used as an alternative to the conventional methods for soil depth mapping and may also be effective for mapping other soil properties.http://www.sciencedirect.com/science/article/pii/S2095311918619367environmental covariate selectionintegrated methodpredictive soil mappingsoil depth |
| spellingShingle | Yuan-yuan LU Feng LIU Yu-guo ZHAO Xiao-dong SONG Gan-lin ZHANG An integrated method of selecting environmental covariates for predictive soil depth mapping Journal of Integrative Agriculture environmental covariate selection integrated method predictive soil mapping soil depth |
| title | An integrated method of selecting environmental covariates for predictive soil depth mapping |
| title_full | An integrated method of selecting environmental covariates for predictive soil depth mapping |
| title_fullStr | An integrated method of selecting environmental covariates for predictive soil depth mapping |
| title_full_unstemmed | An integrated method of selecting environmental covariates for predictive soil depth mapping |
| title_short | An integrated method of selecting environmental covariates for predictive soil depth mapping |
| title_sort | integrated method of selecting environmental covariates for predictive soil depth mapping |
| topic | environmental covariate selection integrated method predictive soil mapping soil depth |
| url | http://www.sciencedirect.com/science/article/pii/S2095311918619367 |
| work_keys_str_mv | AT yuanyuanlu anintegratedmethodofselectingenvironmentalcovariatesforpredictivesoildepthmapping AT fengliu anintegratedmethodofselectingenvironmentalcovariatesforpredictivesoildepthmapping AT yuguozhao anintegratedmethodofselectingenvironmentalcovariatesforpredictivesoildepthmapping AT xiaodongsong anintegratedmethodofselectingenvironmentalcovariatesforpredictivesoildepthmapping AT ganlinzhang anintegratedmethodofselectingenvironmentalcovariatesforpredictivesoildepthmapping AT yuanyuanlu integratedmethodofselectingenvironmentalcovariatesforpredictivesoildepthmapping AT fengliu integratedmethodofselectingenvironmentalcovariatesforpredictivesoildepthmapping AT yuguozhao integratedmethodofselectingenvironmentalcovariatesforpredictivesoildepthmapping AT xiaodongsong integratedmethodofselectingenvironmentalcovariatesforpredictivesoildepthmapping AT ganlinzhang integratedmethodofselectingenvironmentalcovariatesforpredictivesoildepthmapping |