Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Nai-qing FAN"'
Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 113, Iss , Pp 102986- (2022)
The effective use of environmental covariates in characterizing soil–environment relationships is key to successful digital soil mapping. The typical way to use environmental covariates in digital soil mapping is by selecting diverse environmental
Externí odkaz:
https://doaj.org/article/6bbbfffedfb949149237c5aed4ade2c3
Publikováno v:
Journal of Integrative Agriculture, Vol 19, Iss 8, Pp 2127-2136 (2020)
Selecting a proper set of covariates is one of the most important factors that influence the accuracy of digital soil mapping (DSM). The statistical or machine learning methods for selecting DSM covariates are not available for those situations with
Externí odkaz:
https://doaj.org/article/4239db7a69ea4caea9a572e96f3e4791
Publikováno v:
ISPRS International Journal of Geo-Information, Vol 9, Iss 2, p 102 (2020)
Environmental covariates are fundamental inputs of digital soil mapping (DSM) based on the soil−environment relationship. It is normal to have invalid values (or recorded as NoData value) in individual environmental covariates in some regions over
Externí odkaz:
https://doaj.org/article/a5e1663c320d40509663e8097a9e4d9f
Publikováno v:
Journal of Integrative Agriculture, Vol 19, Iss 8, Pp 2127-2136 (2020)
Selecting a proper set of covariates is one of the most important factors that influence the accuracy of digital soil mapping (DSM). The statistical or machine learning methods for selecting DSM covariates are not available for those situations with
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::edf03e23f346b074c45be3762b021808
https://doi.org/10.1016/s2095-3119(19)62857-1
https://doi.org/10.1016/s2095-3119(19)62857-1
Publikováno v:
Earth Science Informatics. 13:719-728
Predictive mapping of environment is an important means for environment assessment and management. The selection of predictor variables (or environmental covariates) is the first and key step in predictive mapping. A number of machine learning and st
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::478931b87cb3ae88cfe6ce45a27cc810
https://doi.org/10.1007/s12145-020-00466-5
https://doi.org/10.1007/s12145-020-00466-5
Publikováno v:
ISPRS International Journal of Geo-Information
Volume 9
Issue 2
ISPRS International Journal of Geo-Information, Vol 9, Iss 2, p 102 (2020)
Volume 9
Issue 2
ISPRS International Journal of Geo-Information, Vol 9, Iss 2, p 102 (2020)
Environmental covariates are fundamental inputs of digital soil mapping (DSM) based on the soil&ndash
environment relationship. It is normal to have invalid values (or recorded as NoData value) in individual environmental covariates in some regi
environment relationship. It is normal to have invalid values (or recorded as NoData value) in individual environmental covariates in some regi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::064b3c59e2b8108ed3d2fd751e4d6cf1
https://doi.org/10.3390/ijgi9020102
https://doi.org/10.3390/ijgi9020102