Spatial prediction of soil organic matter content using multiyear synthetic images and partitioning algorithms
•The performance of bare soil images in different periods to predict soil organic matter (SOM) is different.•The performance of landsat-8 synthetic images in SOM mapping are better than sentinel-2 synthetic images.•The local regression method based on image partition algorithms can improve the accur...
Gespeichert in:
| Veröffentlicht in: | Catena (Giessen) Jg. 211; S. 106023 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Elsevier B.V
01.04.2022
|
| Schlagworte: | |
| ISSN: | 0341-8162, 1872-6887 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | •The performance of bare soil images in different periods to predict soil organic matter (SOM) is different.•The performance of landsat-8 synthetic images in SOM mapping are better than sentinel-2 synthetic images.•The local regression method based on image partition algorithms can improve the accuracy of SOM mapping.•The mapping effect of SOM prediction model based on soil type partitioning is affected by the sample distribution.
Accurate assessment of the spatial distribution of soil organic matter (SOM) is of great significance for regional sustainable development. However, due to the strong spatial variability in soil, there is still a lack of robust SOM mapping methods. The use of remote sensing satellite technology to map soil parameters has been effectively applied in many areas. Soil mapping in Northeast China with less cloud cover, higher planting intensity and longer bare soil period of cultivated land is expected. In this study, multiyear Landsat 8 and Sentinel-2 images of bare soil periods were used to generate median synthetic images according to different time intervals in Google Earth Engine (GEE). Then, the spectral index and image band were used as input variables, and the prediction accuracies of different combinations were evaluated by the random forest (RF) algorithm. Finally, the best combination of two partitioning methods (based on different soil types and cascade simple K-means clustering) was used for SOM prediction of local regression and mapping. The results show that 1) the best time window for SOM prediction in the Songnen Plain is in May, but precipitation will affect the prediction accuracy of SOM; 2) Sentinel-2 synthetic images are not superior to Landsat 8 synthetic images for SOM mapping, although Sentinel-2 has better temporal and spatial resolution; and 3) compared with the global regression model, the local regression method based on two partitioning methods can improve the accuracy of SOM mapping, but the actual mapping effect based on the soil type partitioning algorithms is affected by the distribution of soil samples. This study extends the application of GEE to soil mapping and improve the accuracy of large-scale regional SOM mapping by partitioning images. |
|---|---|
| AbstractList | Accurate assessment of the spatial distribution of soil organic matter (SOM) is of great significance for regional sustainable development. However, due to the strong spatial variability in soil, there is still a lack of robust SOM mapping methods. The use of remote sensing satellite technology to map soil parameters has been effectively applied in many areas. Soil mapping in Northeast China with less cloud cover, higher planting intensity and longer bare soil period of cultivated land is expected. In this study, multiyear Landsat 8 and Sentinel-2 images of bare soil periods were used to generate median synthetic images according to different time intervals in Google Earth Engine (GEE). Then, the spectral index and image band were used as input variables, and the prediction accuracies of different combinations were evaluated by the random forest (RF) algorithm. Finally, the best combination of two partitioning methods (based on different soil types and cascade simple K-means clustering) was used for SOM prediction of local regression and mapping. The results show that 1) the best time window for SOM prediction in the Songnen Plain is in May, but precipitation will affect the prediction accuracy of SOM; 2) Sentinel-2 synthetic images are not superior to Landsat 8 synthetic images for SOM mapping, although Sentinel-2 has better temporal and spatial resolution; and 3) compared with the global regression model, the local regression method based on two partitioning methods can improve the accuracy of SOM mapping, but the actual mapping effect based on the soil type partitioning algorithms is affected by the distribution of soil samples. This study extends the application of GEE to soil mapping and improve the accuracy of large-scale regional SOM mapping by partitioning images. •The performance of bare soil images in different periods to predict soil organic matter (SOM) is different.•The performance of landsat-8 synthetic images in SOM mapping are better than sentinel-2 synthetic images.•The local regression method based on image partition algorithms can improve the accuracy of SOM mapping.•The mapping effect of SOM prediction model based on soil type partitioning is affected by the sample distribution. Accurate assessment of the spatial distribution of soil organic matter (SOM) is of great significance for regional sustainable development. However, due to the strong spatial variability in soil, there is still a lack of robust SOM mapping methods. The use of remote sensing satellite technology to map soil parameters has been effectively applied in many areas. Soil mapping in Northeast China with less cloud cover, higher planting intensity and longer bare soil period of cultivated land is expected. In this study, multiyear Landsat 8 and Sentinel-2 images of bare soil periods were used to generate median synthetic images according to different time intervals in Google Earth Engine (GEE). Then, the spectral index and image band were used as input variables, and the prediction accuracies of different combinations were evaluated by the random forest (RF) algorithm. Finally, the best combination of two partitioning methods (based on different soil types and cascade simple K-means clustering) was used for SOM prediction of local regression and mapping. The results show that 1) the best time window for SOM prediction in the Songnen Plain is in May, but precipitation will affect the prediction accuracy of SOM; 2) Sentinel-2 synthetic images are not superior to Landsat 8 synthetic images for SOM mapping, although Sentinel-2 has better temporal and spatial resolution; and 3) compared with the global regression model, the local regression method based on two partitioning methods can improve the accuracy of SOM mapping, but the actual mapping effect based on the soil type partitioning algorithms is affected by the distribution of soil samples. This study extends the application of GEE to soil mapping and improve the accuracy of large-scale regional SOM mapping by partitioning images. |
| ArticleNumber | 106023 |
| Author | Zhang, Wenqi Liu, Huanjun Wang, Yiang Luo, Chong Zhang, Xinle |
| Author_xml | – sequence: 1 givenname: Chong surname: Luo fullname: Luo, Chong email: luochong@iga.ac.cn organization: Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China – sequence: 2 givenname: Yiang surname: Wang fullname: Wang, Yiang email: 1376021909@qq.com organization: Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China – sequence: 3 givenname: Xinle surname: Zhang fullname: Zhang, Xinle email: zhangxinle@jlau.edu.cn organization: College of Information Technology, Jilin Agricultural University, Changchun 130118, China – sequence: 4 givenname: Wenqi surname: Zhang fullname: Zhang, Wenqi email: wenqi9094@163.com organization: School of Economics and Management, Jilin Agricultural University, Changchun 130118, China – sequence: 5 givenname: Huanjun surname: Liu fullname: Liu, Huanjun email: huanjunliu@yeah.net organization: Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China |
| BookMark | eNqFkU9P3DAQxS1EJRbKN-DgI5cs_pM4hkOlChWKhNRD27M1OOPFq8RObW-l_fY4CqceyulJo_fejH5zTk5DDEjIFWdbzri62W8tFAywFUyIOlJMyBOy4boXjdK6PyUbJlveaK7EGTnPec8Ya_uOb0j4OUPxMNI54eBt8THQ6GiOfqQx7SB4SycoBRO1MdQlhR6yDzs6HcbijwiJ5mMor1iq0U-ww0whDHSGVPzStnhh3MXky-uUP5NPDsaMl-96QX4_fPt1_715_vH4dP_1ubFS3pZGaO169gLQSemUVkOVVjkFvXQW2dD1zrqBKT1YpjtlOWeDxb4TknMFCuUFuV575xT_HDAXM_lscRwhYDxkI5RUbUWgVLXerVabYs4JnbG-wHJ6SeBHw5lZIJu9WSGbBbJZIddw-094TpVCOn4U-7LGsDL46zGZbD0GW1-Q0BYzRP__gjdOOJ0a |
| CitedBy_id | crossref_primary_10_1016_j_jenvman_2023_117810 crossref_primary_10_1002_agj2_21525 crossref_primary_10_1016_j_rse_2024_114592 crossref_primary_10_1109_ACCESS_2023_3288814 crossref_primary_10_1007_s40333_023_0094_4 crossref_primary_10_12791_KSBEC_2024_33_4_352 crossref_primary_10_1016_j_rse_2022_113166 crossref_primary_10_3390_rs15123191 crossref_primary_10_1016_j_compag_2023_108466 crossref_primary_10_3390_rs16152731 crossref_primary_10_1016_j_catena_2023_107336 crossref_primary_10_1038_s41598_024_68424_5 crossref_primary_10_3390_rs14071701 crossref_primary_10_3390_agronomy14051067 crossref_primary_10_3390_rs14102295 crossref_primary_10_1007_s11368_025_04072_0 crossref_primary_10_1016_j_compag_2023_107928 crossref_primary_10_1016_j_still_2024_106270 crossref_primary_10_3390_rs17030553 crossref_primary_10_3390_rs17152547 crossref_primary_10_1016_j_still_2024_106358 crossref_primary_10_3390_agriculture14122145 crossref_primary_10_3390_app15148060 crossref_primary_10_1016_j_envres_2023_117570 crossref_primary_10_1016_j_geoderma_2022_115959 crossref_primary_10_1109_JSTARS_2023_3267102 crossref_primary_10_1016_j_isprsjprs_2023_06_003 crossref_primary_10_1016_j_eswa_2023_121469 crossref_primary_10_1016_j_isprsjprs_2023_07_020 crossref_primary_10_1016_j_catena_2023_107228 crossref_primary_10_3390_su15010469 crossref_primary_10_1016_j_catena_2024_107821 crossref_primary_10_1016_j_catena_2024_108014 crossref_primary_10_3389_fenvs_2024_1420557 crossref_primary_10_1016_j_compag_2023_107668 crossref_primary_10_3390_su15010323 |
| Cites_doi | 10.1111/ejss.12272 10.1016/j.catena.2014.09.004 10.1016/j.rse.2016.02.016 10.1016/j.rse.2020.112117 10.1038/nature10386 10.1021/ci034160g 10.1016/j.rse.2017.03.026 10.1016/S2095-3119(19)62871-6 10.18637/jss.v036.i03 10.1016/j.geoderma.2016.10.033 10.2136/sssaj1992.03615995005600030031x 10.1016/j.aasci.2016.09.015 10.1023/A:1010933404324 10.1016/j.jrurstud.2019.12.013 10.1016/j.still.2019.104544 10.1016/S0167-1987(02)00018-1 10.1016/j.rse.2016.03.025 10.3390/rs11242947 10.1016/j.rse.2018.04.047 10.1016/j.scitotenv.2020.137703 10.3390/rs10020352 10.1016/j.rse.2017.06.031 10.1016/j.geoderma.2019.04.003 10.13031/2013.31826 10.1016/j.geoderma.2019.07.010 10.1016/j.rse.2017.06.019 10.1016/j.rse.2014.12.014 10.1016/j.geoderma.2019.113896 10.1016/j.catena.2020.104703 10.3390/rs9121245 10.1016/j.rse.2018.10.031 10.1016/j.catena.2019.104259 10.1109/JSTARS.2020.3021052 10.1016/j.rse.2011.11.026 10.1016/j.agrformet.2015.12.062 10.1007/s10661-017-5881-y 10.1016/j.soilbio.2013.10.022 10.1007/s10661-008-0385-4 10.1016/j.geoderma.2018.01.023 10.2136/sssaj1999.03615995006300020010x |
| ContentType | Journal Article |
| Copyright | 2022 Elsevier B.V. |
| Copyright_xml | – notice: 2022 Elsevier B.V. |
| DBID | AAYXX CITATION 7S9 L.6 |
| DOI | 10.1016/j.catena.2022.106023 |
| DatabaseName | CrossRef AGRICOLA AGRICOLA - Academic |
| DatabaseTitle | CrossRef AGRICOLA AGRICOLA - Academic |
| DatabaseTitleList | AGRICOLA |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Geography Geology Sciences (General) |
| EISSN | 1872-6887 |
| ExternalDocumentID | 10_1016_j_catena_2022_106023 S0341816222000091 |
| GeographicLocations | China |
| GeographicLocations_xml | – name: China |
| GroupedDBID | --K --M -DZ .~1 0R~ 1B1 1RT 1~. 1~5 29B 4.4 457 4G. 5GY 5VS 6J9 7-5 71M 8P~ 9JM 9JN AABVA AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALCJ AALRI AAOAW AAQFI AAQXK AATLK AAXUO ABFNM ABFRF ABGRD ABJNI ABMAC ABQEM ABQYD ABXDB ABYKQ ACDAQ ACGFO ACGFS ACIUM ACLVX ACRLP ACSBN ADBBV ADEZE ADMUD ADQTV AEBSH AEFWE AEKER AENEX AEQOU AFKWA AFTJW AFXIZ AGHFR AGUBO AGYEJ AHHHB AI. AIEXJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ ASPBG ATOGT AVWKF AXJTR AZFZN BKOJK BLXMC CBWCG CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q GBLVA HLV HMA HMC HVGLF HZ~ IHE IMUCA J1W KOM LW9 LY3 LY9 M41 MO0 N9A O-L O9- OAUVE OHT OZT P-8 P-9 P2P PC. Q38 R2- RIG ROL RPZ SAB SDF SDG SEN SEP SES SEW SPC SPCBC SSA SSE SSZ T5K UNMZH VH1 WUQ XPP Y6R ZMT ~02 ~G- 9DU AAHBH AATTM AAXKI AAYWO AAYXX ABUFD ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO ADVLN AEGFY AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP CITATION EFKBS ~HD 7S9 L.6 |
| ID | FETCH-LOGICAL-c339t-288f70baa533f686d33f46f6a73fce0d57fcfd068dc0856c110dce7523116a6e3 |
| ISICitedReferencesCount | 45 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000790443600002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0341-8162 |
| IngestDate | Wed Oct 01 14:30:45 EDT 2025 Tue Nov 18 21:35:25 EST 2025 Sat Nov 29 07:23:11 EST 2025 Fri Feb 23 02:40:17 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Partitioning Soil Organic Matter Google Earth Engine Multiyear synthetic |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c339t-288f70baa533f686d33f46f6a73fce0d57fcfd068dc0856c110dce7523116a6e3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| PQID | 2636404766 |
| PQPubID | 24069 |
| ParticipantIDs | proquest_miscellaneous_2636404766 crossref_citationtrail_10_1016_j_catena_2022_106023 crossref_primary_10_1016_j_catena_2022_106023 elsevier_sciencedirect_doi_10_1016_j_catena_2022_106023 |
| PublicationCentury | 2000 |
| PublicationDate | April 2022 2022-04-00 20220401 |
| PublicationDateYYYYMMDD | 2022-04-01 |
| PublicationDate_xml | – month: 04 year: 2022 text: April 2022 |
| PublicationDecade | 2020 |
| PublicationTitle | Catena (Giessen) |
| PublicationYear | 2022 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | Liu, Zhang, Zhang (b0120) 2009; 154 Vaudour, Gilliot, Bel, Lefevre, Chehdi (b0200) 2016; 49 Calinski, Harabasz (b0025) 1974; 3 Dong, Xiao, Menarguez, Zhang, Qin, Thau, Biradar, Moore (b0055) 2016; 185 Doxani, Vermote, Roger, Gascon, Adriaensen, Frantz, Hagolle, Hollstein, Kirches, Li, Louis, Mangin, Pahlevan, Pflug, Vanhellemont (b0065) 2018; 10 Demattê, Garcia (b0040) 1999; 63 Amani, Ghorbanian, Ahmadi, Kakooei, Moghimi, Mirmazloumi, Moghaddam, Mahdavi, Ghahremanloo, Parsian, Wu, Brisco (b0005) 2020; 13 Stevens, Ramirez-Lopez (b0185) 2014 Obalum, Chibuike, Peth, Ouyang (b0155) 2017; 189 Zhang, Liu, Zhang, Yu, Dou, Xie, Wang (b0225) 2018; 320 Žížala, Minařík, Zádorová (b0235) 2019; 11 Yang, Zhang, Xu, Shao, Wang, Liu, Wu, Ma, Bao, Zhang, Liu (b0220) 2020; 184 Demattê, Fongaro, Rizzo, Safanelli (b0045) 2018; 212 Fathololoumi, Vaezi, Alavipanah, Ghorbani, Saurette, Biswas (b0085) 2020; 721 Henderson, Baumgardner, Franzmeier, Stott, Coster (b0110) 1992; 56 Jin, Du, Liu, Wang, Song (b0115) 2016; 218-219 Shonk, Gaultney, Schulze, Van Scoyoc (b0175) 1991; 34 Foga, Scaramuzza, Guo, Zhu, Dilley, Beckmann, Schmidt, Dwyer, Joseph Hughes, Laue (b0090) 2017; 194 Franzluebbers (b0095) 2002; 66 Svetnik, Liaw, Tong, Culberson, Sheridan, Feuston (b0190) 2003; 43 Zhu, Wang, Woodcock (b0230) 2015; 159 Castaldi, Palombo, Santini, Pascucci, Pignatti, Casa (b0030) 2016; 179 Meng, Bao, Liu, Liu, Zhang, Zhang, Wang, Tang, Kong (b0140) 2020; 89 WRB, I.W.G., 2006. World reference base for soil resources 2006. A framework for international classification, correlation and communication. World Soil Resources Reports 103. Griffiths, Nendel, Hostert (b0105) 2019; 220 Conforti, Castrignanò, Robustelli, Scarciglia, Stelluti, Buttafuoco (b0035) 2015; 124 Silvero, Demattê, Amorim, Santos, Rizzo, Safanelli, Poppiel, Mendes, Bonfatti (b0180) 2021; 252 Dou, Wang, Liu, Zhang, Meng, Pan, Yu, Cui (b0060) 2019; 356 Nocita, Stevens, Toth, Panagos, van Wesemael, Montanarella (b0150) 2014; 68 Bao, Wu, Ye, Yang, Zhou (b0010) 2017; 288 Roy, Li, Zhang, Yan, Huang, Li (b0160) 2017; 199 Shi, Ji, Viscarra Rossel, Chen, Zhou (b0170) 2015; 66 Viechtbauer (b0205) 2010; 36 Luo, Liu, Fu, Guan, Ye, Zhang, Kong (b0135) 2020; 19 Gorelick, Hancher, Dixon, Ilyushchenko, Thau, Moore (b0100) 2017; 202 Drusch, Del Bello, Carlier, Colin, Fernandez, Gascon, Hoersch, Isola, Laberinti, Martimort, Meygret, Spoto, Sy, Marchese, Bargellini (b0070) 2012; 120 Nelson, D.W., Sommers, L., 1974. A rapid and accurate procedure for estimation of organic carbon in soils, Proceedings of the Indiana Academy of Science, pp. 456–462. Fang, Xu, Guo, Hong (b0080) 2020; 74 Liu, H.J., Ning, D.H., Kang, R., Jin, H.N., Zhang, X.L., Sheng, L., 2017. A study on predicting model of organic matter contend incorporating soil moisture variation. Guang Pu Xue Yu Guang Pu Fen Xi/Spectroscopy and Spectral Analysis 37, 566–570. Diek, Fornallaz, Schaepman, De Jong (b0050) 2017; 9 Fan, Lal, Zhang, Margenot, Wu, Wu, Zhang, Yao, Chen, Gao (b0075) 2020; 198 Ward, Chabrillat, Neumann, Foerster (b0210) 2019; 353 Urushadze, Blum, Kvrivishvili (b0195) 2016; 14 Bao, Meng, Ustin, Wang, Zhang, Liu, Tang (b0015) 2020; 195 Liu, Shen, Chen, Zhao, Biswas, Jia, Shi, Fang (b0130) 2019; 348 Breiman (b0020) 2001; 45 Schmidt, Torn, Abiven, Dittmar, Guggenberger, Janssens, Kleber, Kögel-Knabner, Lehmann, Manning, Nannipieri, Rasse, Weiner, Trumbore (b0165) 2011; 478 Žížala (10.1016/j.catena.2022.106023_b0235) 2019; 11 Nocita (10.1016/j.catena.2022.106023_b0150) 2014; 68 Calinski (10.1016/j.catena.2022.106023_b0025) 1974; 3 Doxani (10.1016/j.catena.2022.106023_b0065) 2018; 10 Viechtbauer (10.1016/j.catena.2022.106023_b0205) 2010; 36 Diek (10.1016/j.catena.2022.106023_b0050) 2017; 9 Svetnik (10.1016/j.catena.2022.106023_b0190) 2003; 43 Silvero (10.1016/j.catena.2022.106023_b0180) 2021; 252 Bao (10.1016/j.catena.2022.106023_b0015) 2020; 195 Demattê (10.1016/j.catena.2022.106023_b0040) 1999; 63 Roy (10.1016/j.catena.2022.106023_b0160) 2017; 199 Bao (10.1016/j.catena.2022.106023_b0010) 2017; 288 Drusch (10.1016/j.catena.2022.106023_b0070) 2012; 120 Liu (10.1016/j.catena.2022.106023_b0120) 2009; 154 Vaudour (10.1016/j.catena.2022.106023_b0200) 2016; 49 Dou (10.1016/j.catena.2022.106023_b0060) 2019; 356 Castaldi (10.1016/j.catena.2022.106023_b0030) 2016; 179 Franzluebbers (10.1016/j.catena.2022.106023_b0095) 2002; 66 10.1016/j.catena.2022.106023_b0215 Fathololoumi (10.1016/j.catena.2022.106023_b0085) 2020; 721 Amani (10.1016/j.catena.2022.106023_b0005) 2020; 13 Henderson (10.1016/j.catena.2022.106023_b0110) 1992; 56 Zhang (10.1016/j.catena.2022.106023_b0225) 2018; 320 Zhu (10.1016/j.catena.2022.106023_b0230) 2015; 159 Dong (10.1016/j.catena.2022.106023_b0055) 2016; 185 Meng (10.1016/j.catena.2022.106023_b0140) 2020; 89 Ward (10.1016/j.catena.2022.106023_b0210) 2019; 353 Yang (10.1016/j.catena.2022.106023_b0220) 2020; 184 Shi (10.1016/j.catena.2022.106023_b0170) 2015; 66 Urushadze (10.1016/j.catena.2022.106023_b0195) 2016; 14 Demattê (10.1016/j.catena.2022.106023_b0045) 2018; 212 Foga (10.1016/j.catena.2022.106023_b0090) 2017; 194 Jin (10.1016/j.catena.2022.106023_b0115) 2016; 218-219 Liu (10.1016/j.catena.2022.106023_b0130) 2019; 348 Luo (10.1016/j.catena.2022.106023_b0135) 2020; 19 Stevens (10.1016/j.catena.2022.106023_b0185) 2014 Conforti (10.1016/j.catena.2022.106023_b0035) 2015; 124 Gorelick (10.1016/j.catena.2022.106023_b0100) 2017; 202 Griffiths (10.1016/j.catena.2022.106023_b0105) 2019; 220 Obalum (10.1016/j.catena.2022.106023_b0155) 2017; 189 10.1016/j.catena.2022.106023_b0145 Fan (10.1016/j.catena.2022.106023_b0075) 2020; 198 Fang (10.1016/j.catena.2022.106023_b0080) 2020; 74 Breiman (10.1016/j.catena.2022.106023_b0020) 2001; 45 Schmidt (10.1016/j.catena.2022.106023_b0165) 2011; 478 Shonk (10.1016/j.catena.2022.106023_b0175) 1991; 34 10.1016/j.catena.2022.106023_b0125 |
| References_xml | – volume: 252 start-page: 112117 year: 2021 ident: b0180 article-title: Soil variability and quantification based on Sentinel-2 and Landsat-8 bare soil images: A comparison publication-title: Remote Sens. Environ. – volume: 721 start-page: 137703 year: 2020 ident: b0085 article-title: Improved digital soil mapping with multitemporal remotely sensed satellite data fusion: A case study in Iran publication-title: Sci. Total Environ. – volume: 66 start-page: 95 year: 2002 end-page: 106 ident: b0095 article-title: Soil organic matter stratification ratio as an indicator of soil quality publication-title: Soil Tillage Res. – reference: WRB, I.W.G., 2006. World reference base for soil resources 2006. A framework for international classification, correlation and communication. World Soil Resources Reports 103. – volume: 288 start-page: 47 year: 2017 end-page: 55 ident: b0010 article-title: Assessing soil organic matter of reclaimed soil from a large surface coal mine using a field spectroradiometer in laboratory publication-title: Geoderma – volume: 348 start-page: 37 year: 2019 end-page: 44 ident: b0130 article-title: Estimating forest soil organic carbon content using vis-NIR spectroscopy: Implications for large-scale soil carbon spectroscopic assessment publication-title: Geoderma – volume: 179 start-page: 54 year: 2016 end-page: 65 ident: b0030 article-title: Evaluation of the potential of the current and forthcoming multispectral and hyperspectral imagers to estimate soil texture and organic carbon publication-title: Remote Sens. Environ. – volume: 356 start-page: 113896 year: 2019 ident: b0060 article-title: Prediction of soil organic matter using multi-temporal satellite images in the Songnen Plain publication-title: China. Geoderma – volume: 14 start-page: 351 year: 2016 end-page: 355 ident: b0195 article-title: Classification of soils on sediments, sedimentary and andesitic rocks in Georgia by the WRB system publication-title: Ann. Agrar. Sci. – volume: 120 start-page: 25 year: 2012 end-page: 36 ident: b0070 article-title: Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services publication-title: Remote Sens. Environ. – volume: 45 start-page: 5 year: 2001 end-page: 32 ident: b0020 article-title: Random forests publication-title: Machine Learning – volume: 202 start-page: 18 year: 2017 end-page: 27 ident: b0100 article-title: Google Earth Engine: Planetary-scale geospatial analysis for everyone publication-title: Remote Sens. Environ. – volume: 212 start-page: 161 year: 2018 end-page: 175 ident: b0045 article-title: Geospatial Soil Sensing System (GEOS3): A powerful data mining procedure to retrieve soil spectral reflectance from satellite images publication-title: Remote Sens. Environ. – volume: 10 start-page: 352 year: 2018 ident: b0065 article-title: Atmospheric Correction Inter-Comparison Exercise publication-title: Remote Sensing – volume: 198 start-page: 104544 year: 2020 ident: b0075 article-title: Variability and determinants of soil organic matter under different land uses and soil types in eastern China publication-title: Soil Tillage Res. – volume: 478 start-page: 49 year: 2011 end-page: 56 ident: b0165 article-title: Persistence of soil organic matter as an ecosystem property publication-title: Nature – volume: 184 start-page: 104259 year: 2020 ident: b0220 article-title: Hyper-temporal remote sensing data in bare soil period and terrain attributes for digital soil mapping in the Black soil regions of China publication-title: Catena – volume: 9 start-page: 1245 year: 2017 ident: b0050 article-title: Barest Pixel Composite for Agricultural Areas Using Landsat Time Series publication-title: Remote Sensing – volume: 89 start-page: 102111 year: 2020 ident: b0140 article-title: Regional soil organic carbon prediction model based on a discrete wavelet analysis of hyperspectral satellite data publication-title: Int. J. Appl. Earth Obs. Geoinf. – volume: 43 start-page: 1947 year: 2003 end-page: 1958 ident: b0190 article-title: Random forest: A classification and regression tool for compound classification and QSAR modeling publication-title: J. Chem. Inf. Comput. Sci. – reference: Liu, H.J., Ning, D.H., Kang, R., Jin, H.N., Zhang, X.L., Sheng, L., 2017. A study on predicting model of organic matter contend incorporating soil moisture variation. Guang Pu Xue Yu Guang Pu Fen Xi/Spectroscopy and Spectral Analysis 37, 566–570. – volume: 3 start-page: 1 year: 1974 end-page: 27 ident: b0025 article-title: A dendrite method for cluster analysis publication-title: Commun. Stat. – volume: 19 start-page: 1885 year: 2020 end-page: 1896 ident: b0135 article-title: Mapping the fallowed area of paddy fields on Sanjiang Plain of Northeast China to assist water security assessments publication-title: J. Integrative Agric. – volume: 154 start-page: 147 year: 2009 end-page: 154 ident: b0120 article-title: Novel hyperspectral reflectance models for estimating black-soil organic matter in Northeast China publication-title: Environ. Monit. Assess. – volume: 185 start-page: 142 year: 2016 end-page: 154 ident: b0055 article-title: Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine publication-title: Remote Sens. Environ. – volume: 320 start-page: 12 year: 2018 end-page: 22 ident: b0225 article-title: Allocate soil individuals to soil classes with topsoil spectral characteristics and decision trees publication-title: Geoderma – volume: 36 start-page: 1 year: 2010 end-page: 48 ident: b0205 article-title: Conducting Meta-Analyses in R with the metafor Package publication-title: J. Stat. Softw. – volume: 159 start-page: 269 year: 2015 end-page: 277 ident: b0230 article-title: Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4–7, 8, and Sentinel 2 images publication-title: Remote Sens. Environ. – volume: 124 start-page: 60 year: 2015 end-page: 67 ident: b0035 article-title: Laboratory-based Vis–NIR spectroscopy and partial least square regression with spatially correlated errors for predicting spatial variation of soil organic matter content publication-title: Catena – volume: 218-219 start-page: 250 year: 2016 end-page: 260 ident: b0115 article-title: Remote estimation of soil organic matter content in the Sanjiang Plain, Northest China: The optimal band algorithm versus the GRA-ANN model publication-title: Agric. For. Meteorol. – volume: 13 start-page: 5326 year: 2020 end-page: 5350 ident: b0005 article-title: Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review publication-title: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. – volume: 63 start-page: 327 year: 1999 end-page: 342 ident: b0040 article-title: Alteration of Soil Properties through a Weathering Sequence as Evaluated by Spectral Reflectance publication-title: Soil Sci. Soc. Am. J. – volume: 56 start-page: 865 year: 1992 end-page: 872 ident: b0110 article-title: High dimensional reflectance analysis of soil organic matter publication-title: Soil Sci. Soc. Am. J. – volume: 49 start-page: 24 year: 2016 end-page: 38 ident: b0200 article-title: Regional prediction of soil organic carbon content over temperate croplands using visible near-infrared airborne hyperspectral imagery and synchronous field spectra publication-title: Int. J. Appl. Earth Obs. Geoinf. – volume: 220 start-page: 135 year: 2019 end-page: 151 ident: b0105 article-title: Intra-annual reflectance composites from Sentinel-2 and Landsat for national-scale crop and land cover mapping publication-title: Remote Sens. Environ. – volume: 66 start-page: 679 year: 2015 end-page: 687 ident: b0170 article-title: Prediction of soil organic matter using a spatially constrained local partial least squares regression and the Chinese vis–NIR spectral library publication-title: Eur. J. Soil Sci. – volume: 194 start-page: 379 year: 2017 end-page: 390 ident: b0090 article-title: Cloud detection algorithm comparison and validation for operational Landsat data products publication-title: Remote Sens. Environ. – volume: 195 start-page: 104703 year: 2020 ident: b0015 article-title: Vis-SWIR spectral prediction model for soil organic matter with different grouping strategies publication-title: CATENA – volume: 34 start-page: 1978 year: 1991 end-page: 1984 ident: b0175 article-title: Spectroscopic sensing of soil organic matter content publication-title: Trans. ASAE – volume: 353 start-page: 297 year: 2019 end-page: 307 ident: b0210 article-title: A remote sensing adapted approach for soil organic carbon prediction based on the spectrally clustered LUCAS soil database publication-title: Geoderma – volume: 11 start-page: 2947 year: 2019 ident: b0235 article-title: Soil Organic Carbon Mapping Using Multispectral Remote Sensing Data: Prediction Ability of Data with Different Spatial and Spectral Resolutions publication-title: Remote Sens. – volume: 189 start-page: 176 year: 2017 ident: b0155 article-title: Soil organic matter as sole indicator of soil degradation publication-title: Environ. Monit. Assess. – volume: 74 start-page: 111 year: 2020 end-page: 123 ident: b0080 article-title: Identifying determinants of straw open field burning in northeast China: Toward greening agriculture base in newly industrializing countries publication-title: J. Rural Stud. – reference: Nelson, D.W., Sommers, L., 1974. A rapid and accurate procedure for estimation of organic carbon in soils, Proceedings of the Indiana Academy of Science, pp. 456–462. – volume: 68 start-page: 337 year: 2014 end-page: 347 ident: b0150 article-title: Prediction of soil organic carbon content by diffuse reflectance spectroscopy using a local partial least square regression approach publication-title: Soil Biol. Biochem. – volume: 199 start-page: 25 year: 2017 end-page: 38 ident: b0160 article-title: Examination of Sentinel-2A multi-spectral instrument (MSI) reflectance anisotropy and the suitability of a general method to normalize MSI reflectance to nadir BRDF adjusted reflectance publication-title: Remote Sens. Environ. – start-page: 3 year: 2014 ident: b0185 article-title: An introduction to the prospectr package. R Package Vignette publication-title: Report No.: R Package Version – volume: 3 start-page: 1 issue: 1 year: 1974 ident: 10.1016/j.catena.2022.106023_b0025 article-title: A dendrite method for cluster analysis publication-title: Commun. Stat. – volume: 66 start-page: 679 issue: 4 year: 2015 ident: 10.1016/j.catena.2022.106023_b0170 article-title: Prediction of soil organic matter using a spatially constrained local partial least squares regression and the Chinese vis–NIR spectral library publication-title: Eur. J. Soil Sci. doi: 10.1111/ejss.12272 – volume: 124 start-page: 60 year: 2015 ident: 10.1016/j.catena.2022.106023_b0035 article-title: Laboratory-based Vis–NIR spectroscopy and partial least square regression with spatially correlated errors for predicting spatial variation of soil organic matter content publication-title: Catena doi: 10.1016/j.catena.2014.09.004 – volume: 185 start-page: 142 year: 2016 ident: 10.1016/j.catena.2022.106023_b0055 article-title: Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2016.02.016 – volume: 252 start-page: 112117 year: 2021 ident: 10.1016/j.catena.2022.106023_b0180 article-title: Soil variability and quantification based on Sentinel-2 and Landsat-8 bare soil images: A comparison publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2020.112117 – volume: 478 start-page: 49 issue: 7367 year: 2011 ident: 10.1016/j.catena.2022.106023_b0165 article-title: Persistence of soil organic matter as an ecosystem property publication-title: Nature doi: 10.1038/nature10386 – volume: 43 start-page: 1947 year: 2003 ident: 10.1016/j.catena.2022.106023_b0190 article-title: Random forest: A classification and regression tool for compound classification and QSAR modeling publication-title: J. Chem. Inf. Comput. Sci. doi: 10.1021/ci034160g – volume: 194 start-page: 379 year: 2017 ident: 10.1016/j.catena.2022.106023_b0090 article-title: Cloud detection algorithm comparison and validation for operational Landsat data products publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2017.03.026 – volume: 19 start-page: 1885 issue: 7 year: 2020 ident: 10.1016/j.catena.2022.106023_b0135 article-title: Mapping the fallowed area of paddy fields on Sanjiang Plain of Northeast China to assist water security assessments publication-title: J. Integrative Agric. doi: 10.1016/S2095-3119(19)62871-6 – volume: 36 start-page: 1 year: 2010 ident: 10.1016/j.catena.2022.106023_b0205 article-title: Conducting Meta-Analyses in R with the metafor Package publication-title: J. Stat. Softw. doi: 10.18637/jss.v036.i03 – volume: 49 start-page: 24 year: 2016 ident: 10.1016/j.catena.2022.106023_b0200 article-title: Regional prediction of soil organic carbon content over temperate croplands using visible near-infrared airborne hyperspectral imagery and synchronous field spectra publication-title: Int. J. Appl. Earth Obs. Geoinf. – volume: 288 start-page: 47 year: 2017 ident: 10.1016/j.catena.2022.106023_b0010 article-title: Assessing soil organic matter of reclaimed soil from a large surface coal mine using a field spectroradiometer in laboratory publication-title: Geoderma doi: 10.1016/j.geoderma.2016.10.033 – volume: 56 start-page: 865 issue: 3 year: 1992 ident: 10.1016/j.catena.2022.106023_b0110 article-title: High dimensional reflectance analysis of soil organic matter publication-title: Soil Sci. Soc. Am. J. doi: 10.2136/sssaj1992.03615995005600030031x – volume: 14 start-page: 351 issue: 4 year: 2016 ident: 10.1016/j.catena.2022.106023_b0195 article-title: Classification of soils on sediments, sedimentary and andesitic rocks in Georgia by the WRB system publication-title: Ann. Agrar. Sci. doi: 10.1016/j.aasci.2016.09.015 – volume: 45 start-page: 5 year: 2001 ident: 10.1016/j.catena.2022.106023_b0020 article-title: Random forests publication-title: Machine Learning doi: 10.1023/A:1010933404324 – volume: 74 start-page: 111 year: 2020 ident: 10.1016/j.catena.2022.106023_b0080 article-title: Identifying determinants of straw open field burning in northeast China: Toward greening agriculture base in newly industrializing countries publication-title: J. Rural Stud. doi: 10.1016/j.jrurstud.2019.12.013 – volume: 198 start-page: 104544 year: 2020 ident: 10.1016/j.catena.2022.106023_b0075 article-title: Variability and determinants of soil organic matter under different land uses and soil types in eastern China publication-title: Soil Tillage Res. doi: 10.1016/j.still.2019.104544 – volume: 66 start-page: 95 issue: 2 year: 2002 ident: 10.1016/j.catena.2022.106023_b0095 article-title: Soil organic matter stratification ratio as an indicator of soil quality publication-title: Soil Tillage Res. doi: 10.1016/S0167-1987(02)00018-1 – ident: 10.1016/j.catena.2022.106023_b0145 – volume: 179 start-page: 54 year: 2016 ident: 10.1016/j.catena.2022.106023_b0030 article-title: Evaluation of the potential of the current and forthcoming multispectral and hyperspectral imagers to estimate soil texture and organic carbon publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2016.03.025 – volume: 11 start-page: 2947 issue: 24 year: 2019 ident: 10.1016/j.catena.2022.106023_b0235 article-title: Soil Organic Carbon Mapping Using Multispectral Remote Sensing Data: Prediction Ability of Data with Different Spatial and Spectral Resolutions publication-title: Remote Sens. doi: 10.3390/rs11242947 – volume: 212 start-page: 161 year: 2018 ident: 10.1016/j.catena.2022.106023_b0045 article-title: Geospatial Soil Sensing System (GEOS3): A powerful data mining procedure to retrieve soil spectral reflectance from satellite images publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2018.04.047 – volume: 721 start-page: 137703 year: 2020 ident: 10.1016/j.catena.2022.106023_b0085 article-title: Improved digital soil mapping with multitemporal remotely sensed satellite data fusion: A case study in Iran publication-title: Sci. Total Environ. doi: 10.1016/j.scitotenv.2020.137703 – volume: 10 start-page: 352 issue: 3 year: 2018 ident: 10.1016/j.catena.2022.106023_b0065 article-title: Atmospheric Correction Inter-Comparison Exercise publication-title: Remote Sensing doi: 10.3390/rs10020352 – volume: 202 start-page: 18 year: 2017 ident: 10.1016/j.catena.2022.106023_b0100 article-title: Google Earth Engine: Planetary-scale geospatial analysis for everyone publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2017.06.031 – volume: 348 start-page: 37 year: 2019 ident: 10.1016/j.catena.2022.106023_b0130 article-title: Estimating forest soil organic carbon content using vis-NIR spectroscopy: Implications for large-scale soil carbon spectroscopic assessment publication-title: Geoderma doi: 10.1016/j.geoderma.2019.04.003 – volume: 34 start-page: 1978 year: 1991 ident: 10.1016/j.catena.2022.106023_b0175 article-title: Spectroscopic sensing of soil organic matter content publication-title: Trans. ASAE doi: 10.13031/2013.31826 – volume: 353 start-page: 297 year: 2019 ident: 10.1016/j.catena.2022.106023_b0210 article-title: A remote sensing adapted approach for soil organic carbon prediction based on the spectrally clustered LUCAS soil database publication-title: Geoderma doi: 10.1016/j.geoderma.2019.07.010 – volume: 199 start-page: 25 year: 2017 ident: 10.1016/j.catena.2022.106023_b0160 article-title: Examination of Sentinel-2A multi-spectral instrument (MSI) reflectance anisotropy and the suitability of a general method to normalize MSI reflectance to nadir BRDF adjusted reflectance publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2017.06.019 – volume: 159 start-page: 269 year: 2015 ident: 10.1016/j.catena.2022.106023_b0230 article-title: Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4–7, 8, and Sentinel 2 images publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2014.12.014 – volume: 356 start-page: 113896 year: 2019 ident: 10.1016/j.catena.2022.106023_b0060 article-title: Prediction of soil organic matter using multi-temporal satellite images in the Songnen Plain publication-title: China. Geoderma doi: 10.1016/j.geoderma.2019.113896 – volume: 195 start-page: 104703 year: 2020 ident: 10.1016/j.catena.2022.106023_b0015 article-title: Vis-SWIR spectral prediction model for soil organic matter with different grouping strategies publication-title: CATENA doi: 10.1016/j.catena.2020.104703 – volume: 9 start-page: 1245 year: 2017 ident: 10.1016/j.catena.2022.106023_b0050 article-title: Barest Pixel Composite for Agricultural Areas Using Landsat Time Series publication-title: Remote Sensing doi: 10.3390/rs9121245 – volume: 220 start-page: 135 year: 2019 ident: 10.1016/j.catena.2022.106023_b0105 article-title: Intra-annual reflectance composites from Sentinel-2 and Landsat for national-scale crop and land cover mapping publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2018.10.031 – volume: 184 start-page: 104259 year: 2020 ident: 10.1016/j.catena.2022.106023_b0220 article-title: Hyper-temporal remote sensing data in bare soil period and terrain attributes for digital soil mapping in the Black soil regions of China publication-title: Catena doi: 10.1016/j.catena.2019.104259 – volume: 13 start-page: 5326 year: 2020 ident: 10.1016/j.catena.2022.106023_b0005 article-title: Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review publication-title: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. doi: 10.1109/JSTARS.2020.3021052 – volume: 120 start-page: 25 year: 2012 ident: 10.1016/j.catena.2022.106023_b0070 article-title: Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2011.11.026 – ident: 10.1016/j.catena.2022.106023_b0215 – volume: 218-219 start-page: 250 year: 2016 ident: 10.1016/j.catena.2022.106023_b0115 article-title: Remote estimation of soil organic matter content in the Sanjiang Plain, Northest China: The optimal band algorithm versus the GRA-ANN model publication-title: Agric. For. Meteorol. doi: 10.1016/j.agrformet.2015.12.062 – volume: 189 start-page: 176 year: 2017 ident: 10.1016/j.catena.2022.106023_b0155 article-title: Soil organic matter as sole indicator of soil degradation publication-title: Environ. Monit. Assess. doi: 10.1007/s10661-017-5881-y – volume: 68 start-page: 337 year: 2014 ident: 10.1016/j.catena.2022.106023_b0150 article-title: Prediction of soil organic carbon content by diffuse reflectance spectroscopy using a local partial least square regression approach publication-title: Soil Biol. Biochem. doi: 10.1016/j.soilbio.2013.10.022 – volume: 89 start-page: 102111 year: 2020 ident: 10.1016/j.catena.2022.106023_b0140 article-title: Regional soil organic carbon prediction model based on a discrete wavelet analysis of hyperspectral satellite data publication-title: Int. J. Appl. Earth Obs. Geoinf. – start-page: 3 issue: 1 year: 2014 ident: 10.1016/j.catena.2022.106023_b0185 article-title: An introduction to the prospectr package. R Package Vignette publication-title: Report No.: R Package Version – volume: 154 start-page: 147 issue: 1-4 year: 2009 ident: 10.1016/j.catena.2022.106023_b0120 article-title: Novel hyperspectral reflectance models for estimating black-soil organic matter in Northeast China publication-title: Environ. Monit. Assess. doi: 10.1007/s10661-008-0385-4 – volume: 320 start-page: 12 year: 2018 ident: 10.1016/j.catena.2022.106023_b0225 article-title: Allocate soil individuals to soil classes with topsoil spectral characteristics and decision trees publication-title: Geoderma doi: 10.1016/j.geoderma.2018.01.023 – volume: 63 start-page: 327 issue: 2 year: 1999 ident: 10.1016/j.catena.2022.106023_b0040 article-title: Alteration of Soil Properties through a Weathering Sequence as Evaluated by Spectral Reflectance publication-title: Soil Sci. Soc. Am. J. doi: 10.2136/sssaj1999.03615995006300020010x – ident: 10.1016/j.catena.2022.106023_b0125 |
| SSID | ssj0004751 |
| Score | 2.5402272 |
| Snippet | •The performance of bare soil images in different periods to predict soil organic matter (SOM) is different.•The performance of landsat-8 synthetic images in... Accurate assessment of the spatial distribution of soil organic matter (SOM) is of great significance for regional sustainable development. However, due to the... |
| SourceID | proquest crossref elsevier |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 106023 |
| SubjectTerms | agricultural land algorithms catenas China cloud cover Google Earth Engine Internet Landsat Multiyear synthetic Partitioning prediction regression analysis Soil Organic Matter soil types sustainable development |
| Title | Spatial prediction of soil organic matter content using multiyear synthetic images and partitioning algorithms |
| URI | https://dx.doi.org/10.1016/j.catena.2022.106023 https://www.proquest.com/docview/2636404766 |
| Volume | 211 |
| WOSCitedRecordID | wos000790443600002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1872-6887 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0004751 issn: 0341-8162 databaseCode: AIEXJ dateStart: 19950201 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3db9MwELeqDgQviA0Q40tG4gGEghonsZ3HCY0BQhMSQ5SnyHVslilzStNO63_P-SNJtQoNkHhJI6uJ094v57vz3e8QeqGVnFFO00iQMo9SydKIKzqLZGz53rJYZkK7ZhPs-JhPp_nn0Wjd1cJc1MwYfnmZz_-rqGEMhG1LZ_9C3P1NYQDOQehwBLHD8Y8Eb5sM2zD4fGH3YDqDsG2qOrRwkq_PHammS1O3qQArFy9wqYVry-vTrg2YhZbJtToXlgPCkQnYqbrorah_NItqeRqoznumA7idcIGHylKSm40ww6eVj8meNmGpdEF8r2i-A0T7wT6CPa1MrbZGvynzs9oMVYCXO2S4uPjZVg2Nr9tK44jHQScrr4Y5IxHlYSkOepp4rbyl83344cwyfsKPfGMnhkE68XXMV9i0v9jp7GyEOPMYHOcdwrKcj9HOwYfD6cehqJa53p3943V1ly45cHuu39k1V1Z4Z7ac3EV3gr-BDzxOdtFImT1060gFpvI9dPPI9XaGs92g51v8MpCRv7qHTIATHuCEG40tnHCAE_ZwwgFO2MEJ93DCPZywhxMGOOFNOOEBTvfR13eHJ2_fR6FHRySTJF9GhHPNJjMhwG3Q8NKX8JFSTQVLtFSTMmNa6nJCeSnBuKcSrM1SKpaBWxFTQVXyAI1NY9RDhJlQschKcPdimdrdYqWUTnWuZ3muCeH7KOn-4UIGAnvbR6UuukzFs8LLpbByKbxc9lHUXzX3BC7XfJ91wiuCEeqNywLwds2VzztZF6Cj7cabMKpZtQWhCU0BUJQ--ue7P0a3h1fqCRovFyv1FN2QF8uqXTwL4P0FaQi8Cw |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Spatial+prediction+of+soil+organic+matter+content+using+multiyear+synthetic+images+and+partitioning+algorithms&rft.jtitle=Catena+%28Giessen%29&rft.au=Luo%2C+Chong&rft.au=Wang%2C+Yiang&rft.au=Zhang%2C+Xinle&rft.au=Zhang%2C+Wenqi&rft.date=2022-04-01&rft.pub=Elsevier+B.V&rft.issn=0341-8162&rft.eissn=1872-6887&rft.volume=211&rft_id=info:doi/10.1016%2Fj.catena.2022.106023&rft.externalDocID=S0341816222000091 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0341-8162&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0341-8162&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0341-8162&client=summon |