Spatial pattern consistency and repeatability of proximal soil sensor data for digital soil mapping
Data from proximal soil sensors can facilitate digital soil mapping at high spatial resolutions. However, their use for predicting static soil properties, such as texture, is affected by spatio‐temporal changes in environmental and measurement conditions. In this research study, seasonal changes in...
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| Published in | European journal of soil science Vol. 74; no. 5 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
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01.09.2023
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| Abstract | Data from proximal soil sensors can facilitate digital soil mapping at high spatial resolutions. However, their use for predicting static soil properties, such as texture, is affected by spatio‐temporal changes in environmental and measurement conditions. In this research study, seasonal changes in spatial patterns and repeatability of data provided by a platform that simultaneously measures the red (Red) and near infrared (NIR) reflectance, apparent soil electrical conductivity (ECa), temperature, and volumetric moisture content of topsoil (at 3–6 cm depth) were assessed. Test fields are located in Southern Finland with textures dominated by clay and fine sandy till. During single scans, mean relative differences between the data from duplicated measurement points ranged from ~4% to 6% and were the highest for temperature and Red values. The consistency of spatial patterns across seasons (spring and autumn 2021 and 2022) was the highest for ECa values, and the lowest for NIR. ECa and moisture were significant for predicting the clay contents at a cereal grain crop site, whereas temperature was significant at grass ley sites. Errors were generally lower when using spring data compared with autumn data (RMSE ranging from 4.8% to 11.1% for the data from different fields and measurement dates). For the fields, where static soil properties change at small spatial scales, spatially detailed moisture and temperature data support the understanding of seasonal changes in the spatial patterns derived from multi‐sensor data, and the corresponding changes in the performance of calibration models. |
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| AbstractList | Data from proximal soil sensors can facilitate digital soil mapping at high spatial resolutions. However, their use for predicting static soil properties, such as texture, is affected by spatio‐temporal changes in environmental and measurement conditions. In this research study, seasonal changes in spatial patterns and repeatability of data provided by a platform that simultaneously measures the red (Red) and near infrared (NIR) reflectance, apparent soil electrical conductivity (ECa), temperature, and volumetric moisture content of topsoil (at 3–6 cm depth) were assessed. Test fields are located in Southern Finland with textures dominated by clay and fine sandy till. During single scans, mean relative differences between the data from duplicated measurement points ranged from ~4% to 6% and were the highest for temperature and Red values. The consistency of spatial patterns across seasons (spring and autumn 2021 and 2022) was the highest for ECa values, and the lowest for NIR. ECa and moisture were significant for predicting the clay contents at a cereal grain crop site, whereas temperature was significant at grass ley sites. Errors were generally lower when using spring data compared with autumn data (RMSE ranging from 4.8% to 11.1% for the data from different fields and measurement dates). For the fields, where static soil properties change at small spatial scales, spatially detailed moisture and temperature data support the understanding of seasonal changes in the spatial patterns derived from multi‐sensor data, and the corresponding changes in the performance of calibration models. |
| Author | Simojoki, Asko Ahrends, Hella Ellen Lajunen, Antti |
| Author_xml | – sequence: 1 givenname: Hella Ellen orcidid: 0000-0001-7790-847X surname: Ahrends fullname: Ahrends, Hella Ellen organization: Department of Agricultural Sciences University of Helsinki Helsinki Finland – sequence: 2 givenname: Asko orcidid: 0000-0003-2397-3553 surname: Simojoki fullname: Simojoki, Asko organization: Department of Agricultural Sciences University of Helsinki Helsinki Finland – sequence: 3 givenname: Antti orcidid: 0000-0002-2175-7833 surname: Lajunen fullname: Lajunen, Antti organization: Department of Agricultural Sciences University of Helsinki Helsinki Finland |
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| Cites_doi | 10.1016/j.geoderma.2020.114808 10.1080/01621459.2017.1415907 10.23986/afsci.72123 10.21105/joss.01686 10.2136/sssaj2006.0059 10.1016/S0169-7439(03)00051-0 10.1016/j.csda.2013.04.014 10.23986/afsci.5670 10.1007/978-90-481-3585-1_126 10.3997/1873-0604.2008031 10.1590/1809-4430-eng.agric.v39nep126-147/2019 10.18637/jss.v063.i19 10.1016/j.biosystemseng.2006.01.002 10.1016/j.sste.2013.07.003 10.1007/s11119-022-09985-1 10.1111/j.1467-9868.2008.00700.x 10.1007/978-981-10-0415-5_15 10.2307/1936256 10.1016/j.jas.2013.11.027 10.14393/BJ-v32n1a2016-26287 10.1016/j.geoderma.2012.09.007 10.1016/j.scitotenv.2017.07.201 10.18637/jss.v084.i06 10.1190/1.1440226 10.1016/j.compag.2004.11.010 10.1515/intag-2016-0005 10.13031/2013.15873 10.2136/vzj2012.0199 10.3390/soilsystems5030048 10.1371/journal.pone.0187602 10.1016/j.biosystemseng.2013.02.004 10.1002/saj2.20475 10.3390/s17112540 10.32614/RJ-2018-009 10.1016/j.still.2004.10.004 10.1111/j.1365-2389.2009.01199.x 10.2136/sssaj1989.03615995005300020020x 10.1007/s11119-016-9460-y 10.1071/SR15036 10.1198/106186007X179257 10.1016/j.geoderma.2017.10.053 10.1038/s41598-020-73879-3 10.1002/vzj2.20080 10.1016/j.catena.2019.104335 10.13031/2013.28498 10.1016/j.compag.2004.03.002 10.1016/j.geoderma.2016.10.018 10.1214/16-STS576 10.1007/s11119-020-09766-8 10.1007/s11629-012-2630-0 10.1002/vzj2.20228 10.1016/j.geoderma.2022.116103 10.1111/j.1365-2389.2008.01093.x 10.1016/j.geoderma.2014.01.011 10.5194/hess-21-495-2017 10.1016/j.compag.2007.02.010 10.1111/ejss.12790 10.18637/jss.v063.i20 10.1016/j.still.2004.03.022 10.1016/j.envsci.2015.08.012 10.1016/j.compag.2015.03.013 10.2136/sssaj2010.0056 10.1016/j.jhydrol.2015.07.023 10.13031/2013.7002 10.2134/agronj2003.4550 10.1201/9781315175584 10.1175/1520-0450(1975)014<0109:TDOBSA>2.0.CO;2 10.1038/s41467-019-11103-1 10.1111/j.1467-9868.2011.00777.x 10.1016/j.still.2006.03.011 10.1016/j.geodrs.2023.e00610 10.1016/j.still.2020.104604 10.2113/JEEG14.1.15 10.2136/vzj2009.0160 10.1016/j.geodrs.2021.e00359 10.1016/j.geoderma.2020.114253 |
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| References | e_1_2_12_4_1 (e_1_2_12_86_1) 2010; 11 e_1_2_12_6_1 e_1_2_12_19_1 e_1_2_12_2_1 e_1_2_12_17_1 e_1_2_12_38_1 (e_1_2_12_5_1) 1986 Molin J. P. (e_1_2_12_52_1) 2013; 70 e_1_2_12_20_1 e_1_2_12_41_1 e_1_2_12_66_1 e_1_2_12_87_1 e_1_2_12_22_1 e_1_2_12_43_1 e_1_2_12_64_1 e_1_2_12_85_1 e_1_2_12_24_1 e_1_2_12_45_1 e_1_2_12_26_1 e_1_2_12_47_1 e_1_2_12_68_1 e_1_2_12_89_1 e_1_2_12_62_1 e_1_2_12_83_1 e_1_2_12_60_1 e_1_2_12_81_1 e_1_2_12_28_1 e_1_2_12_49_1 Richardson A. J. (e_1_2_12_65_1) 1977; 43 e_1_2_12_31_1 e_1_2_12_77_1 e_1_2_12_33_1 e_1_2_12_54_1 e_1_2_12_75_1 e_1_2_12_35_1 e_1_2_12_56_1 Guerrero A. (e_1_2_12_25_1) 2021 e_1_2_12_37_1 e_1_2_12_58_1 e_1_2_12_79_1 e_1_2_12_14_1 e_1_2_12_90_1 e_1_2_12_12_1 McBratney A. B. (e_1_2_12_48_1) 2005; 5 e_1_2_12_8_1 e_1_2_12_10_1 R Core Team (e_1_2_12_61_1) 2021 e_1_2_12_73_1 e_1_2_12_50_1 e_1_2_12_71_1 e_1_2_12_92_1 e_1_2_12_3_1 e_1_2_12_18_1 e_1_2_12_16_1 e_1_2_12_39_1 e_1_2_12_42_1 e_1_2_12_88_1 Zuur A. F. (e_1_2_12_93_1) 2017 e_1_2_12_21_1 e_1_2_12_44_1 e_1_2_12_63_1 e_1_2_12_23_1 e_1_2_12_46_1 e_1_2_12_69_1 e_1_2_12_67_1 e_1_2_12_80_1 e_1_2_12_84_1 e_1_2_12_40_1 e_1_2_12_82_1 e_1_2_12_27_1 e_1_2_12_29_1 e_1_2_12_30_1 e_1_2_12_53_1 e_1_2_12_76_1 e_1_2_12_32_1 e_1_2_12_55_1 e_1_2_12_74_1 e_1_2_12_34_1 e_1_2_12_57_1 e_1_2_12_36_1 e_1_2_12_59_1 e_1_2_12_78_1 e_1_2_12_15_1 e_1_2_12_91_1 e_1_2_12_13_1 e_1_2_12_11_1 e_1_2_12_72_1 e_1_2_12_7_1 e_1_2_12_51_1 e_1_2_12_70_1 e_1_2_12_9_1 |
| References_xml | – volume-title: Beginner's guide to spatial, temporal, and spatial‐temporal ecological data analysis with R‐INLA. Beginner's guide book series year: 2017 ident: e_1_2_12_93_1 – ident: e_1_2_12_19_1 – ident: e_1_2_12_79_1 doi: 10.1016/j.geoderma.2020.114808 – ident: e_1_2_12_21_1 doi: 10.1080/01621459.2017.1415907 – ident: e_1_2_12_29_1 doi: 10.23986/afsci.72123 – ident: e_1_2_12_87_1 doi: 10.21105/joss.01686 – ident: e_1_2_12_7_1 doi: 10.2136/sssaj2006.0059 – ident: e_1_2_12_66_1 doi: 10.1016/S0169-7439(03)00051-0 – ident: e_1_2_12_18_1 – ident: e_1_2_12_46_1 doi: 10.1016/j.csda.2013.04.014 – ident: e_1_2_12_51_1 doi: 10.23986/afsci.5670 – ident: e_1_2_12_4_1 doi: 10.1007/978-90-481-3585-1_126 – ident: e_1_2_12_42_1 doi: 10.3997/1873-0604.2008031 – ident: e_1_2_12_53_1 doi: 10.1590/1809-4430-eng.agric.v39nep126-147/2019 – ident: e_1_2_12_40_1 doi: 10.18637/jss.v063.i19 – ident: e_1_2_12_49_1 doi: 10.1016/j.biosystemseng.2006.01.002 – ident: e_1_2_12_10_1 doi: 10.1016/j.sste.2013.07.003 – ident: e_1_2_12_14_1 doi: 10.1007/s11119-022-09985-1 – ident: e_1_2_12_68_1 doi: 10.1111/j.1467-9868.2008.00700.x – ident: e_1_2_12_59_1 doi: 10.1007/978-981-10-0415-5_15 – ident: e_1_2_12_31_1 – ident: e_1_2_12_70_1 – ident: e_1_2_12_30_1 doi: 10.2307/1936256 – ident: e_1_2_12_47_1 doi: 10.1016/j.jas.2013.11.027 – ident: e_1_2_12_50_1 doi: 10.14393/BJ-v32n1a2016-26287 – ident: e_1_2_12_33_1 doi: 10.1016/j.geoderma.2012.09.007 – ident: e_1_2_12_27_1 doi: 10.1016/j.scitotenv.2017.07.201 – ident: e_1_2_12_81_1 doi: 10.18637/jss.v084.i06 – ident: e_1_2_12_67_1 doi: 10.1190/1.1440226 – ident: e_1_2_12_37_1 – ident: e_1_2_12_78_1 doi: 10.1016/j.compag.2004.11.010 – ident: e_1_2_12_58_1 doi: 10.1515/intag-2016-0005 – ident: e_1_2_12_20_1 doi: 10.13031/2013.15873 – volume: 43 start-page: 1541 year: 1977 ident: e_1_2_12_65_1 article-title: Distinguishing vegetation from soil background information publication-title: Photogrammetric Engineering and Remote Sensing – ident: e_1_2_12_71_1 doi: 10.2136/vzj2012.0199 – ident: e_1_2_12_72_1 – ident: e_1_2_12_17_1 doi: 10.3390/soilsystems5030048 – ident: e_1_2_12_63_1 doi: 10.1371/journal.pone.0187602 – ident: e_1_2_12_36_1 doi: 10.1016/j.biosystemseng.2013.02.004 – volume-title: R: A language and environment for statistical computing year: 2021 ident: e_1_2_12_61_1 – ident: e_1_2_12_15_1 doi: 10.1002/saj2.20475 – ident: e_1_2_12_26_1 doi: 10.3390/s17112540 – start-page: 1 volume-title: Advances in agronomy year: 1986 ident: e_1_2_12_5_1 – ident: e_1_2_12_57_1 doi: 10.32614/RJ-2018-009 – ident: e_1_2_12_69_1 doi: 10.1016/j.still.2004.10.004 – ident: e_1_2_12_34_1 doi: 10.1111/j.1365-2389.2009.01199.x – ident: e_1_2_12_64_1 doi: 10.2136/sssaj1989.03615995005300020020x – ident: e_1_2_12_75_1 doi: 10.1007/s11119-016-9460-y – ident: e_1_2_12_56_1 doi: 10.1071/SR15036 – ident: e_1_2_12_24_1 doi: 10.1198/106186007X179257 – ident: e_1_2_12_82_1 doi: 10.1016/j.geoderma.2017.10.053 – ident: e_1_2_12_38_1 doi: 10.1038/s41598-020-73879-3 – ident: e_1_2_12_9_1 doi: 10.1002/vzj2.20080 – ident: e_1_2_12_89_1 doi: 10.1016/j.catena.2019.104335 – ident: e_1_2_12_77_1 doi: 10.13031/2013.28498 – ident: e_1_2_12_3_1 doi: 10.1016/j.compag.2004.03.002 – ident: e_1_2_12_2_1 doi: 10.1016/j.geoderma.2016.10.018 – ident: e_1_2_12_76_1 doi: 10.1214/16-STS576 – volume: 5 start-page: 503 year: 2005 ident: e_1_2_12_48_1 article-title: Obtaining ‘useful’ high‐resolution soil data from proximally‐sensed electrical conductivity/resistivity (PSEC/R) surveys publication-title: Precision Agriculture – ident: e_1_2_12_11_1 doi: 10.1007/s11119-020-09766-8 – ident: e_1_2_12_39_1 doi: 10.1007/s11629-012-2630-0 – ident: e_1_2_12_6_1 doi: 10.1002/vzj2.20228 – ident: e_1_2_12_74_1 doi: 10.1016/j.geoderma.2022.116103 – ident: e_1_2_12_85_1 – ident: e_1_2_12_35_1 doi: 10.1111/j.1365-2389.2008.01093.x – ident: e_1_2_12_22_1 doi: 10.1016/j.geoderma.2014.01.011 – ident: e_1_2_12_45_1 doi: 10.5194/hess-21-495-2017 – ident: e_1_2_12_13_1 doi: 10.1016/j.compag.2007.02.010 – ident: e_1_2_12_43_1 doi: 10.1111/ejss.12790 – volume: 70 start-page: 1 year: 2013 ident: e_1_2_12_52_1 article-title: Spatial and temporal variability of soil electrical conductivity related to soil moisture publication-title: Acience and Agriculture (Piracicaba, Brazil) – ident: e_1_2_12_8_1 doi: 10.18637/jss.v063.i20 – ident: e_1_2_12_84_1 – ident: e_1_2_12_54_1 doi: 10.1016/j.still.2004.03.022 – ident: e_1_2_12_55_1 doi: 10.1016/j.envsci.2015.08.012 – ident: e_1_2_12_32_1 doi: 10.1016/j.compag.2015.03.013 – ident: e_1_2_12_92_1 doi: 10.2136/sssaj2010.0056 – ident: e_1_2_12_12_1 doi: 10.1016/j.jhydrol.2015.07.023 – ident: e_1_2_12_83_1 doi: 10.13031/2013.7002 – ident: e_1_2_12_16_1 doi: 10.2134/agronj2003.4550 – ident: e_1_2_12_23_1 doi: 10.1201/9781315175584 – ident: e_1_2_12_28_1 doi: 10.1175/1520-0450(1975)014<0109:TDOBSA>2.0.CO;2 – ident: e_1_2_12_91_1 doi: 10.1038/s41467-019-11103-1 – ident: e_1_2_12_41_1 doi: 10.1111/j.1467-9868.2011.00777.x – ident: e_1_2_12_60_1 doi: 10.1016/j.still.2006.03.011 – ident: e_1_2_12_62_1 doi: 10.1016/j.geodrs.2023.e00610 – ident: e_1_2_12_88_1 doi: 10.1016/j.still.2020.104604 – volume: 11 start-page: 3571 year: 2010 ident: e_1_2_12_86_1 article-title: Asymptotic equivalence of bayes cross validation and widely applicable information criterion in singular learning theory publication-title: Journal of Machine Learning Research – ident: e_1_2_12_80_1 doi: 10.2113/JEEG14.1.15 – ident: e_1_2_12_44_1 doi: 10.2136/vzj2009.0160 – ident: e_1_2_12_73_1 doi: 10.1016/j.geodrs.2021.e00359 – start-page: 1 volume-title: Advances in agronomy year: 2021 ident: e_1_2_12_25_1 – ident: e_1_2_12_90_1 doi: 10.1016/j.geoderma.2020.114253 |
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| SubjectTerms | Autumn Clay Consistency Digital mapping Electrical conductivity Electrical resistivity Environmental changes Finland Grain crops grasses Mapping Moisture content Near infrared radiation Reflectance Reproducibility Seasonal variation Seasonal variations Seasons Sensors Soil conductivity soil electrical conductivity Soil mapping Soil properties Soil temperature Spring Spring (season) temperature Temperature data Temporal variations texture Topsoil Water content |
| Title | Spatial pattern consistency and repeatability of proximal soil sensor data for digital soil mapping |
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| Volume | 74 |
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