Sentinel-2-based predictions of soil depth to inform water and nutrient retention strategies in dryland wheat

dc.contributor.authorFordyce, Simon I.
dc.contributor.authorCarr, Patrick M.
dc.contributor.authorJones, Clain
dc.contributor.authorEberly, Jed O.
dc.contributor.authorSigler, W. Adam
dc.contributor.authorEwing, Stephanie
dc.contributor.authorPowell, Scott L.
dc.date.accessioned2024-01-03T17:17:53Z
dc.date.available2024-01-03T17:17:53Z
dc.date.issued2023-11
dc.description.abstractThe thickness or depth of fine-textured soil (zf) dominates water storage capacity and exerts a control on nutrient leaching in semi-arid agroecosystems. At small pixel sizes (< 1 m; ‘fine resolution’), the normalized difference vegetation index (NDVI) of cereal crops during senescence (Zadoks Growth Stages [ZGS] 90–93) offers a promising alternative to destructive sampling of zf using soil pits. However, it is unclear whether correlations between zf and NDVI exist (a) at larger pixel sizes (1–10 m; ‘intermediate resolution’) and (b) across field boundaries. The relationship of zf to NDVI of wheat (Triticum aestivum L.) was tested using images from a combination of multispectral sensors and fields in central Montana. NDVI was derived for one field using sensors of fine and intermediate spatial resolution and for three fields using intermediate resolution sensors only. Among images acquired during crop senescence, zf was correlated with NDVI (p < 0.05) independent of sensor (p = 0.22) and field (p = 0.94). The zf relationship to NDVI was highly dependent on acquisition day (p < 0.05), but only when pre-senescence (ZGS ≤ 89) images were included in the analysis. Results indicate that cereal crop NDVI of intermediate resolution can be used to characterize zf across field boundaries if image acquisition occurs during crop senescence. Based on these findings, an empirical index was derived from multi-temporal Sentinel-2 imagery to estimate zf on fields in and beyond the study area.en_US
dc.identifier.citationFordyce, S. I., Carr, P. M., Jones, C., Eberly, J. O., Sigler, W. A., Ewing, S., & Powell, S. L. (2023). Sentinel-2-based predictions of soil depth to inform water and nutrient retention strategies in dryland wheat. Agricultural Water Management, 289, 108524.en_US
dc.identifier.issn0378-3774
dc.identifier.urihttps://scholarworks.montana.edu/handle/1/18263
dc.language.isoen_USen_US
dc.publisherElsevier BVen_US
dc.rightscc-byen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.subjectmachine learningen_US
dc.subjectNDVIen_US
dc.subjectnitrate leachingen_US
dc.subjectprecision agricultureen_US
dc.subjectsoil thicknessen_US
dc.subjectsoil water storage capacityen_US
dc.titleSentinel-2-based predictions of soil depth to inform water and nutrient retention strategies in dryland wheaten_US
dc.typeArticleen_US
mus.citation.extentfirstpage1en_US
mus.citation.extentlastpage10en_US
mus.citation.journaltitleAgricultural Water Managementen_US
mus.citation.volume289en_US
mus.data.thumbpage2en_US
mus.identifier.doi10.1016/j.agwat.2023.108524en_US
mus.relation.collegeCollege of Agricultureen_US
mus.relation.departmentResearch Centers.en_US
mus.relation.universityMontana State University - Bozemanen_US

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