Mapping Brazilian soil mineralogy using proximal and remote sensing data

被引:18
|
作者
Rosin, Nicolas Augusto [1 ]
Dematte, Jose A. M. [1 ]
Poppiel, Raul Roberto [1 ]
Silvero, Nelida E. Q. [1 ]
Rodriguez-Albarracin, Heidy S. [1 ]
Rosas, Jorge Tadeu Fim [1 ]
Greschuk, Lucas Tadeu [1 ]
Bellinaso, Henrique [1 ,2 ]
Minasny, Budiman [3 ]
Gomez, Cecile [4 ]
Marques Junior, Jose [5 ]
Fernandes, Kathleen [5 ]
机构
[1] Univ Sao Paulo, Luiz de Queiroz Coll Agr ESALQ, Dept Soil Sci, BR-13418900 Piracicaba, SP, Brazil
[2] Coordinat Integrate Tech Assistance Secretariat A, Campos Salles St 507, BR-13400200 Piracicaba, SP, Brazil
[3] Univ Sydney, Sch Life & Environm Sci, Sydney, NSW 2006, Australia
[4] Univ Montpellier, Inst Agro, LISAH, IRD,INRAE, Montpellier, France
[5] Sao Paulo State Univ Unesp, Dept Agr Sci, Sch Agr & Vet Sci, Jaboticabal, SP, Brazil
基金
巴西圣保罗研究基金会;
关键词
Reflectance spectroscopy; Digital soil mapping; Pedometrics; Bare soil image; DIFFUSE-REFLECTANCE SPECTROSCOPY; ORGANIC-MATTER; CLAY-MINERALS; IRON-OXIDES; DEPTH FUNCTIONS; CARBON STORAGE; GOETHITE; CLASSIFICATION; PERFORMANCE; PREDICTION;
D O I
10.1016/j.geoderma.2023.116413
中图分类号
S15 [土壤学];
学科分类号
0903 ; 090301 ;
摘要
Minerals control many soil functions and play a crucial role in addressing global existential issues. Measuring the abundance of soil minerals is a laborious, costly, and time-consuming task; however, soil spectroscopy can be a useful tool to overcome this issue. This work aimed to map the abundance of major mineralogical components of soils in Brazil from surface to 1 m deep and at a spatial resolution of 30 m. Spectral data of the Brazilian Soil Spectral Library with Vis-NIR-SWIR was used to estimate the abundance of haematite, goethite, kaolinite, and gibbsite. These minerals were spatialized using digital soil mapping techniques. We also developed a novel framework to obtain bare soil reflectance for areas without natural or anthropic soil exposure (continuous image) and used it as covariate. Soil minerals and their abundances were successfully estimated by Vis-NIR-SWIR reflectance. Haematite predictions presented the most accurate results with Random Forest models, followed by gibbsite, kaolinite, and goethite. The spatial validation with reference mineralogical data found R2 of 0.64 (haematite), 0.40 (goethite), 0.20 (kaolinite/Kt), 0.29 (gibbsite/Gbs), and 0.40 (Kt/Kt + Gbs). The resulting maps of soil minerals were in accordance with the geology, pedology, climate, and relief of Brazil and revealed the spatial distribution of mineral abundances at a finer resolution than existing geological and pedological maps, reaching a farm level detail.
引用
收藏
页数:19
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