Mapping Particle Size and Soil Organic Matter in Tropical Soil Based on Hyperspectral Imaging and Non-Imaging Sensors

文献类型： 外文期刊

第一作者： Nanni, Marcos Rafael

作者： Nanni, Marcos Rafael;Rodrigues, Marlon;dos Santos, Glaucio Leboso Alemparte Abrantes;Reis, Amanda Silveira;de Oliveira, Karym Mayara;Cezar, Everson;Furlanetto, Renato Herrig;Crusiol, Luis Guilherme Teixeira;Dematte, Jose Alexandre Melo;Crusiol, Luis Guilherme Teixeira;Sun, Liang

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关键词： PLSR; proximal sensing; remote sensing; soil attributes

期刊名称：REMOTE SENSING （ 影响因子：4.509； 五年影响因子：5.001 ）

ISSN：

年卷期： 2021 年 13 卷 9 期

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收录情况： SCI

摘要： We evaluated the use of airborne hyperspectral imaging and non-imaging sensors in the Vis-NIR-SWIR spectral region to assess particle size and soil organic matter in the surface layer of tropical soils (Oxisols, Ultisols, Entisols). The study area is near Piracicaba municipality, Sao Paulo state, Brazil, in a sugarcane cultivation area of 135 hectares. The study area, with bare soil, was imaged in April 2016 by the AisaFENIX aerotransported hyperspectral sensor, with spectral resolution of 3.5 nm between 380 and 970 nm, and 12 nm between 970 and 2500 nm. We collected 66 surface soil samples. The samples were analyzed for particle size and soil organic matter content. Laboratory spectral measurements were performed using a non-imaging spectroradiometer (ASD FieldSpec 3 Jr). Partial Least Square Regression (PLSR) was used to predict clay, silt, sand and soil organic matter (SOM). The PLSR functions developed were applied to the hyperspectral image of the study area, allowing development of a prediction map of clay, sand, and SOM. The developed PLSR models demonstrated the relationship between the predictor variables at the cross-validation step, both for the non-imaging and imaging sensors, when the highest r and R-2 values were obtained for clay, sand, and SOM, with R-2 over 0.67. We did not obtain a satisfactory model for silt content. For the non-imaging sensor at the prediction step, R-2 values for clay and SOM were over 0.7 and sand was lower than 0.54. The imaging sensor yielded models for clay, sand, and SOM with R-2 values of 0.62, 0.66, and 0.67, respectively. Pearson correlation between sensors was greater than 0.849 for the prediction of clay, sand, and SOM. Our study successfully generated, from the imaging sensor, a large-scale and detailed predicted soil maps for particle size and SOM, which are important in the management of tropical soils.

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