数字农科院2.0

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

作者： Nanni Marcos Rafael;Melo Demattê José Alexandre;Rodrigues Marlon;Abrantes Dos Santos Glaucio Leboso Alemparte;Reis Amanda Silveira;De Oliveira Karym Mayara;Cezar Everson;Furlanetto Renato Herrig;Teixeira Crusiol Luís Guilherme;Sun Liang

作者机构：

关键词： PLSR; Proximal sensing; Remote sensing; Soil attributes

期刊名称： Remote Sensing

ISSN： 2072-4292

年卷期： 2021 年 13 卷 9 期

页码：

收录情况： JCR（2021版） ; EI（2021版）

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, São 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 R2 values were obtained for clay, sand, and SOM, with R2 over 0.67. We did not obtain a satisfactory model for silt content. For the non-imaging sensor at the prediction step, R2 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 R2 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. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

分类号：

  • 相关文献

[1]结合植被指数与纹理特征的玉米冠层FAPAR遥感估算研究. 王思宇,聂臣巍,余汛,邵明超,王梓旭,努热曼古丽·托乎提,刘亚东,程明瀚,官云兰,金秀良. 2021

[2]结合植被指数与纹理特征的玉米冠层 FAPAR 遥感估算研究. 王思宇,, 聂臣巍,, 余 汛 ,,邵明超 ,, 王梓旭 ,, 努热曼古丽·托乎提,,刘亚东 ,,程明瀚 ,,官云兰* ,,金秀良*. 2021

[3]Net Surface Shortwave Radiation Retrieval Using Random Forest Method With Modis/Aqua Data. Li, Zhao-Liang,Wu, Hua,Wu, Hua,Wu, Hua,Ying, Wangmin,Ying, Wangmin,Li, Zhao-Liang. 2019

[4]Estimation of evapotranspiration using all-weather land surface temperature and variational trends with warming temperatures for the River Source Region in Southwest China. Ma Y., Zhou J., Liu S., Zhang W., Zhang Y., Xu Z., Song L., Zhao H.. 2022

[5]A Robust Method for Filling the Gaps in MODIS and VIIRS Land Surface Temperature Data. Rui Yao,Lunche Wang,Xin Huang,Liang Sun,Ruiqing Chen,Xiaojun Wu,Wei Zhang,Zigeng Niu. 2021

[6]Characterization of Evodia rutaecarpa (Juss) Benth honey: volatile profile, odor-active compounds and odor properties. Li, Hongxia,Liu, Zhaolong,Shuai, Mengying,Song, Mei,Qiao, Dong,Peng, Wenjun,Chen, Lanzhen. 2023

[7]Using Various Models for Predicting Soil Organic Carbon Based on DRIFT-FTIR and Chemical Analysis. Fatma N. Thabit,Osama I.A. Negim,Mohamed A.E. AbdelRahman,Antonio Scopa,Ali R.A. Moursy. 2024

[8]Improving potato AGB estimation to mitigate phenological stage impacts through depth features from hyperspectral data. Yang Liu,Haikuan Feng,Jibo Yue,Xiuliang Jin,Yiguang Fan,Riqiang Chen,Mingbo Bian,Yanpeng Ma,Jingbo Li,Bo Xu,Guijun Yang. 2024

[9]Development Of A Low-Cost Quadrotor U.av Based On Adrc F or Agricultural Remote Sensing. Xue, Xinyu,Zhang, Songchao,Zhang, Songchao,Sun, Tao,Chen, Chen,Sun, Zhu. 2019

[10]Rapid Detection Of Chlorophyll Content A.nd Distribution In Citrus O rchards Based On Low-Altitude Remote Sensing And Bio-Sensors. Deng, Lie,He, Shaolan,Li, Wentao,Lyu, Qiang,Zheng, Yongqiang,Yi, Shilai,Xie, Rangjin,Wang, Kejian,Ma, Yanyan. 2018

[11]Evaluation Of Machine Learning Algorithms In Spatial Downscaling Of Modis Land Surface Temperature. Wu, Hua,Duan, Si-Bo,Li, Zhao-Liang,Wu, Hua,Li, Zhao-Liang,Li, Wan,Li, Wan,Ni, Li,Wu, Hua. 2019

[12]Water quality related to Conservation Reserve Program (CRP) and cropland areas: Evidence from multi-temporal remote sensing. Yin, Dameng,Wang, Le,Zhu, Zhenduo,Clark, Susan Spierre,Cao, Ying,Besek, Jordan,Dai, Ning. 2021

[13]A Framework For Assessing The E.ffects Of Afforestation And S outh-To-North Water Transfer On Nitrogen And Phosphorus Uptake By Plants In A Critical Riparian Zone. Zhao, HG, Huang, YC, You, SC, Wu, YF, Zheng, FX. 2019

[14]Estimation Of Daily Evapotranspiration Using M.odis Data To Calculate I nstantaneous Decoupling Coefficient And Resistances. Jiang, YZ, Jiang, XG, Tang, RL, Li, ZL, Zhang, YZ, Huang, C, Ru, C. 2017

[15]The Influences Of The South-To-North Water Transfer Project On Nitrogen Removal By Soil In A Terminal Reservoir In A Critical State In China. Xue, WL, Li, FH, Zhao, HG. 2019

[16]Estimating Grassland Lai Using The R.andom Forests Approach And L andsat Imagery In The Meadow Steppe Of Hulunber, China. Li, ZW, Xin, XP, Tang, H, Yang, F, Chen, BR, Zhang, BH. 2017

[17]Perspective Of Chinese Gf-1 High-Resolution S.atellite Data In Agricultural R emote Sensing Monitoring. Zhou, QB, Yu, QY, Liu, J, Wu, WB, Tang, HJ. 2017

[18]An Assessment Of The Spatial And Temporal Distribution Of Soil Salinity In Combination With Field And Satellite Data: A Case Study In Sujawal District. Solangi, KA, Siyal, AA, Wu, YY, Abbasi, B, Solangi, F, Lakhiar, IA, Zhou, GY. 2019

[19]Distributed Correlation Model Mining From R.emote Sensing Big Data B ased On Gene Expression Programming. Yang, LC, Qin, ZH. 2018

[20]Estimation Of Daily Evapotranspiration Using I.nstantaneous Decoupling Coefficient From T he Modis And Field Data. Jiang, YZ, Jiang, XG, Tang, RL, Li, ZL, Zhang, YZ, Huang, C, Ru, C. 2018

作者其他论文 更多>>