• Laser & Optoelectronics Progress
  • Vol. 57, Issue 9, 093002 (2020)
Meiling Tian1、2、3、**, Xiangyu Ge1、2、3, Jianli Ding1、2、3、*, Jingzhe Wang1、2、3, and Zhenhua Zhang1、2、3
Author Affiliations
  • 1College of Resource and Environment Sciences, Xinjiang University, Urumqi, Xinjiang 830046, China
  • 2Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi, Xinjiang 830046, China
  • 3Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, Xinjiang University, Urumqi, Xinjiang 830046, China
  • show less
    DOI: 10.3788/LOP57.093002 Cite this Article Set citation alerts
    Meiling Tian, Xiangyu Ge, Jianli Ding, Jingzhe Wang, Zhenhua Zhang. Coupled Machine Learning and Unmanned Aerial Vehicle Based Hyperspectral Data for Soil moisture Content Estimation[J]. Laser & Optoelectronics Progress, 2020, 57(9): 093002 Copy Citation Text show less
    References

    [1] Park J, Baik J, Choi M. Satellite-based crop coefficient and evapotranspiration using surface soil moisture and vegetation indices in Northeast Asia[J]. Catena, 156, 305-314(2017).

    [2] Zhang Z T, Wang H F, Han W T et al. Inversion of soil moisture content based on multispectral remote sensing of UAVs[J]. Transactions of the Chinese Society for Agricultural Machinery, 49, 173-181(2018).

    [3] Sankey T T. McVay J, Swetnam T L, et al. UAV hyperspectral and lidar data and their fusion for arid and semi-arid land vegetation monitoring[J]. Remote Sensing in Ecology & Conservation, 4, 20-33(2018).

    [4] Cheng H, Shen R L, Chen Y Y et al. Estimating heavy metal concentrations in suburban soils with reflectance spectroscopy[J]. Geoderma, 336, 59-67(2019).

    [5] Ge X Y, Ding J L, Wang J Z et al. Estimation of soil moisture content based on competitive adaptive reweighted sampling algorithm coupled with machine learning[J]. Acta Optica Sinica, 38, 1030001(2018).

    [6] Zhang Z P, Ding J L, Wang J Z. Spectral characteristics of oasis soil in arid area based on harmonic analysis algorithm[J]. Acta Optica Sinica, 39, 0228003(2019).

    [7] Miao S, Wang R, Li J C et al. Retrieval algorithm of phycocyanin concentration in inland lakes from sentinel 3A-OLCI images[J]. Journal of Infrared and Millimeter Waves, 37, 621-630(2018).

    [8] Wang N, Li Q Z, Du X et al. Identification of main crops based on the univariate feature selection in Subei[J]. Journal of Remote Sensing, 21, 519-530(2017).

    [9] Zamani Joharestani M, Cao C X, Ni X L et al. PM2.5 prediction based on random forest, XGBoost, and deep learning using multisource remote sensing data[J]. Atmosphere, 10, 373(2019).

    [10] Song W Z, Jia H F, Huang J F et al. A satellite-based geographically weighted regression model for regional PM2.5 estimation over the Pearl River Delta region in China[J]. Remote Sensing of Environment, 154, 1-7(2014).

    [11] Yuan J, Zhang F, Ge X Y et al. Leaf salt ion content estimation of halophyte plants based on geographically weighted regression model combined with hyperspectral data[J]. Transactions of the Chinese Society of Agricultural Engineering, 35, 115-124(2019).

    [12] Ge X, Wang J, Ding J et al. Combining UAV-based hyperspectral imagery and machine learning algorithms for soil moisture content monitoring[J]. PeerJ, 7, e6926(2019).

    [13] Wang J Z, Ding J L, Ma X K et al. Detection of soil moisture content based on UAV-derived hyperspectral imagery and spectral index in oasis cropland[J]. Transactions of the Chinese Society for Agricultural Machinery, 49, 164-172(2018).

    [14] Sun J, Cong S L, Mao H P et al. CARS-ABC-SVR model for predicting leaf moisture of leaf-used lettuce based on hyperspectral[J]. Transactions of the Chinese Society of Agricultural Engineering, 33, 178-184(2017).

    [15] Menze B H, Kelm B M, Masuch R et al. A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data[J]. BMC Bioinformatics, 10, 213(2009).

    [16] Friedman J H . Greedy function approximation: a gradient boosting machine[J]. Annals of Statistics, 29, 1189-1232(2001).

    [17] Im J, Jensen J R. Hyperspectral remote sensing of vegetation[J]. Geography Compass, 2, 1943-1961(2008).

    [18] Schoo R N, Ray S S, Manjunath K R. Hyperspectral remote sensing of agriculture[J]. Current Science, 108, 848-859(2015).

    [19] Bao Q L, Ding J L, Wang J Z. Prediction of soil moisture content by selecting spectral characteristics using random forest method[J]. Laser & Optoelectronics Progress, 55, 113002(2018).

    [20] Wang J Z, Ding J L, Abulimiti A et al. Quantitative estimation of soil salinity by means of different modeling methods and visible-near infrared (VIS-NIR) spectroscopy, Ebinur Lake Wetland, Northwest China[J]. PeerJ, 6, e4703(2018).

    [21] Jiang Z L, Yang Y S, Sha J M. Application of GWR model in hyperspectral prediction of soil heavy metals[J]. Acta Geographica Sinica, 72, 533-544(2017).

    [22] Luo M, Guo L, Zhang H T et al[J]. Characterization of spatial distribution of soil organic carbon in China based on environmental variables Acta Pedologica Sinica, 2020, 48-69.

    [23] Yue Y M, Wang K L, Zhang B et al. Exploring the relationship between vegetation spectra and eco-geo-environmental conditions in Karst region, Southwest China[J]. Environmental Monitoring and Assessment, 160, 157-168(2010).

    [24] Shi Z, Xu D Y, Teng H F et al. Soil information acquisition based on remote sensing and proximal soil sensing: current status and prospect[J]. Progress in Geography, 37, 79-92(2018).

    Meiling Tian, Xiangyu Ge, Jianli Ding, Jingzhe Wang, Zhenhua Zhang. Coupled Machine Learning and Unmanned Aerial Vehicle Based Hyperspectral Data for Soil moisture Content Estimation[J]. Laser & Optoelectronics Progress, 2020, 57(9): 093002
    Download Citation