Remote Sensing Technology and Application, Volume. 40, Issue 2, 344(2025)
Study on Spectral Characteristics and Quantitative Estimation of Soil Salinity based on Fractional Order Derivative
[1] YANG Jinsong, YAO Rongjiang, WANG Xiangping et al. Research on salt-affected soils in China: History, status quo and prospect. Acta Pedologica Sinica, 59, 10-27(2022).
[2] HASSANI A, AZAPAGIC A, SHOKRI N. Predicting long-term dynamics of soil salinity and sodicity on a global scale. Proceedings of the National Academy of Sciences of the United States of America, 117, 33017-33027(2020).
[3] YUN Xuexue, CHEN Yusheng. International development of saline-alkali land and its enlightenment to China. Territory & Natural Resources Study, 84-87(2020).
[4] IVUSHKIN K, BARTHOLOMEUS H, BREGT A K et al. Global mapping of soil salinity change. Remote Sensing of Environment, 231(2019).
[5] LITALIEN A, ZEEB B. Curing the earth: A review of anthropogenic soil salinization and plant-based strategies for sustainable mitigation. Science of the Total Environment, 698(2020).
[6] WANG S, GUAN K, ZHANG C et al. Using soil library hyperspectral reflectance and machine learning to predict soil organic carbon: Assessing potential of airborne and spaceborne optical soil sensing. Remote Sensing of Environment, 271(2022).
[7] YANG X, YU Y. Estimating soil salinity under various moisture conditions: an experimental study. Ieee Transactions on Geoscience and Remote Sensing, 55, 2525-2533(2017).
[8] YANG N N, HAN L, LIU M. Inversion of soil heavy metals in metal tailings area based on different spectral transformation and modeling methods. Heliyon, 9, 19(2023).
[9] TIAN A H, ZHAO J, FU C B et al. Estimation of SO42-ion in saline soil using VIS-NIR spectroscopy under different human activity stress. Spectrochimica Acta Part a-Molecular and Biomolecular Spectroscopy, 282(2022).
[10] GUO Qing, ZHANG Lifu, QI Wenchao et al. The Hyperspectral Inversion Method of Main Ionic Compounds Content in Groundwater based on BP Neural Network. Remote Sensing Technology and Application, 39, 149-159(2024).
[11] ZHOU W, YANG H, XIE L J et al. Hyperspectral inversion of soil heavy metals in three-river source region based on random forest model. Catena, 202, 10(2021).
[12] HONG Y S, GUO L, CHEN S C et al. Exploring the potential of airborne hyperspectral image for estimating topsoil organic carbon: Effects of fractional-order derivative and optimal band combination algorithm. Geoderma, 365, 12(2020).
[13] QIAO Xingxing, FENG Meicheng, YANG Wude et al. Effect of spectral transformation processes on the PLSR models of soil nitrogen. Journal of Geo-Information Science, 18, 1123-1132(2016).
[14] WANG Yijing, CHEN Ruihua, ZHANG Junhua et al. Hyperspectral inversion of soil water and salt information based on fractional order derivative technology. Chinese Journal of Applied Eology, 34, 1384-1394(2023).
[15] HUANG Huayu, DING Qidong, ZHANG Junhua et al. Ground-based hyperspectral inversion of salinization and alkalinization of different soil layers in farmland in Yinbei area, Ningxia, China. Chinese Journal of Applied Ecology, 35, 3073-3084(2024).
[16] LAO C C, CHEN J Y, ZHANG Z T et al. Predicting the contents of soil salt and major water-soluble ions with fractional-order derivative spectral indices and variable selection. Computers and Electronics in Agriculture, 182, 18(2021).
[17] XING Z, DU C, SHEN Y et al. A method combining FTIR-ATR and Raman spectroscopy to determine soil organic matter: Improvement of prediction accuracy using Competitive Adaptive Reweighted Sampling(CARS). Computers and Electronics in Agriculture, 191(2021).
[18] ZHU C, DING J, ZHANG Z et al. Exploring the potential of UAV hyperspectral image for estimating soil salinity: Effects of optimal band combination algorithm and random forest. Spectrochimica Acta Part a-Molecular and Biomolecular Spectroscopy, 279(2022).
[19] LI F, XU L, YOU T Y et al. Measurement of potentially toxic elements in the soil through NIR, MIR, and XRF spectral data fusion. Computers and Electronics in Agriculture, 187, 9(2021).
[20] PADARIAN J, MINASNY B, MCBRATNEY A B. Machine learning and soil sciences:A review aided by machine learning tools. Soil, 6, 35-52(2020).
[21] MAO Y C, LIU J, CAO W et al. Research on the quantitative inversion model of heavy metals in soda saline land based on visible-near-infrared spectroscopy. Infrared Physics & Technology, 112, 11(2021).
[22] TIAN Yuxin, WANG Zhenghai, XIE Peng. Quantitative hyperspectral inversion of soil heavy metals based on feature screening combined with PSO-BPNN and GA-BPNN algorithms. Remote Sensing Technology and Application, 39, 259-268(2024).
[23] ZHANG Xiaoguang, JIANG Zixuan, KONG Fanchang. Hyperspectral characteristics of coastal saline soil with visible/near infrared spectroscopy. Remote Sensing Technology and Application, 34, 816-821(2019).
[25] YUAN Min. Research of temporal and spatial variability of soil nutrient in Yulin(2015).
[27] WANG Zunqin, ZHU Shouquan, YU Renpei et al. Salt-affected soils of China, 130-216(1993).
[28] XIAO B, LI S Z, DOU S Q et al. Comparison of leaf chlorophyll content retrieval performance of citrus using FOD and CWT methods with field-based full-spectrum hyperspectral reflectance data. Computers and Electronics in Agriculture, 217, 18(2024).
[29] WU Jun, GUO Daqian, LI Guo et al. Prediction of soil organic carbon content in Jiangxi Province by Vis-NIR spectroscopy based on the CARS-BPNN model. Scientia Agricultura Sinica, 55, 3738-3750(2022).
[30] CHEN Yuanzhe, WANG Qiaohua, GAO Sheng et al. Nondestructive testing model for textural quality of freshwater fish in storage using near-infrared spectroscopy. Laser & Optoe-lectronics Progress, 58, 507-515(2021).
[31] YIN Fang, FENG Kai, WU Mengmeng et al. A remote sensing estimation method for heavy metals in soil based on piecewise partial least squares model. Remote Sensing Technology and Application, 36, 1321-1328(2021).
[32] LI Guoxu, GENG Jing, XU Xuanhong et al. Inversion of soil Cd content using WorldView-3 multispectral and key environmental variables. Transactions of the Chinese Society of Agricultural Engineering, 38, 224-232(2022).
[33] ZHANG B, GUO B, ZOU B et al. Retrieving soil heavy metals concentrations based on GaoFen-5 hyperspectral satellite image at an opencast coal mine, Inner Mongolia, China. Environmental Pollution, 300(2022).
[34] MELO D J A, HORAK T I, BEIRIGO R M et al. Genesis and properties of wetland soils by VIS-NIR-SWIR as a technique for environmental monitoring. Journal of Environmental Management, 197, 50-62(2017).
[35] QI Haoping, WENG Yongling, ZHAO Fuyue et al. A study of salinity characteristics and spectral characteristics of salt-affected soil in Caka-Gonghe Basin. Remote Sensing for Land&Resources, 4-8(2010).
[36] ZHANG X, SUN W, CEN Y et al. Predicting cadmium concentration in soils using laboratory and field reflectance spectroscopy. Science of the Total Environment, 650, 321-334(2019).
[37] CROWLEY J K. Visible and near-infrared (0.4-2.5 μm) reflectance spectra of playa evaporite minerals. Journal of Geophysical Research-Solid Earth, 96, 16231-16240(1991).
[38] FARIFTEH J, VAN D M F, VAN D M M et al. Spectral characteristics of salt-affected soils: A laboratory experiment. Geoderma, 145, 196-206(2008).
[39] LIU Huanjun, ZHANG Bai, WANG Zongming et al. Soil saline-alka-lization evaluation basing on spectral reflectance characteristics. Journal of Infrared and Millimeter Waves, 27, 138-142(2008).
[40] DRAKE N A. Reflectance spectra of evaporate minerals (400-2500 nm): applications for remote sensing. International Journal of Remote Sensing, 16, 2555-2571(1995).
[41] WANG Z, ZHANG F, ZHANG X et al. Regional suitability prediction of soil salinization based on remote-sensing derivatives and optimal spectral index. Science of the Total Environment, 775(2021).
[42] JIANG Chuanli, ZHAO Jianyun, DING Yuanyuan et al. Study on soil water retrieval technology of yellow river source based on SPA Algorithm and Machine Learning. Spectroscopy and Spectral Analysis, 43, 1961-1967(2023).
[43] XIA K, WU T X, ZHANG S W et al. A new method for high-precision estimation of soil organic matter using two-dimensional correlation spectroscopy-to support collaborative use of global open soil spectral libraries. Geoderma, 445, 20(2024).
[44] HONG Y S, LIU Y, CHEN Y Y et al. Application of fractional-order derivative in the quantitative estimation of soil organic matter content through visible and near-infrared spectroscopy. Geoderma, 337, 758-769(2019).
[45] ZHANG Z P, DING J L, WANG J Z. et al Prediction of soil organic matter in northwestern China using fractional-order derivative spectroscopy and modified normalized difference indices. Catena, 185, 12(2020).
[46] TIAN Anhong, ZHAO Junsan, ZHANG Shunji et al. Hyperspectral estimation of saline soil electrical conductivity based on fractional derivative. Chinese Journal of Eco-Agriculture, 28, 599-607(2020).
[47] WANG Y L, ZOU B, CHAI L Y et al. Monitoring of soil heavy metals based on hyperspectral remote sensing: A review. Earth-Science Reviews, 254, 21(2024).
[48] LIU J, DONG Z, XIA J et al. Estimation of soil organic matter content based on CARS algorithm coupled with random forest. Spectrochimica Acta Part a-Molecular and Biomolecular Spectroscopy, 258(2021).
[49] ZHANG Nannan, ZHANG Xiao. Analysis of near infrared spectra of southern Xinjiang jujube orchard conductivity of saline soil. Journal of Northeast Agricultural Sciences, 47, 118-121,154(2022).
[50] DING Songtao, ZHANG Xia, SHANG Kun et al. Estimating soil heavy metal from hyperspectral remote sensing images base on fractional order derivative. National Remote Sensing Bulletin, 27, 2191-2205(2023).
[51] LIN Y K, GAO J X, TU Y J et al. Estimating low concentration heavy metals in water through hyperspectral analysis and genetic algorithm-partial least squares regression. Science of the Total Environment, 916, 11(2024).
[52] GOODARZI R, MOKHTARZADE M, ZOEJ M J V. A Robust Fuzzy Neural Network Model for Soil Lead Estimation from Spectral Features. Remote Sensing, 7, 8416-8435(2015).
[53] TAN J, DING J L, WANG Z Y et al. Estimating soil salinity in mulched cotton fields using UAV-based hyperspectral remote sensing and a Seagull Optimization Algorithm-Enhanced Random Forest Model. Computers and Electronics in Agriculture, 221(2024).
[54] GE X Y, DING J L, TENG D X et al. Exploring the capability of Gaofen-5 hyperspectral data for assessing soil salinity risks. International Journal of Applied Earth Observation and Geoinformation, 112(2022).
[55] KUMAR P, TIWARI P, BISWAS A et al. Spatio-temporal assessment of soil salinization utilizing remote sensing derivatives, and prediction modeling: Implications for sustainable development. Geoscience Frontiers, 15(2024).
[56] SHUAI L Y, LI Z Y, CHEN Z et al. A research review on deep learning combined with hyperspectral Imaging in multiscale agricultural sensing. Computers and Electronics in Agriculture, 217(2024).
[57] SUN M Y, LIU H G, LI P F et al. Effects of salt content and particle size on spectral reflectance and model accuracy: Estimating soil salt content in arid, saline-alkali lands. Microchemical Journal, 207(2024).
Get Citation
Copy Citation Text
. Study on Spectral Characteristics and Quantitative Estimation of Soil Salinity based on Fractional Order Derivative[J]. Remote Sensing Technology and Application, 2025, 40(2): 344
Category:
Received: Mar. 6, 2023
Accepted: --
Published Online: May. 23, 2025
The Author Email: