Remote Sensing Technology and Application, Volume. 39, Issue 4, 917(2024)
Sensitivity of Black Soil Organic Matter Content Prediction to the Spectral Resolution and Signal-to-Noise Ratio of Space-based Remote Sensing Loads
[1] JIANG Ming, WEN Ya, SUN Ming et al. The strategic thinking and implementation path of science and technology to make good use of black land-The overall thinking and implementation plan of the strategic pilot science and technology project of the Chinese Academy of Sciences "Black Soil Granary". Journal of the Chinese Academy of Sciences, 36, 1146-1154(2021).
[2] LIU Huanjun, ZHAO Chunjiang, WANG Jihua et al. Remote sensing inversion of soil organic matter in typical black soil areas. Journal of Agricultural Engineering, 27, 211-215(2011).
[3] LIU Huanjun, ZHANG Xinle, ZHENG Shufeng et al. A field hyperspectral prediction model for organic matter content in black soil. Spectroscopy and Spectral Analysis, 30, 3355-3358(2010).
[4] CHENG Bin. Quantitative inversion of organic matter and related elements in black soil of Songliao Plain using remote sensing(2007).
[5] YUMITI Maiming, WANG Xuemei. Hyperspectral estimation of soil organic matter content using continuous wavelet transform. Spectroscopy and Spectral Analysis, 42, 1278-1284(2022).
[6] GASTALDI F, CHABRILLAT S, JONES A et al. Soil organic carbon estimation in croplands by hyperspectral remote APEX data using the LUCAS topsoil database. Remote Sensing, 10, 153-172(2018).
[7] LIU Huanjun, WU Bingfang, ZHAO Chunjiang et al. The influence of spectral resolution on the prediction model of organic matter in black soil. Spectroscopy and Spectral Analysis, 32, 739-742(2012).
[8] YUAN Jing. Inversion of soil organic matter and water content and research on spectral remote sensing parameters(2021).
[9] JIANG Q. Study on Signal-to-noise ratio estimation and compression method of operational modular imaging spectrometer multi-spectral images. Acta Optica Sinica, 23, 1335-1340(2003).
[10] BAOA Y, MENGA X, USTIN S et al. Vis-SWIR spectral prediction model for soil organic matter with diff erentgrouping strategies. Catena, 195, DOI:10.1016/j.catena.2020.104703(2020).
[11] SADEGHI M, JONES S B, Philpot W D. A linear physically based model for remote sensing of soil moisture using short wave infrared bands. Remote Sensing of Environment, 164, 66-76(2015).
[12] GOMEZ C, OLTRA-CARRIO R, BACHA S et al. Evaluating the sensitivity of clay content prediction to atmospheric effects and degradation of image spatial resolution using Hyperspectral VNIR/SWIR imagery. Remote Sensing of Environment, 164, 1-15(2015).
[13] DU S S, LIU L Y, LIU X J et al. The Solar-Induced Chlorophyll Fluorescence Imaging Spectrometer (SIFIS) onboard the First Terrestrial Ecosystem Carbon Inventory Satellite (TECIS-1):Specifications and Prospects. Sensors(Basel Switzerland), 20, 1-21(2020).
[14] CASTALDI F, PALOMBO A, SANTINI F et al. Evaluation of the potential of the current and forthcoming multispectral and hyperspectral imagers to estimate soil texture and organic carbon. Remote Sensing of Environment, 179, 54-65(2016).
[15] VERMOTE E F, TANRE D, DEUZE J L et al. Second simulation of the satellite signal in the solar spectrum, 6S: an overview. IEEE Transactions on Geoscience and Remote Sensing, 35, 675-686(1997).
[16] WEI Bin, ZHAO Jiguang, HUANG Huang. Simulation study of hyperspectral scene radiance model. Computer Measurement and Control, 27, 209-213(2019).
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Zexin LI, Shuang GAO, Denghui HU, Changkun WANG, Guohua LIU. Sensitivity of Black Soil Organic Matter Content Prediction to the Spectral Resolution and Signal-to-Noise Ratio of Space-based Remote Sensing Loads[J]. Remote Sensing Technology and Application, 2024, 39(4): 917
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Received: Jan. 22, 2023
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Published Online: Jan. 6, 2025
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