黄河三角洲不同盐渍度土壤有机质含量的高光谱预测研究

doi:10.3864/j.issn.0578-1752.2023.10.008

Abstract

Abstract:

【Objective】The aim of this study was to investigate the spectral response of soil organic matter and salt, to analyze the effects of different salt content on soil organic matter prediction models, and to establish a rapid and effective hyperspectral prediction model for organic matter content in saline soils.【Method】In this study, according to different salinity contents for indoor hyperspectral measurements, the soil samples were divided into four groups of non-saline (SA), slightly saline (SB), moderately saline (SC), and heavy saline (SD). Then, ANOVA was used to explore the degree of organic matter and salinity spectral response of soils with different salinity degrees respectively. The raw spectra reflectances (raw spectral reflectance, R) were subjected to first order differential reflectance (first order differential reflectance, FD), continuous statistical removal (continuous statistical removal, CR), logarithmic (logarithmic, Log) and multiple scatter correction (multipication scatter correction, MSC) transformations were applied to the raw spectra reflectance; finally, three soil organic matter prediction models, namely multiple linear regression (multiple linear regression, MLR), partial least squares regression (partial least squares regression, PLSR) and support vector regression (support vector machine, SVR), were constructed based on four sets of samples of saline soils combined with the four transformed spectra, respectively.【Result】Soil organic matter and salinity had significant spectral response in the range of 400-900 nm and the change pattern were the same basically, and the sensitive bands of the two overlap. Modeling by dividing different salinity levels could improve the prediction accuracy of soil organic matter, but the prediction accuracy of the model decreased with the increase of salinity content. FD treatment could better highlight the difference of spectral characteristics and improved the correlation between organic matter content and spectral reflectance. Comparing the results of the three models, the highest accuracy of the soil organic matter prediction model was established using FD treatment combined with SVR, and the coefficients of determination R² of the optimal result modeling set and validation set were 0.86 and 0.82, respectively, the root mean square error RMSE was 2.71 and 2.96 g·kg^-1, respectively, and the ratio of prediction to deviation RPD was 2.42.【Conclusion】Soil salinity and organic matter overlapped in the sensitive bands near the visible wavelength (400-900 nm), and the accuracy of the organic matter prediction model could be effectively improved by classifying different salinity levels.

Key words: saline soil, organic matter, spectral response, hyperspectral prediction

HOU HuaGang, WANG DanYang, MA SiQi, PAN JianJun, LI ZhaoFu. Hyperspectral Prediction of Organic Matter in Soils of Different Salinity Levels in the Yellow River Delta[J].Scientia Agricultura Sinica, 2023, 56(10): 1905-1919.

Figures/Tables 11

Fig. 1

Table 1

Table 2

Fig. 2

Fig. 3

Fig. 4

Table 3

Table 4

Table 5

Table 6

Fig. 5

References 46

[1]	方少文, 杨梅花, 赵小敏, 郭熙. 红壤区土壤有机质光谱特征与定量估算: 以江西省吉安县为例. 土壤学报, 2014, 51(5): 1003-1010.
	FANG S W, YANG M H, ZHAO X M, GUO X. Spectral characteristics and quantitative estimation of som in red soil typical of Ji’an County, Jiangxi Province. Acta Pedologica Sinica, 2014, 51(5): 1003-1010. (in Chinese)
[2]	韩兆迎, 朱西存, 刘庆, 房贤一, 王卓远. 黄河三角洲土壤有机质含量的高光谱反演. 植物营养与肥料学报, 2014, 20(6): 1545-1552.
	HAN Z Y, ZHU X C, LIU Q, FANG X Y, WANG Z Y. Hyperspectral inversion models for soil organic matter content in the Yellow River Delta. Journal of Plant Nutrition and Fertilizer, 2014, 20(6): 1545-1552. (in Chinese)
[3]	TANG X L, ZHAO X, BAI Y F, TANG Z Y, WANG W T, ZHAO Y C, WAN H W, XIE Z Q, SHI X Z, WU B F, WANG G X, YAN J H, MA K P, DU S, LI S G, HAN S J, MA Y X, HU H F, HE N P, YANG Y H, HAN W X, HE H L, YU G R, FANG J Y, ZHOU G Y. Carbon pools in China’s terrestrial ecosystems: new estimates based on an intensive field survey. Proceedings of the National Academy of Sciences of the United States of America, 2018, 115(16): 4021-4026.
[4]	NAWAR S, BUDDENBAUM H, HILL J, KOZAK J, MOUAZEN A M. Estimating the soil clay content and organic matter by means of different calibration methods of vis-NIR diffuse reflectance spectroscopy. Soil and Tillage Research, 2016, 155: 510-522. doi: 10.1016/j.still.2015.07.021
[5]	NOCITA M, STEVENS A, TOTH G, PANAGOS P, VAN WESEMAEL B, MONTANARELLA L. Prediction of soil organic carbon content by diffuse reflectance spectroscopy using a local partial least square regression approach. Soil Biology and Biochemistry, 2014, 68: 337-347. doi: 10.1016/j.soilbio.2013.10.022
[6]	VASQUES G M, GRUNWALD S, SICKMAN J O. Comparison of multivariate methods for inferential modeling of soil carbon using visible/near-infrared spectra. Geoderma, 2008, 146(1/2): 14-25. doi: 10.1016/j.geoderma.2008.04.007
[7]	VAŠÁT R, KODEŠOVÁ R, KLEMENT A, BORŮVKA L. Simple but efficient signal pre-processing in soil organic carbon spectroscopic estimation. Geoderma, 2017, 298: 46-53. doi: 10.1016/j.geoderma.2017.03.012
[8]	南锋, 朱洪芬, 毕如田. 黄土高原煤矿区复垦农田土壤有机质含量的高光谱预测. 中国农业科学, 2016, 49(11): 2126-2135. doi: 10.3864/j.issn.0578-1752.2016.11.009. doi: 10.3864/j.issn.0578-1752.2016.11.009
	NAN F, ZHU H F, BI R T. Hyperspectral prediction of soil organic matter content in the reclamation cropland of coal mining areas in the Loess Plateau. Scientia Agricultura Sinica, 2016, 49(11): 2126-2135. doi: 10.3864/j.issn.0578-1752.2016.11.009. (in Chinese) doi: 10.3864/j.issn.0578-1752.2016.11.009
[9]	侯艳军, 塔西甫拉提·特依拜, 买买提·沙吾提, 张飞. 荒漠土壤有机质含量高光谱估算模型. 农业工程学报, 2014, 30(16): 113-120.
	HOU Y J, TASHPOLAT T, MAMAT S, ZHANG F. Hyperspectral estimation model of organic matter content in desert soil. Transactions of the Chinese Society of Agricultural Engineering, 2014, 30(16): 113-120. (in Chinese)
[10]	王永敏, 李西灿, 田林亚, 贾斌, 杨惠. 土壤有机质含量地面高光谱估测模型对比分析. 国土资源遥感, 2019, 31(1): 110-116.
	WANG Y M, LI X C, TIAN L Y, JIA B, YANG H. Comparison and analysis of estimation models of soil organic matter content established by hyperspectral on ground. Remote Sensing for Land & Resources, 2019, 31(1): 110-116. (in Chinese)
[11]	ZHANG Z P, DING J L, ZHU C M, WANG J Z. Combination of efficient signal pre-processing and optimal band combination algorithm to predict soil organic matter through visible and near-infrared spectra. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2020, 240: 118553. doi: 10.1016/j.saa.2020.118553
[12]	王海峰, 张智韬, Arnon Karnieli, 陈俊英, 韩文霆. 基于灰度关联-岭回归的荒漠土壤有机质含量高光谱估算. 农业工程学报, 2018, 34(14): 124-131.
	WANG H F, ZHANG Z T, KARNIELI A, CHEN J Y, HAN W T. Hyperspectral estimation of desert soil organic matter content based on gray correlation-ridge regression model. Transactions of the Chinese Society of Agricultural Engineering, 2018, 34(14): 124-131. (in Chinese)
[13]	朱亚星, 于雷, 洪永胜, 章涛, 朱强, 李思缔, 郭力, 刘家胜. 土壤有机质高光谱特征与波长变量优选方法. 中国农业科学, 2017, 50(22): 4325-4337. doi: 10.3864/j.issn.0578-1752.2017.22.009. doi: 10.3864/j.issn.0578-1752.2017.22.009
	ZHU Y X, YU L, HONG Y S, ZHANG T, ZHU Q, LI S D, GUO L, LIU J S. Hyperspectral features and wavelength variables selection methods of soil organic matter. Scientia Agricultura Sinica, 2017, 50(22): 4325-4337. doi: 10.3864/j.issn.0578-1752.2017.22.009. (in Chinese) doi: 10.3864/j.issn.0578-1752.2017.22.009
[14]	ZHANG Z P, DING J L, WANG J Z, GE X Y. Prediction of soil organic matter in northwestern China using fractional-order derivative spectroscopy and modified normalized difference indices. CATENA, 2020, 185: 104257. doi: 10.1016/j.catena.2019.104257
[15]	HONG Y S, CHEN S C, ZHANG Y, CHEN Y Y, YU L, LIU Y F, LIU Y L, CHENG H, LIU Y. Rapid identification of soil organic matter level via visible and near-infrared spectroscopy: effects of two- dimensional correlation coefficient and extreme learning machine. Science of the Total Environment, 2018, 644: 1232-1243. doi: 10.1016/j.scitotenv.2018.06.319
[16]	WANG X P, ZHANG F, KUNG H T, JOHNSON V C. New methods for improving the remote sensing estimation of soil organic matter content (SOMC) in the Ebinur Lake Wetland National Nature Reserve (ELWNNR) in northwest China. Remote Sensing of Environment, 2018, 218: 104-118. doi: 10.1016/j.rse.2018.09.020
[17]	彭杰, 周清, 张杨珠, 向红英. 有机质对土壤光谱特性的影响研究. 土壤学报, 2013, 50(3): 517-524.
	PENG J, ZHOU Q, ZHANG Y Z, XIANG H Y. Effect of soil organic matter on spectral characteristics of soil. Acta Pedologica Sinica, 2013, 50(3): 517-524. (in Chinese)
[18]	彭杰, 迟春明, 向红英, 滕洪芬, 史舟. 基于连续统去除法的土壤盐分含量反演研究. 土壤学报, 2014, 51(3): 459-469.
	PENG J, CHI C M, XIANG H Y, TENG H F, SHI Z. Inversion of soil salt content based on continuum-removal method. Acta Pedologica Sinica, 2014, 51(3): 459-469. (in Chinese)
[19]	刘亚秋, 陈红艳, 王瑞燕, 常春艳, 陈哲. 基于可见/近红外光谱的黄河口区土壤盐分及其主要离子的定量分析. 中国农业科学, 2016, 49(10): 1925-1935. doi: 10.3864/j.issn.0578-1752.2016.10.009. doi: 10.3864/j.issn.0578-1752.2016.10.009
	LIU Y Q, CHEN H Y, WANG R Y, CHANG C Y, CHEN Z. Quantitative analysis of soil salt and its main ions based on visible/ near infrared spectroscopy in estuary area of Yellow River. Scientia Agricultura Sinica, 2016, 49(10): 1925-1935. doi: 10.3864/j.issn.0578-1752.2016.10.009. (in Chinese) doi: 10.3864/j.issn.0578-1752.2016.10.009
[20]	张俊华, 贾萍萍, 孙媛, 贾科利. 基于高光谱特征的盐渍化土壤不同土层盐分离子含量预测. 农业工程学报, 2019, 35(12): 106-115.
	ZHANG J H, JIA P P, SUN Y, JIA K L. Prediction of salinity ion content in different soil layers based on hyperspectral data. Transactions of the Chinese Society of Agricultural Engineering, 2019, 35(12): 106-115. (in Chinese)
[21]	WANG J Z, TIYIP T, DING J L, ZHANG D, LIU W, WANG F. Quantitative estimation of organic matter content in arid soil using vis-NIR spectroscopy preprocessed by fractional derivative. Journal of Spectroscopy, 2017, 2017: 1-9.
[22]	ZHANG Z P, DING J L, ZHU C M, WANG J Z, MA G L, GE X Y, LI Z S, HAN L J. Strategies for the efficient estimation of soil organic matter in salt-affected soils through Vis-NIR spectroscopy: optimal band combination algorithm and spectral degradation. Geoderma, 2021, 382: 114729. doi: 10.1016/j.geoderma.2020.114729
[23]	张超, 刘咏梅, 孙亚楠, 王雷, 刘建红. 黄土丘陵沟壑区土壤养分含量的高光谱预测模型. 应用生态学报, 2018, 29(9): 2835-2842. doi: 10.13287/j.1001-9332.201809.010
	ZHANG C, LIU Y M, SUN Y N, WANG L, LIU J H. Hyperspectral prediction model of soil nutrient content in the loess hilly-gully region, China. Chinese Journal of Applied Ecology, 2018, 29(9): 2835-2842. (in Chinese)
[24]	玉米提·买明, 王雪梅. 塔里木盆地北缘荒漠土壤有机质含量的高光谱估测. 中国土壤与肥料, 2021(4): 318-326.
	YUMITI MAIMING, WANG X M. Hyperspectral estimation of desert soil organic matter content in the northern margin of Tarim Basin. Soil and Fertilizer Sciences in China, 2021(4): 318-326. (in Chinese)
[25]	郄欣, 齐雁冰, 刘姣姣, 王珂, 陈敏辉. 基于室内高光谱数据的多种类型土壤有机质估算模型比较. 干旱地区农业研究, 2021, 39(4): 109-116, 124.
	QIE X, QI Y B, LIU J J, WANG K, CHEN M H. Comparison of multiple estimation models of soil organic matter based on laboratory hyperspectral reflectance in Shaanxi Province. Agricultural Research in the Arid Areas, 2021, 39(4): 109-116, 124. (in Chinese)
[26]	洪永胜, 于雷, 朱亚星, 李思缔, 郭力, 刘家胜, 聂艳, 周勇. 利用OSC算法消除土壤含水量变化对Vis-NIR光谱估算有机质的影响. 中国农业科学, 2017, 50(19): 3766-3777. doi: 10.3864/j.issn.0578-1752.2017.19.013. doi: 10.3864/j.issn.0578-1752.2017.19.013
	HONG Y S, YU L, ZHU Y X, LI S D, GUO L, LIU J S, NIE Y, ZHOU Y. Using orthogonal signal correction algorithm removing the effects of soil moisture on hyperspectral reflectance to estimate soil organic matter. Scientia Agricultura Sinica, 2017, 50(19): 3766-3777. doi: 10.3864/j.issn.0578-1752.2017.19.013. (in Chinese) doi: 10.3864/j.issn.0578-1752.2017.19.013
[27]	尚璇, 李西灿, 徐邮邮, 刘莎莎. 土壤水与有机质对高光谱的作用及交互作用规律. 中国农业科学, 2017, 50(8): 1465-1475. doi: 10.3864/j.issn.0578-1752.2017.08.010. doi: 10.3864/j.issn.0578-1752.2017.08.010
	SHANG X, LI X C, XU Y Y, LIU S S. The role and interaction of soil water and organic matter on hyper-spectral reflectance. Scientia Agricultura Sinica, 2017, 50(8): 1465-1475. doi: 10.3864/j.issn.0578-1752.2017.08.010. (in Chinese) doi: 10.3864/j.issn.0578-1752.2017.08.010
[28]	鲍士旦. 土壤农化分析. 3版. 北京: 中国农业出版社, 2000.
	BAO S D. Soil and agricultural chemistry analysis. 3rd ed. Beijing: China Agriculture Press, 2000. (in Chinese)
[29]	周倩倩, 丁建丽, 唐梦迎, 杨斌. 干旱区典型绿洲土壤有机质的反演及影响因素研究. 土壤学报, 2018, 55(2): 313-324.
	ZHOU Q Q, DING J L, TANG M Y, YANG B. Inversion of soil organic matter content in oasis typical of arid area and its influencing factors. Acta Pedologica Sinica, 2018, 55(2): 313-324. (in Chinese)
[30]	陈奕云, 齐天赐, 黄颖菁, 万远, 赵瑞瑛, 亓林, 张超, 费腾. 土壤有机质含量可见-近红外光谱反演模型校正集优选方法. 农业工程学报, 2017, 33(6): 107-114.
	CHEN Y Y, QI T C, HUANG Y J, WAN Y, ZHAO R Y, QI L, ZHANG C, FEI T. Optimization method of calibration dataset for VIS-NIR spectral inversion model of soil organic matter content. Transactions of the Chinese Society of Agricultural Engineering, 2017, 33(6): 107-114. (in Chinese)
[31]	黄伯强, 李启才. 带交互作用的双因素方差分析的线性回归建模. 统计与决策, 2021, 37(1): 10-15.
	HUANG B Q, LI Q C. Linear regression modeling of two-factor ANOVA with interaction. Statistics & Decision, 2021, 37(1): 10-15. (in Chinese)
[32]	全国土壤普查办公室. 中国土壤普查技术. 北京: 中国农业出版社, 1992.
	National Soil Census Office. Soil General Survey Technology in China. Beijing: China Agriculture Press, 1992. (in Chinese)
[33]	王丹阳, 陈红艳, 王桂峰, 丛津桥, 王向锋, 魏学文. 无人机多光谱反演黄河口重度盐渍土盐分的研究. 中国农业科学, 2019, 52(10): 1698-1709. doi: 10.3864/j.issn.0578-1752.2019.10.004. doi: 10.3864/j.issn.0578-1752.2019.10.004
	WANG D Y, CHEN H Y, WANG G F, CONG J Q, WANG X F, WEI X W. Salinity inversion of severe saline soil in the Yellow River Estuary based on UAV multi-spectra. Scientia Agricultura Sinica, 2019, 52(10): 1698-1709. doi: 10.3864/j.issn.0578-1752.2019.10.004. (in Chinese) doi: 10.3864/j.issn.0578-1752.2019.10.004
[34]	TERHOEVEN-URSELMANS T, SCHMIDT H, GEORG JOERGENSEN R, LUDWIG B. Usefulness of near-infrared spectroscopy to determine biological and chemical soil properties: importance of sample pre-treatment. Soil Biology and Biochemistry, 2008, 40(5): 1178-1188. doi: 10.1016/j.soilbio.2007.12.011
[35]	PENG X, XU C, ZENG W Z, WU J W, HUANG J S. Elimination of the soil moisture effect on the spectra for reflectance prediction of soil salinity using external parameter orthogonalization method. Journal of Applied Remote Sensing, 2016, 10(1): 015014. doi: 10.1117/1.JRS.10.015014
[36]	QIAN T N, TSUNEKAWA A, PENG F, MASUNAGA T, WANG T, LI R. Derivation of salt content in salinized soil from hyperspectral reflectance data: a case study at Minqin Oasis, Northwest China. Journal of Arid Land, 2019, 11(1): 111-122. doi: 10.1007/s40333-019-0091-9
[37]	WANG S J, CHEN Y H, WANG M G, LI J. Performance comparison of machine learning algorithms for estimating the soil salinity of salt-affected soil using field spectral data. Remote Sensing, 2019, 11(22): 2605. doi: 10.3390/rs11222605
[38]	刘庆生, 王志刚, 荆林海. 岩石实验室光谱对应分析. 遥感学报, 1999, 3(2): 151-156.
	LIU Q S, WANG Z G, JING L H. Correspondence analysis of laboratory spectra of rock. Journal of Remote Sensing, 1999, 3(2): 151-156. (in Chinese)
[39]	国佳欣, 朱青, 赵小敏, 郭熙, 韩逸, 徐喆. 不同土地利用类型下土壤有机碳含量的高光谱反演. 应用生态学报, 2020, 31(3): 863-871. doi: 10.13287/j.1001-9332.202003.014
	GUO J X, ZHU Q, ZHAO X M, GUO X, HAN Y, XU Z. Hyper-spectral inversion of soil organic carbon content under different land use types. Chinese Journal of Applied Ecology, 2020, 31(3): 863-871. (in Chinese) doi: 10.13287/j.1001-9332.202003.014
[40]	GAO L L, ZHU X C, HAN Z Y, WANG L, ZHAO G X, JIANG Y M. Spectroscopy-based soil organic matter estimation in brown forest soil areas of the Shandong peninsula, China. Pedosphere, 2019, 29(6): 810-818. doi: 10.1016/S1002-0160(17)60485-5
[41]	彭杰, 张杨珠, 周清, 刘香伶, 周卫军. 去除有机质对土壤光谱特性的影响. 土壤, 2006, 38(4): 453-458.
	PENG J, ZHANG Y Z, ZHOU Q, LIU X L, ZHOU W J. Spectral characteristics of soils in Hunan Province as affected by removal of soil organic matter. Soils, 2006, 38(4): 453-458. (in Chinese)
[42]	NOCITA M, STEVENS A, VAN WESEMAEL B, AITKENHEAD M, BACHMANN M, BARTHÈS B, BEN DOR E, BROWN D J, CLAIROTTE M, CSORBA A, DARDENNE P, DEMATTÊ J A M, GENOT V, GUERRERO C, KNADEL M, MONTANARELLA L, NOON C, RAMIREZ-LOPEZ L, ROBERTSON J, SAKAI H, WETTERLIND J. Soil spectroscopy: an alternative to wet chemistry for soil monitoring. Advances in Agronomy, 2015, 132: 139-159.
[43]	GOLDSHLEGER N, BEN-DOR E, LUGASSI R, ESHEL G. Soil degradation monitoring by remote sensing: examples with three degradation processes. Soil Science Society of America Journal, 2010, 74(5): 1433-1445. doi: 10.2136/sssaj2009.0351
[44]	郭斗斗, 黄绍敏, 张水清, 聂胜委. 多种潮土有机质高光谱预测模型的对比分析. 农业工程学报, 2014, 30(21): 192-200.
	GUO D D, HUANG S M, ZHANG S Q, NIE S W. Comparative analysis of various hyperspectral prediction models of fluvo-aquic soil organic matter. Transactions of the Chinese Society of Agricultural Engineering, 2014, 30(21): 192-200. (in Chinese)
[45]	WANG J Z, DING J L, YU D L, MA X K, ZHANG Z P, GE X Y, TENG D X, LI X H, LIANG J, LIZAGA I, CHEN X Y, YUAN L, GUO Y H. Capability of Sentinel-2 MSI data for monitoring and mapping of soil salinity in dry and wet seasons in the Ebinur Lake region, Xinjiang, China. Geoderma, 2019, 353: 172-187. doi: 10.1016/j.geoderma.2019.06.040
[46]	TIAN Y C, ZHANG J J, YAO X, CAO W X, ZHU Y. Laboratory assessment of three quantitative methods for estimating the organic matter content of soils in China based on visible/near-infrared reflectance spectra. Geoderma, 2013, 202/203: 161-170. doi: 10.1016/j.geoderma.2013.03.018

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!

盐渍化等级 Salinization grade	样本类型 Sample type	样本数 Sample size	最小值 Min (g·kg^-1)	最大值 Max (g·kg^-1)	均值 Mean (g·kg^-1)	标准差 SD (g·kg^-1)	变异系数 CV (%)
全部样本 Whole set	Ⅰ	209	1.70	35.08	16.43	7.99	48.63
全部样本 Whole set	Ⅱ	102	2.38	35.31	16.66	7.85	47.14
SA (0-2 dS·m^-1)	Ⅰ	52	1.70	29.57	16.74	7.43	44.35
SA (0-2 dS·m^-1)	Ⅱ	25	4.33	27.93	16.82	7.15	42.50
SB (2-4 dS·m^-1)	Ⅰ	47	4.89	35.08	18.73	8.04	42.95
SB (2-4 dS·m^-1)	Ⅱ	23	6.28	34.59	18.95	7.90	41.67
SC (4-8 dS·m^-1)	Ⅰ	46	3.70	34.56	18.23	7.73	42.44
SC (4-8 dS·m^-1)	Ⅱ	22	5.61	32.92	18.23	7.10	39.00
SD (>8 dS·m^-1)	Ⅰ	64	2.08	31.12	13.19	7.69	58.27
SD (>8 dS·m^-1)	Ⅱ	32	2.38	35.31	13.81	8.30	60.10

因素 Factor	水平Level
因素 Factor	1	2	3	4
EC (dS·m^-1)	0.11-1.97	2.03-3.97	4.02-7.97	8.13-93.32
SOM (g·kg^-1)	1.70-9.82	10.01-19.97	20.01-35.31

光谱处理 Spectral processing	特征波段 Characteristic band (nm)
光谱处理 Spectral processing	全部样本 Whole set	SA (0-2 dS·m^-1)	SB (2-4 dS·m^-1)	SC (4-8 dS·m^-1)	SD (>8 dS·m^-1)
R	428、607、1102 1719、2176	638、1075 2135、2256	435、625、859 2057、2361	427、596 610、2351	598、1082 1955
FD	526、672 846、868、1498	526、664、852 999、1347	526、879、1426 1506、2161	438、495、528 858、1272、1433	522、672、881 1374
CR	844、880、998 1074、1116	785、842、880 994、1074	823、859、880 1062	648、808、852 862、1008、1072	769、791、879 1020
Log	596、608 1005、1830	669、1453 1805、2316	435、626、694 2057、2105	427、596 610	597、1090 1955
MSC	596、2109 2216、2283、2332	576、2046 2257、2339、2384	582、1134、1342 2057、2106、2280	581、1146 2174、2284、2351	596、2181 2236、2282、2335

盐渍化等级 Salinization grade	光谱处理 Spectral processing	建模集 Calibration set		验证集 Validation set
盐渍化等级 Salinization grade	光谱处理 Spectral processing	Rc²	RMSEc	Rv²	RMSEv	RPD
全部样本 Whole set	R	0.67	4.65	0.69	4.38	1.79
	FD	0.69	4.48	0.67	4.50	1.75
	CR	0.42	6.17	0.47	5.72	1.37
	Log	0.61	5.02	0.64	4.76	1.65
	MSC	0.45	5.99	0.48	5.65	1.39
SA (0-2 dS·m^-1)	R	0.74	3.94	0.44	5.80	1.23
	FD	0.80	3.52	0.75	3.51	2.04
	CR	0.62	4.80	0.61	4.63	1.54
	Log	0.73	3.99	0.68	4.09	1.75
	MSC	0.80	3.52	0.57	4.72	1.51
SB (2-4 dS·m^-1)	R	0.68	4.83	0.62	5.15	1.53
	FD	0.73	4.40	0.63	5.01	1.58
	CR	0.57	5.52	0.41	6.13	1.29
	Log	0.68	4.83	0.48	5.86	1.35
	MSC	0.59	5.51	0.52	5.33	1.48
SC (4-8 dS·m^-1)	R	0.66	4.73	0.51	5.12	1.39
	FD	0.71	4.50	0.66	4.47	1.59
	CR	0.55	5.59	0.33	6.16	1.15
	Log	0.66	4.67	0.56	4.98	1.43
	MSC	0.56	5.42	0.54	4.95	1.44
SD (>8 dS·m^-1)	R	0.55	5.27	0.62	5.22	1.59
	FD	0.70	4.36	0.71	4.42	1.88
	CR	0.54	5.40	0.36	7.20	1.15
	Log	0.56	5.20	0.61	5.54	1.50
	MSC	0.53	5.48	0.47	5.96	1.39

盐渍化等级 Salinization grade	光谱处理 Spectral processing	建模集 Calibration set		验证集 Validation set
盐渍化等级 Salinization grade	光谱处理 Spectral processing	Rc²	RMSEc	Rv²	RMSEv	RPD
全部样本 Whole set	R	0.66	4.62	0.68	4.48	1.75
	FD	0.69	4.42	0.68	4.47	1.76
	CR	0.42	6.08	0.46	5.75	1.37
	Log	0.61	4.96	0.63	4.76	1.65
	MSC	0.45	5.91	0.47	5.68	1.38
SA (0-2 dS·m^-1)	R	0.71	3.95	0.51	5.15	1.39
	FD	0.76	3.61	0.74	3.60	1.99
	CR	0.62	4.51	0.61	4.63	1.54
	Log	073	3.80	0.68	4.09	1.75
	MSC	0.79	3.33	0.54	4.67	1.53
SB (2-4 dS·m^-1)	R	0.68	4.51	0.56	5.30	1.49
	FD	0.72	4.22	0.60	5.15	1.53
	CR	0.57	5.24	0.42	6.01	1.31
	Log	068	4.51	0.48	5.86	1.35
	MSC	0.58	5.12	0.53	5.34	1.48
SC (4-8 dS·m^-1)	R	0.66	4.47	0.51	5.12	1.39
	FD	0.67	4.42	0.67	4.16	1.71
	CR	0.52	5.31	0.33	6.16	1.15
	Log	0.66	4.46	0.56	4.98	1.43
	MSC	0.56	5.09	0.61	4.51	1.58
SD (>8 dS·m^-1)	R	0.55	5.11	0.61	5.30	1.57
	FD	0.70	4.20	0.70	4.47	1.86
	CR	0.54	5.20	0.36	7.20	1.15
	Log	0.56	5.03	0.61	5.57	1.49
	MSC	0.51	5.33	0.49	5.84	1.42

Hyperspectral Prediction of Organic Matter in Soils of Different Salinity Levels in the Yellow River Delta

RichHTML

PDF

Abstract

Cite this article

share this article

Figures/Tables 11

References 46

Related Articles 15

Metrics

Comments

Recommended 0

[1]	WANG BingJie, QIN ShiHan, LI DeCheng, HU WenYou, JIANG Jun, CHI FengQin, ZHANG Chao, ZHANG JiuMing, XU YingDe, WANG JingKuan. Spatial Distribution Pattern and Transfer Function Construction of Soil Bulk Density in Nenjiang City, Heilongjiang Province [J]. Scientia Agricultura Sinica, 2025, 58(9): 1791-1803.
[2]	HAN ZheQun, SU Ying, GAO QiQi, LIU MeiYing, JIA AngYuan, ZHANG HaiRui, NAN ShanShan, XU QinZheng, WANG Qiang, WANG LiJun, WU XuePing. Intelligent Drip Irrigation Water-Fertilizer Coupling Regime Realizes Synergistic Improvement of Soil Water Conservation, Salt Control and Sunflower Yield and Quality [J]. Scientia Agricultura Sinica, 2025, 58(20): 4158-4177.
[3]	YUAN HuiLin, LI YaYing, GU WenJie, XU PeiZhi, LU YuSheng, SUN LiLi, ZHOU ChangMin, LI WanLing, QIU RongLiang. Characterization and Correlation Analysis of Soil Dissolved Organic Matter and Microbial Communities Under Long-Term Application of Fresh and Composted Manure [J]. Scientia Agricultura Sinica, 2025, 58(2): 307-325.
[4]	ZHAO Jian, REN Tao, FANG YaTing, YANG Xin, SHENG QianNan, LI XiaoKun, ZHU Jun, LU JianWei. Effect of Nitrogen Application on Organic Nitrogen Mineralization Functional Genes in Rapeseed and Wheat Rhizosphere Soils Under Different Rotation Patterns [J]. Scientia Agricultura Sinica, 2025, 58(19): 3919-3931.
[5]	LI YaZhen, HAN TianFu, QU XiaoLin, MA ChangBao, DU JiangXue, LIU KaiLou, HUANG Jing, LIU ShuJun, LIU LiSheng, SHEN Zhe, ZHANG HuiMin. Spatio-Temporal Variations of Fertilizer Contribution Rate for Rice in China and Its Influencing Factors [J]. Scientia Agricultura Sinica, 2023, 56(4): 674-685.
[6]	HUO RunXia, ZHANG Zhe, LI WenPing, ZHANG YangYang, LIAO ShiPeng, REN Tao, LI XiaoKun, LU ZhiFeng, CONG RiHuan, LU JianWei. 40 Years’ Change Characteristics of Soil Basic Properties in the Main Planting Area of Winter Oilseed Rape [J]. Scientia Agricultura Sinica, 2023, 56(23): 4696-4705.
[7]	CHEN ShuoTong, XIA Xin, DING YuanJun, FENG Xiao, LIU XiaoYu, Marios Drosos, LI LianQing, PAN GenXing. Changes in Topsoil Organic Matter Content and Composition of a Gleyic Stagnic Anthrosol Amended with Maize Residue in Different Forms from the Tai Lake Plain, China [J]. Scientia Agricultura Sinica, 2023, 56(13): 2518-2529.
[8]	ZHANG WeiLi, FU BoJie, XU AiGuo, YANG Peng, CHEN Tao, ZHANG RenLian, SHI Zhou, WU WenBin, LI JianBing, JI HongJie, LIU Feng, LEI QiuLiang, LI ZhaoJun, FENG Yao, LI YanLi, XU YongBing, PEI Wei. Geostatistical Characteristics of Soil Data from National Soil Survey Works in China [J]. Scientia Agricultura Sinica, 2022, 55(13): 2572-2583.
[9]	CUI Shuai,LIU ShuoRan,WANG Yin,XIA ChenZhen,YAN Li,FENG GuoZhong,GAO Qiang. Soil Available Sulfur Content in Jilin Province and Its Correlation with Soil Organic Matter and Soil Total Nitrogen [J]. Scientia Agricultura Sinica, 2022, 55(12): 2372-2383.
[10]	BIAN RongJun,LIU XiaoYu,ZHENG JuFeng,CHENG Kun,ZHANG XuHui,LI LianQing,PAN GenXing. Chemical Composition and Bioactivity of Dissolvable Organic Matter in Biochars [J]. Scientia Agricultura Sinica, 2022, 55(11): 2174-2186.
[11]	FANG Rui,YU ZhenHua,LI YanSheng,XIE ZhiHuang,LIU JunJie,WANG GuangHua,LIU XiaoBing,CHEN Yuan,LIU JuDong,ZHANG ShaoQing,WU JunJiang,Stephen J HERBERT,JIN Jian. Effects of Elevated CO₂ Concentration and Warming on Soil Carbon Pools and Microbial Community Composition in Farming Soil [J]. Scientia Agricultura Sinica, 2021, 54(17): 3666-3679.
[12]	Lei QIAO,WuPing ZHANG,MingJing HUANG,GuoFang WANG,Jian REN. Mapping of Soil Organic Matter and Its Driving Factors Study Based on MGWRK [J]. Scientia Agricultura Sinica, 2020, 53(9): 1830-1844.
[13]	FengZhi SHI,RuiYan WANG,YuHuan LI,Hong YAN,XiaoXin ZHANG. LAI Estimation Based on Multi-Spectral Remote Sensing of UAV and Its Application in Saline Soil Improvement [J]. Scientia Agricultura Sinica, 2020, 53(9): 1795-1805.
[14]	XiaoLei LI,YuJun ZHANG,FengMin SHEN,GuiYing JIANG,Fang LIU,KaiLou LIU,ShiLiang LIU. The Effects of Long-Term Fertilization on the Labile Organic Matter and Carbon Pool Management Index in Different Soil Layers in Red Soil [J]. Scientia Agricultura Sinica, 2020, 53(6): 1189-1201.
[15]	ZHANG WeiLi,KOLBE H,ZHANG RenLian,JI HongJie. Soil Organic Carbon Management and Farmland Organic Matter Balance Method [J]. Scientia Agricultura Sinica, 2020, 53(2): 332-345.