Inversion of coastal soil salinity in Qinzhou Bay based on domestic ZY1-02D satellite and machine learning algorithm
TIAN Yi-chao1,2, ZHENG Dan-lin1, ZHANG Qiang1, LU Fang1, HUANG You-ju3, TAO Jin1, ZHANG Ya-li1, LIN Jun-liang1, YAO Gui-zhao1, YAO Yuan-yuan1
1. Beibu Gulf Ocean Development Research Center, College of Resources and Environment, Beibu Gulf University, Qinzhou 535000, China; 2. Guangxi Key Laboratory of Marine Environment Change and Disaster in Beibu Gulf, Key Laboratory of Marine Geographic Information Resources Development and Utilization in the Beibu Gulf, Beibu Gulf University, Qinzhou 535000, China; 3. Guangxi Zhuang Autonomous Region Remote Sensing Institute of Natural Resources, Nanning 530028, China
Abstract：The relevant feature parameters extracted from the domestic ZY1-02D multispectral satellite were used to characterize the soil salinity over the coastal area of Qinzhou Bay with the support of Ada Boot, LightGBM, XGBoost, RFR, and CatBoost machine learning algorithms. The performance of each model was evaluated with the coefficient of determination (R2) and root mean square error (RMSE). The results show that the soil total salt content in the research area was measured to range from 0.740 to 10.352g/kg with an average of 1.739g/kg. Model simulation results demonstrate that CatBoost had the best predictive performance over AdaBoost, LightGBM, XGBoost, and RFR, and combined CatBoost with the highest accuracy (R2=0.8317, RMSE=0.396g/kg); and of all variables in a group, the mean of texture features was most sensitive to soil salinity and made the highest contribution; The soil salt content was simulated to range from 0to 8.784g/kg, with an average of 2.478g/kg, in which mild salinity mainly occurred in the western part of the study area and scattered in the eastern part. The combination of domestic resource satellite remote sensing data and CatBoost model has shown good performance in retrieving soil salinity in the coastal area of Qinzhou Bay, providing a new approach to characterizing coastal soil salinity at a large-scale.
田义超, 郑丹琳, 张强, 卢芳, 黄友菊, 陶进, 张亚丽, 林俊良, 姚贵钊, 姚媛元. 基于国产资源一号02D卫星和机器学习算法的钦州湾滨海土壤盐分反演[J]. 中国环境科学, 2024, 44(1): 371-385.
TIAN Yi-chao, ZHENG Dan-lin, ZHANG Qiang, LU Fang, HUANG You-ju, TAO Jin, ZHANG Ya-li, LIN Jun-liang, YAO Gui-zhao, YAO Yuan-yuan. Inversion of coastal soil salinity in Qinzhou Bay based on domestic ZY1-02D satellite and machine learning algorithm. CHINA ENVIRONMENTAL SCIENCECE, 2024, 44(1): 371-385.
Metternicht G I, Zinck J A.Remote sensing of soil salinity:potentials and constraints [J].Remote Sensing of Environment, 2003,85(1):1-20.
[2]
Castañeda C, Herrero J.Assessing the degradation of saline wetlands in an arid agricultural region in Spain [J].Catena, 2008,72(2):205-213.
[3]
Dwivedi R S, Rao B R M.The selection of the best possible Landsat TM band combination for delineating salt-affected soils [J].International Journal of Remote Sensing, 1992,13(11):2051-2058.
[4]
彭杰,迟春明,向红英,等.基于连续统去除法的土壤盐分含量反演研究[J].土壤学报, 2014,51(3):459-469.Peng J, Chi C M, Xiang H Y, et al.Inversion of soil salt content based on continuum-removal method [J].Acta Pedologica Sinica, 2014,51(3):459-469.
[5]
卢霞.滨海盐土盐分含量与其光谱特征的关系研究[J].水土保持通报, 2012,32(5):186-190.Lu X.Relationship between saline concentration and its reflectance spectra for seashore saline soil [J].Bulletin of Soil and Water Conservation, 2012,32(5):186-190.
[6]
Weng Y, Gong P, Zhu Z.Reflectance spectroscopy for the assessment of soil salt content in soils of the Yellow River Delta of China [J].International Journal of Remote Sensing, 2008,29(19):5511-5531.
[7]
屈永华,段小亮,高鸿永,等.内蒙古河套灌区土壤盐分光谱定量分析研究[J].光谱学与光谱分析, 2009,29(5):1362-1366.Qu Y H, Duan X L, Gao H Y, et al.Quantitative retrieval of soil salinity using hyperspectral data in the region of Inner Mongolia Hetao irrigation district [J].Spectroscopy and Spectral Analysis, 2009,29(5):1362-1366.
[8]
翁永玲,戚浩平,方洪宾,等.基于PLSR方法的青海茶卡-共和盆地土壤盐分高光谱遥感反演[J].土壤学报, 2010,47(6):1255-1263.Weng Y L, Qi H P, Fang H B, et al.PLSR-based hyperspectral remote sensing retrieval of soil salinity of Chaka Gonghe Basin in Qinghai Province [J].Acta Pedologica Sinica, 2010,47(6):1255-1263.
[9]
刘娅,潘贤章,王昌昆,等.基于可见-近红外光谱的滨海盐土土壤盐分预测方法[J].土壤学报, 2012,49(4):824-829.Liu Y, Pan X Z, Wang C K, et al.Prediction of coastal saline soil salinity based on VIS-NIR reflectance spectroscopy [J].Acta Pedologica Sinica, 2012,49(4):824-829.
[10]
王静,刘湘南,黄方,等.基于ANN技术和高光谱遥感的盐渍土盐分预测[J].农业工程学报, 2009,25(12):161-166.Wang J, Liu X N, Huang F, et al.Salinity forecasting of saline soil based on ANN and hyperspectral remote sensing [J].Transactions of the Chinese Society of Agricultural Engineering, 2009,25(12):161-166.
[11]
杨丽萍,任杰,王宇,等.基于多源遥感数据的居延泽地区土壤盐分估算模型[J].农业机械学报, 2022,53(11):226-235.Yang L P, Ren J, Wang Y, et al.Soil salinity estimation model in Juyanze based on multi-source remote sensing data [J].Transactions of the Chinese Society for Agricultural Machinery, 2022,53(11):226-235.
[12]
蒙莉娜,丁建丽,王敬哲,等.基于环境变量的渭干河-库车河绿洲土壤盐分空间分布[J].农业工程学报, 2020,36(1):175-181.Meng L N, Ding J L, Wang J Z, et al.Spatial distribution of soil salinity in Ugan-Kuqa River delta oasis based on environmental variables [J].Transactions of the Chinese Society of Agricultural Engineering, 2020,36(1):175-181.
[13]
丁玮祺,吾木提·艾山江,阿不都艾尼·阿不里,等.基于机器学习的干旱区土壤盐渍化定量估算[J].土壤通报, 2022,53(5):1038-1048.Ding W Q, UMUT Hasan, Abdughenit abliz, et al.Quantitative estimation soil salinization in arid areas based on machine learning [J].Chinese Journal of Soil Science, 2022,53(5):1038-1048.
[14]
Wang X, Zhang F, Ding J, et al.Estimation of soil salt content (SSC) in the Ebinur Lake Wetland National Nature Reserve (ELWNNR), Northwest China, based on a Bootstrap-BP neural network model and optimal spectral indices [J].Science of the Total Environment, 2018, 615:918-930.
[15]
Vermeulen D, Van Niekerk A.Machine learning performance for predicting soil salinity using different combinations of geomorphometric covariates [J].Geoderma, 2017,299:1-12.
[16]
Wang N, Xue J, Peng J, et al.Integrating remote sensing and landscape characteristics to estimate soil salinity using machine learning methods:a case study from Southern Xinjiang, China [J].Remote Sensing, 2020,12(24):4118.
[17]
Wang J, Ding J, Yu D, et al.Machine learning-based detection of soil salinity in an arid desert region, Northwest China:A comparison between Landsat-8OLI and Sentinel-2MSI [J].Science of the Total Environment, 2020,707:136092.
Bartlett P, Freund Y, Lee W S, et al.Boosting the margin:A new explanation for the effectiveness of voting methods [J].The Annals of Statistics, 1998,26(5):1651-1686.
[20]
王飞,杨胜天,丁建丽,等.环境敏感变量优选及机器学习算法预测绿洲土壤盐分[J].农业工程学报, 2018,34(22):102-110.Wang F, Yang S T, Ding J L, et al.Environmental sensitive variable optimization and machine learning algorithm using in soil salt prediction at oasis [J].Transactions of the Chinese Society of Agricultural Engineering, 2018,34(22):102-110.
[21]
Ma G, Ding J, Han L, et al.Digital mapping of soil salinization based on Sentinel-1and Sentinel-2data combined with machine learning algorithms [J].Regional Sustainability, 2021,2(2):177-188.
[22]
康俊锋,黄烈星,张春艳,等.多机器学习模型下逐小时PM2.5预测及对比分析[J].中国环境科学, 2020,40(5):1895-1905.Kang J F, Huang L X, Zhang C Y, et al.Hourly PM2.5 prediction and its comparative analysis under multi-machine learning model [J].China Environmental Science, 2020,40(5):1895-1905.
[23]
周超,殷坤龙,曹颖,等.基于集成学习与径向基神经网络耦合模型的三峡库区滑坡易发性评价[J].地球科学, 2020,45(6):1865-1876.Zhou C, Yin K L, Cao Y, et al.Landslide susceptibility assessment by applying the coupling method of radial basis neural network and adaboost:A case study from the three gorges reservoir area [J].Earth Science, 2020,45(6):1865-1876.
[24]
卢雪梅,苏华.基于OLCI数据的福建近海悬浮物浓度遥感反演[J].环境科学学报, 2020,40(8):2819-2827.Lu X M, Su H.Retrieving total suspended matter concentration in Fujian coastal waters using OLCI data [J].Acta Scientiae Circumstantiae, 2020,40(8):2819-2827.
[25]
巴逢辰,赵羿.中国海涂土壤资源[J].土壤通报, 1997,28(2):49-51.Ba F C, Zhao Y.Soil resources in China's coastal areas [J].Chinese Journal of Soil Science, 1997,28(2):49-51.
[26]
王晓利,侯西勇.1982~2014年中国沿海地区归一化植被指数(NDVI)变化及其对极端气候的响应[J].地理研究, 2019,38(4):807-821.Wang X L, Hou X Y.Variation of normalized difference vegetation index and its response to extreme climate in coastal China during 1982~2014[J].Geographical Research, 2019,38(4):807-821.
[27]
Mulder J P M, Hommes S, Horstman E M.Implementation of coastal erosion management in the Netherlands [J].Ocean & coastal management, 2011,54(12):888-897.
[28]
田义超,黄远林,张强,等.北部湾典型海岛生态系统服务价值空间异质性对比研究[J].海洋科学, 2019,43(2):60-68.Tian Y C, Huang Y L, Zhang Q, et al.A comparative study of spatial heterogeneity of ecosystem service value in typical islands in Beibu Gulf [J].Marine Sciences, 2019,43(2):60-68.
[29]
劳燕玲.滨海湿地生态安全评价研究-以钦州湾为例[D].武汉:中国地质大学, 2013.Lao Y L.Research on ecological security evaluation of coastal wetland:A case study of Qinzhou Bay [D].Wuhan:China University of Geosciences, 2013.
[30]
王卓然,赵庚星,高明秀,等.黄河三角洲垦利县夏季土壤水盐空间变异及土壤盐分微域特征[J].生态学报, 2016,36(4):1040-1049.Wang Z R, Zhao G X, Gao M X, et al.Spatial variation of soil water and salt and microscopic variation of soil salinity in summer in typical area of the Yellow River Delta in Kenli County [J].Acta Ecologica Sinica, 2016,36(4):1040-1049.
[31]
魏丹丹,赵世湖,肖晨超,等.资源一号02D卫星高光谱数据叶面积指数估算方法[J].航天器工程, 2020,29(6):169-173.Wei D D, Zhao S H, Xiao C C, et al.Leaf area index estimation algorithm of ZY-1-02D satellite [J].Spacecraft Engineering, 2020, 29(6):169-173.
[32]
唐洪钊,肖晨超,梁树能,等.资源一号02D卫星在轨辐射定标精度验证与分析[J].航天器工程, 2020,29(6):142-147.Tang H Z, Xiao C C, Liang S N, et al.On-orbit radiometric calibration and validation of ZY-1-02D satellite [J].Spacecraft Engineering, 2020,29(6):142-147.
[33]
郭铌.植被指数及其研究进展[J].干旱气象, 2003,21(4):71-75.Guo N.Veqetation index and its advances [J].Journal of Arid Meteorology, 2003,21(4):71-75.
[34]
Tucker C J.Red and photographic infrared linear combinations for monitoring vegetation [J].Remote Sensing of Environment, 1979, 8(2):127-150.
[35]
Scudiero E, Skaggs T H, Corwin D L.Regional-scale soil salinity assessment using Landsat ETM+ canopy reflectance [J].Remote Sensing of Environment, 2015,169:335-343.
[36]
Jordan C F.Derivation of leaf-area index from quality of light on the forest floor [J].Ecology, 1969,50(4):663-666.
[37]
易秋香.基于Sentinel-2多光谱数据的棉花叶面积指数估算[J].农业工程学报, 2019,35(16):189-197.Yi Q X.Remote estimation of cotton LAI using Sentinel-2multispectral data [J].Transactions of the Chinese Society of Agricultural Engineering, 2019,35(16):189-197.
[38]
Guo S, Ruan B, Chen H, et al.Characterizing the spatiotemporal evolution of soil salinization in Hetao Irrigation District (China) using a remote sensing approach [J].International Journal of Remote Sensing, 2018,39(20):6805-6825.
[39]
Wu W.The generalized difference vegetation index (GDVI) for dryland characterization [J].Remote Sensing, 2014,6(2):1211-1233.
[40]
方孝荣,高俊峰,谢传奇,等.农作物冠层光谱信息检测技术及方法综述[J].光谱学与光谱分析, 2015,35(7):1949-1955.Fang X R, Gao J F, Xie C Q, et al.Review of crop canopy spectral information detection technology and methods [J].Spectroscopy and Spectral Analysis, 2015,35(7):1949-1955.
[41]
Fernandez-Buces N, Siebe C, Cram S, et al.Mapping soil salinity using a combined spectral response index for bare soil and vegetation:A case study in the former lake Texcoco, Mexico [J].Journal of Arid Environments, 2006,65(4):644-667.
[42]
Gitelson A A, Kaufman Y J, Merzlyak M N.Use of a green channel in remote sensing of global vegetation from EOS-MODIS [J].Remote Sensing of Environment, 1996,58(3):289-298.
[43]
Allbed A, Kumar L, Aldakheel Y Y.Assessing soil salinity using soil salinity and vegetation indices derived from IKONOS high-spatial resolution imageries:Applications in a date palm dominated region [J].Geoderma, 2014,230:1-8.
[44]
Abbas A, Khan S.Using remote sensing techniques for appraisal of irrigated soil salinity [C]//International Congress on Modelling and Simulation (MODSIM).Modelling and Simulation Society of Australia and New Zealand, 2007:2632-2638.
[45]
Nicolas H, Walter C.Detecting salinity hazards within a semiarid context by means of combining soil and remote-sensing data [J].Geoderma, 2006,134(1/2):217-230.
[46]
Wang J, Ding J, Yu D, et al.Capability of Sentinel-2MSI data for monitoring and mapping of soil salinity in dry and wet seasons in the Ebinur Lake region, Xinjiang, China [J].Geoderma, 2019,353:172-187.
[47]
Khan N M, Rastoskuev V V, Sato Y, et al.Assessment of hydrosaline land degradation by using a simple approach of remote sensing indicators [J].Agricultural Water Management, 2005,77(1-3):96-10.
[48]
Kadiyala A, Kumar A.Applications of python to evaluate the performance of decision tree-based boosting algorithms [J].Environmental Progress & Sustainable Energy, 2018,37(2):618-623.
[49]
Littlestone N, Warmuth M K.The weighted majority algorithm [J].Information and Computation, 1994,108(2):212-261.
[50]
Freund Y, Schapire R E.A decision-theoretic generalization of on-line learning and an application to boosting [J].Journal of Computer and System Sciences, 1997,55(1):119-139.
Chen T, Guestrin C.Xgboost:A scalable tree boosting system [C]//Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining.2016:785-794.
[53]
谢勇,项薇,季孟忠,等.基于Xgboost和LightGBM算法预测住房月租金的应用分析[J].计算机应用与软件, 2019,36(9):151-155, 191.Xie Y, Xiang W, Ji M Z, et al.An application and analysis of forecast housing rental based on xgboost and lightgbm algorithms [J].Computer Applications and Software, 2019,36(9):151-155,191.
[54]
Ke G, Meng Q, Finley T, et al.Lightgbm:A highly efficient gradient boosting decision tree [J].Advances in neural information processing systems, 2017,30.
[55]
Prokhorenkova L, Gusev G, Vorobev A, et al.CatBoost:unbiased boosting with categorical features [J].In Advances in neural information processing systems, 2017,3148-3156.
[56]
陈点点,陈芸芝,冯险峰,等.基于超参数优化CatBoost算法的河流悬浮物浓度遥感反演[J].地球信息科学学报, 2022,24(4):780-791.Chen D D, Chen Y Z, Feng X F, et al.Retrieving suspended matter concentration in rivers based on hyperparameter optimized catboost algorithm [J].Journal of Geo-information Science, 2022,24(4):780-791.
[57]
Powell S L, Cohen W B, Healey S P, et al.Quantification of live aboveground forest biomass dynamics with Landsat time-series and field inventory data:A comparison of empirical modeling approaches [J].Remote Sensing of Environment, 2010,114(5):1053-1068.
[58]
鲍士旦.土壤农化分析[M].北京:中国农业出版社, 2000:187.Bao S D.Agrochemical analysis of soil [M].Beijing:China Agricultural Press, 2000:187.
[59]
奚雪,赵庚星,高鹏,等.基于Sentinel卫星及无人机多光谱的滨海冬小麦种植区土壤盐分反演研究——以黄三角垦利区为例[J].中国农业科学, 2020,53(24):5005-5016.Xi X, Zhao G X, Gao P, et al.Inversion of soil salinity in coastal winter wheat growing area based on sentinel satellite and unmanned aerial vehicle multi-spectrum——A case study in Kenli district of the Yellow River Delta [J].Scientia Agricultura Sinica, 2020,53(24):5005-5016.
[60]
王多多,贾文晓,王志保,等.基于Landsat影像的崇明岛东滩土壤盐分遥感反演技术[J].中国农业科技导报, 2018,20(3):55-63.Wang D, Jia W X, Wang Z B, et al.Retrieving coastal soil saline based on landsat image in Chongming Dongtan [J].Journal of Agricultural Science and Technology, 2018,20(3):55-63.
[61]
冯雪力,刘全明.基于多源遥感协同反演的区域性土壤盐渍化监测[J].农业机械学报, 2018,49(7):127-133.Feng X L, Liu Q M.Regional soil salinity monitoring based on multi-source collaborative remote sensing data [J].Transactions of the Chinese Society for Agricultural Machinery, 2018,49(7):127-133.
[62]
张雅莉,塔西甫拉提·特依拜,阿尔达克·克里木,等.基于Landsat8OLI影像光谱的土壤盐分估算模型研究[J].国土资源遥感, 2018, 30(1):87-94.Zhang Y L, Tashpolat T, Ardak K, et al.Estimation model of soil salinization based on Landsat8OLI image spectrum [J]. Remote Sensing for Land and Resources, 2018,30(1):87-94.
[63]
边玲玲,王卷乐,郭兵,等.基于特征空间的黄河三角洲垦利县土壤盐分遥感提取[J].遥感技术与应用, 2020,35(1):211-218.Bian L L, Wang J L, Guo B, et al.Remote sensing extraction of soil salinity in Yellow River Delta Kenli County based on feature space [J].Remote Sensing Technology and Application, 2020,35(1):211-218.
[64]
Aldakheel Y Y, Elprince A M, Al-Hosaini A I.Mapping of salt-affected soils of irrigated lands in arid regions using remote sensing and GIS [C]//Proceedings of 2nd International Conference on Recent Advances in Space Technologies, 2005.RAST 2005.Istanbul, Turkey:IEEE, 2005:467-472.
[65]
Metternicht G I, Zinck J A.Remote sensing of soil salinity:potentials and constraints [J].Remote sensing of Environment, 2003,85(1):1-20.
[66]
Judkins G, Myint S.Spatial variation of soil salinity in the Mexicali valley, Mexico:Application of a practical method for agricultural monitoring [J].Environmental Management, 2012,50(3):478-489.
[67]
刘俊,毕华兴,朱沛林,等.基于ALOS遥感数据纹理及纹理指数的柞树蓄积量估测[J].农业机械学报, 2014,45(7):245-254.Liu J, Bi H X, Zhu P L, et al.Estimating stand volume of xylosma racemosum forest based on texture parameters and derivative texture indices of ALOS imagery [J].Transactions of the Chinese Society for Agricultural Machinery, 2014,45(7):245-254.
[68]
Ren J H, Li X J, Zhao K, et al.Study of an on-line measurement method for the salt parameters of soda-saline soils based on the texture features of cracks [J].Geoderma, 2016,263:60-69.
[69]
台翔.植被覆盖条件下的无人机多光谱遥感土壤含盐量监测模型[D].杨凌:西北农林科技大学, 2022.Tai X.Monitoring model of soil salt content by UAV multi-spectral remote sensing under vegetation cover [D].Yanglin:Northwest Agricultural and Forestry University College, 2022.
[70]
Hoa P V, Giang N V, Binh N A, et al.Soil salinity mapping using SAR Sentinel-1data and advanced machine learning algorithms:a case study at Ben Tre Province of the Mekong River Delta (Vietnam) [J].Remote Sensing, 2019,11(2):128.
[71]
Bouaziz M, Matschullat J, Gloaguen R.Improved remote sensing detection of soil salinity from a semi-arid climate in Northeast Brazil [J].Comptes Rendus Geoscience, 2011,343(11/12):795-803.
[72]
Fan X, Liu Y, Tao J, et al.Soil salinity retrieval from advanced multi-spectral sensor with partial least square regression [J].Remote Sensing, 2015,7(1):488-511.
[73]
Metternicht G, Zinck J A.Spatial discrimination of salt-and sodium-affected soil surfaces [J].International Journal of Remote Sensing, 1997,18(12):2571-2586.
[74]
边慧芹,王雪梅.基于多光谱影像的干旱区绿洲耕层土壤盐分估算[J].干旱区资源与环境, 2022,36(5):110-118.Bian H Q, Wang X M.Estimation of soil salinity in cultivated layers of oasis in arid areas based on multispectral images [J].Journal of Arid Land Resources and Environment, 2022,36(5):110-118.
