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何爽，张森，田家，卢霞.2023.结合多模态数据的滨海湿地碱蓬叶面积指数无人机高光谱反演.遥感学报，27（6）： 1441-1453
He S，Zhang S，Tian J and Lu X. 2023. UAV hyperspectral inversion of Suaeda Salsa leaf area index in coastal wetlands combined with multimodal data. National Remote Sensing Bulletin， 27（6）：1441-1453
叶面积指数（LAI）是表征植被对光合辐射的吸收和拦截以及植被生长状况的重要参数。为准确、快速地提高叶面积指数估算精度，选取黄河三角洲碱蓬滩湿地，以中国土著植物碱蓬为研究对象，获取无人机高光谱遥感影像和测定地面光谱，结合区域土壤因子、植被光谱特征、高光谱影像纹理特征和植被覆盖度构建多模态数据，发展随机森林RF（Random Forest）和粒子群PSO（Particle Swarm Optimization）双优化策略的深度极限学习机DELM（Deep Extreme Learning Machine）算法构建滨海湿地碱蓬叶面积指数反演模型，决定系数（
<math id="M1"><msup><mrow><mi>R</mi></mrow><mrow><mn mathvariant="normal">2</mn></mrow></msup></math>
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2.96333337
2.53999996
）和均方根误差（RMSE）分别为0.9546，0.1341。与基于支持向量机SVM（Support Vector Machine）、BP神经网络BP（Back Propagation Neural Network）、极限学习机ELM（Extreme Learning Machine）、深度极限学习机DELM（Deep Extreme Learning Machine）、粒子群优化的深度极限学习机（PSO-DELM）5种算法构建的碱蓬叶面积指数反演模型精度相比，决定系数
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2.96333337
2.53999996
最高提高了0.2654，均方根误差RMSE最大降低了0.0828。与传统的反演模型SVM相比，RF-PSO-DELM模型具有更好的泛化性，融合多模态数据则可以有效提高反演模型精度。该研究进一步丰富了基于无人机高光谱遥感技术实现盐沼植被精准监测的理论和技术。
Salt marsh vegetation is an important part of the blue carbon ecosystem
which has strong carbon sequestration and carbon storage capacity. The Leaf Area Index (LAI) determines its growth
photosynthetic active radiation absorption ratio
and biomass. LAI is often used to simulate the photosynthesis
respiration
and transpiration of vegetation
thus playing a crucial role in improving the yield of
Suaeda salsa
. An accurate estimation of
Suaeda salsa
can provide an important basis for judging the growth status of alkali ponies and thus provide an effective aid for monitoring salt marsh wetlands.
To improve the accuracy of LAI estimation accurately and rapidly
the Yellow River Delta
Suaeda salsa
shoal wetland was selected
and the indigenous plant
Suaeda salsa
was used as the research object. A UAV hyperspectral remote sensing image was obtained
and the ground spectrum was measured in combination with regional soil factors
vegetation spectral characteristics
hyperspectral image texture characteristics
and vegetation coverage. Multimodal data
through Random Forest (RF) feature selection for multimodal data
and the RF-PSO-DELM algorithm with a dual optimization strategy was developed to construct an inversion model of the LAI of
Suaeda salsa
in coastal wetlands.
The coefficient of determination (
R
2
) and the Root Mean Square Error (RMSE) were 0.9546 and 0.1341
respectively. Compared with the inversion model accuracy of
Suaeda salsa
LAI constructed on the basis of the five algorithms of SVM
BP
ELM
DELM
and PSO-DELM
R
2
was increased by 0.2654 at most
and the RMSE was reduced by 0.0828 at most.
Compared with the traditional inversion model (SVM)
the RF-PSO-DELM model had better generalization; moreover
the fusion of multimodal data could effectively improve the accuracy of the inversion model. This study further enriched the theory and technology for the accurate monitoring of salt marsh vegetation based on UAV hyperspectral remote sensing technology. Multisource modal data
such as soil factors
texture features
spectral features
and vegetation cover affecting the growth of alkali ponies in coastal wetlands
were comprehensively considered
and the important influencing factors sensitive to the LAI of alkali ponies’ LAI were extracted by the random forest feature preference algorithm
which effectively reduced the complexity of model inversion and greatly improved the accuracy of model prediction.
无人机多模态数据碱蓬叶面积指数随机森林粒子群算法深度极限学习机黄河三角洲
UAVmultimodal dataSuaeda salsaleaf area indexrandom forestparticle swarm optimizationdeep extreme learning machineYellow River Delta
Chen G W, Jin R J, Ye Z J, Li Q, Gu J L, Luo M, Luo Y M, Christakos G, Morris J, He J Y, Li D, Wang H W, Song L, Wang Q X and Wu J P. 2022. Spatiotemporal mapping of salt marshes in the intertidal zone of China during 1985-2019. Journal of Remote Sensing, 2022: 9793626 [DOI: 10.34133/2022/9793626http://dx.doi.org/10.34133/2022/9793626]
Chen Z X, Ren J Q, Tang H J, Shi Y, Leng P, Liu J, Wang L M, Wu W B, Yao Y M and Hasiyuya. 2016. Progress and perspectives on agricultural remote sensing research and applications in China. Journal of Remote Sensing, 20(5): 748-767
陈仲新, 任建强, 唐华俊, 史云, 冷佩, 刘佳, 王利民, 吴文斌, 姚艳敏, 哈斯图亚. 2016. 农业遥感研究应用进展与展望. 遥感学报, 20(5): 748-767 [DOI: 10.11834/jrs.20166214http://dx.doi.org/10.11834/jrs.20166214]
Cutler A, Cutler D R and Stevens J R. 2012. Random forests//Zhang C, Ma Y Q, eds. Ensemble Machine Learning. New York: Springer: 157-175 [DOI: 10.1007/978-1-4419-9326-7_5http://dx.doi.org/10.1007/978-1-4419-9326-7_5]
Duan B, Liu Y T, Gong Y, Peng Y, Wu X T, Zhu R S and Fang S H. 2019. Remote estimation of rice LAI based on Fourier spectrum texture from UAV image. Plant Methods, 15(1): 124 [DOI: 10.1186/s13007-019-0507-8http://dx.doi.org/10.1186/s13007-019-0507-8]
Fang H L, Baret F, Plummer S and Schaepman-Strub G. 2019. An overview of global leaf area index (LAI): methods, products, validation, and applications. Reviews of Geophysics, 57(3): 739-799 [DOI: 10.1029/2018RG000608http://dx.doi.org/10.1029/2018RG000608]
Gao L, Wang X F, Gu X F, Tian Q J, Jiao J N, Wang P Y and Li D. 2017a. Exploring the influence of soil types underneath the canopy in winter wheat leaf area index remote estimating. Chinese Journal of Plant Ecology, 41(12): 1273-1288
高林, 王晓菲, 顾行发, 田庆久, 焦俊男, 王培燕, 李丹. 2017a. 植冠下土壤类型差异对遥感估算冬小麦叶面积指数的影响. 植物生态学报, 41(12): 1273-1288 [DOI: 10.17521/cjpe.2017.0231http://dx.doi.org/10.17521/cjpe.2017.0231]
Gao L, Yang G J, Li C C, Feng H K, Xu B, Wang L, Dong J H and Fu K. 2017b. Application of an improved method in retrieving leaf area index combined spectral index with PLSR in hyperspectral data generated by unmanned aerial vehicle snapshot camera. Acta Agronomica Sinica, 43(4): 549-557
高林, 杨贵军, 李长春, 冯海宽, 徐波, 王磊, 董锦绘, 付奎. 2017b. 基于光谱特征与PLSR结合的叶面积指数拟合方法的无人机画幅高光谱遥感应用. 作物学报, 43(4): 549-557 [DOI: 10.3724/SP.J.1006.2017.00549http://dx.doi.org/10.3724/SP.J.1006.2017.00549]
Guo Y K, Liu Y L, Zhang X J and Xu M. 2019. LAI inversion using radiation transfer model and random forest regression. Engineering of Surveying and Mapping, 28(6): 17-21, 29
郭云开, 刘雨玲, 张晓炯, 许敏. 2019. 利用辐射传输模型和随机森林回归反演LAI. 测绘工程, 28(6): 17-21, 29 [DOI: 10.19349/j.cnki.issn1006-7949.2019.06.004http://dx.doi.org/10.19349/j.cnki.issn1006-7949.2019.06.004]
Hao X L, Zhou J J, Zhang M M, Wu J, Zhang F G and Wang Y J. 2020. Study on the change of soil nutrient content of robinia pseudoacacia forest of different converted years in hilly and gully areas of Southern Shanxi Province. Forest Resources Management, 6: 105-110, 115
郝小玲, 周佳佳, 张咪咪, 吴洁, 张粉果, 王永吉. 2020. 晋南丘陵沟壑区不同退耕年限刺槐林土壤养分含量变化. 林业资源管理, (6): 105-110, 115 [DOI: 10. 13466/j. cnki. lyzygl. 2020. 06. 017http://dx.doi.org/10.13466/j.cnki.lyzygl.2020.06.017]
He W J, Han G X, Yan K, Guan B, Wang G M, Lu F, Zhou Y F, Zhang L L and Zhou L. 2021. Effects of microtopography on plant biomass and the distribution of both soil water and salinity in coastal saline-alkali land. Chinese Journal of Ecology, 40(11): 3585-3597
贺文君, 韩广轩, 颜坤, 管博, 王光美, 路峰, 周英锋, 张乐乐, 周莉. 2021. 微地形对滨海盐碱地土壤水盐分布和植物生物量的影响. 生态学杂志, 40(11): 3585-3597 [DOI: 10.13292/j.1000-4890.202111.002http://dx.doi.org/10.13292/j.1000-4890.202111.002]
Hernández J A, Olmos E, Corpas F J, Sevilla F and Del Río L A. 1995. Salt-induced oxidative stress in chloroplasts of pea plants. Plant Science, 105(2): 151-167 [DOI: 10.1016/0168-9452(94)04047-8http://dx.doi.org/10.1016/0168-9452(94)04047-8]
Huang J F, Wang Y, Wang F M and Liu Z Y. 2006. Red edge characteristics and leaf area index estimation model using hyperspectral data for rape. Transactions of the CSAE, 22(8): 22-26
黄敬峰, 王渊, 王福民, 刘占宇. 2006. 油菜红边特征及其叶面积指数的高光谱估算模型. 农业工程学报, 22(8): 22-26 [DOI: 10.3321/j.issn:1002-6819.2006.08.005http://dx.doi.org/10.3321/j.issn:1002-6819.2006.08.005]
Jia J, Bai J H, Wang W, Zhang G L, Wang X, Zhao Q Q and Zhang S. 2018. Changes of biogenic elements in Phragmites australis and Suaeda salsa from salt marshes in Yellow River Delta, China. Chinese Geographical Science, 28(3): 411-419 [DOI: 10.1007/s11769-018-0959-1http://dx.doi.org/10.1007/s11769-018-0959-1]
Jiang C H, Chen Y W, Wu H H, Li W, Zhou H, Bo Y M, Shao H, Song S J, Puttonen E and Hyyppä J. 2019. Study of a high spectral resolution hyperspectral LiDAR in vegetation red edge parameters extraction. Remote Sensing, 11(17): 2007 [DOI: 10.3390/rs11172007http://dx.doi.org/10.3390/rs11172007]
Jing S H, Liu J Z, Chen Y J and Zhang T J. 2018. Effects of plant communities on soil phosphorus availability in coastal wetlands. Chinese Journal of Soil Science, 49(2): 392-401
靖淑慧, 刘加珍, 陈永金, 张天举. 2018. 滨海湿地不同群落对土壤磷的有效性影响研究. 土壤通报, 49(2): 392-401 [DOI: 10.19336/j.cnki.trtb.2018.02.19http://dx.doi.org/10.19336/j.cnki.trtb.2018.02.19]
Kanke Y, Tubaña B, Dalen M and Harrell D. 2016. Evaluation of red and red-edge reflectance-based vegetation indices for rice biomass and grain yield prediction models in paddy fields. Precision Agriculture, 17(5): 507-530 [DOI: 10.1007/s11119-016-9433-1http://dx.doi.org/10.1007/s11119-016-9433-1]
Li C J, Sun Y Q, Fan J L, Zhai Z Z, Yang S R, Fan J W, Wang T T, Wang S J and Zhang H. 2015. Ecological significance of planting suaeda salsa in saline/alkali soils in the lop nur potash mine. Arid Zone Research, 32(6): 1160-1166
李从娟, 孙永强, 范敬龙, 翟志忠, 杨司睿, 范井伟, 王婷婷, 王世杰, 张恒. 2015. 盐地碱蓬在高盐碱土环境中的生态学意义. 干旱区研究, 32(6): 1160-1166 [DOI: 10.13866/j.azr.2015.06.16http://dx.doi.org/10.13866/j.azr.2015.06.16]
Li D, Cheng T, Zhou K, Zheng H B, Yao X, Tian Y C, Zhu Y and Cao W X. 2017. WREP: a wavelet-based technique for extracting the red edge position from reflectance spectra for estimating leaf and canopy chlorophyll contents of cereal crops. ISPRS Journal of Photogrammetry and Remote Sensing, 129: 103-117 [DOI: 10.1016/j.isprsjprs.2017.04.024http://dx.doi.org/10.1016/j.isprsjprs.2017.04.024]
Li J S, Hussain T, Feng X H, Guo K, Chen H Y, Yang C and Liu X J. 2019. Comparative study on the resistance of Suaeda glauca and Suaeda salsa to drought, salt, and alkali stresses. Ecological Engineering, 140: 105593 [DOI: 10.1016/j.ecoleng.2019.105593http://dx.doi.org/10.1016/j.ecoleng.2019.105593]
Li Q, Zhang X Y, Ma T J, Jiao C L, Wang H and Hu W. 2021. A multi-step ahead photovoltaic power prediction model based on similar day, enhanced colliding bodies optimization, variational mode decomposition, and deep extreme learning machine. Energy, 224: 120094 [DOI: 10.1016/j.energy.2021.120094http://dx.doi.org/10.1016/j.energy.2021.120094]
Liu F D, Mo X, Kong W J and Song Y. 2020. Soil bacterial diversity, structure, and function of Suaeda salsa in rhizosphere and non-rhizosphere soils in various habitats in the Yellow River Delta, China. Science of the Total Environment, 740: 140144 [DOI: 10.1016/j.scitotenv.2020.140144http://dx.doi.org/10.1016/j.scitotenv.2020.140144]
Lu J S, Chen S M, Huang W M and Hu T T. 2021. Estimation of aboveground biomass and leaf area index of summer maize using SEPLS_ELM model. Transactions of the Chinese Society of Agricultural Engineering, 37(18): 128-135
陆军胜, 陈绍民, 黄文敏, 胡田田. 2021. 采用SEPLS_ELM模型估算夏玉米地上部生物量和叶面积指数. 农业工程学报, 37(18): 128-135 [DOI: 10.11975/j.issn.1002-6819.2021.18.015http://dx.doi.org/10.11975/j.issn.1002-6819.2021.18.015]
Luo G L, He S Q, Tan W and Qi Y J. 2022. Determination of leaf area index in Pinus massoniana plantations of different ages. Journal of Central South University of Forestry and Technology, 42(2): 55-64
罗光浪, 何世强, 谭伟, 戚玉娇. 2022. 不同林龄马尾松人工林叶面积指数的测定. 中南林业科技大学学报, 42(2): 55-64 [DOI: 10.14067/j.cnki.1673-923x.2022.02.007http://dx.doi.org/10.14067/j.cnki.1673-923x.2022.02.007]
Ma T T, Li X W, Bai J H, Ding S Y, Zhou F W and Cui B S. 2019. Four decades’ dynamics of coastal blue carbon storage driven by land use/land cover transformation under natural and anthropogenic processes in the Yellow River Delta, China. Science of the Total Environment, 655: 741-750 [DOI: 10.1016/j.scitotenv.2018.11.287http://dx.doi.org/10.1016/j.scitotenv.2018.11.287]
Ma Y R, Lü X, Yi X, Ma L L, Qi Y Q, Hou T Y and Zhang Z. 2021. Monitoring of cotton leaf area index using machine learning. Transactions of the Chinese Society of Agricultural Engineering, 37(13): 152-162
马怡茹, 吕新, 易翔, 马露露, 祁亚琴, 侯彤瑜, 张泽. 2021. 基于机器学习的棉花叶面积指数监测. 农业工程学报, 37(13): 152-162 [DOI: 10.11975/j.issn.1002-6819.2021.13.018http://dx.doi.org/10.11975/j.issn.1002-6819.2021.13.018]
Ma Y R, Zhang Q, Yi X, Ma L L, Zhang L F, Huang C P, Zhang Z and Lv X. 2022. Estimation of Cotton Leaf Area Index (LAI) based on spectral transformation and vegetation index. Remote Sensing, 14(1): 136 [DOI: 10.3390/rs14010136http://dx.doi.org/10.3390/rs14010136]
Parker G G. 2020. Tamm review: Leaf Area Index (LAI) is both a determinant and a consequence of important processes in vegetation canopies. Forest Ecology and Management, 477: 118496 [DOI: 10.1016/j.foreco.2020.118496http://dx.doi.org/10.1016/j.foreco.2020.118496]
Qi C H, Chen M, Song J and Wang B S. 2009. Increase in aquaporin activity is involved in leaf succulence of the euhalophyte Suaeda salsa, under salinity. Plant Science, 176(2): 200-205 [DOI: 10.1016/j.plantsci.2008.09.019http://dx.doi.org/10.1016/j.plantsci.2008.09.019]
Qu Y, Liu S H and Xie Y. 2008. Computer simulation model of fractional vegetation cover and its parameters sensitivity. Acta Agronomica Sinica, 34(11): 1964-1969
瞿瑛, 刘素红, 谢云. 2008. 植被覆盖度计算机模拟模型与参数敏感性分析. 作物学报, 34(11): 1964-1969 [DOI: 10.3724/SP.J.1006.2008.01964http://dx.doi.org/10.3724/SP.J.1006.2008.01964]
Sha Z Y, Wang Y W, Bai Y F, Zhao Y J, Jin H, Na Y and Meng X L. 2019. Comparison of leaf area index inversion for grassland vegetation through remotely sensed spectra by unmanned aerial vehicle and field-based spectroradiometer. Journal of Plant Ecology, 12(3): 395-408 [DOI: 10.1093/jpe/rty036http://dx.doi.org/10.1093/jpe/rty036]
Su Z B, Lu Y W, Gu J T, Gao R, Ma Z and Kong Q M. 2021. Research on rice leaf area index inversion model based on improved QGA-ELM algorithm. Spectroscopy and Spectral Analysis, 41(4): 1227-1233
苏中滨, 陆艺伟, 谷俊涛, 高睿, 马铮, 孔庆明. 2021. 改进的QGA-ELM算法水稻叶面积指数反演模型. 光谱学与光谱分析, 41(4): 1227-1233 [DOI: 10.3964/j.issn.1000-0593(2021)04-1227-07http://dx.doi.org/10.3964/j.issn.1000-0593(2021)04-1227-07]
Sun K, Zhang J S, Zhang C X and Hu J Y. 2017. Generalized extreme learning machine autoencoder and a new deep neural network. Neurocomputing, 230: 374-381 [DOI: 10.1016/j.neucom.2016.12.027http://dx.doi.org/10.1016/j.neucom.2016.12.027]
Wang L, Wang P X, Liang S L, Qi X, Li L and Xu L X. 2019. Monitoring maize growth conditions by training a BP neural network with remotely sensed vegetation temperature condition index and leaf area index. Computers and Electronics in Agriculture, 160: 82-90 [DOI: 10.1016/j.compag.2019.03.017http://dx.doi.org/10.1016/j.compag.2019.03.017]
Wang X Q, Liu S, Li Q Y and Ma K B. 2021. Classification and discrimination of surrounding rock of tunnel based on SVM of K-fold cross validation. Mining and Metallurgical Engineering, 41(6): 126-128, 133
汪学清, 刘爽, 李秋燕, 马凯彬. 2021. 基于K折交叉验证的SVM隧道围岩分级判别. 矿冶工程, 41(6): 126-128, 133 [DOI: 10.3969/j.issn.0253-6099.2021.06.031http://dx.doi.org/10.3969/j.issn.0253-6099.2021.06.031]
Wu W B, Li J Y, Zhang Z B, Ling C J, Lin X K and Chang X L. 2018. Estimation model of LAI and nitrogen content in tea tree based on hyperspectral image. Transactions of the Chinese Society of Agricultural Engineering, 34(3): 195-201
吴伟斌, 李佳雨, 张震邦, 凌彩金, 林贤柯, 常星亮. 2018. 基于高光谱图像的茶树LAI与氮含量反演. 农业工程学报, 34(3): 195-201 [DOI: 10.11975/j.issn.1002-6819.2018.03.026http://dx.doi.org/10.11975/j.issn.1002-6819.2018.03.026]
Xia J B, Ren J Y, Zhang S Y, Wang Y H and Fang Y. 2019. Forest and grass composite patterns improve the soil quality in the coastal saline-alkali land of the Yellow River Delta, China. Geoderma, 349: 25-35 [DOI: 10.1016/j.geoderma.2019.04.032http://dx.doi.org/10.1016/j.geoderma.2019.04.032]
Xie Q Y, Dash J, Huang W J, Peng D L, Qin Q M, Mortimer H, Casa R, Pignatti S, Laneve G, Pascucci S, Dong Y Y and Ye H C. 2018. Vegetation indices combining the red and red-edge spectral information for leaf area index retrieval. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(5): 1482-1493 [DOI: 10.1109/JSTARS.2018.2813281http://dx.doi.org/10.1109/JSTARS.2018.2813281]
Xie Q Y, Huang W J, Cai S H, Liang D, Peng D L, Zhang Q, Huang L S, Yang G J and Zhang D Y. 2014. Comparative study on remote sensing invertion methods for estimating winter wheat leaf area index. Spectroscopy and Spectral Analysis, 34(5): 1352-1356
谢巧云, 黄文江, 蔡淑红, 梁栋, 彭代亮, 张清, 黄林生, 杨贵军, 张东彦. 2014. 冬小麦叶面积指数遥感反演方法比较研究. 光谱学与光谱分析, 34(5): 1352-1356 [DOI: 10.3964/j.issn.1000-0593(2014)05-1352-05http://dx.doi.org/10.3964/j.issn.1000-0593(2014)05-1352-05]
Yan G J, Hu R H, Luo J H, Weiss M, Jiang H L, Mu X H, Xie D H and Zhang W M. 2019. Review of indirect optical measurements of leaf area index: recent advances, challenges, and perspectives. Agricultural and Forest Meteorology, 265: 390-411 [DOI: 10.1016/j.agrformet.2018.11.033http://dx.doi.org/10.1016/j.agrformet.2018.11.033]
Yang H Y, Song J D, Zhou T, Jin G Z, Jiang F and Liu Z L. 2019. Influences of stand, soil and space factors on spatial heterogeneity of leaf area index in a spruce-fir valley forest in Xiao Hinggan Ling, China. Chinese Journal of Plant Ecology, 43(4): 342-351
杨焕莹, 宋建达, 周焘, 金光泽, 姜峰, 刘志理. 2019. 林分、土壤及空间因子对谷地云冷杉林叶面积指数空间异质性的影响. 植物生态学报, 43(4): 342-351 [DOI: 10.17521/cjpe.2018.0310http://dx.doi.org/10.17521/cjpe.2018.0310]
Yao X, Yu K Y, Yang Y J, Zeng Q, Chen Z H and Liu J. 2017. Estimation of forest leaf area index based on random forest model and remote sensing data. Transactions of the Chinese Society for Agricultural Machinery, 48(5): 159-166
姚雄, 余坤勇, 杨玉洁, 曾琪, 陈樟昊, 刘健. 2017. 基于随机森林模型的林地叶面积指数遥感估算. 农业机械学报, 48(5): 159-166 [DOI: 10.6041/j.issn.1000-1298.2017.05.019http://dx.doi.org/10.6041/j.issn.1000-1298.2017.05.019]
Yu F H, Feng S, Zhao Y R, Wang D K, Xing S M and Xu T Y. 2020. Inversion model of chlorophyll content in japonica rice canopy based on PSO-ELM and hyper-spectral remote sensing. Journal of South China Agricultural University, 41(6): 59-66
于丰华, 冯帅, 赵依然, 王定康, 邢思敏, 许童羽. 2020. 粳稻冠层叶绿素含量PSO-ELM高光谱遥感反演估算. 华南农业大学学报, 41(6): 59-66 [DOI: 10.7671/j.issn.1001-411X.202007044http://dx.doi.org/10.7671/j.issn.1001-411X.202007044]
Yu Y H, Wang J L, Liu G J and Cheng F. 2019. Forest leaf area index inversion based on Landsat OLI Data in the Shangri-La City. Journal of the Indian Society of Remote Sensing, 47(6): 967-976 [DOI: 10.1007/s12524-019-00950-6http://dx.doi.org/10.1007/s12524-019-00950-6]
Zhang L, Gong Z N, Wang Q W, Jin D D and Wang X. 2019. Wetland mapping of Yellow River Delta wetlands based on multi-feature optimization of Sentinel-2 images. Journal of Remote Sensing, 23(2): 313-326
张磊, 宫兆宁, 王启为, 金点点, 汪星. 2019. Sentinel-2影像多特征优选的黄河三角洲湿地信息提取. 遥感学报, 23(2): 313-326 [DOI: 10.11834/jrs.20198083http://dx.doi.org/10.11834/jrs.20198083]
Zhang M, Kang G Q, Wu L F and Guan Y. 2022. A method for capacity prediction of lithium-ion batteries under small sample conditions. Energy, 238: 122094 [DOI: 10.1016/j.energy.2021.122094http://dx.doi.org/10.1016/j.energy.2021.122094]
Zhao S S, Pu H Y and Wei H F. 2020. Effects of different treatments on seed germination and growth of Suaeda salsa. Chinese Wild Plant Resources, 39(2): 1-6
赵珊珊, 蒲红宇, 魏海峰. 2020. 不同处理对碱蓬种子萌发和土壤氮磷含量的影响. 中国野生植物资源, 39(2): 1-6 [DOI: 10.3969/j.issn.1006-9690.2020.02.001http://dx.doi.org/10.3969/j.issn.1006-9690.2020.02.001]
Zhu T, Yu J, Xie D H and Liu L M. 2014. Particle swarm optimization in PolSAR image unspuervised classification. Journal of Geomatics Science and Technology, 31(1): 57-61
朱腾, 余洁, 谢东海, 刘利敏. 2014. 粒子群优化算法在全极化SAR影像非监督分类中的应用. 测绘科学技术学报, 31(1): 57-61 [DOI: 10.3969/j.issn.1673-6338.2014.01.013http://dx.doi.org/10.3969/j.issn.1673-6338.2014.01.013]
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