浏览全部资源
扫码关注微信
Published： 07 March 2023 ，
扫 描 看 全 文
汪静平，吴小丹，马杜娟，闻建光，肖青.2023.基于机器学习的遥感反演：不确定性因素分析.遥感学报，27（3）： 790-801
Wang J P，Wu X D，Ma D J，Wen J G and Xiao Q. 2023. Remote sensing retrieval based on machine learning algorithm： Uncertainty analysis. National Remote Sensing Bulletin， 27（3）：790-801
遥感反演是定量遥感的核心内容之一，只有实现高精度的参数反演，才能提高遥感数据的利用效率，从而进一步推进定量遥感的发展。基于机器学习的遥感参数反演充分利用了卫星大数据的优势，能够避免因物理模型带来的复杂处理和计算过程，同时减小由遥感数据预处理包括几何校正、辐射校正、大气校正等引起的不确定性，因此被应用于遥感反演中。然而，机器学习在遥感反演中的效用和能力需要客观看待。本文重点对机器学习在遥感反演中的应用进行论述。首先在文献检索的基础上总结了目前机器学习在遥感反演方面的应用现状。然后总结了几种典型的机器学习算法的原理及特点。最后，重点论述了基于机器学习的遥感反演不确定性产生的几种主要因素，包括机器学习算法的选择、辅助变量的选择、变量数据集的来源及准确性、训练样本的选择和模型的跨尺度、跨区域应用等。在机器学习算法的实际应用中，应综合考虑这些因素可能会引起的不确定性，从而选择最为合适的算法和样本，提高结果的准确度和可信度。
Remote sensing retrieval is one of the core issues of quantitative remote sensing. High-precision retrieval can improve the utilization efficiency of satellite data and promote the development of quantitative remote sensing. Machine learning algorithms have been increasingly used in remote sensing domains due to the outstanding advantages in dealing with complex and nonlinear problems. They can avoid the complicated processing and calculation in physical models
and minimize the uncertainty resulting from data preprocessing such as geometric correction
radiometric correction
and atmospheric correction. However
the utility of machine learning algorithms in retrieval needs to be viewed objectively. For instance
which machine learning algorithm is the most appropriate or which parameter configuration is optimal in order to obtain more accurate retrieval results. What we are concerned about most is which factors might influence the accuracy of retrievals. Hence
this paper systematically combs the current situation and principles of the application of machine learning algorithms in remote sensing retrieval with the focus on the main uncertainty factors in machine learning-based retrieval.
It was found that the number of relevant articles surged especially after 2018. Several mainstream algorithms including random forests
support vector machine
and artificial neural network have been widely used in remote sensing retrieval. And the retrievals of vegetation index
leaf area index
soil moisture
chlorophyll content
and biomass are the main research hotspots at present. MODIS and Sentinel datasets are the most widely used data. The process of machine learning-based retrieval can be summarized as the acquisition of training samples as well as the construction and application of the training model. The main factors causing the uncertainty of retrievals including the selection of machine learning models
the selection of auxiliary variables
the source and accuracy of the variable datasets
the selection of training samples
and the cross-regional and cross-area application of models were discussed in this paper. The findings are helpful in deepening the consciousness and understanding of the uncertainties of the retrieval results based on machine learning algorithms
which is necessary to select the most suitable algorithm and samples for improving the accuracy and reliability of the results.
Moreover
it is important to note that there is a trade-off between the model accuracy and complexity for machine learning algorithms. Therefore
they may be not feasible in solving different remote sensing problems. Further retrieval based on machine learning models may be developed from the following four aspects: 1) Selecting more detailed information to fully capture the spatial heterogeneity of land surface and the spatiotemporal variation characteristics of parameters; 2) The training samples should be more representative to make the models more universal; 3) The scale mismatch between the
in situ
measurements and auxiliary variables extracted from satellite products should be fully taken into consideration to reduce the uncertainty caused by the scale effect; 4) The combination of physical models and machine learning algorithms can improve the representativeness of training samples
providing a new way for the construction of training samples.
机器学习反演不确定性定量遥感深度学习文献分析
machine learningretrievaluncertaintyquantitative remote sensingdeep learningliterature analysis
Ahmad S, Kalra A and Stephen H. 2010. Estimating soil moisture using remote sensing data: a machine learning approach. Advances in Water Resources, 33(1): 69-80 [DOI: 10.1016/j.advwatres.2009.10.008http://dx.doi.org/10.1016/j.advwatres.2009.10.008]
Atzberger C, Guérif M, Baret F and Werner W. 2010. Comparative analysis of three chemometric techniques for the spectroradiometric assessment of canopy chlorophyll content in winter wheat. Computers and Electronics in Agriculture, 73(2): 165-173 [DOI: 10.1016/j.compag.2010.05.006http://dx.doi.org/10.1016/j.compag.2010.05.006]
Baret F and Guyot G. 1991. Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sensing of Environment, 35(2/3): 161-173 [DOI: 10.1016/0034-4257(91)90009-Uhttp://dx.doi.org/10.1016/0034-4257(91)90009-U]
Breiman L. 2001. Random forests. Machine Learning, 45(1): 5-32 [DOI: 10.1023/A:1010933404324http://dx.doi.org/10.1023/A:1010933404324]
Chen J M, Pavlic G, Brown L, Cihlar J, Leblanc S G, White H P, Hall R J, Peddle D R, King D J, Trofymow J A, Swift E, Van Der Sanden J and Pellikka P K E. 2002. Derivation and validation of Canada-wide coarse-resolution leaf area index maps using high-resolution satellite imagery and ground measurements. Remote Sensing of Environment, 80(1): 165-184 [DOI: 10.1016/S0034-4257(01)00300-5http://dx.doi.org/10.1016/S0034-4257(01)00300-5]
Ebrahimy H and Azadbakht M. 2019. Downscaling MODIS land surface temperature over a heterogeneous area: an investigation of machine learning techniques, feature selection, and impacts of mixed pixels. Computers and Geosciences, 124: 93-102 [DOI: 10.1016/j.cageo.2019.01.004http://dx.doi.org/10.1016/j.cageo.2019.01.004]
Ghulam A, Qin Q M, Teyip T and Li Z L. 2007. Modified perpendicular drought index(MPDI): a real-time drought monitoring method. ISPRS Journal of Photogrammetry and Remote Sensing, 62(2): 150-164 [DOI: 10.1016/j.isprsjprs.2007.03.002http://dx.doi.org/10.1016/j.isprsjprs.2007.03.002]
Goovaerts P. 1997. Geostatistics for Natural Resources Evaluation. New York: Oxford University Press: 28
Guan X D, Huang J P, Guo N, Bi J R and Wang G Y. 2009. Variability of soil moisture and its relationship with surface albedo and soil thermal parameters over the Loess Plateau. Advances in Atmospheric Sciences, 26(4): 692-700 [DOI: 10.1007/s00376-009-8198-0http://dx.doi.org/10.1007/s00376-009-8198-0]
Hansen P M and Schjoerring J K. 2003. Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sensing of Environment, 86(4): 542-553 [DOI: 10.1016/S0034-4257(03)00131-7http://dx.doi.org/10.1016/S0034-4257(03)00131-7]
Hufkens K, Bogaert J, Dong Q H, Lu L, Huang C L, Ma M G, Che T, Li X, Veroustraete F and Ceulemans R. 2008. Impacts and uncertainties of upscaling of remote-sensing data validation for a semi-arid woodland. Journal of Arid Environments, 72(8): 1490-1505 [DOI: 10.1016/j.jaridenv.2008.02.012http://dx.doi.org/10.1016/j.jaridenv.2008.02.012]
Hutengs C and Vohland M. 2016. Downscaling land surface temperatures at regional scales with random forest regression. Remote Sensing of Environment, 178: 127-141 [DOI: 10.1016/j.rse.2016.03.006http://dx.doi.org/10.1016/j.rse.2016.03.006]
Im J, Park S, Rhee J, Baik J and Choi M. 2016. Downscaling of AMSR-E soil moisture with MODIS products using machine learning approaches. Environmental Earth Sciences, 75(15): 1120 [DOI: 10.1007/s12665-016-5917-6http://dx.doi.org/10.1007/s12665-016-5917-6]
Ji T, Liu R, Yang H, He T R and Wu J F. 2015. Spatial downscaling of precipitation using multi-source remote sensing data: a case study of Sichuan-Chongqing region. Journal of Geo-Information Science, 17(1): 108-117
嵇涛, 刘睿, 杨华, 何太蓉, 吴建峰. 2015. 多源遥感数据的降水空间降尺度研究——以川渝地区为例. 地球信息科学学报, 17(1): 108-117 [DOI: 10.3724/SP.J.1047.2015.00108http://dx.doi.org/10.3724/SP.J.1047.2015.00108]
Jiang D, Yang X, Clinton N and Wang N. 2004. An artificial neural network model for estimating crop yields using remotely sensed information. International Journal of Remote Sensing, 25(9): 1723-1732 [DOI: 10.1080/0143116031000150068http://dx.doi.org/10.1080/0143116031000150068]
Ke Y H, Im J, Park S and Gong H L. 2016. Downscaling of MODIS One kilometer evapotranspiration using Landsat-8 data and machine learning approaches. Remote Sensing, 8(3): 215 [DOI: 10.3390/rs8030215http://dx.doi.org/10.3390/rs8030215]
Li W, Ni L, Li Z L, Duan S B and Wu H. 2019. Evaluation of machine learning algorithms in spatial downscaling of modis land surface temperature. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(7): 2299-2307 [DOI: 10.1109/JSTARS.2019.2896923http://dx.doi.org/10.1109/JSTARS.2019.2896923]
Li W, Niu L, Chen H and Wu H. 2020. Robust downscaling method of land surface temperature by using random forest algorithm. Journal of Geo-Information Science, 22(8): 1666-1678
李婉, 牛陆, 陈虹, 吴骅. 2020. 基于随机森林算法的地表温度鲁棒降尺度方法. 地球信息科学学报, 22(8): 1666-1678 [DOI: 10.12082/dqxxkx.2020.190142http://dx.doi.org/10.12082/dqxxkx.2020.190142]
Li X C, Xu X G, Bao Y S, Huang W J, Luo J H, Dong Y Y, Song X Y and Wang J H. 2012. Retrieving LAI of winter wheat based on sensitive vegetation index by the segmentation method. Scientia Agricultura Sinica, 45(17): 3486-3496
李鑫川, 徐新刚, 鲍艳松, 黄文江, 罗菊花, 董莹莹, 宋晓宇, 王纪华. 2012. 基于分段方式选择敏感植被指数的冬小麦叶面积指数遥感反演. 中国农业科学, 45(17): 3486-3496
Liang L, Di L P, Huang T, Wang J H, Lin L, Wang L J and Yang M H. 2018. Estimation of leaf nitrogen content in wheat using new hyperspectral indices and a random forest regression algorithm. Remote Sensing, 10(12): 1940 [DOI: 10.3390/rs10121940http://dx.doi.org/10.3390/rs10121940]
Liang S L, Cheng J, Jia K, Jiang B, Liu Q, Liu S H, Xiao Z Q, Xie X H, Yao Y J, Yuan W P, Zhang X T and Zhao X. 2016. Recent progress in land surface quantitative remote sensing. Journal of Remote Sensing, 20(5): 875-898
梁顺林, 程洁, 贾坤, 江波, 刘强, 刘素红, 肖志强, 谢先红, 姚云军, 袁文平, 张晓通, 赵祥. 2016. 陆表定量遥感反演方法的发展新动态. 遥感学报, 20(5): 875-898 [DOI: 10.11834/jrs.20166258http://dx.doi.org/10.11834/jrs.20166258]
Liu H Z, Wang B M and Fu C B. 2008. Relationships between surface albedo, soil thermal parameters and soil moisture in the semi-arid area of Tongyu, northeastern China. Advances in Atmospheric Sciences, 25(5): 757-764 [DOI: 10.1007/s00376-008-0757-2http://dx.doi.org/10.1007/s00376-008-0757-2]
Liu Y, Liu R G, Chen J M, Cheng X and Zheng G. 2013. Current status and perspectives of leaf area index retrieval from optical remote sensing data. Journal of Geo-Information Science, 15(5): 734-743
刘洋, 刘荣高, 陈镜明, 程晓, 郑光. 2013. 叶面积指数遥感反演研究进展与展望. 地球信息科学学报, 15(5): 734-743 [DOI: 10.3724/SP.J.1047.2013.00734http://dx.doi.org/10.3724/SP.J.1047.2013.00734]
Liu Y, Liu R G, Liu S L, Liu J Y, Chen Z X, Wang L M and Zou J Q. 2010. Estimation of Crop LAI based on the neural network trained from physical model. Journal of Geo-Information Science, 12(3): 426-435
刘洋, 刘荣高, 刘斯亮, 刘纪远, 陈仲新, 王利民, 邹金秋. 2010. 基于物理模型训练神经网络的作物叶面积指数遥感反演研究. 地球信息科学学报, 12(3): 426-435
Liu Z J, Shao Q Q, Tao J and Chi W F. 2015. Intra-annual variability of satellite observed surface albedo associated with typical land cover types in China. Journal of Geographical Sciences, 25(1): 35-44 [DOI: 10.1007/s11442-015-1151-5http://dx.doi.org/10.1007/s11442-015-1151-5]
Mao K B, Yang J, Han X Z, Tang S H, Yuan Z J and Gao C Y. 2018. Retrieving land surface temperature based on deep dynamic learning NN algorithm and radiation transmission model. China Agricultural Informatics, 30(5): 47-57
毛克彪, 杨军, 韩秀珍, 唐世浩, 袁紫晋, 高春雨. 2018. 基于深度动态学习神经网络和辐射传输模型地表温度反演算法研究. 中国农业信息, 30(5): 47-57 [DOI: 10.12105/j.issn.1672-0423.20180506http://dx.doi.org/10.12105/j.issn.1672-0423.20180506]
Masson V, Champeaux J L, Chauvin F, Meriguet C and Lacaze R. 2003. A global database of land surface parameters at 1-km resolution in meteorological and climate models. Journal of Climate, 16(9): 1261-1282 [DOI: 10.1175/1520-0442(2003)16<1261:AGDOLS>2.0.CO;2http://dx.doi.org/10.1175/1520-0442(2003)16<1261:AGDOLS>2.0.CO;2]
Meyer H, Kühnlein M, Appelhans T and Nauss T. 2016. Comparison of four machine learning algorithms for their applicability in satellite-based optical rainfall retrievals. Atmospheric Research, 169: 424-433 [DOI: 10.1016/j.atmosres.2015.09.021http://dx.doi.org/10.1016/j.atmosres.2015.09.021]
Pan S F, Pan N Q, Tian H Q, Friedlingstein P, Sitch S, Shi H, Arora V K, Haverd V, Jain A K, Kato E, Lienert S, Lombardozzi D, Nabel J E M S, Ottlé C, Poulter B, Zaehle S and Running S W. 2020. Evaluation of global terrestrial evapotranspiration using state-of-the-art approaches in remote sensing, machine learning and land surface modeling. Hydrology and Earth System Sciences, 24(3): 1485-1509 [DOI: 10.5194/hess-24-1485-2020http://dx.doi.org/10.5194/hess-24-1485-2020]
Peng J J, Liu Q, Wen J G, Liu Q H, Tang Y, Wang L Z, Dou B C, You D Q, Sun C K, Zhao X J, Feng Y B and Shi J. 2015. Multi-scale validation strategy for satellite albedo products and its uncertainty analysis. Science China Earth Sciences, 58(4): 573-588 [DOI: 10.1007/s11430-014-4997-yhttp://dx.doi.org/10.1007/s11430-014-4997-y]
Qin W M, Wang L C, Lin A W, Zhang M and Bilal M. 2018. Improving the estimation of daily aerosol optical depth and aerosol radiative effect using an optimized artificial neural network. Remote Sensing, 10(7): 1022 [DOI: 10.3390/rs10071022http://dx.doi.org/10.3390/rs10071022]
Salomonson V V and Appel I. 2004. Estimating fractional snow cover from MODIS using the normalized difference snow index. Remote Sensing of Environment, 89(3): 351-360. [DOI: 10.1016/j.rse.2003.10.016http://dx.doi.org/10.1016/j.rse.2003.10.016]
Srinet R, Nandy S and Patel N R. 2019. Estimating leaf area index and light extinction coefficient using Random Forest regression algorithm in a tropical moist deciduous forest, India. Ecological Informatics, 52: 94-102 [DOI: 10.1016/j.ecoinf.2019.05.008http://dx.doi.org/10.1016/j.ecoinf.2019.05.008]
Wang J P, Wu X D, Wen J G, Xiao Q, Gong B C, Ma D J, Cui Y R, Lin X W and Bao Y F. 2022. Upscaling in situ site-based albedo using machine learning models: main controlling factors on results. IEEE Transactions on Geoscience and Remote Sensing, 60: 4403516 [DOI: 10.1109/TGRS.2021.3095153http://dx.doi.org/10.1109/TGRS.2021.3095153]
Wang J W, Yuan Q Q, Shen H F, Liu T T, Li T W, Yue L W, Shi X G and Zhang L P. 2020. Estimating snow depth by combining satellite data and ground-based observations over Alaska: a deep learning approach. Journal of Hydrology, 585: 124828. [DOI: 10.1016/j.jhydrol.2020.124828http://dx.doi.org/10.1016/j.jhydrol.2020.124828]
Wang K C, Wang P C, Liu J M, Sparrow M, Haginoya S and Zhou X J. 2005. Variation of surface albedo and soil thermal parameters with soil moisture content at a semi-desert site on the western Tibetan Plateau. Boundary-Layer Meteorology, 116(1): 117-129 [DOI: 10.1007/s10546-004-7403-zhttp://dx.doi.org/10.1007/s10546-004-7403-z]
Wang L A, Zhou X D, Zhu X K, Dong Z D and Guo W S. 2016. Estimation of biomass in wheat using random forest regression algorithm and remote sensing data. The Crop Journal, 4(3): 212-219 [DOI: 10.1016/j.cj.2016.01.008http://dx.doi.org/10.1016/j.cj.2016.01.008]
Wang L H and Xing Y Q. 2008. Remote sensing estimation of natural forest biomass based on an artificial neural network. The Journal of Applied Ecology, 19(2): 261-266
王立海, 邢艳秋. 2008. 基于人工神经网络的天然林生物量遥感估测.应用生态学报, 19(2): 261-266
Wang Q. 2021. Progress of environmental remote sensing monitoring technology in China and some related frontier issues. National Remote Sensing Bulletin, 25(1): 25-36
王桥. 2021. 中国环境遥感监测技术进展及若干前沿问题. 遥感学报, 25(1): 25-36 [DOI: 10.11834/jrs.20210572http://dx.doi.org/10.11834/jrs.20210572]
Weng Q H and Hu X F. 2008. Medium spatial resolution satellite imagery for estimating and mapping urban impervious surfaces using LSMA and ANN. IEEE Transactions on Geoscience and Remote Sensing, 46(8): 2397-2406 [DOI: 10.1109/TGRS.2008.917601http://dx.doi.org/10.1109/TGRS.2008.917601]
Wu X D, Wen J G, Xiao Q and You D Q. 2020. Upscaling of single-site-based measurements for validation of long-term coarse-pixel albedo products. IEEE Transactions on Geoscience and Remote Sensing, 58(5): 3411-3425 [DOI: 10.1109/TGRS.2019.2954879http://dx.doi.org/10.1109/TGRS.2019.2954879]
Wu X D, Wen J G, Xiao Q, Liu Q, Peng J J, Dou B C, Li X H, You D Q, Tang Y and Liu Q H. 2016. Coarse scale in situ albedo observations over heterogeneous snow-free land surfaces and validation strategy: a case of MODIS albedo products preliminary validation over northern China. Remote Sensing of Environment, 184: 25-39 [DOI: 10.1016/j.rse.2016.06.013http://dx.doi.org/10.1016/j.rse.2016.06.013]
Wu X D, Wen J G, Xiao Q, You D Q, Liu Q and Lin X W. 2018. Forward a spatio-temporal trend surface for long-term ground-measured albedo upscaling over heterogeneous land surface. International Journal of Digital Earth, 11(5): 470-484 [DOI: 10.1080/17538947.2017.1334097http://dx.doi.org/10.1080/17538947.2017.1334097]
Xiao Z Q, Liang S L, Wang T T and Jiang B. 2016. Retrieval of Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) from VIIRS time-series data. Remote Sensing, 8(4): 351 [DOI: 10.3390/rs8040351http://dx.doi.org/10.3390/rs8040351]
Xu B D, Li J, Park T, Liu Q H, Zeng Y L, Yin G F, Zhao J, Fan W L, Yang L, Knyazikhin Y and Myneni R B. 2018a. An integrated method for validating long-term leaf area index products using global networks of site-based measurements. Remote Sensing of Environment, 209: 134-151 [DOI: 10.1016/j.rse.2018.02.049http://dx.doi.org/10.1016/j.rse.2018.02.049]
Xu G H, Liu Q H, Chen L F and Liu L Y. 2016. Remote sensing for China’s sustainable development: Opportunities and challenges. Journal of Remote Sensing, 20(5): 679-688
徐冠华, 柳钦火, 陈良富, 刘良云. 2016. 遥感与中国可持续发展: 机遇和挑战. 遥感学报, 20(5): 679-688 [DOI: 10.11834/jrs.20166308http://dx.doi.org/10.11834/jrs.20166308]
Xu T R, Guo Z X, Liu S M, He X L, Meng Y F Y, Xu Z W, Xia Y L, Xiao J F, Zhang Y, Ma Y F and Song L S. 2018b. Evaluating different machine learning methods for upscaling evapotranspiration from flux towers to the regional scale. Journal of Geophysical Research: Atmospheres, 123(16): 8674-8690 [DOI: 10.1029/2018JD028447http://dx.doi.org/10.1029/2018JD028447]
Yang G J, Pu R L, Huang W J, Wang J H and Zhao C J. 2010. A novel method to estimate subpixel temperature by fusing solar-reflective and thermal-infrared remote-sensing data with an artificial neural network. IEEE Transactions on Geoscience and Remote Sensing, 48(4): 2170-2178 [DOI: 10.1109/TGRS.2009.2033180http://dx.doi.org/10.1109/TGRS.2009.2033180]
Yang G J, Zhao C J, Xing Z R, Huang W J and Wang J H. 2011. LAI inversion of spring wheat based on PROBA/CHRIS hyperspectral multi-angular data and PROSAIL model. Transactions of the CSAE, 27(10): 88-94
杨贵军, 赵春江, 邢著荣, 黄文江, 王纪华. 2011. 基于PROBA/CHRIS遥感数据和PROSAIL模型的春小麦LAI反演. 农业工程学报, 27(10): 88-94 [DOI: 10.3969/j.issn.1002-6819.2011.10.016http://dx.doi.org/10.3969/j.issn.1002-6819.2011.10.016]
Yang R M, Zhang G L, Liu F, Lu Y Y, Yang F, Yang F, Yang M, Zhao Y G and Li D C. 2016. Comparison of boosted regression tree and random forest models for mapping topsoil organic carbon concentration in an alpine ecosystem. Ecological Indicators, 60: 870-878 [DOI: 10.1016/j.ecolind.2015.08.036http://dx.doi.org/10.1016/j.ecolind.2015.08.036]
Yang Y B, Cao C, Pan X, Li X L and Zhu X. 2017. Downscaling land surface temperature in an arid area by using multiple remote sensing indices with random forest regression. Remote Sensing, 9(8): 789 [DOI: 10.3390/rs9080789http://dx.doi.org/10.3390/rs9080789]
Yao Y J, Liang S L, Li X L, Chen J Q, Liu S M, Jia K, Zhang X T, Xiao Z Q, Fisher J B, Mu Q Z, Pan M, Liu M, Cheng J, Jiang B, Xie X H, Grünwald T, Bernhofer C and Roupsard O. 2017. Improving global terrestrial evapotranspiration estimation using support vector machine by integrating three process-based algorithms. Agricultural and Forest Meteorology, 242: 55-74 [DOI: 10.1016/j.agrformet.2017.04.011http://dx.doi.org/10.1016/j.agrformet.2017.04.011]
Yuan H H, Yang G J, Li C C, Wang Y J, Liu J G, Yu H Y, Feng H K, Xu B, Zhao X Q and Yang X D. 2017. Retrieving soybean leaf area index from unmanned aerial vehicle hyperspectral remote sensing: analysis of RF, ANN, and SVM regression models. Remote Sensing, 9(4): 309 [DOI: 10.3390/rs9040309http://dx.doi.org/10.3390/rs9040309]
Yuan Q Q, Xu H Z, Li T W, Shen H F and Zhang L P. 2020. Estimating surface soil moisture from satellite observations using a generalized regression neural network trained on sparse ground-based measurements in the continental U.S. Journal of Hydrology, 580: 124351 [DOI: 10.1016/j.jhydrol.2019.124351http://dx.doi.org/10.1016/j.jhydrol.2019.124351]
Yue J B, Yang G J, Li C C, Li Z H, Wang Y J, Feng H K and Xu B. 2017. Estimation of winter wheat above-ground biomass using unmanned aerial vehicle-based snapshot hyperspectral sensor and crop height improved models. Remote Sensing, 9(7): 708 [DOI: 10.3390/rs9070708http://dx.doi.org/10.3390/rs9070708]
Zhang L P and Wu C. 2017. Advance and future development of change detection for multi-temporal remote sensing imagery. Acta Geodaetica et Cartographica Sinica, 46(10): 1447-1459
张良培, 武辰. 2017. 多时相遥感影像变化检测的现状与展望. 测绘学报, 46(10): 1447-1459 [DOI: 10.11947/j.AGCS.2017.20170340http://dx.doi.org/10.11947/j.AGCS.2017.20170340]
Zhao W, Sánchez N, Lu H and Li A N. 2018. A spatial downscaling approach for the SMAP passive surface soil moisture product using random forest regression. Journal of Hydrology, 563: 1009-1024 [DOI: 10.1016/j.jhydrol.2018.06.081http://dx.doi.org/10.1016/j.jhydrol.2018.06.081]
Zhao X Z, Yu B L, Liu Y, Chen Z Q, Li Q X, Wang C X and Wu J P. 2019. Estimation of poverty using random forest regression with multi-source data: a case study in Bangladesh. Remote Sensing, 11(4): 375 [DOI: 10.3390/rs11040375http://dx.doi.org/10.3390/rs11040375]
Zheng H P, Yan J Z, Liu L S, Li L H and Zhang Y L. 2017. Research advances in grassland remote sensing based on bibliometrology. Chinese Journal of Grassland, 39(4): 101-110, 115
郑海朋, 阎建忠, 刘林山, 李兰晖, 张镱锂. 2017. 基于文献计量的草地遥感研究进展. 中国草地学报, 39(4): 101-110, 115 [DOI: 10.16742/j.zgcdxb.2017-04-16http://dx.doi.org/10.16742/j.zgcdxb.2017-04-16]
Zhu J S, Ji X Y and Song Z Z. 2019. Water Depth Inversion Based on Support Vector Machine and BP Neural Network. Geomatics and Spatial Information Technology, 42(6): 11-14
朱金山, 纪轩禹, 宋珍珍. 2019. 基于支持向量机和BP神经网络的水深反演研究. 测绘与空间地理信息, 42(6): 11-14
相关文章
相关作者
相关机构
京公网安备11010802024621