Comparison of Machine Learning Regression Algorithms for Cotton Leaf Area Index Retrieval Using Sentinel-2 Spectral Bands
Leaf area index (LAI) is a crucial crop biophysical parameter that has been widely used in a variety of fields. Five state-of-the-art machine learning regression algorithms (MLRAs), namely, artificial neural network (ANN), support vector regression (SVR), Gaussian process regression (GPR), random fo...
Uloženo v:
| Vydáno v: | Applied sciences Ročník 9; číslo 7; s. 1459 |
|---|---|
| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Basel
MDPI AG
01.04.2019
|
| Témata: | |
| ISSN: | 2076-3417, 2076-3417 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Leaf area index (LAI) is a crucial crop biophysical parameter that has been widely used in a variety of fields. Five state-of-the-art machine learning regression algorithms (MLRAs), namely, artificial neural network (ANN), support vector regression (SVR), Gaussian process regression (GPR), random forest (RF) and gradient boosting regression tree (GBRT), have been used in the retrieval of cotton LAI with Sentinel-2 spectral bands. The performances of the five machine learning models are compared for better applications of MLRAs in remote sensing, since challenging problems remain in the selection of MLRAs for crop LAI retrieval, as well as the decision as to the optimal number for the training sample size and spectral bands to different MLRAs. A comprehensive evaluation was employed with respect to model accuracy, computational efficiency, sensitivity to training sample size and sensitivity to spectral bands. We conducted the comparison of five MLRAs in an agricultural area of Northwest China over three cotton seasons with the corresponding field campaigns for modeling and validation. Results show that the GBRT model outperforms the other models with respect to model accuracy in average ( R 2 ¯ = 0.854, R M S E ¯ = 0.674 and M A E ¯ = 0.456). SVR achieves the best performance in computational efficiency, which means it is fast to train, and to validate that it has great potentials to deliver near-real-time operational products for crop management. As for sensitivity to training sample size, GBRT behaves as the most robust model, and provides the best model accuracy on the average among the variations of training sample size, compared with other models ( R 2 ¯ = 0.884, R M S E ¯ = 0.615 and M A E ¯ = 0.452). Spectral bands sensitivity analysis with dCor (distance correlation), combined with the backward elimination approach, indicates that SVR, GPR and RF provide relatively robust performance to the spectral bands, while ANN outperforms the other models in terms of model accuracy on the average among the reduction of spectral bands ( R 2 ¯ = 0.881, R M S E ¯ = 0.625 and M A E ¯ = 0.480). A comprehensive evaluation indicates that GBRT is an appealing alternative for cotton LAI retrieval, except for its computational efficiency. Despite the different performance of the ML models, all models exhibited considerable potential for cotton LAI retrieval, which could offer accurate crop parameters information timely and accurately for crop fields management and agricultural production decisions. |
|---|---|
| AbstractList | Leaf area index (LAI) is a crucial crop biophysical parameter that has been widely used in a variety of fields. Five state-of-the-art machine learning regression algorithms (MLRAs), namely, artificial neural network (ANN), support vector regression (SVR), Gaussian process regression (GPR), random forest (RF) and gradient boosting regression tree (GBRT), have been used in the retrieval of cotton LAI with Sentinel-2 spectral bands. The performances of the five machine learning models are compared for better applications of MLRAs in remote sensing, since challenging problems remain in the selection of MLRAs for crop LAI retrieval, as well as the decision as to the optimal number for the training sample size and spectral bands to different MLRAs. A comprehensive evaluation was employed with respect to model accuracy, computational efficiency, sensitivity to training sample size and sensitivity to spectral bands. We conducted the comparison of five MLRAs in an agricultural area of Northwest China over three cotton seasons with the corresponding field campaigns for modeling and validation. Results show that the GBRT model outperforms the other models with respect to model accuracy in average ( R 2 ¯ = 0.854, R M S E ¯ = 0.674 and M A E ¯ = 0.456). SVR achieves the best performance in computational efficiency, which means it is fast to train, and to validate that it has great potentials to deliver near-real-time operational products for crop management. As for sensitivity to training sample size, GBRT behaves as the most robust model, and provides the best model accuracy on the average among the variations of training sample size, compared with other models ( R 2 ¯ = 0.884, R M S E ¯ = 0.615 and M A E ¯ = 0.452). Spectral bands sensitivity analysis with dCor (distance correlation), combined with the backward elimination approach, indicates that SVR, GPR and RF provide relatively robust performance to the spectral bands, while ANN outperforms the other models in terms of model accuracy on the average among the reduction of spectral bands ( R 2 ¯ = 0.881, R M S E ¯ = 0.625 and M A E ¯ = 0.480). A comprehensive evaluation indicates that GBRT is an appealing alternative for cotton LAI retrieval, except for its computational efficiency. Despite the different performance of the ML models, all models exhibited considerable potential for cotton LAI retrieval, which could offer accurate crop parameters information timely and accurately for crop fields management and agricultural production decisions. According to Figure 7, different sensitivity performances of the sample size variation are found among the ML models. ANN and SVR are very sensitive to the training samples. [...]all models behave more robust with the growth of training sample size according to the variation of standard error. [...]some other crop biophysical parameters (e.g., Chlorophyll content and FVC) demand evaluation as well as ML regression methods, as different results may appear with different ML algorithms and different crop biophysical parameters. [...]the performance of five MLRAs has not yet been evaluated for large amounts of data (e.g., tens of thousands of data) due to the limited number of samples in our study, however, it remains to be seen whether a favorable performance can be obtained. |
| Author | Fang, Huiting Zhang, Qiankun Mao, Huihui Ji, Fujiang Meng, Jihua |
| Author_xml | – sequence: 1 givenname: Huihui orcidid: 0000-0001-7397-5181 surname: Mao fullname: Mao, Huihui – sequence: 2 givenname: Jihua surname: Meng fullname: Meng, Jihua – sequence: 3 givenname: Fujiang surname: Ji fullname: Ji, Fujiang – sequence: 4 givenname: Qiankun surname: Zhang fullname: Zhang, Qiankun – sequence: 5 givenname: Huiting surname: Fang fullname: Fang, Huiting |
| BookMark | eNptkVFrHCEQx6Wk0DTNSz-B0LfCprrquvt4Pdrm4EKhaZ5l1hsvHnu6UROSbx_TK0ko9UGH__zmr868J0chBiTkI2dnQgzsC8zzwDSXanhDjlumu0ZIro9exe_Iac47VtfARc_ZMXlYxv0MyecYaHT0Auy1D0jXCCn4sKW_cJswZ1_Ti2kbky_X-0xdTHQZS6lqJR1dJAS6Chu8rwUlebyDiV7lJ4NLDKU6Tk1LL2e0JdXMVwib_IG8dTBlPP17npCr799-L8-b9c8fq-Vi3VjR8dJA54RibMPrzjuJvVUW1KhH4WSLMHIAXlVh6x9RghvQIdeW67FVI3ImTsjq4LuJsDNz8ntIDyaCN3-EmLYGUvF2QqN0x_XghFCgJDAJPTI7dgqtRtf3unp9OnjNKd7cYi5mF29TqM83reBScqGHvlKfD5RNMeeE7vlWzszTpMzLpCrM_oGtL1Bqw2ur_PS_kkcVR5gn |
| CitedBy_id | crossref_primary_10_1007_s11119_022_09893_4 crossref_primary_10_1080_07038992_2021_2011180 crossref_primary_10_1109_TGRS_2022_3219981 crossref_primary_10_3390_rs14153776 crossref_primary_10_3390_app9122446 crossref_primary_10_3390_rs14061392 crossref_primary_10_3390_rs14235977 crossref_primary_10_3390_rs16122250 crossref_primary_10_1007_s00704_023_04779_5 crossref_primary_10_1016_j_jhydrol_2024_132423 crossref_primary_10_1155_2022_4214332 crossref_primary_10_1016_j_asr_2024_02_031 crossref_primary_10_3390_app11167208 crossref_primary_10_1080_01431161_2021_1998714 crossref_primary_10_3390_rs14225633 crossref_primary_10_1016_j_fcr_2022_108655 crossref_primary_10_1016_j_rsase_2025_101493 crossref_primary_10_1080_01431161_2019_1674461 crossref_primary_10_1002_agj2_21504 crossref_primary_10_3390_agronomy11071363 crossref_primary_10_3390_ijgi12090361 crossref_primary_10_3390_su15043278 crossref_primary_10_1007_s12524_024_01901_6 crossref_primary_10_3390_rs17122014 crossref_primary_10_3390_rs14020331 crossref_primary_10_1080_15481603_2022_2163046 crossref_primary_10_3390_agronomy11050915 crossref_primary_10_1007_s11356_024_33939_x crossref_primary_10_1016_j_eja_2025_127632 crossref_primary_10_1007_s11119_024_10129_w crossref_primary_10_1080_10106049_2022_2037730 crossref_primary_10_3390_land13111840 crossref_primary_10_1007_s11269_019_02357_x crossref_primary_10_1186_s12302_020_00397_4 crossref_primary_10_1080_01431161_2020_1823043 crossref_primary_10_1080_10106049_2021_1959654 crossref_primary_10_1016_j_scitotenv_2020_139486 crossref_primary_10_3390_agriengineering6030134 crossref_primary_10_1029_2024EA003554 crossref_primary_10_3390_rs13214314 crossref_primary_10_3390_rs14051066 crossref_primary_10_1117_1_JRS_18_046511 crossref_primary_10_1016_j_agwat_2022_108056 crossref_primary_10_3390_s21082861 crossref_primary_10_3390_rs13142841 |
| Cites_doi | 10.1109/TNN.2003.809401 10.3390/rs9040309 10.1002/9780470748992 10.1109/TIT.2016.2514489 10.1016/j.mcm.2012.12.013 10.1007/s10462-016-9506-6 10.1080/13504851.2014.907468 10.1109/TGRS.2003.812910 10.1080/01431161.2018.1433343 10.1007/978-3-319-65633-5 10.1016/j.rse.2012.12.027 10.1016/j.gsf.2015.07.003 10.1029/2007JG000635 10.1016/j.eja.2012.12.001 10.1016/j.rser.2014.07.108 10.1046/j.1466-822X.2003.00026.x 10.1175/1520-0442(2001)014<3536:EOTUOS>2.0.CO;2 10.3390/s140815348 10.3390/s17010081 10.1016/j.ijrmms.2005.06.007 10.3389/fnbot.2013.00021 10.1177/0309133307084626 10.1002/jgrg.20051 10.1016/j.isprsjprs.2010.11.001 10.1080/01621459.2012.695654 10.1016/j.rse.2011.01.001 10.1016/j.rse.2006.07.014 10.1109/TCYB.2016.2609408 10.1038/nature14539 10.2747/1548-1603.45.2.229 10.1111/j.1365-3040.1992.tb00992.x 10.1023/A:1010933404324 10.1214/aos/1013203451 10.3390/rs10050763 10.1016/j.agrformet.2008.07.014 10.1080/00949655.2014.928820 10.1109/JSTARS.2015.2461136 10.3390/app7101046 10.1016/j.compag.2017.12.007 10.1038/323533a0 10.3390/rs8040303 10.3390/ijgi6020057 10.1016/j.fcr.2018.02.002 10.1016/0893-6080(89)90020-8 10.1007/s10712-018-9478-y 10.1080/01431161.2015.1084438 10.1016/j.rse.2006.09.031 10.1016/j.rse.2011.11.002 10.1016/j.scitotenv.2018.04.251 10.1109/TGRS.2011.2168962 10.1080/10106049.2014.997303 10.1214/009053607000000505 10.1016/j.fcr.2012.02.012 10.1109/ICMLA.2016.0182 10.1016/j.isprsjprs.2015.05.005 10.3390/s18010018 10.1007/BF00994018 10.2134/agronj2005.0418 10.1080/00207720601051463 |
| ContentType | Journal Article |
| Copyright | 2019. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: 2019. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | AAYXX CITATION ABUWG AFKRA AZQEC BENPR CCPQU DWQXO PHGZM PHGZT PIMPY PKEHL PQEST PQQKQ PQUKI DOA |
| DOI | 10.3390/app9071459 |
| DatabaseName | CrossRef ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials ProQuest Central ProQuest One Community College ProQuest Central ProQuest Central Premium ProQuest One Academic Publicly Available Content Database (subscription) ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic (retired) ProQuest One Academic UKI Edition Directory of Open Access Journals (DOAJ) |
| DatabaseTitle | CrossRef Publicly Available Content Database ProQuest Central ProQuest One Academic Middle East (New) ProQuest One Academic UKI Edition ProQuest Central Essentials ProQuest Central Korea ProQuest One Academic Eastern Edition ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest Central (New) ProQuest One Academic ProQuest One Academic (New) |
| DatabaseTitleList | CrossRef Publicly Available Content Database |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Sciences (General) |
| EISSN | 2076-3417 |
| ExternalDocumentID | oai_doaj_org_article_576179f335a54a04a8e0cb65ec7ef887 10_3390_app9071459 |
| GeographicLocations | Beijing China United States--US China |
| GeographicLocations_xml | – name: China – name: Beijing China – name: United States--US |
| GroupedDBID | .4S 2XV 5VS 7XC 8CJ 8FE 8FG 8FH AADQD AAFWJ AAYXX ADBBV ADMLS AFFHD AFKRA AFPKN AFZYC ALMA_UNASSIGNED_HOLDINGS APEBS ARCSS BCNDV BENPR CCPQU CITATION CZ9 D1I D1J D1K GROUPED_DOAJ IAO IGS K6- K6V KC. KQ8 L6V LK5 LK8 M7R MODMG M~E OK1 P62 PHGZM PHGZT PIMPY PROAC TUS ABUWG AZQEC DWQXO PKEHL PQEST PQQKQ PQUKI |
| ID | FETCH-LOGICAL-c361t-a6f3500d1350164e8c5ca5b7b3f42eab1aa14e83c076e4af9efe17c17b25be103 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 58 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000466547500194&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2076-3417 |
| IngestDate | Mon Nov 10 04:35:22 EST 2025 Mon Jun 30 11:17:57 EDT 2025 Sat Nov 29 07:10:53 EST 2025 Tue Nov 18 21:36:12 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 7 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c361t-a6f3500d1350164e8c5ca5b7b3f42eab1aa14e83c076e4af9efe17c17b25be103 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0001-7397-5181 |
| OpenAccessLink | https://doaj.org/article/576179f335a54a04a8e0cb65ec7ef887 |
| PQID | 2314413798 |
| PQPubID | 2032433 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_576179f335a54a04a8e0cb65ec7ef887 proquest_journals_2314413798 crossref_primary_10_3390_app9071459 crossref_citationtrail_10_3390_app9071459 |
| PublicationCentury | 2000 |
| PublicationDate | 2019-04-01 |
| PublicationDateYYYYMMDD | 2019-04-01 |
| PublicationDate_xml | – month: 04 year: 2019 text: 2019-04-01 day: 01 |
| PublicationDecade | 2010 |
| PublicationPlace | Basel |
| PublicationPlace_xml | – name: Basel |
| PublicationTitle | Applied sciences |
| PublicationYear | 2019 |
| Publisher | MDPI AG |
| Publisher_xml | – name: MDPI AG |
| References | ref_50 Yuan (ref_5) 2011; 115 Kiniry (ref_11) 2005; 97 Verrelst (ref_55) 2012; 118 ref_14 ref_57 Friedman (ref_73) 2001; 29 Adelabu (ref_52) 2015; 30 Li (ref_78) 2012; 107 Rumelhart (ref_58) 1986; 323 Kundu (ref_80) 2017; 47 ref_19 ref_16 Chen (ref_21) 2018; 636 ref_15 (ref_56) 2017; 11 Ramedani (ref_68) 2014; 39 Szekely (ref_77) 2007; 35 Madhiarasan (ref_61) 2017; 48 Baret (ref_47) 2013; 137 Basak (ref_66) 2007; 11 Durbha (ref_25) 2007; 107 Wang (ref_34) 2018; 219 Liang (ref_12) 2007; 31 ref_69 Guyon (ref_81) 2003; 3 ref_24 ref_23 Twele (ref_39) 2008; 45 ref_67 ref_22 ref_20 ref_64 Chen (ref_1) 1992; 15 Hornik (ref_59) 1989; 2 ref_29 Dong (ref_9) 2013; 58 Verrelst (ref_27) 2012; 50 Cortes (ref_63) 1995; 20 Viterbo (ref_6) 2003; 108 LeCun (ref_85) 2015; 521 ref_70 Sonmez (ref_60) 2006; 43 Zhong (ref_79) 2015; 85 Verrelst (ref_13) 2015; 108 ref_36 ref_35 Lary (ref_18) 2016; 7 Gong (ref_38) 2003; 41 Dube (ref_54) 2014; 14 ref_76 ref_31 ref_75 Hong (ref_82) 2007; 38 Asner (ref_3) 2003; 12 Scornet (ref_71) 2016; 62 Siegmann (ref_84) 2015; 36 Pedregosa (ref_51) 2011; 12 Jego (ref_10) 2012; 131 Buermann (ref_4) 2001; 14 Fang (ref_43) 2013; 118 Martinez (ref_32) 2009; 149 Bacour (ref_28) 2006; 105 ref_83 Natekin (ref_74) 2013; 7 Guneralp (ref_30) 2014; 33 Huang (ref_62) 2003; 14 Maxwell (ref_17) 2018; 39 Delegido (ref_37) 2013; 46 ref_46 ref_45 ref_44 ref_42 ref_41 ref_40 Mountrakis (ref_65) 2011; 66 ref_2 ref_49 ref_48 Omer (ref_53) 2015; 8 Breiman (ref_72) 2001; 45 ref_8 Karimi (ref_26) 2018; 144 ref_7 (ref_33) 2014; 21 |
| References_xml | – volume: 14 start-page: 274 year: 2003 ident: ref_62 article-title: Learning capability and storage capacity of two-hidden-layer feedforward networks publication-title: IEEE Trans. Neural Netw. doi: 10.1109/TNN.2003.809401 – ident: ref_49 – ident: ref_83 doi: 10.3390/rs9040309 – volume: 12 start-page: 2825 year: 2011 ident: ref_51 article-title: Scikit-learn: Machine learning in Python publication-title: J. Mach. Learn. Res. – ident: ref_57 doi: 10.1002/9780470748992 – volume: 62 start-page: 1485 year: 2016 ident: ref_71 article-title: Random forests and Kernel methods publication-title: IEEE Trans. Inf. Theory doi: 10.1109/TIT.2016.2514489 – volume: 58 start-page: 871 year: 2013 ident: ref_9 article-title: Integrating a very fast simulated annealing optimization algorithm for crop leaf area index variational assimilation publication-title: Math. Comput. Model. doi: 10.1016/j.mcm.2012.12.013 – volume: 48 start-page: 449 year: 2017 ident: ref_61 article-title: Comparative analysis on hidden neurons estimation in multi layer perceptron neural networks for wind speed forecasting publication-title: Artif. Intell. Rev. doi: 10.1007/s10462-016-9506-6 – ident: ref_16 – volume: 21 start-page: 1031 year: 2014 ident: ref_33 article-title: Assessing the impact of China net imports on the world cotton price publication-title: Appl. Econ. Lett. doi: 10.1080/13504851.2014.907468 – volume: 41 start-page: 1355 year: 2003 ident: ref_38 article-title: Estimation of forest leaf area index using vegetation indices derived from Hyperion hyperspectral data publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2003.812910 – volume: 39 start-page: 2784 year: 2018 ident: ref_17 article-title: Implementation of machine-learning classification in remote sensing: An applied review publication-title: Int. J. Remote Sens. doi: 10.1080/01431161.2018.1433343 – ident: ref_42 – ident: ref_22 doi: 10.1007/978-3-319-65633-5 – ident: ref_35 – volume: 137 start-page: 299 year: 2013 ident: ref_47 article-title: GEOV1: LAI and FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part1: Principles of development and production publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2012.12.027 – volume: 7 start-page: 3 year: 2016 ident: ref_18 article-title: Machine learning in geosciences and remote sensing publication-title: Geosci. Front. doi: 10.1016/j.gsf.2015.07.003 – ident: ref_2 doi: 10.1029/2007JG000635 – volume: 46 start-page: 42 year: 2013 ident: ref_37 article-title: A red-edge spectral index for remote sensing estimation of green LAI over agroecosystems publication-title: Eur. J. Agron. doi: 10.1016/j.eja.2012.12.001 – volume: 39 start-page: 1005 year: 2014 ident: ref_68 article-title: Potential of radial basis function based support vector regression for global solar radiation prediction publication-title: Renew. Sust. Energ. Rev. doi: 10.1016/j.rser.2014.07.108 – volume: 12 start-page: 191 year: 2003 ident: ref_3 article-title: Global synthesis of leaf area index observations: Implications for ecological and remote sensing studies publication-title: Glob. Ecol. Biogeogr. doi: 10.1046/j.1466-822X.2003.00026.x – volume: 14 start-page: 3536 year: 2001 ident: ref_4 article-title: Evaluation of the utility of satellite-based vegetation leaf area index data for climate simulations publication-title: J. Clim. doi: 10.1175/1520-0442(2001)014<3536:EOTUOS>2.0.CO;2 – ident: ref_8 – volume: 14 start-page: 15348 year: 2014 ident: ref_54 article-title: Intra-and-inter species biomass prediction in a plantation forest: Testing the utility of high spatial resolution spaceborne multispectral rapideye sensor and advanced machine learning algorithms publication-title: Sensors doi: 10.3390/s140815348 – ident: ref_19 doi: 10.3390/s17010081 – ident: ref_48 – ident: ref_69 – volume: 43 start-page: 224 year: 2006 ident: ref_60 article-title: Estimation of rock modulus: For intact rocks with an artificial neural network and for rock masses with a new empirical equation publication-title: Int. J. Rock Mech. Min. Sci. doi: 10.1016/j.ijrmms.2005.06.007 – volume: 7 start-page: 21 year: 2013 ident: ref_74 article-title: Gradient boosting machines, a tutorial publication-title: Front. Neurorobot. doi: 10.3389/fnbot.2013.00021 – volume: 31 start-page: 501 year: 2007 ident: ref_12 article-title: Recent developments in estimating land surface biogeophysical variables from optical remote sensing publication-title: Prog. Phys. Geogr. doi: 10.1177/0309133307084626 – ident: ref_41 – volume: 33 start-page: 119 year: 2014 ident: ref_30 article-title: Estimation of floodplain aboveground biomass using multispectral remote sensing and nonparametric modeling publication-title: Int. J. Appl. Earth Obs. Geoinf. – volume: 118 start-page: 529 year: 2013 ident: ref_43 article-title: Characterization and intercomparison of global moderate resolution leaf area index (LAI) products: Analysis of climatologies and theoretical uncertainties publication-title: J. Geophys. Res.-Biogeosci. doi: 10.1002/jgrg.20051 – volume: 66 start-page: 247 year: 2011 ident: ref_65 article-title: Support vector machines in remote sensing: A review publication-title: ISPRS-J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2010.11.001 – volume: 108 start-page: 4191 year: 2003 ident: ref_6 article-title: Impact of leaf area index seasonality on the annual land surface evaporation in a global circulation model publication-title: J. Geophys. Res.-Atmos. – ident: ref_45 – volume: 107 start-page: 1129 year: 2012 ident: ref_78 article-title: Feature screening via distance correlation learning publication-title: J. Am. Stat. Assoc. doi: 10.1080/01621459.2012.695654 – volume: 115 start-page: 1171 year: 2011 ident: ref_5 article-title: Reprocessing the MODIS Leaf Area Index products for land surface and climate modelling publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2011.01.001 – volume: 11 start-page: 7 year: 2017 ident: ref_56 article-title: Computational foundations of natural intelligence publication-title: Front. Comput. Neurosci. – ident: ref_76 – volume: 105 start-page: 313 year: 2006 ident: ref_28 article-title: Neural network estimation of LAI, fAPAR, fCover and LAIxC(ab), from top of canopy MERIS reflectance data: Principles and validation publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2006.07.014 – volume: 47 start-page: 4356 year: 2017 ident: ref_80 article-title: Feature selection through message passing publication-title: IEEE Trans. Cybern. doi: 10.1109/TCYB.2016.2609408 – volume: 3 start-page: 1157 year: 2003 ident: ref_81 article-title: An introduction to variable and feature selection publication-title: J. Mach. Learn. Res. – ident: ref_24 – volume: 521 start-page: 436 year: 2015 ident: ref_85 article-title: Deep learning publication-title: Nature doi: 10.1038/nature14539 – volume: 45 start-page: 229 year: 2008 ident: ref_39 article-title: Spatially explicit estimation of leaf area index using EO-1 hyperion and landsat ETM+ data: Implications of spectral bandwidth and shortwave infrared data on prediction accuracy in a tropical montane environment publication-title: GISci. Remote Sens. doi: 10.2747/1548-1603.45.2.229 – ident: ref_40 – volume: 15 start-page: 421 year: 1992 ident: ref_1 article-title: Defining leaf-area index for non-flat leaves publication-title: Plant Cell Environ. doi: 10.1111/j.1365-3040.1992.tb00992.x – volume: 45 start-page: 5 year: 2001 ident: ref_72 article-title: Random forests publication-title: Mach. Learn. doi: 10.1023/A:1010933404324 – ident: ref_67 – volume: 29 start-page: 1189 year: 2001 ident: ref_73 article-title: Greedy function approximation: A gradient boosting machine publication-title: Ann. Stat. doi: 10.1214/aos/1013203451 – ident: ref_15 doi: 10.3390/rs10050763 – volume: 149 start-page: 130 year: 2009 ident: ref_32 article-title: Derivation of high-resolution leaf area index maps in support of validation activities: Application to the cropland Barrax site publication-title: Agric. For. Meteorol. doi: 10.1016/j.agrformet.2008.07.014 – ident: ref_44 – volume: 85 start-page: 2331 year: 2015 ident: ref_79 article-title: An iterative approach to distance correlation-based sure independence screening publication-title: J. Stat. Comput. Simul. doi: 10.1080/00949655.2014.928820 – volume: 8 start-page: 4825 year: 2015 ident: ref_53 article-title: Performance of support vector machines and artificial neural network for mapping endangered tree species using WorldView-2 data in Dukuduku Forest, South Africa publication-title: IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. doi: 10.1109/JSTARS.2015.2461136 – ident: ref_23 doi: 10.3390/app7101046 – volume: 144 start-page: 232 year: 2018 ident: ref_26 article-title: Generalizability of gene expression programming and random forest methodologies in estimating cropland and grassland leaf area index publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2017.12.007 – volume: 323 start-page: 533 year: 1986 ident: ref_58 article-title: Learning representations by back-propagating errors publication-title: Nature doi: 10.1038/323533a0 – ident: ref_7 doi: 10.3390/rs8040303 – ident: ref_31 doi: 10.3390/ijgi6020057 – volume: 219 start-page: 169 year: 2018 ident: ref_34 article-title: Coupling effects of water and fertilizer on yield, water and fertilizer use efficiency of drip-fertigated cotton in northern Xinjiang, China publication-title: Field Crop. Res. doi: 10.1016/j.fcr.2018.02.002 – volume: 2 start-page: 359 year: 1989 ident: ref_59 article-title: Multilayer feedforward networks are universal approximators publication-title: Neural Netw. doi: 10.1016/0893-6080(89)90020-8 – ident: ref_75 – ident: ref_50 – ident: ref_14 doi: 10.1007/s10712-018-9478-y – ident: ref_46 – volume: 36 start-page: 4519 year: 2015 ident: ref_84 article-title: Comparison of different regression models and validation techniques for the assessment of wheat leaf area index from hyperspectral data publication-title: Int. J. Remote Sens. doi: 10.1080/01431161.2015.1084438 – volume: 107 start-page: 348 year: 2007 ident: ref_25 article-title: Support vector machines regression for retrieval of leaf area index from multiangle imaging spectroradiometer publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2006.09.031 – ident: ref_64 – volume: 118 start-page: 127 year: 2012 ident: ref_55 article-title: Machine learning regression algorithms for biophysical parameter retrieval: Opportunities for Sentinel-2 and -3 publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2011.11.002 – volume: 636 start-page: 52 year: 2018 ident: ref_21 article-title: A machine learning method to estimate PM2.5 concentrations across China with remote sensing, meteorological and land use information publication-title: Sci. Total Environ. doi: 10.1016/j.scitotenv.2018.04.251 – volume: 50 start-page: 1832 year: 2012 ident: ref_27 article-title: Retrieval of vegetation biophysical parameters using Gaussian process techniques publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2011.2168962 – volume: 30 start-page: 810 year: 2015 ident: ref_52 article-title: Testing the reliability and stability of the internal accuracy assessment of random forest for classifying tree defoliation levels using different validation methods publication-title: Geocarto Int. doi: 10.1080/10106049.2014.997303 – ident: ref_36 – ident: ref_70 – volume: 35 start-page: 2769 year: 2007 ident: ref_77 article-title: Measuring and testing dependence by correlation of distances publication-title: Ann. Stat. doi: 10.1214/009053607000000505 – volume: 11 start-page: 203 year: 2007 ident: ref_66 article-title: Support vector regression publication-title: Neural Inf. Process. Lett. Rev. – volume: 131 start-page: 63 year: 2012 ident: ref_10 article-title: Using Leaf Area Index, retrieved from optical imagery, in the STICS crop model for predicting yield and biomass of field crops publication-title: Field Crop. Res. doi: 10.1016/j.fcr.2012.02.012 – ident: ref_29 doi: 10.1109/ICMLA.2016.0182 – volume: 108 start-page: 273 year: 2015 ident: ref_13 article-title: Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties—A review publication-title: ISPRS-J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2015.05.005 – ident: ref_20 doi: 10.3390/s18010018 – volume: 20 start-page: 273 year: 1995 ident: ref_63 article-title: Support-vector networks publication-title: Mach. Learn. doi: 10.1007/BF00994018 – volume: 97 start-page: 418 year: 2005 ident: ref_11 article-title: Large-area maize yield forecasting using leaf area index based yield model publication-title: Agron. J. doi: 10.2134/agronj2005.0418 – volume: 38 start-page: 101 year: 2007 ident: ref_82 article-title: Backward elimination model construction for regression and classification using leave-one-out criteria publication-title: Int. J. Syst. Sci. doi: 10.1080/00207720601051463 |
| SSID | ssj0000913810 |
| Score | 2.3867955 |
| Snippet | Leaf area index (LAI) is a crucial crop biophysical parameter that has been widely used in a variety of fields. Five state-of-the-art machine learning... According to Figure 7, different sensitivity performances of the sample size variation are found among the ML models. ANN and SVR are very sensitive to the... |
| SourceID | doaj proquest crossref |
| SourceType | Open Website Aggregation Database Enrichment Source Index Database |
| StartPage | 1459 |
| SubjectTerms | Accuracy Algorithms Artificial intelligence Cotton International conferences leaf area index (LAI) Machine learning Remote sensing Satellites sensitivity analysis Sentinel-2 spectral bands Statistical methods Studies training sample size Vegetation |
| SummonAdditionalLinks | – databaseName: ProQuest Central dbid: BENPR link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3BbtQwELWg5UAPhRYQ2xZkCQ70YBHHdhyfqu6qFRxYVQWk3iLbsbdIS9JuAhJ_z0zi3VYCceFqT-RIMx7PjMfvEfK2sHkUXlqmg9dM1iZjLtaGeallVloNc_VANqHn8_LqylykgluX2irXPnFw1HXrsUb-HuIQOLmFNuXJzS1D1ii8XU0UGg_JNiKVgZ1vT8_mF5ebKguiXpY8G3FJBeT3eC9s8NEOgpPeO4kGwP4__PFwyJw_-d_fe0p2U3hJT0d72CMPQrNPdu6BDu6TvbSdO_ouYU4fPyO_Zhs-QtpG-mlosQw0oa8u6GVYjA2zDT1dLmDh_vp7RyHgpbO2h_ARJSMsGyz9iPiL8AEydYEZ06EpgX7GrqQmLFlOkfIe6yt0is-Mn5Ov52dfZh9YYmVgXhS8Z7aIQmVZzfFKspCh9Mpb5bQTUebBOm4th1HhM10EaaMJMXDtuXa5coFn4gXZatomvCTUBiM4vsV1QUkNuYoTpoy5KIxTEPQXE3K81lDlE2Q5MmcsK0hdUJvVnTYn5M1G9mYE6vir1BQVvZFAcO1hoF0tqrRXK0jBwE1FIZRV0mbSliHzrlBgzSGCU56Qo7UNVGnHd9WdARz8e_qQPIagy4zdP0dkq1_9CK_II_-z_9atXicD_g1P-fuD priority: 102 providerName: ProQuest |
| Title | Comparison of Machine Learning Regression Algorithms for Cotton Leaf Area Index Retrieval Using Sentinel-2 Spectral Bands |
| URI | https://www.proquest.com/docview/2314413798 https://doaj.org/article/576179f335a54a04a8e0cb65ec7ef887 |
| Volume | 9 |
| WOSCitedRecordID | wos000466547500194&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2076-3417 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913810 issn: 2076-3417 databaseCode: DOA dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2076-3417 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913810 issn: 2076-3417 databaseCode: M~E dateStart: 20110101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2076-3417 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913810 issn: 2076-3417 databaseCode: BENPR dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2076-3417 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913810 issn: 2076-3417 databaseCode: PIMPY dateStart: 20110101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Na9wwEBUl7SE9hHzSTdIgaA_JwSBZkmUds0tCc8iypA0kJyNpR5vA1lt23UL_fWdsZ7vQQi89Wh5bRjOSZqyZ9xj7WPg8qah9ZiHaTE-dyEKauixqq0XpLd6btmQTdjwuHx7cZIPqi3LCOnjgbuAwYMc91iWljDfaC-1LEDEUBl8NCWcIrb7Cuo1gql2DnSToqg6PVGFcT-fBjop1CJR0Ywdqgfr_WIfbzeV6l-30XiG_7L5mj72Cep-93cAK3Gd7_Sxc8fMeKvrigP0crWkE-SLx2zYzEngPmjrjdzDr8lxrfjmfLZbPzdPXFUc_lY8WDXp9JJmwW_D8hmAT8QEi2ELr420uAf9MyUQ1zLOcE1M9_RbhQ6oOPmT311dfRp-ynkwhi6qQTeaLpIwQU0kniYWGMproTbBBJZ2DD9J7ia0qCluA9slBAmmjtCE3AaRQR2yrXtTwjnEPTkkqoQ1gtMUQIyhXplwVLhj01YsBu3gZ4Cr2SONEeDGvMOIgZVS_lTFgH9ay3zp8jb9KDUlPawnCxG4b0FKq3lKqf1nKgJ2-aLnqJ-qqQvcWHUJlXXn8P_o4YdvoUbkuteeUbTXL7_CevYk_mufV8oy9Hl6NJ3dnra3i1eTmdvL4C0Zf70g |
| linkProvider | Directory of Open Access Journals |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VLRJwAFpALBSwBEj0EJHEThwfEGoXqq7aXa1okdpTcBx7QVqSsgmg_il-IzN5bCuBuPXA1XZixfk8D3vmG4AXsQ4dN0J70hrpiVz5XuZy5RkhhZ9oiX15U2xCTqfJyYmarcGvPheGwip7mdgI6rw0dEb-Gu0Q1NxcquTt2TePqkbR7WpfQqOFxYE9_4kuW_Vm_A7_78sw3Ht_PNr3uqoCnuFxUHs6djzy_TygK7VY2MRERkeZzLgTodVZoHWArdygh2-Fdso6G0gTyCyMMhv4HN97DdYFgX0A67PxZHa6OtUhls0k8FseVM6VT_fQipKEiAz1kuZrCgT8If8bpbZ3539bjrtwuzOf2U6L9w1Ys8Um3LpEqrgJG524qtirjlN7-x6cj1b1Flnp2KQJIbWsY5edsw923gYEF2xnMccPrT9_rRga9GxU1mge00iH01rNxsQviQ9QJTLcpqwJumBHFHVV2IUXsiNKXsVp2S6lUd-Hj1eyIA9gUJSFfQhMW8UDyjXObCQk-mIZV4kLeayyCJ2aeAjbPSJS01GyU2WQRYquGaEnvUDPEJ6vxp61RCR_HbVLwFqNIPLwpqFcztNOFqXoYqIYdpxHOhLaFzqxvsniCHerdah0hrDVYy7tJFqVXgDu0b-7n8GN_ePJYXo4nh48hptoYKo20mkLBvXyu30C182P-ku1fNptHgafrhqgvwErqVmi |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Nb9QwEB2VLUJwAFpAXShgCZDoIWoSO3F8QKjdsmJVulpRkMop2I69VFqSsgmg_jV-HePE2VYCceuBq-3EivM8H_bMG4DnqYwt1UwG3GgesEKEgbKFCDTjLMwkx76iLTbBp9Ps5ETM1uBXnwvjwip7mdgK6qLS7ox8F-0Q1NyUi2zX-rCI2cH49dm3wFWQcjetfTmNDiKH5vwnum_1q8kB_usXcTx-82H0NvAVBgJN06gJZGppEoZF5K7XUmYynWiZKK6oZbGRKpIywlaq0ds3TFphrIm4jriKE2WikOJ7r8E6muQsHsD6bHI0-7Q64XGMm1kUdpyolIrQ3UkLlzDkiFEvacG2WMAfuqBVcOM7__PS3IXb3qwme90-2IA1U27CrUtki5uw4cVYTV56ru2de3A-WtVhJJUlR21oqSGedXZO3pt5Fyhckr3FHD-0-fK1Jmjok1HVoNnsRlqc1kgycbyT-ICrUIbbl7TBGOTYRWOVZhHE5NglteK0ZN-lV9-Hj1eyIA9gUFal2QIijaCRy0FWJmEcfTRFRWZjmgqVoLOTDmGnR0euPVW7qxiyyNFlc0jKL5A0hGersWcdQclfR-07kK1GOFLxtqFaznMvo3J0PVE8W0oTmTAZMpmZUKs0wV1sLCqjIWz3-Mu9pKvzC_A9_Hf3U7iBqMzfTaaHj-Am2p2iC4DahkGz_G4ew3X9ozmtl0_8PiLw-arx-RvKcmJi |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Comparison+of+Machine+Learning+Regression+Algorithms+for+Cotton+Leaf+Area+Index+Retrieval+Using+Sentinel-2+Spectral+Bands&rft.jtitle=Applied+sciences&rft.au=Mao%2C+Huihui&rft.au=Meng%2C+Jihua&rft.au=Ji%2C+Fujiang&rft.au=Zhang%2C+Qiankun&rft.date=2019-04-01&rft.issn=2076-3417&rft.eissn=2076-3417&rft.volume=9&rft.issue=7&rft.spage=1459&rft_id=info:doi/10.3390%2Fapp9071459&rft.externalDBID=n%2Fa&rft.externalDocID=10_3390_app9071459 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2076-3417&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2076-3417&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2076-3417&client=summon |