Combining both spectral and textural indices for alleviating saturation problem in forest LAI estimation using Sentinel-2 data

References

• Addabbo P, Focareta M, Marcuccio S, Votto C, Ullo SL. 2016. Contribution of Sentinel – 2 data for applications in vegetation monitoring. Acta Imeko. 5(2):44–54.
   Google Scholar
• Aragao LEOC, Shimabukuro YE, Espirito Santo FDB, Williams M. 2005. Landscape pattern and spatial variability of leaf area index in Eastern Amazonia. For Ecol Manage. 211(3):240–256.
   Web of Science ®Google Scholar
• Asner GP, Scurlock JMO, Hicke JA. 2003. Global synthesis of leaf area index observations: implications for ecological and remote sensing studies. Glob Ecol Biogeogr. 12(3):191–205. (2003):
   Web of Science ®Google Scholar
• Bharati MH, Liu JJ, Macgregor JF. 2004. Image texture analysis : methods and comparisons. Chemom Inteliligent Lab Syst. 72(1):57–71.
   Web of Science ®Google Scholar
• Broge NH, Leblanc E. 2001. Vegetation indices for estimation of green leaf area index and canopy comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sens Environ. 76(2):156–172.
   Web of Science ®Google Scholar
• Campos-Taberner M, García-Haro FJ, Busetto L, Ranghetti L, Martínez B, Gilabert MA, Camps-Valls G, Camacho F, Boschetti M. 2018. A critical comparison of remote sensing Leaf Area Index estimates over rice-cultivated areas: From Sentinel-2 and Landsat-7/8 to MODIS, GEOV1 and EUMETSAT polar system. Remote Sens. 10(5):763.
   Web of Science ®Google Scholar
• Campos-Taberner M, García-Haro FJ, Camps-Valls G, Grau-Muedra G, Nutini F, Busetto L, Katsantonis D, Stavrakoudis D, Minakou C, Gatti L. 2017. Exploitation of SAR and optical sentinel data to detect rice crop and estimate seasonal dynamics of leaf area index. Remote Sens. 9(3):1–17.
   Google Scholar
• Cao Z, Cheng T, Ma X, Tian Y, Zhu Y, Yao X, Chen Q, Liu S, Guo Z, Zhen Q, et al. 2017. A new three-band spectral index for mitigating the saturation in the estimation of leaf area index in wheat. Int J Remote Sens. 38(13):3865–3885.
   Web of Science ®Google Scholar
• Černý J, Haninec P, Pokorný R. 2020. Leaf area index estimated by direct, semi-direct, and indirect methods in European beech and sycamore maple stands. J for Res. 31(3):827–836.
   Google Scholar
• Chang CW, Laird DA, Mausbach MJ, Hurburgh CR. 2001. Near-infrared reflectance spectroscopy-principal components regression analyses of soil properties. Soil Sci Soc Am J. 65(2):480–490.
   Web of Science ®Google Scholar
• Chen JM. 1996. Optically-based methods for measuring seasonal variation of leaf area index in boreal conifer stands. Agric for Meteorol. 80 (2-4):135–163.
   Web of Science ®Google Scholar
• Chen JM. 1996. Evaluation of vegetation indices and a modified simple ratio for boreal applications. Can J Remote Sens. 22(3):229–242.
   Google Scholar
• Chen JM, Pavlic G, Brown L, Cihlar J, Leblanc SG, White HP, Hall RJ, Peddle DR, King DJ, Trofymow JA, et al. 2002. Derivation and validation of Canada-wide coarse-resolution leaf area index maps using high-resolution satellite imagery and ground measurements. Remote Sens Environ. 80(1):165–184.
   Web of Science ®Google Scholar
• Chen JM, Rich PM, Gower ST, Norman JM, Plummer S. 1997. Leaf area index of boreal forests- Theory, techniques, and measurements. J Geophys Res. 102(D24):29429–29443.
   Web of Science ®Google Scholar
• Claverie M, Vermote EF, Weiss M, Baret F, Hagolle O, Demarez V. 2013. Validation of coarse spatial resolution LAI and FAPAR time series over cropland in southwest France. Remote Sens Environ. 139:216–230.
   Web of Science ®Google Scholar
• Colombo R. Bellingeri D, Fasolini D, Marino C. 2003. Retrieval of leaf area index in different vegetation types using high resolution satellite data. Remote Sens Environ. 86(1):120–131.
   Web of Science ®Google Scholar
• Darvishzadeh R, Wang T, Skidmore A, Vrieling A, Connor BO, Gara TW, Ens BJ, Paganini M. 2019. Analysis of Sentinel-2 and rapideye for retrieval of leaf area index in a Saltmarsh using a radiative transfer model. Remote Sens. 11(6):671.
   Google Scholar
• Delegido J, Verrelst J, Alonso L, Moreno J. 2011. Evaluation of sentinel-2 red-edge bands for empirical estimation of green LAI and chlorophyll content. Sensors (Basel)). 11(7):7063–7081.
   PubMed Web of Science ®Google Scholar
• Frampton WJ, Dash J, Watmough G, Milton EJ. 2013. Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation. ISPRS J Photogramm Remote Sens. 82:83–92.
   Web of Science ®Google Scholar
• Fu WJ, Jiang PK, Zhou GM, Zhao KL. 2014. Using Moran’s i and GIS to study the spatial pattern of forest litter carbon density in a subtropical region of southeastern China. Biogeosciences. 11(8):2401–2409.
   Web of Science ®Google Scholar
• Fuster B, Sánchez-Zapero J, Camacho F, García-Santos V, Verger A, Lacaze R, Weiss M, Baret F, Smets B. 2020. Quality assessment of PROBA-V LAI, fAPAR and fCOVER collection 300 m products of copernicus global land service. Remote Sens. 12(6):1017.
   Web of Science ®Google Scholar
• García-Haro FJ, Campos-Taberner M, Muñoz-Marí J, Laparra V, Camacho F, Sánchez-Zapero J, Camps-Valls G. 2018. Derivation of global vegetation biophysical parameters from EUMETSAT Polar System. ISPRS J Photogramm. Remote Sens. 139:57–74.
   Web of Science ®Google Scholar
• Gikungu SW, Waititu A, Kihoro J. 2015. Modeling inflation in Kenya : Comparison of SARIMA and generalized least squares models. Math Theory Model. 5(12):67–73.
   Google Scholar
• Gonsamo A, Walter JMN, Pellikka P. 2011. CIMES: A package of programs for determining canopy geometry and solar radiaStion regimes through hemispherical photographs. Comput Electron Agric. 79(2):207–215.
   Web of Science ®Google Scholar
• Gray J, Song C. 2012. Mapping leaf area index using spatial, spectral, and temporal information from multiple sensors. Remote Sens Environ. 119:173–183.
   Web of Science ®Google Scholar
• Haboudane D, Miller JR, Pattey E, Zarco-Tejada PJ, Strachan IB. 2004. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sens Environ. 90(3):337–352.
   Web of Science ®Google Scholar
• Hall-Beyer M. 2017. GLCM Texture: A Tutorial v. 3.0 (March). University of Calgary, Canada.
   Google Scholar
• Haralick RM, Shanmugam K, Dinstein I. 1973. Textural features for image classification. IEEE Trans Syst Man Cybern. SMC. 3(6):610–621.
   Web of Science ®Google Scholar
• Hill MJ. 2013. Vegetation index suites as indicators of vegetation state in grassland and savanna: An analysis with simulated SENTINEL 2 data for a North American transect. Remote Sens Environ. 137:94–111.
   Web of Science ®Google Scholar
• Inoue Y, Peñuelas J, Miyata A, Mano M. 2008. Normalized difference spectral indices for estimating photosynthetic efficiency and capacity at a canopy scale derived from hyperspectral and CO2 flux measurements in rice. Remote Sens Environ. 112(1):156–172.
   Web of Science ®Google Scholar
• Jin H, Xu W, Li A, Xie X, Zhang Z, Xia H. 2019. Spatially and temporally continuous leaf area index mapping for crops through assimilation of multi-resolution satellite data. Remote Sens. 11(21):2517.
   Google Scholar
• Jolliffe IT, Cadima J. 2016. Principal component analysis: A review and recent developments. Philos Trans A Math Phys Eng Sci. 374(2065):20150202–20150216.
   PubMed Web of Science ®Google Scholar
• Kayitakire F, Hamel C, Defourny P. 2006. Retrieving forest structure variables based on image texture analysis and IKONOS-2 imagery. Remote Sens Environ. 102(3-4):390–401.
   Web of Science ®Google Scholar
• Kiala Z, Odindi J, Mutanga O. 2017. Potential of interval partial least square regression in estimating leaf area index. S Afr J Sci. 113(9–10):1–9.
   Web of Science ®Google Scholar
• Knyazikhin Y, Martonchik JV, Myneni RB, Diner DJ, Running SW. 1998. Synergistic algorithm for estimating vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from MODIS and MISR data. J Geophys Res. 103(D24):32257–32275.
   Web of Science ®Google Scholar
• Kuhn MM, Wing J, Weston S, Williams A, Keefer C, Engelhardt A, Cooper T, Mayer Z, Kenkel B, Benesty M. 2020. Caret: Classification and Regression Training.
   Google Scholar
• Li P, Wang Q. 2013. Developing and validating novel hyperspectral indices for leaf area index estimation: Effect of canopy vertical heterogeneity. Ecol Indic. 32:123–130.
   Web of Science ®Google Scholar
• Li S, Yuan F, Ata-Ui-Karim ST, Zheng H, Cheng T, Liu X, Tian Y, Zhu Y, Cao W, Cao Q. 2019. Combining color indices and textures of UAV-based digital imagery for rice LAI estimation. Remote Sens. 11(15):1763.
   Web of Science ®Google Scholar
• Li W, Weiss M, Waldner F, Defourny P, Demarez V, Morin D, Hagolle O, Baret F. 2015. A generic algorithm to estimate LAI, FAPAR and FCOVER variables from SPOT4_HRVIR and landsat sensors: Evaluation of the consistency and comparison with ground measurements. Remote Sens. 7(11):15494–15516.
   Web of Science ®Google Scholar
• Li X, Zhang Y, Bao Y, Luo J, Jin X, Xu X, Song X, Yang G. 2014. Exploring the best hyperspectral features for LAI estimation using partial least squares regression. Remote Sens. 6(7):6221–6241.
   Google Scholar
• Liang S, Zhao X, Liu S, Yuan W, Cheng X, Xiao Z, Zhang X, Liu Q, Cheng J, Tang H, et al. 2013. A long-term Global LAnd Surface Satellite (GLASS) data-set for environmental studies. Int J Digit Earth. 6(sup1):5–33.
   Web of Science ®Google Scholar
• Liebhold AM, Sharov AA. 1998. Testing for correlation in the presence of spatial autocorrelation in insect count data. Popul Community Ecol Insect Manag Conserv. CRC Press 8
   Google Scholar
• Liu Z, Chen JM, Jin G, Qi Y. 2015. Agricultural and Forest Meteorology Estimating seasonal variations of leaf area index using litterfall collection and optical methods in four mixed evergreen – deciduous forests. Agric for Meteorol. 209–210:36–48.
   Web of Science ®Google Scholar
• Liu Z, Jiang F, Zhu Y, Li F, Jin G. 2018. Spatial heterogeneity of leaf area index in a temperate old-growth forest: Spatial autocorrelation dominates over biotic and abiotic factors. Sci Total Environ. 634:287–295.
   PubMed Web of Science ®Google Scholar
• Luo T, Neilson RP, Tian H, Vörösmarty CJ, Zhu H, Liu S. 2002. A model for seasonality and distribution of leaf area index of forests and its application to China. J Veg Sci. 13(6):817–830.
   Web of Science ®Google Scholar
• Ma Y, Van Dam RL, Jayawickreme DH. 2014. Soil moisture variability in a temperate deciduous forest: Insights from electrical resistivity and throughfall data. Environ Earth Sci. 72(5):1367–1381.
   Web of Science ®Google Scholar
• Majasalmi T, Rautiainen M. 2016. The potential of Sentinel-2 data for estimating biophysical variables in a boreal forest: A simulation study. Remote Sens Lett. 7(5):427–436. (February 2016):
   Web of Science ®Google Scholar
• Mananze S, Pôças I, Cunha M. 2018. Retrieval of maize leaf area index using hyperspectral and multispectral data. Remote Sens. 10(12):30.
   Google Scholar
• Mao H, Meng J, Ji F, Zhang Q, Fang H. 2019. Comparison of machine learning regression algorithms for cotton leaf area index retrieval using Sentinel-2 spectral bands. Appl Sci. 9(7):1459.
   Google Scholar
• Melnikova I, Awaya Y, Saitoh TM, Muraoka H, Sasai T. 2018. Estimation of leaf area index in a mountain forest of central Japan with a 30-m spatial resolution based on landsat operational land imager imagery: An application of a simple model for seasonal monitoring. Remote Sens. 10(2):1–24.
   Google Scholar
• Meng J, Li S, Wang W, Liu Q, Xie S, Ma W. 2016. Mapping forest health using spectral and textural information extracted from SPOT-5 satellite images. Remote Sens. 8(9):719.
   Google Scholar
• Mevik B-H, Wehrens R. 2007. The pls Package: Principal Component and Partial Least Squares Regression in R. J Stat Softw. 18(2):1–23.
   Web of Science ®Google Scholar
• Meyer LH, Heurich M, Beudert B, Premier J, Pflugmacher D. 2019. Comparison of Landsat-8 and Sentinel-2 data for estimation of leaf area index in temperate forests. Remote Sens. 11(10):1–16.
   Google Scholar
• Muraoka H, Saigusa N, Nasahara KN, Noda H, Yoshino J, Saitoh TM, Nagai S, Murayama S, Koizumi H. 2010. Effects of seasonal and interannual variations in leaf photosynthesis and canopy leaf area index on gross primary production of a cool-temperate deciduous broadleaf forest in Takayama, Japan. J Plant Res. 123(4):563–576.
   PubMed Web of Science ®Google Scholar
• Ozdemir I, Karnieli A. 2011. Predicting forest structural parameters using the image texture derived from worldview-2 multispectral imagery in a dryland forest, Israel. Int J Appl Earth Obs Geoinf. 13(5):701–710.
   Web of Science ®Google Scholar
• Pu R, Cheng J. 2015. Mapping forest leaf area index using reflectance and textural information derived from WorldView-2 imagery in a mixed natural forest area in Florida, US. Int J Appl Earth Obs Geoinf. 42:11–23.
   Web of Science ®Google Scholar
• Qi J, Chehbouni A, Huete AR, Kerr YH, Sorooshian S. 1994. A modified soil adjusted vegetation index. Remote Sens Environ. 48(2):119–126.
   Web of Science ®Google Scholar
• Richter K, Atzberger C, Vuolo F, D'Urso G. 2011. Evaluation of Sentinel-2 spectral sampling for radiative transfer model based LAI estimation of wheat, sugar beet, and maize. IEEE J Sel Top Appl Earth Observations Remote Sensing. 4(2):458–464.
   Web of Science ®Google Scholar
• Rouse W, Haas H, Deering W. 1974. Monitoring vegetation systems in the great plains with ERTS. Third Earth Resour Technol Satell Symp. 1:309–317.
   Google Scholar
• Sarker LR, Nichol JE. 2011. Improved forest biomass estimates using ALOS AVNIR-2 texture indices. Remote Sens Environ. 115(4):968–977.
   Web of Science ®Google Scholar
• Song G, Wang Q, Jin J. 2021. Including leaf trait information helps empirical estimation of jmax from vcmax in cool-temperate deciduous forests. Plant Physiol Biochem. 166:839–848.
   PubMed Web of Science ®Google Scholar
• Sonobe R, Wang Q. 2017. Towards a universal hyperspectral index to assess chlorophyll content in deciduous forests. Remote Sens. 9(3):191.
   Google Scholar
• Stenberg P, Rautiainen M, Manninen T, Voipio P, Smolander H. 2004. Reduced simple ratio better than NDVI for estimating LAI in Finnish pine and spruce stands. Silva Fennica. 38(1):3–14.
   Google Scholar
• Szantoi Z, Strobl P. 2019. Copernicus Sentinel-2 calibration and validation. Eur J Remote Sens. 52(1):253–255.
   Web of Science ®Google Scholar
• Wang F, Yi Q, Hu J, Xie L, Yao X, Xu T, Zheng J. 2021. Combining spectral and textural information in UAV hyperspectral images to estimate rice grain yield. Int J Appl Earth Obs Geoinf. 102(1):102397.
   Google Scholar
• Wang H, Zhao Y, Pu R, Zhang Z. 2015. Mapping Robinia pseudoacacia forest health conditions by using combined spectral, spatial, and textural information extracted from IKONOS imagery and random forest classifier. Remote Sens. 7(7):9020–9044.
   Google Scholar
• Wold S, Sjöström M, Eriksson L. 2001. PLS-regression: A basic tool of chemometrics. Chemom Intell Lab Syst. 58(2):109–130.
   Web of Science ®Google Scholar
• Wood EM, Pidgeon AM, Radeloff VC, Keuler NS. 2012. Image texture as a remotely sensed measure of vegetation structure. Remote Sens Environ. 121:516–526.
   Web of Science ®Google Scholar
• Wulder MA, LeDrew EF, Franklin SE, Lavigne MB. 1998. Aerial image texture information in the estimation of northern deciduous and mixed wood forest leaf area index (LAI). Remote Sens Environ. 64(1):64–76.
   Web of Science ®Google Scholar
• Xie Q, Huang W, Zhang B, Chen P, Song X, Pascucci S, Pignatti S, Laneve G, Dong Y. 2016. Estimating Winter Wheat Leaf Area Index from Ground and Hyperspectral Observations Using Vegetation Indices. IEEE J Sel Top Appl Earth Observations Remote Sensing. 9(2):771–780.
   Web of Science ®Google Scholar
• Yan G, Hu R, Luo J, Weiss M, Jiang H, Mu X, Xie D, Zhang W. 2019. Review of indirect optical measurements of leaf area index: Recent advances, challenges, and perspectives. Agric For Meteorol. 265:390–411.
   Web of Science ®Google Scholar
• Yin G, Li J, Liu Q, Fan W, Xu B, Zeng Y, Zhao J. 2015. Regional leaf area index retrieval based on remote sensing: The role of radiative transfer model selection. Remote Sens. 7(4):4604–4625.
   Google Scholar
• Yue J, Yang G, Tian Q, Feng H, Xu K, Zhou C. 2019. Estimate of winter-wheat above-ground biomass based on UAV ultrahigh-ground-resolution image textures and vegetation indices. ISPRS J Photogramm Remote Sens. 150:226–244.
   Web of Science ®Google Scholar
• Zhang Y, Chen JM, Miller JR. 2005. Determining digital hemispherical photograph exposure for leaf area index estimation. Agric for Meteorol. 133(1-4):166–181.
   Web of Science ®Google Scholar
• Zheng G, Moskal LM. 2009. Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors. Sensors (Basel)). 9(4):2719–2745.
   PubMed Web of Science ®Google Scholar
• Zheng H, Cheng T, Zhou M, Li D, Yao X, Tian Y, Cao W, Zhu Y. 2019. Improved estimation of rice aboveground biomass combining textural and spectral analysis of UAV imagery. Precision Agric. 20(3):611–629.
   Web of Science ®Google Scholar
• Zheng H, Du P, Chen J. 2017. Performance Evaluation of Downscaling Sentinel-2 Imagery for Land Use and Land Cover Classification by Spectral-Spatial Features. Remote Sens. 9:1–17.
   Google Scholar
• Zheng H, Ma J, Zhou M, Li D, Yao X, Cao W, Zhu Y, Cheng T. 2020. Enhancing the nitrogen signals of rice canopies across critical growth stages through the integration of textural and spectral information from unmanned aerial vehicle (UAV) multispectral imagery. Remote Sens. 12(6):957.
   Google Scholar
• Zhou J, Yan Guo R, Sun M, Di TT, Wang S, Zhai J, Zhao Z. 2017. The Effects of GLCM parameters on LAI estimation using texture values from Quickbird Satellite Imagery. Sci Rep. 7(1):1–12.
   PubMedGoogle Scholar
• Zhu W, Xiang W, Pan Q, Zeng Y, Ouyang S, Lei P, Deng X, Fang X, Peng C. 2016. Spatial and seasonal variations of leaf area index (LAI) in subtropical secondary forests related to floristic composition and stand characters. Biogeosciences. 13(12):3819–3831.
   Web of Science ®Google Scholar
• Zhu X, Skidmore AK, Wang T, Liu J, Darvishzadeh R, Shi Y, Premier J, Heurich M. 2018. Improving leaf area index (LAI) estimation by correcting for clumping and woody effects using terrestrial laser scanning. Agric for Meteorol. 263:276–286.
   Web of Science ®Google Scholar
• Zvoleff A. 2019. GLCM: Calculate Textures from Grey-Level Co-Occurrence Matrices (GLCMs)https://github.com/azvoleff/glcm
   Google Scholar