Skip to main content
SpringerLink
Log in

Moderate resolution LAI prediction using Sentinel-2 satellite data and indirect field measurements in Sikkim Himalaya

  • Published:
Environmental Monitoring and Assessment Aims and scope Submit manuscript

Abstract

The leaf area index (LAI) has been traditionally used as a photosynthetic variable. LAI plays an essential role in forest cover monitoring and has been identified as one of the important climate variables. However, due to challenges in field sampling, complex topography, and availability of cloud-free optical satellite data, LAI assessment on larger scale is still unexplored in the Sikkim Himalayan area. We used two optical instruments, digital hemispherical photography (DHP) and LAI-2200C, to assess the LAI across four different forests following 20 × 20 m2 elementary sampling units (ESUs) in the Himalayan state of Sikkim, India. The use of Sentinel-2 derived vegetation indices (VIs) demonstrated a better correlation with the DHP based LAI estimates than using LAI-2200C. Further, the combination of both reflectance bands and VIs were integrated to predict the LAI maps using random forest model. The temperate evergreen forests demonstrated the highest LAI value, while the predicted maps exhibited LAI maxima of 3.4. The estimated vs predicted LAI for DHP and LAI-2200C based estimation demonstrated reasonably good (R2 = 0.63 and R2 = 0.68, respectively) agreement. Further, improvements on the LAI prediction can be attempted by minimizing errors from the inherent field protocols, optimizing the density of field measurements, and representing heterogeneity. The recent rise of frequent forest fires in Sikkim Himalaya prompts for better understanding of fuel load in terms of surface fuel or canopy fuel that can be linked to LAI. The high-resolution LAI map could serve as input to forest fuel bed characterization, especially in seasonal forests with significant variations in green leaves and litter, thereby offering inputs for forest management in changing climate.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Data availability

Data were derived from the following resources available in the public domain: [https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR].

References

  • Arroyo-Rodríguez, V., Melo, F. P. L., Martínez-Ramos, M., Bongers, F., Chazdon, R. L., Meave, J. A., Norden, N., Santos, B. A., Leal, I. R., & Tabarelli, M. (2017). Multiple successional pathways in human-modified tropical landscapes: New insights from forest succession, forest fragmentation and landscape ecology research. Biological Reviews, 92(1), 326–340. https://doi.org/10.1111/brv.12231

    Article  Google Scholar 

  • Barik, S. K., & Behera, M. D. (2020). Studies on ecosystem function and dynamics in Indian sub-continent and emerging applications of Satellite remote sensing technique. Tropical Ecology, 61(1), 1–4. https://doi.org/10.1007/s42965-020-00078-6

    Article  CAS  Google Scholar 

  • Basu, R., Misra, G., & Sarkar, D. (2021). A remote sensing based analysis of climate change in Sikkim supported by evidence from the field. Journal of Mountain Science, 18(5), 1256–1267.

    Article  Google Scholar 

  • Behera, M. D., Barnwal, S., Paramanik, S., Das, P., Bhattyacharya, B. K., Jagadish, B., Roy, P. S., Ghosh, S. M., & Behera, S. K. (2021). Species-level classification and mapping of a mangrove forest using random forest—Utilisation of AVIRIS-NG and sentinel data. Remote Sensing, 13(11). https://doi.org/10.3390/rs13112027

  • Behera, M. D., Kushwaha, S. P. S., & Roy, P. S. (2001). Forest vegetation characterization & mapping using IRS-1C satellite images in eastern Himalayan region. Geocarto International, 16(3), 53–62. https://doi.org/10.1080/10106040108542204

    Article  Google Scholar 

  • Behera, M. D., Neeti, A. J., & Jayaraman, V. (2005). Analysis of land cover change and misregistration bias in North District of Sikkim, India. International Journal of Geoinformatics, 1(4).

  • Behera, S. K., Behera, M., & Tuli, R. (2015). An indirect method of estimating leaf area index in a tropical deciduous forest of India. Ecological Indicators, 58, 356–364.

    Article  Google Scholar 

  • Belgiu, M., & Drăgu, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24–31. https://doi.org/10.1016/j.isprsjprs.2016.01.011

    Article  Google Scholar 

  • Breda, N. J. (2003). Ground-based measurements of leaf area index: A review of methods, instruments and current controversies. Journal of Experimental Botany, 54(392), 2403–2417.

    Article  CAS  Google Scholar 

  • Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324

    Article  Google Scholar 

  • Cescatti, A. (2007). Indirect Estimates of Canopy Gap Fraction Based on the Linear Conversion of Hemispherical Photographs Methodology and Comparison with Standard Thresholding Techniques., 143, 1–12. https://doi.org/10.1016/j.agrformet.2006.04.009

    Article  Google Scholar 

  • Chen, J. M., & Black, T. A. (1991). Defining leaf area index for non-flat leaves. 421–429.

  • Chhabra, A., & Panigrahy, S. (2011). Analysis of spatio-temporal patterns of leaf area index in different forest types of India using high temporal remote sensing data. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 38(8), W20.

    Google Scholar 

  • Claverie, M., Vermote, E. F., 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 Sensing of Environment, 139, 216–230.

    Article  Google Scholar 

  • Cohen, W. B., Maiersperger, T. K., Gower, S. T., & Turner, D. P. (2003). An improved strategy for regression of biophysical variables and Landsat ETM + data. 84, 561–571.

  • Danner, M., Locherer, M., Hank, T., Richter, K., Consortium, E., et al. (2015). Measuring Leaf Area Index (LAI) with the LI-Cor LAI 2200C or LAI-2200 (+ 2200Clear Kit)–theory, measurement, problems, interpretation.

  • Das, P., Behera, M. D., Barik, S. K., Mudi, S., Jagadish, B., Sarkar, S., Joshi, S. R., Adhikari, D., Behera, S. K., Sarma, K., Srivastava, P. K., & Chauhan, P. S. (2022). Shifting cultivation induced burn area dynamics using ensemble approach in Northeast India. Trees, Forests and People, 7(100183), 1–10. https://doi.org/10.1016/j.tfp.2021.100183

    Article  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, 11(7), 7063–7081. https://doi.org/10.3390/s110707063

    Article  Google Scholar 

  • Dobrowski, S. Z. (2011). A climatic basis for microrefugia: The influence of terrain on climate. Global Change Biology, 17(2), 1022–1035.

    Article  Google Scholar 

  • Donohue, R. J., Roderick, M. L., & McVicar, T. R. (2006). On the importance of including vegetation dynamics in Budyko? s hydrological model. Hydrology and Earth System Sciences Discussions, 3(4), 1517–1551.

    Google Scholar 

  • Drusch, M., Bello, U. D., Carlier, S., Colin, O., Fernandez, V., Gascon, F., Hoersch, B., Isola, C., Laberinti, P., Martimort, P., Meygret, A., Spoto, F., Sy, O., Marchese, F., & Bargellini, P. (2012). Sentinel-2: ESA's optical high-resolution mission for GMES operational services. Remote Sensing of Environment, 120, 25–36. https://doi.org/10.1016/j.rse.2011.11.026

  • Duan, S.-B., Li, Z.-L., Wu, H., Tang, B.-H., Ma, L., Zhao, E., & Li, C. (2014). Inversion of the PROSAIL model to estimate leaf area index of maize, potato, and sunflower fields from unmanned aerial vehicle hyperspectral data. International Journal of Applied Earth Observation and Geoinformation, 26, 12–20.

    Article  Google Scholar 

  • Fan, L. Y., Gao, Y. Z., Brück, H. E. B. C., & Bernhofer, C. (2009). Investigating the relationship between NDVI and LAI in semi-arid grassland in Inner Mongolia using in-situ measurements. Theoretical and Applied Climatology, 95(1), 151–156.

    Article  Google Scholar 

  • Fang, H., Baret, F., Plummer, S., & Schaepman-Strub, G. (2019). An overview of global Leaf Area Index (LAI): Methods, products, validation, and applications. Reviews of Geophysics, 57(3), 739–799. https://doi.org/10.1029/2018RG000608

    Article  Google Scholar 

  • Forest Survey of India (FSI). (2019). State of Forest Report. https://fsi.nic.in/forest-report

  • Garrigues, S., Lacaze, R., Baret, F., Morisette, J., Weiss, M., Nickeson, J., Fernandes, R., Plummer, S., Shabanov, N., Myneni, R., & others. (2008). Validation and intercomparison of global Leaf Area Index products derived from remote sensing data. Journal of Geophysical Research: Biogeosciences, 113(G2).

  • GCOS. (2021). The Status of the Global Climate Observing System 2021: The GCOS Status Report (GCOS-240), pub WMO, Geneva.

  • Genuer, R., Poggi, J. M., & Tuleau-Malot, C. (2010). Variable selection using random forests. Pattern Recognition Letters, 31(14), 2225–2236. https://doi.org/10.1016/j.patrec.2010.03.014

    Article  Google Scholar 

  • Ghosh, S. M., & Behera, M. D. (2021). Aboveground biomass estimates of tropical mangrove forest using Sentinel-1 SAR coherence data—The superiority of deep learning over a semi-empirical model. Computers & Geosciences, 150, 104737. https://doi.org/10.1016/j.cageo.2021.104737

    Article  Google Scholar 

  • Ghosh, S. M., Behera, M. D., & Paramanik, S. (2020). Canopy height estimation using sentinel series images through machine learning models in a mangrove forest. In Remote Sensing (Vol. 12). https://doi.org/10.3390/rs12091519

  • Gonsamo, A., & Pellikka, P. (2012). The sensitivity based estimation of leaf area index from spectral vegetation indices. ISPRS Journal of Photogrammetry and Remote Sensing, 70, 15–25. https://doi.org/10.1016/j.isprsjprs.2012.03.009

    Article  Google Scholar 

  • Grier, C. G., & Running, S. W. (1977). Leaf area of mature northwestern coniferous forests: Relation to site water balance. Ecology, 58(4), 893–899.

    Article  Google Scholar 

  • Hadi, K., & L., Hovi, A., Rönnholm, P., & Rautiainen, M. (2016). The accuracy of large-area forest canopy cover estimation using Landsat in boreal region. International Journal of Applied Earth Observation and Geoinformation, 53, 118–127. https://doi.org/10.1016/j.jag.2016.08.009

    Article  Google Scholar 

  • Hales, K., Neelin, J. D., & Zeng, N. (2004). Sensitivity of tropical land climate to leaf area index: Role of surface conductance versus albedo. Journal of Climate, 17(7), 1459–1473.

    Article  Google Scholar 

  • Hao, L., Pan, C., Liu, P., Zhou, D., Zhang, L., Xiong, Z., & Sun, G. (2016). Detection of the coupling between vegetation leaf area and climate in a multifunctional watershed. Northwestern China. Remote Sensing, 8(12), 1032.

    Article  Google Scholar 

  • Hardwick, S. R., Toumi, R., Pfeifer, M., Turner, E. C., Nilus, R., & Ewers, R. M. (2015). The relationship between leaf area index and microclimate in tropical forest and oil palm plantation: Forest disturbance drives changes in microclimate. Agricultural and Forest Meteorology, 201, 187–195.

    Article  Google Scholar 

  • Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295–309.

    Article  Google Scholar 

  • Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, L. G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1–2), 195–213.

    Article  Google Scholar 

  • Iiames, J. S., Congalton, R. G., Lewis, T. E., & Pilant, A. N. (2015). Uncertainty analysis in the creation of a fine-resolution leaf area index (LAI) reference map for validation of moderate resolution LAI products. Remote Sensing, 7(2), 1397–1421.

    Article  Google Scholar 

  • Jackson, R. D., & Huete, A. R. (1991). Interpreting vegetation indices. Preventive Veterinary Medicine, 11(3–4), 185–200.

    Article  Google Scholar 

  • Jonckheere, I., Nackaerts, K., Muys, B., & Coppin, P. (2005). Assessment of Automatic Gap Fraction Estimation of Forests from Digital Hemispherical Photography., 132, 96–114. https://doi.org/10.1016/j.agrformet.2005.06.003

    Article  Google Scholar 

  • Kalácska, M., Sánchez-Azofeifa, G. A., Rivard, B., Calvo-Alvarado, J. C., Journet, A. R. P., Arroyo-Mora, J. P., & Ortiz-Ortiz, D. (2004). Leaf area index measurements in a tropical moist forest: A case study from Costa Rica. Remote Sensing of Environment, 91(2), 134–152. https://doi.org/10.1016/j.rse.2004.02.011

    Article  Google Scholar 

  • Kamal, M., Phinn, S., & Johansen, K. (2016). Assessment of multi-resolution image data for mangrove leaf area index mapping. Remote Sensing of Environment, 176, 242–254. https://doi.org/10.1016/j.rse.2016.02.013

    Article  Google Scholar 

  • Kang, Y., Ozdogan, M., Gao, F., Anderson, M. C., White, W. A., Yang, Y., Yang, Y., & Erickson, T. A. (2021). A data-driven approach to estimate leaf area index for Landsat images over the contiguous US. Remote Sensing of Environment, 258, 112383. https://doi.org/10.1016/j.rse.2021.112383

    Article  Google Scholar 

  • Kim, J. B., Monier, E., Sohngen, B., Pitts, G. S., Drapek, R., McFarland, J., & Cole, J. (2017). Assessing climate change impacts, benefits of mitigation, and uncertainties on major global forest regions under multiple socioeconomic and emissions scenarios. Environmental Research Letters, 12(4), 045001.

    Article  Google Scholar 

  • Knudby, A., LeDrew, E., & Brenning, A. (2010). Predictive mapping of reef fish species richness, diversity and biomass in Zanzibar using IKONOS imagery and machine-learning techniques. Remote Sensing of Environment, 114(6), 1230–1241.

    Article  Google Scholar 

  • Korhonen, L., Korhonen, K. T., Stenberg, P., Maltamo, M., & Rautiainen, M. (2007). Local models for forest canopy cover with beta regression. Silva Fennica, 41(4), 671–685.

    Article  Google Scholar 

  • Le Maire, G., Marsden, C., Nouvellon, Y., Stape, J. -L., & Ponzoni, F. J. (2012). Calibration of a species-specific Spectral Vegetation Index for Leaf Area Index (LAI) monitoring: example with MODIS reflectance time-series on eucalyptus plantations. Remote Sensing, 4(12). https://doi.org/10.3390/rs4123766

  • Leblanc, S. G., Chen, J. M., Fernandes, R., Deering, D. W., & Conley, A. (2005). Methodology comparison for canopy structure parameters extraction from digital hemispherical photography in boreal forests. Agricultural and Forest Meteorology, 129(3–4), 187–207.

    Article  Google Scholar 

  • Li, X., Du, H., Mao, F., Zhou, G., Chen, L., Xing, L., Fan, W., Xu, X., Liu, Y., Cui, L., Li, Y., Zhu, D., & Liu, T. (2018). Estimating bamboo forest aboveground biomass using EnKF-assimilated MODIS LAI spatiotemporal data and machine learning algorithms. Agricultural and Forest Meteorology, 256–257, 445–457. https://doi.org/10.1016/j.agrformet.2018.04.002

    Article  Google Scholar 

  • Liu, J., Pattey, E., & Jégo, G. (2012). Assessment of vegetation indices for regional crop green LAI estimation from Landsat images over multiple growing seasons. Remote Sensing of Environment, 123, 347–358. https://doi.org/10.1016/j.rse.2012.04.002

    Article  Google Scholar 

  • Macfarlane, C. (2011). Agricultural and Forest Meteorology Classification method of mixed pixels does not affect canopy metrics from digital images of forest overstorey. Agricultural and Forest Meteorology, 151(7), 833–840. https://doi.org/10.1016/j.agrformet.2011.01.019

    Article  Google Scholar 

  • Macfarlane, C., Hoffman, M., Eamus, D., Kerp, N., Higginson, S., Mcmurtrie, R., & Adams, M. (2007). Estimation of Leaf Area Index in Eucalypt Forest Using Digital Photography., 143, 176–188. https://doi.org/10.1016/j.agrformet.2006.10.013

    Article  Google Scholar 

  • Mahanand, S., Behera, M. D. & Roy, P. S. (2022). Rapid assessment of plant diversity using MODIS biophysical proxies. Journal of Environmental Management, 311:1. 114778. 1–16. https://doi.org/10.1016/j.jenvman.2022.114778

  • Malhi, R. K. M., Anand, A., Srivastava, P. K., Chaudhary, S. K., Pandey, M. K., Behera, M. D., Kumar, A., Singh, P., & Sandhya Kiran, G. (2021). Synergistic evaluation of Sentinel 1 and 2 for biomass estimation in a tropical forest of India. Advances in Space Research. https://doi.org/10.1016/j.asr.2021.03.035

    Article  Google Scholar 

  • Malhi, Y., Gardner, T. A., Goldsmith, G. R., Silman, M. R., & Zelazowski, P. (2014). Tropical Forests in the Anthropocene. Annual Review of Environment and Resources, 39(1), 125–159. https://doi.org/10.1146/annurev-environ-030713-155141

    Article  Google Scholar 

  • Meyer, L. H., 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 Sensing, 11(10). https://doi.org/10.3390/rs11101160

  • Mousivand, A., Menenti, M., Gorte, B., & Verhoef, W. (2015). Multi-temporal, multi-sensor retrieval of terrestrial vegetation properties from spectral–directional radiometric data. Remote Sensing of Environment, 158, 311–330. https://doi.org/10.1016/j.rse.2014.10.030

    Article  Google Scholar 

  • Mudi, S., Behera, M. D., Paramanik, S., Prakash, A. J., & Prusty, B. K. (2021). Recent rise in wildfires in community forests and other natural vegetation: Geospatial basis. Current Science, 121(7), 891–893.

    Google Scholar 

  • Mutanga, O., Adam, E., & Cho, M. A. (2012). High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm. International Journal of Applied Earth Observation and Geoinformation, 18, 399–406.

    Article  Google Scholar 

  • Omer, G., Mutanga, O., Abdel-Rahman, E. M., & Adam, E. (2016a). Empirical prediction of Leaf Area Index (LAI) of endangered tree species in intact and fragmented indigenous forests ecosystems using WorldView-2 data and two robust machine learning algorithms. Remote Sensing, 8(4). https://doi.org/10.3390/rs8040324

  • Omer, G., Mutanga, O., Abdel-Rahman, E. M., & Adam, E. (2016b). Empirical prediction of Leaf Area Index (LAI) of endangered tree species in intact and fragmented indigenous forests ecosystems using WorldView-2 data and two robust machine learning algorithms. Remote Sensing, 8(4). https://doi.org/10.3390/rs8040324

  • Padalia, H., Sinha, S. K., Bhave, V., Trivedi, N. K., & Senthil Kumar, A. (2020). Estimating canopy LAI and chlorophyll of tropical forest plantation (North India) using Sentinel-2 data. Advances in Space Research, 65(1), 458–469. https://doi.org/10.1016/j.asr.2019.09.023

    Article  Google Scholar 

  • Paramanik, S., Behera, M. D., & Dash, J. (2022). Symbolic regression-based allometric model development of a mangrove forest LAI using structural variables and digital hemispherical photography. Applied Geography, 139, 102649.

    Article  Google Scholar 

  • Paramanik, S., Behera, M. D., Bhattacharya, B., & Tripathi, S. (2019). Evaluation and validation of the modis lai algorithm with digital hemispherical photography at bhitar kanika mangrove forest, india. IGARSS 2019–2019 IEEE International Geoscience and Remote Sensing Symposium, 6558–6561.

  • Piao, S., Yin, G., Tan, J., Cheng, L., Huang, M., Li, Y., & Wang, Y. (2015). Detection and attribution of vegetation greening trend in China over the last 30 years. Global Change Biology, 21(4), 1601–1609.

    Article  Google Scholar 

  • Pope, G., & Treitz, P. (2013). Leaf Area Index (LAI) estimation in boreal mixedwood forest of Ontario, Canada using Light detection and ranging (LiDAR) and worldview-2 imagery. Remote Sensing, 5(10), 5040–5063. https://doi.org/10.3390/rs5105040

    Article  Google Scholar 

  • Rodriguez-Galiano, V., Sanchez-Castillo, M., Chica-Olmo, M., & Chica-Rivas, M. (2015). Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines. Ore Geology Reviews, 71, 804–818.

    Article  Google Scholar 

  • Running, S. W., & Nemani, R. R. (1991). Regional hydrologic and carbon balance responses of forests resulting from potential climate change. Climatic Change, 19(4), 349–368.

    Article  CAS  Google Scholar 

  • Sanchez-Azofeifa, A., Antonio Guzmán, J., Campos, C. A., Castro, S., Garcia-Millan, V., Nightingale, J., & Rankine, C. (2017). Twenty-first century remote sensing technologies are revolutionizing the study of tropical forests. Biotropica, 49(5), 604–619. https://doi.org/10.1111/btp.12454

    Article  Google Scholar 

  • Sharma, R., Chaudhry, S., & Sharma, N. K. (2020). Litterfall Dynamics in different Forest Types of Kumaun Himalaya. Research & Reviews: Journal of Ecology, 9(1), 23–30.

    Google Scholar 

  • Sinha, S. K., Padalia, H., Dasgupta, A., Verrelst, J., & Rivera, J. P. (2020). Estimation of leaf area index using PROSAIL based LUT inversion, MLRA-GPR and empirical models: Case study of tropical deciduous forest plantation, North India. International Journal of Applied Earth Observation and Geoinformation, 86, 102027. https://doi.org/10.1016/j.jag.2019.102027

    Article  Google Scholar 

  • Sirri, N. F., Libalah, M. B., Momo Takoudjou, S., Ploton, P., Medjibe, V., Kamdem, N. G., Mofack, G., Sonké, B., & Barbier, N. (2019). Allometric models to estimate leaf area for tropical african broadleaved forests. Geophysical Research Letters, 46(15), 8985–8994. https://doi.org/10.1029/2019GL083514

    Article  Google Scholar 

  • Smith, A. M. S., Kolden, C. A., Tinkham, W. T., Talhelm, A. F., Marshall, J. D., Hudak, A. T., Boschetti, L., Falkowski, M. J., Greenberg, J. A., Anderson, J. W., Kliskey, A., Alessa, L., Keefe, R. F., & Gosz, J. R. (2014). Remote sensing the vulnerability of vegetation in natural terrestrial ecosystems. Remote Sensing of Environment, 154, 322–337. https://doi.org/10.1016/j.rse.2014.03.038

    Article  Google Scholar 

  • Srinet, R., Nandy, S., & 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. https://doi.org/10.1016/j.ecoinf.2019.05.008

    Article  Google Scholar 

  • Tesemma, Z. K., Wei, Y., Western, A. W., & Peel, M. C. (2014). Leaf area index variation for crop, pasture, and tree in response to climatic variation in the Goulburn-Broken catchment. Australia. Journal of Hydrometeorology, 15(4), 1592–1606.

    Article  Google Scholar 

  • Tian, Y., Woodcock, C. E., Wang, Y., Privette, J. L., Shabanov, N. V., Zhou, L., Zhang, Y., Buermann, W., Dong, J., Veikkanen, B., Häme, T., Andersson, K., Ozdogan, M., Knyazikhin, Y., & Myneni, R. B. (2002). Multiscale analysis and validation of the MODIS LAI product: I. Uncertainty Assessment. Remote Sensing of Environment, 83(3), 414–430. https://doi.org/10.1016/S0034-4257(02)00047-0

    Article  Google Scholar 

  • Tripathi, P., Patel, N. R., Kushwaha, S. P. S., & Dadhwal, V. K. (2014). Upscaling of leaf area index in Terai forest plantations using fine-and moderate-resolution satellite data. International Journal of Remote Sensing, 35(22), 7749–7762.

    Article  Google Scholar 

  • Turner, D. P., Cohen, W. B., Kennedy, R. E., Fassnacht, K. S., & Briggs, J. M. (1999). Relationships between Leaf Area Index and Landsat TM Spectral Vegetation Indices across Three Temperate Zone Sites. Remote Sensing of Environment, 70(1), 52–68. https://doi.org/10.1016/S0034-4257(99)00057-7

    Article  Google Scholar 

  • Wang, J., Xiao, X., Bajgain, R., Starks, P., Steiner, J., Doughty, R. B., & Chang, Q. (2019). Estimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images. ISPRS Journal of Photogrammetry and Remote Sensing, 154, 189–201. https://doi.org/10.1016/j.isprsjprs.2019.06.007

    Article  Google Scholar 

  • Watson, D. J. (1947). Comparative Physiological Studies on the Growth of Field Crops: I. Variation in net assimilation rate and leaf area between species and varieties, and within and between Years. Annals of Botany, 11(41), 41–76. JSTOR.

  • Wei, S., Yin, T., Dissegna, M. A., Whittle, A. J., Ow, G. L. F., Yusof, M. L. M., Lauret, N., & Gastellu-Etchegorry, J.-P. (2020). An assessment study of three indirect methods for estimating leaf area density and leaf area index of individual trees. Agricultural and Forest Meteorology, 292, 108101.

    Article  Google Scholar 

  • Weiss, M., & Baret, F. (2016). S2ToolBox Level 2 products: LAI, FAPAR, FCOVER. In Sentinel2 ToolBox Level2 Products.

  • Xie, R., Darvishzadeh, R., Skidmore, A. K., Heurich, M., Holzwarth, S., Gara, T. W., & Reusen, I. (2021). Mapping leaf area index in a mixed temperate forest using Fenix airborne hyperspectral data and Gaussian processes regression. International Journal of Applied Earth Observation and Geoinformation, 95, 102242.

    Article  Google Scholar 

  • Yin, G., Cao, B., Li, J., Fan, W., Zeng, Y., Xu, B., & Zhao, W. (2020). Path length correction for improving leaf area index measurements over sloping terrains: A deep analysis through computer simulation. IEEE Transactions on Geoscience and Remote Sensing, 58(7), 4573–4589.

    Article  Google Scholar 

  • Zhang, L., Hickel, K., Dawes, W. R., Chiew, F. H., Western, A. W., & Briggs, P. R. (2004). A rational function approach for estimating mean annual evapotranspiration. Water Resources Research, 40(2).

  • Zhao, Q., Yu, S., Zhao, F., Tian, L., & Zhao, Z. (2019). Comparison of machine learning algorithms for forest parameter estimations and application for forest quality assessments. Forest Ecology and Management, 434, 224–234.

    Article  Google Scholar 

Download references

Acknowledgements

MDB acknowledges the support from Meity, ISRO, and SERB-DST in form of research projects, and the authorities of IIT Kharagpur for instrumental support and facilities provided. SM acknowledges Meity, GOI for assistantship and support for field work; SP acknowledges MoE for a senior research fellowship to pursue PhD; AJP acknowledges ISRO (NISAR AO; and PhenoCam AO Project) for a junior research fellowship; NR acknowledges SERB-DST for a junior research fellowship; SK acknowledges MoE for a junior research fellowship to pursue PhD, respectively. The Sikkim state forest and wildlife department is thanked for permission to conduct field work.

Author information

Authors and Affiliations

  1. Centre for Oceans, Rivers, Atmosphere and Land Sciences, IIT Kharagpur, Kharagpur, 721302, India

    Sujoy Mudi, Somnath Paramanik, Mukunda Dev Behera, A Jaya Prakash, Nikhil Raj Deep & Shubham Kumar

  2. CDAC 3Rd Floor, RMZ Westend Center 3, Westend IT Park, Nagras Road, Aundh, Pune, 411007, India

    Manish P. Kale & Manoj Chavan

  3. Department of Science and Technology, Vigyan Bhawan, Deorali Gangtok, 737102, Sikkim, India

    Narpati Sharma, Prerna Pradhan & Dhiren G. Shrestha

  4. World Resources Institute, New Delhi, 110016, India

    Partha Sarathi Roy

Authors
  1. Sujoy Mudi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Somnath Paramanik
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mukunda Dev Behera
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. A Jaya Prakash
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Nikhil Raj Deep
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Manish P. Kale
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Shubham Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Narpati Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Prerna Pradhan
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Manoj Chavan
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Partha Sarathi Roy
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Dhiren G. Shrestha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Somnath Paramanik.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mudi, S., Paramanik, S., Behera, M.D. et al. Moderate resolution LAI prediction using Sentinel-2 satellite data and indirect field measurements in Sikkim Himalaya. Environ Monit Assess 194, 897 (2022). https://doi.org/10.1007/s10661-022-10530-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10661-022-10530-w

Keywords

Search

Navigation