Education, Science, Technology, Innovation and Life
Open Access
Sign In
  1. Home
  2. Journals
  3. Advances in Computer, Signals and Systems
  4. Vol 8, Issue 5, 2024

Estimating LAI and uncertainty in grassland using UAV hyperspectral data and PROSAIL

Download as PDF

DOI: 10.23977/acss.2024.080502 | Downloads: 20 | Views: 1078

Author(s)

Zixiao Ding 1,2, Xiaohua Zhu 1, Lingling Ma 1, Yongguang Zhao 1

Affiliation(s)

1 National Engineering Laboratory for Satellite Remote Sensing Applications, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China
2 University of Chinese Academy of Sciences, Beijing, 100094, China

Corresponding Author

Zixiao Ding

ABSTRACT

Leaf area index is an important structural parameter that characterizes vegetation growth conditions. Quickly and accurately obtaining the leaf area index of grassland vegetation can provide support for grassland ecosystem assessment and terrestrial carbon cycle research. Based on PROSAIL model and UAV hyperspectral data, this study established a LAI inversion model for natural grassland through feature band selection, PROSAIL parameter sensitivity analysis, lookup table combined with cost function. Through comparison with the measured data on the ground, the accuracy of LAI inversion was R2=0.8289, RMSE=0.3921m2/ m2. Based on this, the possibility of improving the inversion model is proposed by analyzing the uncertainty of the image reflectance. After adding 5% Gaussian noise to ESI, the inversion accuracy is R2=0.8394 and RMSE=0.3900 m2/ m2, which proves the accuracy of uncertainty analysis.

KEYWORDS

Leaf area index, Hyperspectral, PROSAIL, Uncertainty

CITE THIS PAPER

Zixiao Ding, Xiaohua Zhu, Lingling Ma, Yongguang Zhao, Estimating LAI and uncertainty in grassland using UAV hyperspectral data and PROSAIL. Advances in Computer, Signals and Systems (2024) Vol. 8: 13-22. DOI: http://dx.doi.org/10.23977/acss.2024.080502.

REFERENCES

[1] Asam S, Verrelst J, Klein D, et al. Leaf Area Index derivation from hyperspectral and multispectral remote sensing data in heterogeneous grassland[C]//9th EARSeL SIG Imaging Spectroscopy workshop.2015.
[2] Duan S B, Li Z L, Wu H, et al. Inversion of the PROSAIL model to estimate leaf area index of maize, potato, and sunflower fields from unmanned aerial vehicle hyperspectral data[J]. International Journal of Applied Earth Observations & Geoinformation, 2014, 26:12-20.DOI:10.1016/j.jag.2013.05.007.
[3] Pignatti S, Casa R, Harfouche A, et al. Maize Crop and Weeds Species Detection by Using Uav Vnir Hyperpectral Data[C]// IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2019.DOI:10.1109/IGARSS.2019.8900241.
[4] Pan Y, Wu W, Zhang J, et al. Estimating leaf nitrogen and chlorophyll content in wheat by correcting canopy structure effect through multi-angular remote sensing[J]. Computers and Electronics in Agriculture, 2023:208.
[5] Singh M S. Hyperspectral Data-Based Waveform Parameters Response for Forest Leaf Area Index[J]. Climate Change and Environmental Sustainability, 2018, 6(1).
[6] Song X N, Ma J W, Li X T, et al. Estimation of Vegetation Canopy Water Content Using Hyperion Hyperspectral Data[J]. Spectroscopy & Spectral Analysis, 2013, 33(10):2833.DOI:info:doi/10.3964/j.issn.1000-0593(2013)10-2833-05.
[7] Zhu X , Li C , Tang L ,et al. Retrieval and scale effect analysis of LAI over typical farmland from UAV-based hyperspectral data[C]//Remote Sensing for Agriculture, Ecosystems, and Hydrology XXI.2019.DOI:10.1117/12.2535478.
[8] Du R, Chen J, Xiang Y, et al. Incremental learning for crop growth parameters estimation and nitrogen diagnosis from hyperspectral data[J]. Computers and Electronics in Agriculture, 2023:215.
[9] Wang L. Leaf Area Index Estimation Algorithm for GF-5 Hyperspectral Data Based on Different Feature Selection and Machine Learning Methods[J]. Remote Sensing, 2020, 12.DOI:10.3390/rs12132110. 
[10] Locherer M, Hank T, Danner M, et al. Systematic analysis of the LUT-based inversion of PROSAIL using full range hyperspectral data for the retrieval of leaf area index in view of the future EnMAP mission[J]. IEEE, 2015.DOI:10.1109/IGARSS.2015.7326705.
[11] Daniel, Doktor, Angela, et al. Extraction of Plant Physiological Status from Hyperspectral Signatures Using Machine Learning Methods[J]. Remote Sensing, 2014.DOI:10.3390/rs61212247.
[12] Chen H, Huang W, Li W, et al. Estimation of LAI in Winter Wheat from Multi-Angular Hyperspectral VNIR Data: Effects of View Angles and Plant Architecture[J]. Remote Sensing, 2018, 10(10):1630-. DOI:10.3390/rs10101630.
[13] Sahoo R N, Rejith R G, Gakhar S, et al. Estimation of wheat biophysical variables through UAV hyperspectral remote sensing using machine learning and radiative transfer models[J]. Computers and Electronics in Agriculture, 2024, 221. DOI:10.1016/j.compag.2024.108942. 
[14] Zhao Y, Sun Y, Lu X, et al. Hyperspectral retrieval of leaf physiological traits and their links to ecosystem productivity in grassland monocultures[J]. Ecological Indicators, 2021, 122:107267. DOI:10.1016/j.ecolind.2020.107267.
[15] Chen Q, Zheng B, Chen T, et al. Integration of APSIM and PROSAIL models to develop more precise radiometric estimation of crop traits using deep learning[J]. Cold Spring Harbor Laboratory, 2021.DOI:10.1101/2021.02.02.429471.

Subscription
E-Mail Alert
Most popular papers
Call for Special Issue
Publication Ethics & OA Statement
Plagiarism Policy
Contact Us
Downloads: 38553
Visits: 697951

Sponsors, Associates, and Links

All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.