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Advances in Leaf Area Index Estimation: Methods, Products, and Applications
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Advances in Leaf Area Index Estimation: Methods, Products, and Applications

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 8715

Special Issue Editor

Dr. Lin Du

E-Mail Website
Guest Editor
School of Geography and Information Engineering, China University of Geoscience, Wuhan 430079, China
Interests: laser scanning; vegetation parameter retrieval (chlorophyll, biomass, LAI, etc.); bio-physical models for vegetation simulation; data assimilation for vegetation estimation

Special Issue Information

Dear Colleagues,

Leaf Area Index (LAI) is significant indicator for vegetation growing status, and it is also the key parameter of vegetation canopy in response to global change. Meanwhile, LAI relates to many bio-physical processes of vegetation, i.e., photosynthesis, respiration, transpiration, carbon cycle, and precipitation interception. There are various methods in LAI estimation, especially with the development of advanced technology in remote sensing such as lidar, hyperspectral imaging, and some methods with theoretical models These developments bring some great opportunities and challenges. Therefore, the main goal of this Special Issue is to summarize the development achievements of LAI estimation, discuss and look forward to the future development path, and promote the rapid application of LAI products in different fields.

This Special Issue encourages the methods and products of LAI for retrieving vegetation parameters, monitoring environmental quality, and estimating vegetation growing status, among others. Additionally, processing and applications of LAI products in landslides, earthquakes, smart cities, or ecological remediation in the ecologically vulnerable areas (like mines, disater-hit area etc.) are welcome. Related hardware design and retrieval algorithms for ground, airborne, and space-based sensors for LAI estimation are all encouraged. Furthermore, data fusion and assimilation approaches for acquiring new LAI data with higher accuracy and temporal and spatial resolution are encouraged. In summary, we invite submissions exploring cutting-edge research and recent advances in the fields of LAI estimation in this Special Issue; both theoretical and experimental studies are welcome, as well as comprehensive review and survey papers.

Dr. Lin Du
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • LAI retrieval with advanced algorithm, theory, and technology
  • LAI products with high temporal and spatial resolution
  • LAI application in environment, climate, ecosystem, and smart city
  • advanced sensors for LAI estimation
  • relationship of LAI to other vegetation parameter (biomass, chlorophyll content, etc.)
  • LAI mapping in global or larger scale

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Published Papers (3 papers)

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Research

21 pages, 15312 KiB  
Article
Enhancing Leaf Area Index Estimation with MODIS BRDF Data by Optimizing Directional Observations and Integrating PROSAIL and Ross–Li Models
by Hu Zhang, Xiaoning Zhang, Lei Cui, Yadong Dong, Yan Liu, Qianrui Xi, Hongtao Cao, Lei Chen and Yi Lian
Remote Sens. 2023, 15(23), 5609; https://doi.org/10.3390/rs15235609 - 2 Dec 2023
Cited by 2 | Viewed by 2749
Abstract
The Leaf Area Index (LAI) is a crucial vegetation parameter for climate and ecological models. Reflectance anisotropy contains valuable supplementary information for the retrieval of properties of an observed target surface. Previous studies have utilized multi-angular reflectance data and physically based Bidirectional Reflectance [...] Read more.
The Leaf Area Index (LAI) is a crucial vegetation parameter for climate and ecological models. Reflectance anisotropy contains valuable supplementary information for the retrieval of properties of an observed target surface. Previous studies have utilized multi-angular reflectance data and physically based Bidirectional Reflectance Distribution Function (BRDF) models with detailed vegetation structure descriptions for LAI estimation. However, the optimal selection of viewing angles for improved inversion results has received limited attention. By optimizing directional observations and integrating the PROSAIL and Ross–Li models, this study aims to enhance LAI estimation from MODIS BRDF data. A dataset of 20,000 vegetation parameter combinations was utilized to identify the directions in which the PROSAIL model exhibits higher sensitivity to LAI changes and better consistency with the Ross–Li BRDF models. The results reveal significant variations in the sensitivity of the PROSAIL model to LAI changes and its consistency with the Ross–Li model over the viewing hemisphere. In the red band, directions with high sensitivity to LAI changes and strong model consistency are mainly found at smaller solar and viewing zenith angles. In the near-infrared band, these directions are distributed at positions with larger solar and viewing zenith angles. Validation using field measurements and LAI maps demonstrates that the proposed method achieves comparable accuracy to an algorithm utilizing 397 viewing angles by utilizing reflectance data from only 30 directions. Moreover, there is a significant improvement in computational efficiency. The accuracy of LAI estimation obtained from simulated multi-angle data is relatively high for LAI values below 3.5 when compared with the MODIS LAI product from two tiles. Additionally, there is also a slight improvement in the results when the LAI exceeds 4.5. Overall, our results highlight the potential of utilizing multi-angular reflectance in specific directions for vegetation parameter inversion, showcasing the promise of this method for large-scale LAI estimation. Full article
(This article belongs to the Special Issue Advances in Leaf Area Index Estimation: Methods, Products, and Applications)
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19 pages, 10310 KiB  
Article
AM–GM Algorithm for Evaluating, Analyzing, and Correcting the Spatial Scaling Bias of the Leaf Area Index
by Jingyu Zhang, Rui Sun, Zhiqiang Xiao, Liang Zhao and Donghui Xie
Remote Sens. 2023, 15(12), 3068; https://doi.org/10.3390/rs15123068 - 12 Jun 2023
Cited by 1 | Viewed by 1616
Abstract
The leaf area index (LAI) is a crucial variable in climate, ecological, and land surface modeling. However, the estimation of the LAI from coarse-resolution remote sensing data can be affected by the spatial scaling bias, which arises from the nonlinearity of retrieval models [...] Read more.
The leaf area index (LAI) is a crucial variable in climate, ecological, and land surface modeling. However, the estimation of the LAI from coarse-resolution remote sensing data can be affected by the spatial scaling bias, which arises from the nonlinearity of retrieval models and the heterogeneity of the land surface. This study provides an algorithm named Arithmetic Mean and Geometric Mean (AM–GM) to correct the spatial scaling bias. It is established based on negative logarithmic functions and avoids second-order stationarity. In this algorithm, relationships are derived between the scaling bias of LAI and the arithmetic and geometric means of directional gap probability for two commonly used remote sensing models, the Beer–Lambert law and a semi-empirical transfer function, respectively. According to the AM–GM algorithm, the expression representing the model nonlinearity is derived and utilized for the analysis of LAI scaling bias. Furthermore, the AM–GM algorithm is simplified by a linear relationship, which is constructed between two quantities related to the directional gap probability between two specific resolutions. Two scenes simulated by the LargE-Scale remote sensing data and image Simulation framework (LESS) model and three sites are used to evaluate the proposed algorithm and analyze the scaling bias of LAI. The validation results show that the AM–GM algorithm provides accurate correction of LAI scaling bias. The analyses based on the AM–GM algorithm demonstrate that the scaling bias of LAI increases with the increase in the LAI value, with stronger surface heterogeneity and coarser spatial resolution. The validation results of the simplified AM–GM algorithm demonstrate that at the Sud-Ouest site, the absolute value of the bias for the estimated LAI decreases from 0.10, 0.22, 0.29, and 0.31 to 0.04, 0.01, 0.04, and 0.05 at 200 m, 500 m, 1000 m, and 1500 m resolutions, respectively. In conclusion, the proposed algorithm is effective in the analysis and correction of the scaling bias for coarse-resolution LAI. Full article
(This article belongs to the Special Issue Advances in Leaf Area Index Estimation: Methods, Products, and Applications)
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Graphical abstract

22 pages, 3539 KiB  
Article
Quantitative Monitoring of Leaf Area Index in Rice Based on Hyperspectral Feature Bands and Ridge Regression Algorithm
by Shu Ji, Chen Gu, Xiaobo Xi, Zhenghua Zhang, Qingqing Hong, Zhongyang Huo, Haitao Zhao, Ruihong Zhang, Bin Li and Changwei Tan
Remote Sens. 2022, 14(12), 2777; https://doi.org/10.3390/rs14122777 - 9 Jun 2022
Cited by 19 | Viewed by 3206
Abstract
Leaf area index (LAI) is one of the indicators measuring the growth of rice in the field. LAI monitoring plays an important role in ensuring the stable increase of grain yield. In this study, the canopy reflectance spectrum of rice was obtained by [...] Read more.
Leaf area index (LAI) is one of the indicators measuring the growth of rice in the field. LAI monitoring plays an important role in ensuring the stable increase of grain yield. In this study, the canopy reflectance spectrum of rice was obtained by ASD at the elongation, booting, heading and post-flowering stages of rice, and the correlations between the original reflectance (OR), first-derivative transformation (FD), reciprocal transformation (1/R), and logarithmic transformation (LOG) with LAI were analyzed. Characteristic bands of spectral data were then selected based on the successive projections algorithm (SPA) and Pearson correlation. Moreover, ridge regression (RR), partial least squares (PLS), and multivariate stepwise regression (MSR) were conducted to establish estimation models based on characteristic bands and vegetation indices. The research results showed that the correlation between canopy spectrum and LAI was significantly improved after FD transformation. Modeling using SPA to select FD characteristic bands performed better than using Pearson correlation. The optimal modeling combination was FD-SPA-VI-RR, with the coefficient of determination (R2) of 0.807 and the root-mean-square error (RMSE) of 0.794 for the training set, R2 of 0.878 and RMSE of 0.773 for the validation set 1, and R2 of 0.705 and RMSE of 1.026 for the validation set 2. The results indicated that the present model may predict the rice LAI accurately, meeting the requirements of large-scale statistical monitoring of rice growth indicators in the field. Full article
(This article belongs to the Special Issue Advances in Leaf Area Index Estimation: Methods, Products, and Applications)
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