Forest and Crop Leaf Area Index Estimation Using Remote Sensing: Research Trends and Future Directions

Abstract

Leaf area index (LAI) is an important vegetation leaf structure parameter in forest and agricultural ecosystems. Remote sensing techniques can provide an effective alternative to field-based observation of LAI. Differences in canopy structure result in different sensor types (active or passive), platforms (terrestrial, airborne, or satellite), and models being appropriate for the LAI estimation of forest and agricultural systems. This study reviews the application of remote sensing-based approaches across different system configurations (passive, active, and multisource sensors on different collection platforms) that are used to estimate forest and crop LAI and explores uncertainty analysis in LAI estimation. A comparison of the difference in LAI estimation for forest and agricultural applications given the different structure of these ecosystems is presented, particularly as this relates to spatial scale. The ease of use of empirical models supports these as the preferred choice for forest and crop LAI estimation. However, performance variation among different empirical models for forest and crop LAI estimation limits the broad application of specific models. The development of models that facilitate the strategic incorporation of local physiology and biochemistry parameters for specific forests and crop growth stages from various temperature zones could improve the accuracy of LAI estimation models and help develop models that can be applied more broadly. In terms of scale issues, both spectral and spatial scales impact the estimation of LAI. Exploration of the quantitative relationship between scales of data from different sensors could help forest and crop managers more appropriately and effectively apply different data sources. Uncertainty coming from various sources results in reduced accuracy in estimating LAI. While Bayesian approaches have proven effective to quantify LAI estimation uncertainty based on the uncertainty of model inputs, there is still a need to quantify uncertainty from remote sensing data source, ground measurements and related environmental factors to mitigate the impacts of model uncertainty and improve LAI estimation.

A Review of Hybrid Approaches for Quantitative Assessment of Crop Traits Using Optical Remote Sensing: Research Trends and Future Directions

“…The accuracy of estimates using VIs as model inputs can be affected if the study does not identify an appropriate index through model inversion [64]. Further information about this technique presenting the general concept of VIs can be found in these studies: [65][66][67] for LAI, ref. [68] for fCover, and [69] for CC.…”

Evaluating Optical Remote Sensing Methods for Estimating Leaf Area Index for Corn and Soybean

“…There are several review research studies on LAI estimation using remote sensing [47][48][49][50][51][52]. These studies show the importance, limitations, and strategies to improve the accuracy of each of these methods [49,[53][54][55].…”

“…These studies show the importance, limitations, and strategies to improve the accuracy of each of these methods [49,[53][54][55]. Most of these studies focused on forest cover, and very few reviewed the existing LAI estimation methods for cropland using optical remote sensing including multispectral, hyperspectral and LiDAR [47,52]. All of the existing studies focused on reviewing existing studies and underscoring the advantages and limitations of current approaches.…”

Comparison of Spaceborne and UAV-Borne Remote Sensing Spectral Data for Estimating Monsoon Crop Vegetation Parameters

“…When considering other photosynthetically-active plant parts besides the leaves, it is called the green area index or plant area index [ 8 ]. Crop LAI estimation using RS reflectance data and empirical modelling approaches (both parametric and non-parametric) have shown promising results, but also considerable variation in prediction quality (coefficient of determination (R 2 ) ranges from 0.36 to 0.97) [ 16 ].…”

“…However, RS data application has not been examined for crops like finger millet and lablab, which are major monsoon crops in the tropical region (e.g., Bengaluru, Southern India). Furthermore, few studies have compared different remote sensing platforms (e.g., in-situ vs airborne vs spaceborne) and sensors (multispectral vs hyperspectral) for crop vegetation parameters estimation [ 16 , 28 ]. Thus, this study sought to fill the identified research and knowledge gap for RS for monsoon crop monitoring.…”