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.
Highlights
Forest and agricultural systems are dominant components of the global ecosystem [1], and understanding how management actions impact their growth patterns [2,3] and their effect on global climate is important [4,5,6]
This search yielded 314 results from Web of Science and 590 from the Google Scholar database. These results were filtered to eliminate review papers, conference proceedings, Leaf area index (LAI) estimated for vegetation cover types not of interest, studies focused on LAI application for estimating other parameters, and ground-based LAI estimates from handheld devices
This paper reviews 225 studies from the past three decades that focused on LAI estimation; of these about 60% related to forests and 40% to crops (Table 2)
Summary
Forest and agricultural systems are dominant components of the global ecosystem [1], and understanding how management actions impact their growth patterns [2,3] and their effect on global climate is important [4,5,6]. Based on the gap fraction, which describes light penetration and the amount and distribution of openings in the canopy [27], indirect ground measurements quantify effective LAI (eLAI). Baret and Buis [32] described methods and challenges with canopy characteristic estimation from remote sensing observations, and suggested ways to improve retrieval performance, including using prior information, and incorporating spatial or temporal constraints. This paper updates and extends these prior studies by exploring recent advances of LAI estimation in forest and agricultural systems. Based on the synthesis of information from 190 papers published over the past three decades, we present the advantages, disadvantages, and research trends in the application of different sensor types and models, discuss scale and uncertainty issues, and propose future directions of LAI estimation to support forest and crop management
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