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

LAI estimation based on physical model combining airborne LiDAR waveform and Sentinel-2 imagery

Leaf area index (LAI) is an important biophysical parameter of vegetation and serves as a significant indicator for assessing forest ecosystems. Multi-source remote sensing data enables large-scale and dynamic surface observations, providing effective data for quantifying various indices in forest and evaluating ecosystem changes. However, employing single-source remote sensing spectral or LiDAR waveform data poses limitations for LAI inversion, making the integration of multi-source remote sensing data a trend. Currently, the fusion of active and passive remote sensing data for LAI inversion primarily relies on empirical models, which are mainly constructed based on field measurements and do not provide a good explanation of the fusion mechanism. In this study, we aimed to estimate LAI based on physical model using both spectral imagery and LiDAR waveform, exploring whether data fusion improved the accuracy of LAI inversion. Specifically, based on the physical model geometric-optical and radiative transfer (GORT), a fusion strategy was designed for LAI inversion. To ensure inversion accuracy, we enhanced the data processing by introducing a constraint-based EM waveform decomposition method. Considering the spatial heterogeneity of canopy/ground reflectivity ratio in regional forests, calculation strategy was proposed to improve this parameter in inversion model. The results showed that the constraint-based EM waveform decomposition method improved the decomposition accuracy with an average 12% reduction in RMSE, yielding more accurate waveform energy parameters. The proposed calculation strategy for the canopy/ground reflectivity ratio, considering dynamic variation of parameter, effectively enhanced previous research that relied on a fixed value, thereby improving the inversion accuracy that increasing on the correlation by 5% to 10% and on R2 by 62.5% to 132.1%. Based on the inversion strategy we proposed, data fusion could effectively be used for LAI inversion. The inversion accuracy achieved using both spectral and LiDAR data (correlation=0.81, R2 = 0.65, RMSE=1.01) surpassed that of using spectral data or LiDAR alone. This study provides a new inversion strategy for large-scale and high-precision LAI inversion, supporting the field of LAI research.

Drought and water-use efficiency are dominant environmental factors affecting greenness in the Yellow River Basin, China

Revegetation is accelerating globally because of its benefits in terms of ecosystem restoration, desertification prevention, and warming mitigation. The Yellow River Basin (YRB), as an ecological barrier in northern China, has implemented revegetation projects (such as the 'Grain for Green' program) for over two decades. However, a consensus on whether a significant change in greenness has been achieved and to what extent have environmental factors contributed to this change, as well as their importance ranking, is lacking. Leaf area index (LAI) is a critical indicator for estimating global greenness and projecting the dynamics of climate change. Herein, we apply four methods (Geodetector, random forest, multiple linear regression, and structural equation models) to explore the contribution of different environmental factors to greenness using the LAI in the YRB. We found that greenness has been increasing (greening over 67.22% (p < 0.05; 47.7%) of the YRB) with great spatial heterogeneity in the entire basin since 2000. Specifically, the greening process differed with elevation and slope. Temperature vegetation dryness index (TVDI) and water-use efficiency (WUE) dominated the greening; however, the three subregions evaluated revealed differing performance. In the upstream region, LAI increased by 0.031 y^-1. The primary positive factors of greening change were WUE and the annual highest value of daily minimum temperature; the negative factors were TVDI and the highest number of consecutive days when precipitation <1 mm. In the midstream region, LAI increased by 0.025 y^-1; greenness was mainly affected by the negative interaction of TVDI and the positive interaction of WUE. Annual maximum consecutive 5-day precipitation and annual count when daily minimum temperature < 0 °C had a great indirect impact on greenness, mainly through TVDI and WUE. In the downstream region, LAI increased by 0.045 y^-1, and the main driving factors were the annual lowest value of daily minimum temperature with a negative influence and the annual lowest value of daily maximum temperature with a positive influence. In addition, we found that the effect of the interaction of any two driving factors on greenness was greater than or equal to the single effect of a driving factor. This study concludes that drought and WUE are important predictors to evaluate the greenness in arid and semi-arid regions. We emphasise that the selection and assessment of greenness factors should follow a scientific and rigorous process rather than experience, and increased attention should be paid to the interaction of multiple factors. Furthermore, the perspective of system analysis will deepen our understanding of vegetation change in a vulnerable ecosystem.

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

Remote sensing technology allows to provide information about biochemical and biophysical crop traits and monitor their spatiotemporal dynamics of agriculture ecosystems. Among multiple retrieval techniques, hybrid approaches have been found to provide outstanding accuracy, for instance, for the inference of leaf area index (LAI), fractional vegetation cover (fCover), and leaf and canopy chlorophyll content (LCC and CCC). The combination of radiative transfer models (RTMs) and data-driven models creates an advantage in the use of hybrid methods. Through this review paper, we aim to provide state-of-the-art hybrid retrieval schemes and theoretical frameworks. To achieve this, we reviewed and systematically analyzed publications over the past 22 years. We identified two hybrid-based parametric and hybrid-based nonparametric regression models and evaluated their performance for each variable of interest. From the results of our extensive literature survey, most research directions are now moving towards combining RTM and machine learning (ML) methods in a symbiotic manner. In particular, the development of ML will open up new ways to integrate innovative approaches such as integrating shallow or deep neural networks with RTM using remote sensing data to reduce errors in crop trait estimations and improve control of crop growth conditions in very large areas serving precision agriculture applications.

Stand Canopy Closure Estimation in Planted Forests Using a Geometric-Optical Model Based on Remote Sensing

Canopy closure, which is the ratio of the vertical projection area of the crowns to the area of forest land, can indicate the growth and tending situation of a forest and is of great significance for forest management planning. In this study, a geometric-optical model (GOST model) was used to simulate the canopy gap fraction of a forest. Then, a canopy closure estimation method using the gap fraction was discussed. In this study, three typical planted forest farms (the Mengjiagang (MJG), Gaofeng (GF), and Wangyedian (WYD) forest farms) containing the most commonly planted tree species in the north and south regions of China were selected, and field measurements were executed. The results show that the gap fraction (Pvg-c) had a higher correlation with the average projected area of the tree crowns, and the relationship was an exponential function, with R2 and RMSE values of 0.5619 and 0.0723, respectively. Finally, the applicability and accuracy of this method were evaluated using line transects, and a fisheye camera measured the canopy closure. The accuracy of the canopy closure estimated by the Pvg-c was 86.69%. This research can provide a reference for canopy closure estimation using a geometric-optical model.

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

Various remote sensing data have been successfully applied to monitor crop vegetation parameters for different crop types. Those successful applications mostly focused on one sensor system or a single crop type. This study compares how two different sensor data (spaceborne multispectral vs unmanned aerial vehicle borne hyperspectral) can estimate crop vegetation parameters from three monsoon crops in tropical regions: finger millet, maize, and lablab. The study was conducted in two experimental field layouts (irrigated and rainfed) in Bengaluru, India, over the primary agricultural season in 2018. Each experiment contained n = 4 replicates of three crops with three different nitrogen fertiliser treatments. Two regression algorithms were employed to estimate three crop vegetation parameters: leaf area index, leaf chlorophyll concentration, and canopy water content. Overall, no clear pattern emerged of whether multispectral or hyperspectral data is superior for crop vegetation parameter estimation: hyperspectral data showed better estimation accuracy for finger millet vegetation parameters, while multispectral data indicated better results for maize and lablab vegetation parameter estimation. This study's outcome revealed the potential of two remote sensing platforms and spectral data for monitoring monsoon crops also provide insight for future studies in selecting the optimal remote sensing spectral data for monsoon crop parameter estimation.