Leaf area index (LAI) is a critical vegetation structural variable and is essential in the feedback of vegetation to the climate system. The advancement of the global Earth Observation has enabled ...the development of global LAI products and boosted global Earth system modeling studies. This overview provides a comprehensive analysis of LAI field measurements and remote sensing estimation methods, the product validation methods and product uncertainties, and the application of LAI in global studies. First, the paper clarifies some definitions related to LAI and introduces methods to determine LAI from field measurements and remote sensing observations. After introducing some major global LAI products, progresses made in temporal compositing and prospects for future LAI estimation are analyzed. Subsequently, the overview discusses various LAI product validation schemes, uncertainties in global moderate resolution LAI products, and high resolution reference data. Finally, applications of LAI in global vegetation change, land surface modeling, and agricultural studies are presented. It is recommended that (1) continued efforts are taken to advance LAI estimation algorithms and provide high temporal and spatial resolution products from current and forthcoming missions; (2) further validation studies be conducted to address the inadequacy of current validation studies, especially for underrepresented regions and seasons; and (3) new research frontiers, such as machine learning algorithms, light detection and ranging technology, and unmanned aerial vehicles be pursued to broaden the production and application of LAI.
Key Points
LAI, one half the total leaf area per unit surface area, is a fundamental vegetation attribute and an essential climate variable
The paper gives an overview of LAI field and remote sensing estimation methods, and LAI product validation, uncertainties, and applications
Gaps in current studies and new frontiers are analyzed; recommendations for future LAI estimations and validations are given
Achieving the Global Climate Observing System goal of 10 m resolution leaf area index (LAI) maps is critical for applications related to climate adaptation, sustainable agriculture, and ecosystem ...monitoring. Five strategies for producing 10 m LAI maps from Sentinel-2 (S2) imagery are evaluated: i. bi-cubic interpolation of 20 m resolution S2 LAI maps from the Simplified Level 2 Prototype Processor Version 1 (SL2PV1) as currently performed by the Sentinel Applications Platform (SNAP), ii. applying SL2PV1 to S2 reflectance bands spatially downscaled to 10 m using bi-cubic interpolation (BICUBIC), iii. Applying SL2PV1 to S2 reflectance bands spatially downscaled to 10 m using Area to Point Regression Kriging (ATPRK), iv. using a recalibrated version of SL2PV1 (SL2PV2) requiring only three S2 10m bands, and iv) a novel use of the previously developed Active Learning Regularization (ALR) approach to locally approximate the SL2PV1 algorithm using only 10 m bands.
Algorithms were assessed in terms of per-pixel accuracy and spatial metrics when comparing 10 m LAI maps produced using either actual S2 imagery or S2 imagery synthesized from airborne hyperspectral imagery to reference 10 m LAI maps traceable to in-situ fiducial reference measurements at 10 sites across the continental US. ATPRK and ALR algorithms had the lowest precision error of ∼0.15 LAI, compared to 0.19 LAI for SNAP and BICUBIC and 0.35 LAI for SL2PV2, and ranked highest in terms of local correlation and Structural Similarity Index measure as well as qualitative agreement with reference maps. SL2PV2 LAI showed evidence of saturation over forests related to decreased sensitivity of input visible reflectance. All algorithms had a similar uncertainty of ∼0.55 LAI compared to traceable reference maps, due to the trade-off between bias and precision. However, ATPRK and ALR uncertainty reduced to 0.11 LAI and 0.16 LAI, respectively, when compared to reference maps that ignored canopy clumping. These results suggest that both ATPRK and ALR are suitable for producing 10 m S2 LAI maps assuming bias due to local clumping can be corrected in the underlying SL2PV1 algorithm.
10 m resolution reference LAI maps based on input 10 m synthetic imagery for S2 bands (REFRM) traceable to in-situ fiducial reference measurements and downscaling algorithm estimates (BICUBIC. SNAP, SL2PV2, ATPRK, ALR) using synthetic S2 imagery for KONZA National Ecological Observatory Network site. Display omitted
•Validation of five Sentinel-2 10 m LAI algorithms at 10 sites across US.•Reflectance downscaling (ATPRK) and transfer learning (ALR) offer best spatial precision.•Spatial interpolation results in worst spatial precision and significant blurring.•ATPRK and ALR uncertainty ≤0.11 LAI and ≤ 0.16 LAI respectively ignoring clumping bias.•Simplified Level 2 Prototype Processor LAI using only 10 m bands saturates over forests.
Leaf area index (LAI) is an important vegetation biophysical variable and has been widely used for crop growth monitoring and yield estimation, land-surface process simulation, and global change ...studies. Several LAI products currently exist, but most have limited temporal coverage. A long-term high-quality global LAI product is required for greatly expanded application of LAI data. In this paper, a method previously proposed was improved to generate a long time series of Global LAnd Surface Satellite (GLASS) LAI product from Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MOD!S) reflectance data. The GLASS LAI product has a temporal resolution of eight days and spans from 1981 to 2014. During 1981-1999, the LAI product was generated from AVHRR reflectance data and was provided in a geographic latitude/longitude projection at a spatial resolution of 0.05°. During 2000-2014, the LAI product was derived from MODIS surface-reflectance data and was provided in a sinusoidal projection at a spatial resolution of 1 km. The GLASS LAI values derived from MODIS and AVHRR reflectance data form a consistent data set at a spatial resolution of 0.05°. Comparison of the GLASS LAI product with the MODIS LAI product (MOD15) and the first version of the Geoland2 (GEOV1) LAI product indicates that the global consistency of these LAI products is generally good. However, relatively large discrepancies among these LAI products were observed in tropical forest regions, where the GEOV1 LAI values were clearly lower than the GLASS and MOD15 LAI values, particularly in January. A quantitative comparison of temporal profiles shows that the temporal smoothness of the GLASS LAI product is superior to that of the GEOV1 and MODIS LAI products. Direct validation with the mean values of high-resolution LAI maps demonstrates that the GLASS LAI values were closer to the mean values of the high-resolution LAI maps (RMSE = 0.7848 and R 2 = 0.8095) than the GEOV1 LAI values (RMSE = 0.9084 and R 2 = 0.7939) and the MOD15 LAI values (RMSE = 1.1173 and R 2 = 0.6705).
Understanding the long‐term performance of global satellite leaf area index (LAI) products is important for global change research. However, few effort has been devoted to evaluating the long‐term ...time‐series consistencies of LAI products. This study compared four long‐term LAI products (GLASS, GLOBMAP, LAI3g, and TCDR) in terms of trends, interannual variabilities, and uncertainty variations from 1982 through 2011. This study also used four ancillary LAI products (GEOV1, MERIS, MODIS C5, and MODIS C6) from 2003 through 2011 to help clarify the performances of the four long‐term LAI products. In general, there were marked discrepancies between the four long‐term LAI products. During the pre‐MODIS period (1982–1999), both linear trends and interannual variabilities of global mean LAI followed the order GLASS>LAI3g>TCDR>GLOBMAP. The GLASS linear trend and interannual variability were almost 4.5 times those of GLOBMAP. During the overlap period (2003–2011), GLASS and GLOBMAP exhibited a decreasing trend, TCDR no trend, and LAI3g an increasing trend. GEOV1, MERIS, and MODIS C6 also exhibited an increasing trend, but to a much smaller extent than that from LAI3g. During both periods, the R2 of detrended anomalies between the four long‐term LAI products was smaller than 0.4 for most regions. Interannual variabilities of the four long‐term LAI products were considerably different over the two periods, and the differences followed the order GLASS>LAI3g>TCDR>GLOBMAP. Uncertainty variations quantified by a collocation error model followed the same order. Our results indicate that the four long‐term LAI products were neither intraconsistent over time nor interconsistent with each other. These inconsistencies may be due to NOAA satellite orbit changes and MODIS sensor degradation. Caution should be used in the interpretation of global changes derived from the four long‐term LAI products.
Evaluation on consistencies of long‐term global satellite products is prerequisite to global change research. We intercompared four long‐term LAI products, GLASS, GLOBMAP, LAI3g, and TCDR, in terms of trends, interannual variabilities and uncertainty variations from 1982 through 2011. Our study indicated that the four long‐term LAI products are neither intraconsistent nor interconsistent. Caution should be used in the interpretation of global changes derived from the four long‐term LAI products at present.
Global greening trends have been widely reported based on long‐term remote sensing data of terrestrial ecosystems. Typically, a hypothesis test is performed for each grid cell; this leads to multiple ...hypothesis testing and false positive trend detection. We reanalyze global greening and account for this issue with a novel statistical method that allows robust inference on greening regions. Based on leaf area index (LAI) data, our methods reduce the detected greening from 35.2% to 15.3% of the terrestrial land surface; this reduction is most notable in nonwoody vegetation. Our results confirm several greening regions (China, India, Europe, Sahel, North America, Brazil, and Siberia), that are also supported by independent data products. We also report evidence for an increasing seasonal amplitude in LAI north of 35°N. Considering the widespread use of spatially replicated trend tests in global change research, we recommend adopting the proposed multiple testing procedure to control false positive outcomes.
Plain Language Summary
Using satellite data, recent studies have detected an increase in vegetation greenness around the globe. These studies attribute this vegetation increase to different factors, such as warming or land use change. However, we argue that the commonly used analysis method is detecting too many regions with trends. In this work, we reanalyze vegetation data using the leaf area index, which measures the area occupied by leaves in any given area. With our refined methods, we too detect greening regions around the world, however these regions are smaller and less abundant. Our research introduces a step in the statistical analysis that increases the reliability of the detected vegetation greening. This can help establish more consensus on what the main contributing factors are for the observed vegetation increase.
Key Points
Many studies have consistently reported on global greening trends, but repeatedly without rigorous significance testing
Although global greening has been overestimated, significant greening can still be rigorously detected
We observe an increase in the seasonal amplitude of leaf area index around the glob
Keywords
global greening |leaf area index (LAI) | multiple testing |statistical significance
The estimation of leaf area index (LAI) from optical remotely sensed data based on vegetation indices (VIs) is a quick and practical approach to acquire LAI over vast areas. Reflectance in the ...red-edge bands is sensitive to vegetation status, and its information is thought to be useful in agricultural applications. Based on three red-edge band observations (represented as RE1, RE2, and RE3 for bands 5-7) from the Multispectral Instrument (MSI) onboard the Sentinel-2 satellite, this article aims to investigate the feasibility and performance of using red-edge bands for LAI estimates with the VI method and ground-measured LAI data sets. Sensitivity analysis from PROSAIL simulations revealed that RE1 is mainly affected by the influence of the leaf chlorophyll content, and this uncertainty should not be ignored during LAI estimation. For the normalized difference vegetation index (NDVI), modified simple ratio (MSR), chlorophyll index (CI), and wide dynamic range vegetation index (WDRVI), the optimal combination of Sentinel-2 bands for LAI estimation was RE2 and RE3, with a minimum root-mean-square error (RMSE) of 0.75. Four 3-band red-edge VIs were proposed to exploit the full content of the red-edge bands of Sentinel-2, and their performance in LAI estimation improved slightly. However, both 2-band red-edge VIs and 3-band red-edge VIs remained slightly saturated at high LAI levels; therefore, a segmental estimation with a threshold was suggested for large LAIs. The results indicate that the optimal 2-band red-edge VIs and proposed 3-band red-edge VIs are effective tools for crop LAI estimation in multiple-growth stages with Sentinel-2 MSI images.
Abstract African pastoralists suffer recurrent droughts that cause high livestock mortality and vulnerability to climate change. The index-based livestock insurance (IBLI) program offers protection ...against drought impacts. However, the current IBLI design relying on the normalized difference vegetation index (NDVI) may pose limitation because it does not consider the mixed composition of rangelands (including herbaceous and woody plants) and the diverse feeding habits of grazers and browsers. To enhance IBLI, we assessed the efficacy of utilizing distinct browse and grazing forage estimates from woody LAI (LAI W ) and herbaceous LAI (LAI H ), respectively, derived from aggregate leaf area index (LAI A ), as an alternative to NDVI for refined IBLI design. Using historical livestock mortality data from northern Kenya as reference ground dataset, our analysis compared two competing models for (1) aggregate forage estimates including sub-models for NDVI, LAI (LAI A ); and (2) partitioned biomass model (LAI P ) comprising LAI H and LAI W . By integrating forage estimates with ancillary environmental variables, we found that LAI P , with separate forage estimates, outperformed the aggregate models. For total livestock mortality, LAI P yielded the lowest RMSE (5.9 TLUs) and higher R 2 (0.83), surpassing NDVI and LAI A models RMSE (9.3 TLUs) and R 2 (0.6). A similar pattern was observed for species-specific livestock mortality. The influence of environmental variables across the models varied, depending on level of mortality aggregation or separation. Overall, forage availability was consistently the most influential variable, with species-specific models showing the different forage preferences in various animal types. These results suggest that deriving distinct browse and grazing forage estimates from LAI P has the potential to reduce basis risk by enhancing IBLI index accuracy.
This paper presents an operational chain for high-resolution leaf area index (LAI) retrieval from multiresolution satellite data specifically developed for Mediterranean rice areas. The proposed ...methodology is based on the inversion of the PROSAIL radiative transfer model through the state-of-the-art nonlinear Gaussian process regression (GPR) method. Landsat and SPOT5 data were used for multitemporal LAI retrievals at high-resolution. LAI estimates were validated using time series of in situ LAI measurements collected during the rice season in Spain and Italy. Ground LAI data were collected with smartphones using PocketLAI, a specific phone application for LAI estimation. Temporal evolution of the LAI estimates using Landsat and SPOT5 data followed consistently the temporal evolution of the in situ LAI measurements acquired on several Mediterranean rice varieties. The estimates had a root-mean-square-error (RMSE) of 0.39 and 0.51m2/m2 in Spain and 0.38 and 0.47m2/m2 in Italy for Landsat and SPOT5 respectively, with a strong correlation (R2>0.92) for both cases. Spatial-temporal assessment of the estimated LAI from Landsat and SPOT5 data confirmed the robustness and consistency of the retrieval chain. This paper demonstrates the importance of an adequate characterization of the underlying rice background in order to address changes in background condition related to water management. Results highlight the potential of the proposed chain for deriving multitemporal near real-time decametric LAI maps fundamental for operational rice crop monitoring, and demonstrate the readiness of the proposed method for the processing of data such as the recently launched Sentinel-2.
•Developed an operational system for LAI estimation for Mediterranean rice crops•LAI maps produced by inverting PROSAIL with Gaussian processes regression•Multitemporal decametric LAI estimations obtained from Landsat and SPOT5•Direct map validation performed with a smartphone app•Assessed the influence of rice background conditions in LAI retrievals
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•The LAI was estimated based on the UAV-based image analysis.•Morphological parameters and vegetation indices were extracted to evaluate the dynamic changes of LAI.•The fusion of ...morphological data and spectral vegetation indices could improve the accuracy of LAI estimation.
As an important indicator reflecting plant growth and canopy structure, accurate and rapid monitoring of the leaf area index (LAI) is very important for modern precision agriculture. The purpose of this study is to explore the potential of fusion of morphological information and spectral information in multiple growth periods of maize to improve the accuracy of LAI dynamic estimation. The multi-spectral sensor carried by the unmanned aerial vehicle (UAV) was used to collect remote sensing images of the maize canopy during the six growth stages. Three morphological parameters (canopy height, canopy coverage, and canopy volume) and two vegetation indices (normalized vegetation index (NDVI) and visible atmospheric vegetation index (VARI)) were extracted from image information and spectral information, respectively, and a LAI estimation model was constructed based on parameters fusion. The results showed that the morphological parameters and vegetation indices had the same time distribution law as LAI, and could be used to monitor crop LAI. At the same time, the study found that the fusion of canopy height, canopy coverage and canopy volume could further characterize the external morphological changes of crops and improved the accuracy of LAI dynamic estimation based on morphology, but there were still limitations in the seedling and milk stages. Furthermore, the fusion of canopy morphological parameters and vegetation indices could further improve the dynamic estimate accuracy of maize LAI, and showed better performance in all growth stages (Seedling stage: Rv2 = 0.688, RMSEP = 0.0493; Jointing stage: Rv2 = 0.860, RMSEP = 0.0847; Tasseling stage: Rv2 = 0.780, RMSEP = 0.1829; Silking stage: Rv2 = 0.794, RMSEP = 0.1981; Blister stage: Rv2 = 0.793, RMSEP = 0.1584; Milk stage: Rv2 = 0.708, RMSEP = 0.1396; All: Rv2 = 0.943, RMSEP = 0.2618). The results show that the fusion of image information and spectral information can improve the estimation accuracy of crop LAI and provide a feasible method for crop growth information monitoring based on UAV platform.
Using a spectral vegetation index (VI) is an efficient approach for monitoring plant phenology from remotely-sensed data. However, the quantitative biophysical meaning of most VIs is still unclear, ...and, particularly at high northern latitudes characterized by low green biomass renewal rate and snow-affected VI signals, it is difficult to use them for tracking seasonal vegetation growth and retrieving phenology. In this study we propose a physically-based new vegetation index for characterizing terrestrial vegetation canopy green leaf area dynamics: the plant phenology index (PPI). PPI is derived from the solution to a radiative transfer equation, is computed from red and near-infrared (NIR) reflectance, and has a nearly linear relationship with canopy green leaf area index (LAI), enabling it to depict canopy foliage density well. This capability is verified with stacked-leaf measurements, canopy reflectance model simulations, and field LAI measurements from international sites. Snow influence on PPI is shown by modeling and satellite observations to be less severe than on the Normalized Difference Vegetation Index (NDVI) or the Enhanced Vegetation Index (EVI), while soil brightness variations in general have moderate influence on PPI. Comparison of satellite-derived PPI to ground observations of plant phenology and gross primary productivity (GPP) shows strong similarity of temporal patterns over several Nordic boreal forest sites. The proposed PPI can thus serve as an efficient tool for estimating plant canopy growth, and will enable improved vegetation monitoring, particularly of evergreen needle-leaf forest phenology at high northern latitudes.
•The plant phenology index (PPI) is derived from radiative transfer equation.•PPI, formulated with red and NIR bands, has nearly linear relationship with LAI.•PPI is insensitive to snow and noise during the phenology transition period.•PPI tracks canopy green foliage dynamics and shows strong correlation with GPP.•PPI provides an operational and efficient approach to retrieving plant phenology.
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