Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors

Zheng, Guang; Moskal, L. Monika

doi:10.3390/s90402719

Cited by 440 publications

(281 citation statements)

References 120 publications

(143 reference statements)

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“…Biophysical variables can be derived from remote sensing data using statistical, physical and hybrid retrieval methods [7][8][9]. Statistical methods rely on models to relate spectral data with the biophysical variable of interest, usually through some form of regression.…”

Section: Introductionmentioning

confidence: 99%

Global Estimation of Biophysical Variables from Google Earth Engine Platform

et al. 2018

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This paper proposes a processing chain for the derivation of global Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fraction Vegetation Cover (FVC), and Canopy water content (CWC) maps from 15-years of MODIS data exploiting the capabilities of the Google Earth Engine (GEE) cloud platform. The retrieval chain is based on a hybrid method inverting the PROSAIL radiative transfer model (RTM) with Random forests (RF) regression. A major feature of this work is the implementation of a retrieval chain exploiting the GEE capabilities using global and climate data records (CDR) of both MODIS surface reflectance and LAI/FAPAR datasets allowing the global estimation of biophysical variables at unprecedented timeliness. We combine a massive global compilation of leaf trait measurements (TRY), which is the baseline for more realistic leaf parametrization for the considered RTM, with large amounts of remote sensing data ingested by GEE. Moreover, the proposed retrieval chain includes the estimation of both FVC and CWC, which are not operationally produced for the MODIS sensor. The derived global estimates are validated over the BELMANIP2.1 sites network by means of an inter-comparison with the MODIS LAI/FAPAR product available in GEE. Overall, the retrieval chain exhibits great consistency with the reference MODIS product (R 2 = 0.87, RMSE = 0.54 m 2 /m 2 and ME = 0.03 m 2 /m 2 in the case of LAI, and R 2 = 0.92, RMSE = 0.09 and ME = 0.05 in the case of FAPAR). The analysis of the results by land cover type shows the lowest correlations between our retrievals and the MODIS reference estimates (R 2 = 0.42 and R 2 = 0.41 for LAI and FAPAR, respectively) for evergreen broadleaf forests. These discrepancies could be attributed mainly to different product definitions according to the literature. The provided results proof that GEE is a suitable high performance processing tool for global biophysical variable retrieval for a wide range of applications.

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Section: Introductionmentioning

confidence: 99%

Global Estimation of Biophysical Variables from Google Earth Engine Platform

et al. 2018

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“…The best model performances were shown when using a 5-m spatial resolution. According to Zheng and Moskal [47], the accuracy of LAI estimation depends on two main reasons: overlapping and clumping between leaves within canopies due to the non-random distribution of foliage and light obstruction from canopy branches, trunks and stems. The first reason relates to this study, as well, due to mangrove canopy overlapping.…”

Section: Predicting Laimentioning

confidence: 99%

Estimating Mangrove Biophysical Variables Using WorldView-2 Satellite Data: Rapid Creek, Northern Territory, Australia

2016

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Abstract:Mangroves are one of the most productive coastal communities in the world. Although we acknowledge the significance of ecosystems, mangroves are under natural and anthropogenic pressures at various scales. Therefore, understanding biophysical variations of mangrove forests is important. An extensive field survey is impossible within mangroves. WorldView-2 multi-spectral images having a 2-m spatial resolution were used to quantify above ground biomass (AGB) and leaf area index (LAI) in the Rapid Creek mangroves, Darwin, Australia. Field measurements, vegetation indices derived from WorldView-2 images and a partial least squares regression algorithm were incorporated to produce LAI and AGB maps. LAI maps with 2-m and 5-m spatial resolutions showed root mean square errors (RMSEs) of 0.75 and 0.78, respectively, compared to validation samples. Correlation coefficients between field samples and predicted maps were 0.7 and 0.8, respectively. RMSEs obtained for AGB maps were 2.2 kg/m 2 and 2.0 kg/m 2 for a 2-m and a 5-m spatial resolution, and the correlation coefficients were 0.4 and 0.8, respectively. We would suggest implementing the transects method for field sampling and establishing end points of these transects with a highly accurate positioning system. The study demonstrated the possibility of assessing biophysical variations of mangroves using remotely-sensed data.

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“…LAI is a key input for climate and large-scale ecosystem models and also is a key structural characteristic of forest ecosystems (Chen et al 1997;Myneni et al 1997;Wang et al 2004;Zheng and Moskal 2009). In the last decades, LAI has been successfully retrieved using hyperspectral data in the visible/near-infrared (0.35-1.0 µm, VNIR) and short-wave infrared (1.0-2.5 µm, SWIR) regions (Zheng and Moskal 2009). Despite the broadly recognized importance of LAI across ecological research, to our knowledge, LAI has not estimated from thermal infrared (8-14 µm, TIR) hyperspectral data.…”

Section: Introductionmentioning

confidence: 99%

Leaf Area Index Retrieved From Thermal Hyperspectral Data

Neinavaz¹,

Skidmore²,

Darvishzadeh³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Leaf area index (LAI) is an important essential biodiversity variable due to its role in many terrestrial ecosystem processes such as evapotranspiration, energy balance, and gas exchanges as well as plant growth potential. A novel approach presented here is the retrieval of LAI using thermal infrared (8-14 μm, TIR) measurements. Here, we evaluate LAI retrieval using TIR hyperspectral data. Canopy emissivity spectral measurements were recorded under controlled laboratory conditions using a MIDAC (M4401-F) illuminator Fourier Transform Infrared spectrometer for two plant species during which LAI was destructively measured. The accuracy of retrieval for LAI was then assessed using partial least square regression (PLSR) and narrow band index calculated in the form of normalized difference index from all possible combinations of wavebands. The obtained accuracy from the PLSR for LAI retrieval was relatively higher than narrow-band vegetation index (0.54

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Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors

Cited by 440 publications

References 120 publications

Global Estimation of Biophysical Variables from Google Earth Engine Platform

Global Estimation of Biophysical Variables from Google Earth Engine Platform

Estimating Mangrove Biophysical Variables Using WorldView-2 Satellite Data: Rapid Creek, Northern Territory, Australia

Leaf Area Index Retrieved From Thermal Hyperspectral Data

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