Characterizing the Error and Bias of Remotely Sensed LAI Products: An Example for Tropical and Subtropical Evergreen Forests in South China

Yuan Zhao,Xiaoqiu Chen,Mathew Williams,David T Milodowski,Sophie Flack-Prain,Thomas Luke Smallman

doi:10.3390/rs12193122

Yuan Zhao, Xiaoqiu Chen + Show 4 more

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https://doi.org/10.3390/rs12193122

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Abstract

Leaf area is a key parameter underpinning ecosystem carbon, water and energy exchanges via photosynthesis, transpiration and absorption of radiation, from local to global scales. Satellite-based Earth Observation (EO) can provide estimates of leaf area index (LAI) with global coverage and high temporal frequency. However, the error and bias contained within these EO products and their variation in time and across spatial resolutions remain poorly understood. Here, we used nearly 8000 in situ measurements of LAI from six forest environments in southern China to evaluate the magnitude, uncertainty, and dynamics of three widely used EO LAI products. The finer spatial resolution GEOV3 PROBA-V 300 m LAI product best estimates the observed LAI from a multi-site dataset (R2 = 0.45, bias = −0.54 m2 m−2, RMSE = 1.21 m2 m−2) and importantly captures canopy dynamics well, including the amplitude and phase. The GEOV2 PROBA-V 1 km LAI product performed the next best (R2 = 0.36, bias = −2.04 m2 m−2, RMSE = 2.32 m2 m−2) followed by MODIS 500 m LAI (R2 = 0.20, bias = −1.47 m2 m−2, RMSE = 2.29 m2 m−2). The MODIS 500 m product did not capture the temporal dynamics observed in situ across southern China. The uncertainties estimated by each of the EO products are substantially smaller (3–5 times) than the observed bias for EO products against in situ measurements. Thus, reported product uncertainties are substantially underestimated and do not fully account for their total uncertainty. Overall, our analysis indicates that both the retrieval algorithm and spatial resolution play an important role in accurately estimating LAI for the dense canopy forests in Southern China. When constraining models of the carbon cycle and other ecosystem processes are run, studies should assume that current EO product LAI uncertainty estimates underestimate their true uncertainty value.

Highlights

The Leaf Area Index (LAI), the total leaf area per unit ground area, is a key biophysical variable playing an important role in global carbon, water, and energy cycles [1,2]
There are differences in the method used for temporal compositing, where temporal smoothing and gap filling using a climatology are used for GEOV2 1km and interpolation applied in GEOV3 300 m) [78]
Differences in the applied gap-filling approach between these two products do not impact our conclusion that resolution is the primary driver of performance improvement at 300 m relative to 1 km, as all gap-filled and interpolated retrievals were removed in our study

Summary

Introduction

The Leaf Area Index (LAI), the total leaf area per unit ground area, is a key biophysical variable playing an important role in global carbon, water, and energy cycles [1,2] As such, it acts as an important parameter for several applications, such as land surface models [3], ecological models [4], and yield prediction models [5]. Retrieving LAI over large areas and having a good knowledge of their yearly variations, errors, and bias is extremely important Such information is central to accurately estimating primary productivity, understanding land surface-atmosphere exchanges, and detecting the response of terrestrial vegetation to climate change [7]. Such robust evaluations are rarely possible due to the scarcity of in situ data at appropriate temporal and spatial resolutions [14,15]

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