Satellite-derived LAI products exhibit large discrepancies and can lead to substantial uncertainty in simulated carbon and water fluxes

Liu, Yibo; Xiao, Jingfeng; Ju, Weimin; Zhu, Guannan; Wu, Xiaocui; Fan, Weiliang; Li, Dengqiu; Zhou, Yanlian

doi:10.1016/j.rse.2017.12.024

Cited by 124 publications

(93 citation statements)

References 69 publications

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“…Jiang et al () compared modern long‐term satellite data sets and reported that the available long‐term LAI satellite products are not interconsistent over time or latitude. The major reasons for differences in the LAI products are differences in the input surface reflectance, the reflectance preprocessing algorithms, the retrieval algorithms, and the type‐based treatments of vegetation (Jiang et al, ; Liu et al, ). A comparison of interannual LAI variation with prognostic LAI estimates (Figure S3 and Table S2) shows larger linear LAI trends than have been reported previously (Smith et al, ).…”

Section: Resultsmentioning

confidence: 99%

Effects of Anthropogenic Activity on Global Terrestrial Gross Primary Production

Melnikova

Sasai

2020

JGR Biogeosciences

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Gross primary production (GPP) has been identified as the largest terrestrial carbon flux and the major driver of the growing biosphere uptake of carbon. Factorial simulations using several biosphere models have been used to estimate the effects of long‐term (>50 years) climate change on global terrestrial GPP. However, no study has integrated large‐ensemble climate simulation data into a biosphere model to realistically estimate global terrestrial GPP with associated uncertainty. Here we present a novel approach to estimate the global terrestrial long‐term GPP with associated climate data‐induced uncertainty that combines a diagnostic‐type biosphere model with a large‐ensemble climate simulation data set. We distinguish the effects of recent anthropogenic activity on global GPP (the anthropogenic GPP effect) from the effects of interannual climate variability (the natural GPP effect) by comparing GPP model estimates forced by historical and “nonwarming” climate data. We provide evidence for an increasing anthropogenic effect on global terrestrial GPP. The anthropogenic GPP effect is driven by CO2 fertilization, which is projected to weaken or saturate by 2050–2150, depending on the representative concentration pathway scenario used. Model results suggest that shortwave radiation couples with El Niño–Southern Oscillation conditions and volcanic eruptions to drive the natural GPP effect. Because shortwave radiation at the surface is related to cloud cover, we encourage future studies to focus on cloud‐radiation feedbacks on the carbon cycle.

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Section: Resultsmentioning

confidence: 99%

Effects of Anthropogenic Activity on Global Terrestrial Gross Primary Production

Melnikova

Sasai

2020

JGR Biogeosciences

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“…Optical remote sensors have provided the primary data sources in these studies. Examples of these sensors (and their spatial resolutions) include the Advanced Very High Resolution Radiometer (AVHRR, ≈1-km) (Claverie et al, 2016;Jia et al, 2016), the MEdium Resolution Imaging Spectrometer (MERIS, 300-m/1200-m) (Bacour et al, 2006;Foody and Dash, 2010), the Moderate Resolution Imaging Spectroradiometer (MODIS, 250-m/500-m) (John et al, 2018;Liu et al, 2018;Myneni et al, 2002;Pasolli et al, 2015), the Landsat MSS/ TM/ETM+ (30-m) (Chen and Cihlar, 1996;Chen et al, 2002;Friedl et al, 1994;Turner et al, 1999;Zhang et al, 2018), the Satellite Pour I'Observation de la Terre (SPOT, 10-m/20-m) (Grant et al, 2012;Guneralp et al, 2014;Houborg et al, 2009), and other high spatial resolution satellite and airborne images (< 10-m) (Atzberger et al, 2015;Colombo et al, 2003;Darvishzadeh et al, 2011). However, optical sensor data have several limitations for estimating LAI and AGB, including: (1) the acquisition of good quality data is often constrained by weather conditions; (2) the optical data captures the information mainly from the top of canopy rather than the vegetation structure; and (3) saturation of surface reflectance and vegetation indices occurs at moderate to high vegetation cover (Chang and Shoshany, 2016;Lu, 2006).…”

Section: Introductionmentioning

confidence: 99%

Estimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images

Wang

Xiao

Starks

et al. 2019

ISPRS Journal of Photogrammetry and Remote Sensing

218

126

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Grassland degradation has accelerated in recent decades in response to increased climate variability and human activity. Rangeland and grassland conditions directly affect forage quality, livestock production, and regional grassland resources. In this study, we examined the potential of integrating synthetic aperture radar (SAR, Sentinel-1) and optical remote sensing (Landsat-8 and Sentinel-2) data to monitor the conditions of a native pasture and an introduced pasture in Oklahoma, USA. Leaf area index (LAI) and aboveground biomass (AGB) were used as indicators of pasture conditions under varying climate and human activities. We estimated the seasonal dynamics of LAI and AGB using Sentinel-1 (S1), Landsat-8 (LC8), and Sentinel-2 (S2) data, both individually and integrally, applying three widely used algorithms: Multiple Linear Regression (MLR), Support Vector Machine (SVM), and Random Forest (RF). Results indicated that integration of LC8 and S2 data provided sufficient data to capture the seasonal dynamics of grasslands at a 10-30-m spatial resolution and improved assessments of critical phenology stages in both pluvial and dry years. The satellite-based LAI and AGB models developed from ground measurements in 2015 reasonably predicted the seasonal dynamics and spatial heterogeneity of LAI and AGB in 2016. By comparison, the integration of S1, LC8, and S2 has the potential to improve the estimation of LAI and AGB more than 30% relative to the performance of S1 at low vegetation cover (LAI < 2 m 2 /m 2 , AGB < 500 g/m 2) and optical data of LC8 and S2 at high vegetation cover (LAI > 2 m 2 /m 2 , AGB > 500 g/m 2). These results demonstrate the potential of combining S1, LC8, and S2 monitoring grazing tallgrass prairie to provide timely and accurate data for grassland management.

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“…It is well known that descriptions of vegetation processes, definitions of land use/cover (LUC) and relevant vegetation character parameters (e.g., NDVI, LAI, and/or VOD) are needed; thus, their differences and uncertainties potentially propagate into the ET estimates [113]. There are a number of available LUC (e.g., Table S3) and NDVI/LAI/VOD products derived from different data sources (e.g., various satellite images), algorithms, and classification schemes [114,115].…”

Section: Model Inputsmentioning

confidence: 99%

“…It should be noted, however, that these datasets were produced for specific purposes and applications, including analyses of LUC and vegetation changes and their impacts on the climate, hydrology, and ecosystem, and the developments of various geo-scientific models; thus, obvious discrepancies and even errors in these products have been reported, especially at the regional scale [115][116][117][118][119][120][121][122][123][124][125]. Therefore, without considering the suitability of LUC and NDVI/LAI/VOD products, biases originating from raw data and inconsistencies among the selected products and uncertainties owing to product selection and processing can be of the same magnitude as those from the representation of the processes under investigation [113,121,[126][127][128][129]. For example, Branger et al [126] investigated the impact of different LUC datasets on the long-term water balance of the Yzeron peri-urban catchment of France and stated that most water quantities (including ET) were sensitive to LUC selections.…”

Section: Model Inputsmentioning

confidence: 99%

“…For example, Branger et al [126] investigated the impact of different LUC datasets on the long-term water balance of the Yzeron peri-urban catchment of France and stated that most water quantities (including ET) were sensitive to LUC selections. Liu et al [113] quantified uncertainties of simulated water fluxes using MODIS (MOD15), GLASS, and the Four-Scale Geometric Optical Model (FSGOM)-based LAI, and concluded that LAI products could lead to substantial uncertainties in the ET estimates. For these selected ET products, different LUCs and vegetation character parameters are used and cause differences in performances and uncertainties of the ET estimates.…”

Section: Model Inputsmentioning

confidence: 99%

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Assessment of Multi-Source Evapotranspiration Products over China Using Eddy Covariance Observations

Wang

Sun

et al. 2018

Remote Sensing

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As an essential variable in linking water, carbon, and energy cycles, evapotranspiration (ET) is difficult to measure. Remote sensing, reanalysis, and land surface model-based ET products offer comprehensive alternatives at different spatio-temporal intervals, but their performance varies. In this study, we selected four popular ET global products: The Global Land Evaporation Amsterdam Model version 3.0a (GLEAM3.0a), the Modern Era Retrospective-Analysis for Research and Applications-Land (MERRA-Land) project, the Global Land Data Assimilation System version 2.0 with the Noah model (GLDAS2.0-Noah) and the EartH2Observe ensemble (EartH2Observe-En). Then, we comprehensively evaluated the performance of these products over China using a stratification method, six validation criteria, and high-quality eddy covariance (EC) measurements at 12 sites. The aim of this research was to provide important quantitative information to improve and apply the ET models and to inform choices about the appropriate ET product for specific applications. Results showed that, within one stratification, the performance of each ET product based on a certain criterion differed among classifications of this stratification. Furthermore, the optimal ET (OET) among these products was identified by comparing the magnitudes of each criterion. Results suggested that, given a criterion (a stratification classification), the OETs varied among stratification classifications (the selected six criteria). In short, no product consistently performed best, according to the selected validation criterion. Thus, multi-source ET datasets should be employed in future studies to enhance confidence in ET-related conclusions.

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Satellite-derived LAI products exhibit large discrepancies and can lead to substantial uncertainty in simulated carbon and water fluxes

Cited by 124 publications

References 69 publications

Effects of Anthropogenic Activity on Global Terrestrial Gross Primary Production

Effects of Anthropogenic Activity on Global Terrestrial Gross Primary Production

Estimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images

Assessment of Multi-Source Evapotranspiration Products over China Using Eddy Covariance Observations

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