Validation of global moderate resolution leaf area index (LAI) products over croplands in northeastern China

Fang, Hongliang; Zhang, Yinghui; Wei, Shanshan; Li, Wenjuan; Ye, Yongchang; Sun, Tao; Liu, Weiwei

doi:10.1016/j.rse.2019.111377

Cited by 86 publications

(69 citation statements)

References 67 publications

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“…Indeed, the comparison at a spatial resolution of 3 km shows similar product accuracy (just slightly better) to that at a resolution of 300 m. The accuracy of Collection 300 m V1 is similar to that of the other reference satellite 1 km products for LAI and improved for fAPAR. All the satellite products show large overestimations for the Albufera rice crop site during the early and growing periods of development (June-July), and this result was previously observed for LAI products by Campos-Taberner et al [68] and by Fang et al [69] for rice crops in China. Satellite retrieval algorithms misinterpret the decreased reflectance due to strong water absorption as a denser canopy (i.e., increasing artificially LAI, fAPAR and fCOVER values).…”

Section: Discussionsupporting

confidence: 75%

Quality Assessment of PROBA-V LAI, fAPAR and fCOVER Collection 300 m Products of Copernicus Global Land Service

et al. 2020

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The Copernicus Global Land Service (CGLS) provides global time series of leaf area index (LAI), fraction of absorbed photosynthetically active radiation (fAPAR) and fraction of vegetation cover (fCOVER) data at a resolution of 300 m and a frequency of 10 days. We performed a quality assessment and validation of Version 1 Collection 300 m products that were consistent with the guidelines of the Land Product Validation (LPV) subgroup of the Committee on Earth Observation System (CEOS) Working Group on Calibration and Validation (WGCV). The spatiotemporal patterns of Collection 300 m V1 LAI, fAPAR and fCOVER products are consistent with CGLS Collection 1 km V1, Collection 1 km V2 and Moderate Resolution Imagery Spectroradiometer Collection 6 (MODIS C6) products. The Collection 300 m V1 products have good precision and smooth temporal profiles, and the interannual variations are consistent with similar satellite products. The accuracy assessment using ground measurements mainly over crops shows an overall root mean square deviation of 1.01 (44.3%) for LAI, 0.12 (22.2%) for fAPAR and 0.21 (42.6%) for fCOVER, with positive mean biases of 0.36 (15.5%), 0.05 (10.3%) and 0.16 (32.2%), respectively. The products meet the CGLS user accuracy requirements in 69.1%, 62.5% and 29.7% of the cases for LAI, fAPAR and fCOVER, respectively. The CGLS will continue the production of Collection 300 m V1 LAI, fAPAR and fCOVER beyond the end of the PROBA-V mission by using Sentinel-3 OLCI as input data.

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Section: Discussionsupporting

confidence: 75%

Quality Assessment of PROBA-V LAI, fAPAR and fCOVER Collection 300 m Products of Copernicus Global Land Service

et al. 2020

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“…The most likely explanation for the low model efficiency of predictions driven by corrected MODIS LAI is the uncertainty of MODIS LAI itself. Many previous studies have pointed to a large underestimation of LAI by the MODIS product, compared to LAI values measured in situ (Fang et al, 2019;Fu et al, 2016;Sharp et al, 2018), with multiple causes (Yang et al, 2007). In addition, remote sensing of LAI in croplands suffers from specific problems due to their fragmented distribution and close association with settlements.…”

Section: Prediction Of Ab and Yield Variationsmentioning

confidence: 98%

Extending a first-principles primary production model to predict wheat yields

Qiao

Wang

Prentice

et al. 2020

Agricultural and Forest Meteorology

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Climate exerts a major influence on crop development and yield. Despite extensive modelling efforts, there is still considerable uncertainty about the consequences of a changing climate for the yields of major crops. Existing crop models are complex and rely on many assumptions and parameters, motivating a quest for more parsimonious models with stronger theoretical and empirical foundations. This paper presents a prototype of such a model for wheat, informed by measurements of gross primary production (GPP), biomass and yield at research sites across the wheat-growing regions of China. First, GPP was predicted using a recently developed first-principles model driven only by climate, carbon dioxide (CO 2 ) concentration, and light absorbed by leaves. Modelled GPP was shown to agree well with eddy-covariance measurements. Second, the data were used to show that above-ground biomass (AB) is proportional to time-integrated GPP, and that grain yield shows a saturating relationship with AB. Simple empirical equations based on these findings were combined with modelled GPP to predict yield, including propagated errors due to parameter uncertainty in both the GPP model and the empirical equations. The resulting 'hybrid' model, applied in a variety of climates, successfully predicted measured interannual variations in AB and yield. Third, the model was extended to include a phenology scheme, a mass-balance equation relating mean leaf area index to accumulated GPP over the growth phase, and an independently observed response of leaf massper-area to CO 2 . Sensitivity analyses and scenario runs with this extended model showed a positive but saturating (at ~600 ppm) response of yield to rising CO 2 , consistent with experimental evidence. This positive effect was partially counteracted by a net negative response of yield to increasing temperature, caused by increasing photorespiration and an accelerated growth cycle.

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“…Leaf area index (LAI) is defined as one half the total green leaf area per unit horizontal ground surface area (Chen and Black 1992), is one of the basic climate variables defined by the Global Climate Observing System (GCOS). LAI determines the effective cross-section of the interaction between the earth and atmosphere and is an important biophysical parameter in vegetation growth monitoring (Jin et al 2016;Fang et al 2019b;Song et al 2020), global climate change (Martin et al 2016;Tao, Chen, and Fu 2020) and land surface process models (Chaney, Metcalfe, and Wood 2016;Xie et al 2019). In order to run largescale ecosystem models, we need regional and even global LAI data (Myneni et al 2002).…”

Section: Introductionmentioning

confidence: 99%

“…The validation networks usually include flat and relatively homogeneous surfaces, such as the Benchmark Land Multisite Analysis and Inter-comparison of Products (BELMANIP) (Baret et al 2006), BigFoot (Cohen et al 2006), Validation Network for Remote-Sensing Products in China (VRPC) (Ma et al 2015), and Implementing Multi-Scale Agricultural Indicators Exploiting Sentinels (FP7/ImagineS) (Camacho et al 2014). In previous studies, data from these validation networks have been used to validate MODIS, VEGETATION, GLASS, and GEOV1 LAI products under various conditions of land cover types and environment (Claverie et al 2013;Cohen et al 2006;Fang, Wei, and Liang 2012;Pisek and Chen 2007;Jin et al 2017;Stern, Doraiswamy, and Hunt 2014;Yang et al 2017;Fang et al 2019b;Hu et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Validation and inter-comparison of the FY-3B/MERSI LAI product with GLOBMAP and MYD15A2H

Zhang

Zhu

Sun

et al. 2020

International Journal of Remote Sensing

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The leaf area index (LAI) describes the structure of vegetation and is a key variable for Earth system process modelling and ecohydrological models at regional and global scales. The Medium-Resolution Spectral Imager (MERSI) onboard China's new generation of polar-orbiting meteorological satellite series FengYun-3 (FY-3) can provide continuous and global observations of the land surface. Therefore, it could be a potential data source for global LAI retrieval. In this study, a LAI product was generated from FY-3B/MERSI data using the GLOBCARBON LAI algorithm. Cross-calibration of the FY-3B/MERSI spectral response function with Land Remote-Sensing Satellite (Landsat) Thematic Mapper (TM), allowed to correct the influence of the spectral response function difference on the inversion results. Field measurements of LAI and scale-converted LAI reference images provided by the ImagineS project were used to validate and inter-compare the FY-3B/MERSI LAI product with two widely used medium-resolution LAI products, GLOBMAP LAI product and Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product (MYD15A2 H). The results demonstrate that FY-3B/MERSI LAI has the lowest uncertainty of the three products. The low uncertainty of FY-3B/MERSI LAI in shrub-grass mixed areas (root mean square error, RMSE = 0.07), in part, by the generally underestimated LAI value. For deciduous broadleaf forest, of the three products tested, FY-3B/MERSI LAI is closest to the LAI obtained from the reference image (coefficient of determination, R 2 = 0.70) and yields the lowest uncertainty (RMSE = 0.81). GLOBMAP (R 2 = 0.58), which uses the same algorithm, and surface cover data as FY-3B/MERSI LAI, significantly overestimates the LAI. This overestimation may partly due to the use of a relatively lower clumping index. MYD15A2H shows a relatively weak correlation with the reference data (R 2 = 0.25) and a higher uncertainty (RMSE = 1.45). For mature crops, all three LAI products display systematic underestimation of LAI. FY-3B/MERSI LAI yields the greatest underestimation (about 50%), followed by GLOBMAP (about 35%) and MYD15A2H (about 15%). Our inter-comparison of the three LAI products demonstrates that all have higher correlation for low LAI values. FY-3B/MERSI shows similar capabilities and quality to those of the MODIS sensor with respect to the top of atmosphere observations. However, different atmospheric correction processes may ARTICLE HISTORY

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Validation of global moderate resolution leaf area index (LAI) products over croplands in northeastern China

Cited by 86 publications

References 67 publications

Quality Assessment of PROBA-V LAI, fAPAR and fCOVER Collection 300 m Products of Copernicus Global Land Service

Quality Assessment of PROBA-V LAI, fAPAR and fCOVER Collection 300 m Products of Copernicus Global Land Service

Extending a first-principles primary production model to predict wheat yields

Validation and inter-comparison of the FY-3B/MERSI LAI product with GLOBMAP and MYD15A2H

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