High Spatial Resolution and High Temporal Frequency(30-m/15-day) Fractional Vegetation Cover Estimation over China Using Multiple Remote Sensing Datasets:Method Development and Validation

High spatial resolution and high temporal frequency fractional vegetation cover(FVC) products have been increasingly in demand to monitor and research land surface processes. This paper develops an algorithm to estimate FVC at a 30-m/15-day resolution over China by taking advantage of the spatial and temporal information from different types of sensors: the 30-m resolution sensor on the Chinese environment satellite(HJ-1) and the 1-km Moderate Resolution Imaging Spectroradiometer(MODIS). The algorithm was implemented for each main vegetation class and each land cover type over China. First, the high spatial resolution and high temporal frequency normalized difference vegetation index(NDVI) was acquired by using the continuous correction(CC) data assimilation method. Then, FVC was generated with a nonlinear pixel unmixing model. Model coefficients were obtained by statistical analysis of the MODIS NDVI. The proposed method was evaluated based on in situ FVC measurements and a global FVC product(GEOV1 FVC). Direct validation using in situ measurements at 97 sampling plots per half month in 2010 showed that the annual mean errors(MEs) of forest, cropland, and grassland were-0.025, 0.133, and 0.160, respectively, indicating that the FVCs derived from the proposed algorithm were consistent with ground measurements [R2 = 0.809,root-mean-square deviation(RMSD) = 0.065]. An intercomparison between the proposed FVC and GEOV1 FVC demonstrated that the two products had good spatial–temporal consistency and similar magnitude(RMSD approximates 0.1). Overall, the approach provides a new operational way to estimate high spatial resolution and high temporal frequency FVC from multiple remote sensing datasets.

Quantitative Evaluation of Leaf Inclination Angle Distribution on Leaf Area Index Retrieval of Coniferous Canopies

Both leaf inclination angle distribution(LAD)and leaf area index(LAI)dominate optical remote sensing signals.The G-function,which is a function of LAD and remote sensing geometry,is often set to 0.5 in the LAI retrieval of coniferous canopies even though this assumption is only valid for spherical LAD.Large uncertainties are thus introduced.However,because numerous tiny leaves grow on conifers,it is nearly impossible to quantitatively evaluate such uncertainties in LAI retrieval.In this study,we proposed a method to characterize the possible change of G-function of coniferous canopies as well as its effect on LAI retrieval.Specifically,a Multi-Directional Imager(MDI)was developed to capture stereo images of the branches,and the needles were reconstructed.The accuracy of the inclination angles calculated from the reconstructed needles was high.Moreover,we analyzed whether a spherical distribution is a valid assumption for coniferous canopies by calculating the possible range of the G-function from the measured LADs of branches of Larch and Spruce and the true G-functions of other species from some existing inventory data and threedimensional(3D)tree models.Results show that the constant G assumption introduces large errors in LAI retrieval,which could be as large as 53%in the zenithal viewing direction used by spaceborne LiDAR.As a result,accurate LAD estimation is recommended.In the absence of such data,our results show that a viewing zenith angle between 45 and 65 degrees is a good choice,at which the errors of LAI retrieval caused by the spherical assumption will be less than 10%for coniferous canopies.

Remote sensing experiments for earth system science

The Earth system is an integrated system that can be divided into six main subsystems:geosphere,atmosphere,hydrosphere,cryosphere,biosphere,and anthrosphere.These subsystems are interconnected through the flows of global energy,water,and carbon,which are fundamental constituent cycles within the Earth system.To improve our predictive understanding of the subsystem on our changing planet,there is a need to better represent these subsystems through modelling and observations.The central role of these fundamental cycles has prompted scientific institutions or meteorological centers to develop digital platforms dedicated to the integration,modelling,and assimilation of Earth observation data,e.g.,Big Earth Data Science Engineering(Guo,Li,and Qiu 2020).