Gross primary productivity (GPP) through photosynthesis is a crucial ecosystem function that significantly influences food security, carbon cycle, and climate change. Current remote sensing estimates of GPP rely on look-up tables containing biome-specific parameters to model light-use efficiency (ε) using coarse resolution interpolated meteorology data, resulting in significant uncertainties in global GPP estimates. To address this challenge, we propose a simple yet effective ecosystem light-use efficiency (eLUE) model to GPP from FLUXNET tower sites to a global scale. Defined as GPP/PAR, eLUE differs from the traditional LUE (GPP/APAR, or ε) in that eLUE essentially integrates canopy light absorption (fAPAR) and the physiological efficiency of photosynthesis (ε), thus eliminating the need for a separate estimate of ε. eLUE was calibrated as a function of MODIS Enhanced Vegetation Index (EVI), and then GPP can be modelled directly as eLUE × PAR. To quantify the carbon cycle error budget, we analytically derived GPP uncertainty based on the law of error propagation. Cross-validation against 120 global FLUXNET sites, encompassing 11 plant functional types (PFTs), demonstrated satisfactory performance of the eLUE model (R2= 0.74, RMSE = 2.05 g C m-2d-1, NSE = 0.74), outperforming or performing comparably to more sophisticated models. Our estimate of global total terrestrial GPP, averaged between 2001 and 2024, is 135.12±11.02 Pg C yr-1. Meanwhile, we found a significant increasing trend in global total GPP at a rate of 0.26±0.06 Pg C yr-1(p2sequestration in terrestrial ecosystems across the Northern Hemisphere. We suggest that our eLUE model, with its robust performance and clear error representation, will help constrain the global carbon budget and improve the diagnostic analysis of carbon cycle dynamics and climate change feedback. The eLUE-GPP product, available at both global scale and FLUXNET sites, can be accessed for free at: https://doi.org/10.5061/dryad.v9s4mw74h.

Presents corrections to the paper, (“Global Upscaling of Gross Primary Productivity Using a Simple and Robust Modeling Scheme”).

Tensors capture the multi-dimensional structure of multi-view data naturally, resulting in richer and more meaningful data representations. This produces more accurate clustering results for challenging incomplete multi-view clustering (IMVC) tasks. However, previous tensor learning-based IMVC (TLIMVC) methods often build a tensor representation by simply stacking view-specific representations. Consequently, the learned tensor representation lacks good interpretability since each entry of it could not directly reveals the similarity relationship of the corresponding two samples. In addition, most of them only focus on exploring the high-order correlations among views, while the underlying consensus information is not fully exploited. To this end, we propose a novel TLIMVC method named Nonnegative Tensor Representation with Cross-view Consensus (NTRC$^{2}$) in this paper. Specifically, a nonnegative constraint and view-specific consensus are jointly integrated into the framework of the tensor based self-representation learning, which enables the method to simultaneously explore the consensus and complementary information of multi-view data more fully. An Augmented Lagrangian Multiplier based optimization algorithm is derived to optimize the objective function. Experiments on several challenging benchmark datasets verify our NTRC$^{2}$method's effectiveness and competitiveness against state-of-the-art methods.

Medium-resolution satellites have been instrumental in monitoring global vegetation dynamics over the past decades. The Fengyun (FY) 3-D satellite, a second-generation medium-resolution polar-orbiting meteorological satellite launched by the China National Meteorological Administration in 2017, plays a pivotal role in the low-orbiting group network for meteorological, oceanic, and land surface observations. Second-generation medium-resolution spectral imager (MERSI-II), a key component of FY-3D designed with inspiration from Moderate Resolution Imaging Spectroradiometer (MODIS), holds significant yet untapped potential for analyzing vegetation dynamics. This study embarks on a systematic analysis of FY-3D MERSI-II’s applicability in vegetation research, comparing it with Aqua MODIS. First, the spectrums of MERSI-II and MODIS are very close to each other, and compared to MODIS, MERSI-II is slightly overestimated in the red band and slightly underestimated in NIR bands; both reflectance products maintain good temporal stability when examined through desert sites, with the data being more fluctuating when the observation angle is larger, and the data availability for MERSI-II is slightly lower than that of MODIS due to its more stringent cloud detection algorithm. Finally, the results of the enhanced vegetation index with two bands (EVI2) and the vegetation parameter, green vegetation fraction (GVF), show that MERSI-II is also capable of monitoring vegetation dynamics with an optimal temporal resolution of 12 days and a spatial resolution of 2 km. Our comprehensive assessment confirms the remarkable capability of FY-3D MERSI-II in dynamic vegetation monitoring and underscores the need to make the most of its valuable observations. Our findings support the advancement of vegetation monitoring techniques and aid in adjusting the optimal spatial and temporal resolution of related products.

Changes in the sun zenith angle (SZA) alter the normalized difference vegetation index (NDVI) and introduce uncertainties into the estimation of vegetation biochemical and biophysical parameters. For the NDVI obtained from narrow swath width sensors, there is not a unified and easy-to-use approach to correct the sun-angle effect. In this study, the cosine correction model (CCM) was proposed to reduce the sun-angle effect on NDVI based on the path length (PL) of light calculated from the SZA without the need for multi-angle observations. The PL was found to be closely correlated to the simple ratio vegetation index (SR) and can mitigate the impact on the NDVI caused by SZA variations. The CCM performed well when correcting the sun-angle effect on NDVI for different types of data. After correction for the simulated data (e.g., the reference SZA of 10°), the coefficient of variation (CV) of the NDVI concerning SZA variations from 10° to 60° was reduced by 5.42%, and the root-mean-square error (RMSE) was reduced by 0.049. For the field-measured data, the CV of the NDVI under various SZAs was reduced by up to 5.55% after correction, and the maximum difference between the uncorrected and corrected NDVI was 0.099. The RMSE of corrected nadir NDVI from MODIS satellite data was reduced by 34.2% on average. The CCM, as an easily-implemented method, can attenuate the sun-angle effect on NDVI without relying on the BRDF products and hence has the potential to improve the accuracy of remote sensing monitoring of vegetation dynamics.

Vegetation phenology represents a key attribute of an ecosystem and plays an important role in regulating terrestrial carbon and water cycles. Here we used observations from the Advanced Himawari Imager (AHI) onboard the new generation Japanese geostationary (GEO) satellite Himawari-8. The objective was to assess the potentials of retrieving savanna phenology from H8/AHI vegetation index time series along a 1100 km ecological rainfall gradient, known as the North Australian Tropical Transect (NATT). Key phenology transition dates (start, peak, end, and length of season) were extracted from H8/AHI Enhanced Vegetation Index (EVI) time series and then compared to those extracted from MODIS EVI. Results showed that H8/AHI with its higher temporal resolution offers several advantages in monitoring savanna vegetation dynamics than MODIS. The denser EVI time series from H8/AHI not only avoids the artefacts caused by data interpolation but also enabled a more certain characterization of seasonal vegetation growth patterns than MODIS. The short lived, rainfall pulse-driven vegetation cycles in dry savannas were also better detected using H8/AHI.

Let $\Gamma$ be a graph with vertex set $V(\Gamma)$. A subset $C$ of $V(\Gamma)$ is called a perfect code in $\Gamma$ if $C$ is an independent set of $\Gamma$ and every vertex in $V(\Gamma)\setminus C$ is adjacent to exactly one vertex in $C$. A subset $C$ of a group $G$ is called a perfect code of $G$ if there exists a Cayley graph of $G$ which admits $C$ as a perfect code. A group $G$ is said to be code-perfect if every proper subgroup of $G$ is a perfect code of $G$. In this paper we prove that a group is code-perfect if and only if it has no elements of order 4. We also prove that a proper subgroup $H$ of an abelian group $G$ is a perfect code of $G$ if and only if the Sylow 2-subgroup of $H$ is a perfect code of the Sylow 2-subgroup of $G$. This reduces the problem of determining when a given subgroup of an abelian group is a perfect code to the case of abelian 2-groups. Finally, we determine all subgroup perfect codes in any generalized quaternion group.

Rangelands comprise ∼81% of Australia's landmass, extend over a broad range of climates and vegetation types, and provide important social-economical functions (Fig. 1). The climate of Australia's rangelands is extremely variable. This variability was reflected in recent events of extreme flooding immediately following one of the most intense droughts in history over the early 21stcentury [1,2]. These extreme climatic events provide an opportunity to assess how Australian rangelands respond to hydroclimatic variations, and further generalise knowledge regarding resilience of these ecosystems to contrasting drought and wet extremes.