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”).

Large inaccuracies remain in the traditional convective cloud identification system over the plateau area struggles to capture mid- and low-level clouds due to the complex topographic effects influencing cloud pressure. Besides, the lack of efficient nighttime cloud-type products hinders progress in the research on the diurnal cycle and seasonal variation in convective clouds (including deep convection and cumulus clouds) over the Tibet Plateau (TP). In this study, we incorporated Shapley additive explanation (SHAP) tuning into the fundamental machine learning CatBoost Classifier technology, which was applied to a 24-h convective cloud detection algorithm utilizing cloud top temperature (CTT) and optical thickness data derived from the Himawari-8 infrared channels. This specifically tackles the problem of underestimating cumulus clouds in plateau areas. This innovative product enables capturing important processes of deep convection, especially for cumulus clouds, facilitating a comprehensive spatial-temporal analysis of the entire TP region. The results confirm that the new algorithm shows significant improvements in cumulus detection compared to the official cloud product of Himawari-8. In addition, the deep convective clouds have also improved from 35.85% to 63.05% for hit rate (HR) value. The analysis reveals a notable diurnal variation in convective cloud activity over the TP, predominantly occurring from noon to night. This finding underscores the influential heating role of the TP in convective activity.

Remote Sensing Image Super-Resolution (RSISR) techniques play a crucial role in various remote sensing applications. However, deep learning-based methods applied to RSISR encounter difficulties in learning complex features of remote sensing images and modeling long-term correlations between pixels. This study proposes aMulti-Scale Sliding Window Attention Generation Adversarial Network (MSWAGAN), which combines the advantages of Convolutional Neural Networks (CNN) and Transformers to overcome these limitations. The MSWAGAN consists of three main parts. In the shallow feature extraction part, CNN is used to extract shallow features from remote sensing images. The deep feature extraction part is divided into two stages. Firstly, amulti-scale sliding window attention (MSWA)is designed to replace the multi-head attention (MHA) in the Transformer. MSWA can learn local multi-scale complex features of remote sensing images without increasing the number of parameters in MHA. Then, the Transformer is utilized to learn global image features and model the long-range correlations between pixels. The image reconstruction part utilizes sub-pixel convolution for feature upsampling. Furthermore, in order to extend the application of super-resolution remote sensing images, a cross-sensor real multi-spectral RSISR dataset consisting of Landsat-8 (L8) and Sentinel-2 (S2) images was constructed, and a series of experiments to improve the spatial resolution of L8 images from 30m to 10m in B, G, R and Near Infrared (NIR) bands were conducted. Experimental results demonstrate that our method outperforms some of the latest SR methods.

In the absence of high-resolution sensors, super-resolution( SR) algorithms for remote sensing imagery improve the spatial resolution of the images. Currently, most of the SR algorithms are based on deep learning methods e.g., convolutional neural networks(CNN). Particularly, the generative adversarial networks(GANs) have demonstrated accepted performances in image super-resolution owing to their powerful generative capabilities. However, remote sensing images have complex feature types, which largely limits the performances of GAN-based SR methods for real satellite images. To address this issue, an attention mechanism and a multi-scale structure are introduced into the generator of the GAN network, and a multi-scale attention GAN(MSAGAN) is constructed in this study. We sequentially arrange the channel attention module and the spatial attention module after the multi-scale structure to emphasize important information, suppress unimportant information details, improve the model’s performance. Furthermore, we add residual connections and dense blocks to further enhance the performance of the generative network by increasing its depth. We compared to other existing deep learning-based SR methods, our proposed MSAGAN algorithm performed better in generating high spatial satellite images.

Spatiotemporal data fusion is a methodology to generate images with both high spatial and temporal resolution. Most spatiotemporal data fusion methods generate the fused image at a prediction date based on pairs of input images from other dates. The performance of spatiotemporal data fusion is greatly affected by the selection of the input image pair. There are two criteria for selecting the input image pair: the “similarity” criterion, in which the image at the base date should be as similar as possible to that at the prediction date, and the “consistency” criterion, in which the coarse and fine images at the base date should be consistent in terms of their radiometric characteristics and imaging geometry. Unfortunately, the “consistency” criterion has not been quantitatively considered by previous selection strategies. We thus develop a novel method (called “cross-fusion”) to address the issue of the determination of the base image pair. The new method first chooses several candidate input image pairs according to the “similarity” criterion and then takes the “consistency” criterion into account by employing all of the candidate input image pairs to implement spatiotemporal data fusion between them. We applied the new method to MODIS-Landsat Normalized Difference Vegetation Index (NDVI) data fusion. The results show that the cross-fusion method performs better than four other selection strategies, with lower average absolute difference (AAD) values and higher correlation coefficients in various vegetated regions including a deciduous forest in Northeast China, an evergreen forest in South China, cropland in North China Plain, and grassland in the Tibetan Plateau. We simulated scenarios for the inconsistency between MODIS and Landsat data and found that the simulated inconsistency is successfully quantified by the new method. In addition, the cross-fusion method is less affected by cloud omission errors. The fused NDVI time-series data generated by the new method tracked various vegetation growth trajectories better than previous selection strategies. We expect that the cross-fusion method can advance practical applications of spatiotemporal data fusion technology.

GCOM-C SGLI land products are being developed toward better understandings of terrestrial carbon cycle and future climate change projection. In this paper, we will introduce two research products, Leaf Area Index (LAI) and Net Primary Productivity (NPP) and their application of these products to earth system models (ESMs). The LAI product as research product is an improved version from standard product. The LAI retrieval is based on FLiES canopy radiative transfer model with improved treatment to boreal to arctic ecosystems by including overstory and understory vegetation. NPP and GPP products are based on improved version of BESS (Breathing Earth System Simulator) model, and initial results are discussed. Using these potential products, our goal is to evaluate and improve ESMs. We will show initial comparison of satellite-based products of LAI, GPP, NPP with ESM outputs, and evaluate similarity and differences between satellite products and model outputs.

Remote estimation of fractional vegetation cover (FVC) in arid and semiarid areas is crucial for understanding their roles in global climate changes and maintaining their ecological sustainability. Among the existing algorithms for remote estimation of FVC, the linear spectral mixture analysis (LSMA) has been widely adopted owing to its simplicity and flexibility. However, the spectral variability of endmembers is still a big challenge that would largely decrease the estimation accuracy of LSMA. In this letter, we proposed a novel unmixing algorithm by integrating an orthogonal Fisher transformation into the LSMA (fLSMA). Two evaluation experiments were conducted: one was based on simulations; the other was based on a field survey in Xilingol grassland, China. The proposed fLSMA yielded remarkably higher accuracies and precisions than the conventional LSMA (cLSMA), weighted SMA (wSMA) in the first experiment. In the second experiment, a root-mean-square error (RMSE) of 0.11 was derived for the fLSMA, compared with the RMSE values larger than 0.36 for the cLSMA and wSMA. Although the performance of fLSMA was somehow similar to the multiple endmember SMA (MESMA) in the two evaluation experiments, the fLSMA was much less time-consuming than the MESMA in massive computations. The results indicate the potential of the proposed fLSMA in long-term monitoring of FVC in semiarid areas based on satellite observations.

Remote estimation of the vertically averaged diffuse attenuation coefficient over a water layer K̅d(λ) is of great importance in understanding and modeling the physical, chemical, and biological processes in water bodies. A semianalytical algorithm was previously proposed to remotely estimate K̅d( λ) based on the retrieval of the total absorption and backscattering coefficients ( a and bb ). The algorithm has been effectively applied to clear and slightly turbid oceanic waters, but its applicability in turbid inland waters is still unknown. In this study, the relationship between a and bb on the one hand with K̅d(λ) used in the semianalytical algorithm was first validated by the measured a and bb. Second, the semianalytical algorithm was combined with the so-called QAA_Turbid (quasi-analytical algorithm for turbid waters) to remotely estimate K̅d(λ) for highly turbid waters. We tested the performance of the combined algorithm at wavelengths of 443, 556, and 669 nm by using a data set collected from a turbid lake in Japan. The validation results demonstrated that it could estimate K̅d(λ) (ranging from 1.94 to 9.47 m-1) with root-mean-square error and relative error values of 0.17 and 12.96%, respectively. These results indicate the great potential of the semianalytical algorithm to accurately monitor the spectra of K̅d(λ) for turbid inland waters from satellite observations.

The Fraunhofer line discriminator (FLD) principle applied on the atmospheric oxygen absorption feature around 761 nm ( O2-A band) has been widely used to retrieve solar-induced chlorophyll fluorescence (Fs) from remotely sensed data. In this letter, however, we address a violation of the basic assumption caused by O2absorption feature changes, and we evaluate the impact of diminishing O2absorption with the increase in ground altitude on Fsretrieval accuracy. The Fs retrieval accuracy substantially decreases in higher ground altitude areas for the standard FLD and three-band FLD methods, in which relative estimation errors increase approximately 70%-80% and 10%-16%, respectively, with an increase in ground altitude from 0.01 to 4.5 km. However, this increasing trend in Fs retrieval error does not occur with the use of the improved FLD (iFLD) method, which exhibits smaller than 5% of changes in relative estimation errors. Analytical analyses reveal the causes of the changes in Fs retrieval accuracy by the three methods. Based on these findings, the iFLD method is recommended to be used in cross-altitude studies or for Fs estimation in higher ground altitude areas under conditions of low radiometric noise, although its high sensitivity to noise should be taken into account. The investigations in this letter further indicate that the impact of ground altitude should be included in the uncertainty budgets of Fs retrievals and should be considered in the interpretation of Fs signals at the O2-A absorption feature.

Remote estimation of inherent optical properties (IOPs) for water bodies cannot only provide indicators of water quality, but also be used in the study on biological and biogeochemical processes of waters. The quasi-analytical algorithm (QAA) is a simple and effective method to retrieve IOPs from remote-sensing reflectance (Rrs). The QAA has been widely validated and applied in oceans, but its application in inland waters is far less extensive. In this paper, the QAA was enhanced to retrieve IOPs for turbid inland waters based on the bandwidths of Medium Resolution Imaging Spectrometer (MERIS). The enhancement was achieved by proposing a semi-analytical model to estimate the spectral slope of particle backscattering, as well as a novel estimation model for phytoplankton absorption coefficient at 443 nm. Two data sets (i.e., noise-free synthetic data and in-situ data) were collected to assess the performance of the enhanced algorithm. Results show that the algorithm yields almost error-free estimations for total absorption and backscattering coefficients and estimations for phytoplankton absorption at 443 nm with acceptable accuracy in the case of synthetic data set. For the in-situ data set, the algorithm retrieves the total absorption coefficients (ranging 0.337-8.331 m-1) with root-mean-square-error in log scale (RMSE) and bias in log scale lower than 0.130 and 0.094, respectively, and phytoplankton absorption at 443 nm (ranging 0.378-4.669 m-1) with RMSE and bias in log scale of 0.151 and 0.096, respectively. These results indicate the potential of the enhanced QAA to accurately retrieve the IOPs from MERIS satellite observations for inland waters.

The matrix inversion method (MIM) is an effective algorithm for estimating water constituent concentrations in case II waters. To apply this method, appropriate and accurate specific inherent optical properties (SIOPs) for each constituent in water are essential. However, many routine observations of lake water quality do not in fact provide SIOPs, thus limiting the application of the MIM. In this paper, an alternative MIM method based on linear matrix inversion theory was proposed to relax the requirement of SIOPs measurement. For this, so-called ESIOPs (Estimated SIOPs) were first derived by an unusual application of MIM based on adequate calibration samples; then the water constituent concentrations for the whole study area were retrieved by the standard application of MIM based on the derived ESIOPs. For each calibration sample, measurement of the reflectance spectrum and corresponding water constituent concentrations, which can be obtained from periodical satellite data and routine field surveys, is required. The performance of the proposed method was evaluated using the simulation data from Hydrolight and three MEdium Resolution Imaging Spectrometer Instrument (MERIS) images. The results showed that this method yielded satisfactory estimations of the water constituent concentrations for the noise-contaminated simulation data sets. For MERIS data in our study area (Lake Kasumigaura, Japan), the average bias (mean normalized bias or MNB) and relative random uncertainty (normalized root mean square error, or NRMS) were in the range of -11.2% to 3.4% and 4.8% to 29.7% for each water constituent concentration. These findings imply that the algorithm proposed in this study is theoretically reasonable and practically applicable.

A three-band index was previously proposed and successfully utilized to estimate the chlorophyll-a concentration (Chl-a) in case-II waters. However, this index shows uncertainties in highly turbid situations. In this study, an enhanced three-band index is proposed to solve this problem. Since the new index employs bands that are identical to those of the original threeband index, it can be applied to Medium Resolution Imaging Spectrometer (MERIS) data. The performance of the index was evaluated using the data collected from two turbid Asian lakes: Lake Kasumigaura, Japan, and Lake Dianchi, China. The results showed that the Chl-a predicted by the enhanced threeband index was strongly correlated with the measured Chl-a (R2> 0.83), and the root-mean-square error (rmse) and the normalized root-mean-square error (NRMS) were both reduced for the two lakes (for Lake Kasumigaura, rmse from 13.97 to 8.68 mg · m-3and NRMS from 19.01% to 12.30%; for Lake Dianchi, rmse from 41.29 to 15.28 mg · m-3and NRMS from 35.83% to 21.34%). These findings imply that, if accurately atmospheric-corrected MERIS data are available, the enhanced three-band index could be used for mapping Chl-a even in highly turbid case-II waters.

Several spectral unmixing techniques have been developed for subpixel mapping using hyperspectral data in the past two decades, among which the fully constrained least squares method based on the linear spectral mixture model (LSMM) has been widely accepted. However, the shortage of this method is that the Euclidean spectral distance measure is used, and therefore, it is sensitive to the magnitude of the spectra. While other spectral matching criteria are available, such as spectral angle mapping (SAM) and spectral information divergence (SID), the current unmixing algorithm is unable to be extended to these measures. In this paper, we propose a unified subpixel mapping framework that models the unmixing process as a best match of the unknown pixel's spectrum to a weighted sum of the endmembers' spectra. We introduce sequential quadratic programming to solve the nonlinear optimization problem encountered in the implementation of this framework. The main feature of this proposed method is that it is not restricted to any particular similarity measures. Experiments were conducted with both simulated and Hyperion data. The tests demonstrated the proposed framework's advantage in accommodating various spectral similarity measures and provided performance comparisons of the Euclidean distance measure with other spectral matching criteria including SAM, spectral correlation measure, and SID.