3D hand pose estimation has garnered great attention in recent years due to its critical applications in human-computer interaction, virtual reality, and related fields. Accurate estimation of hand joints is essential for high-quality hand pose estimation. However, existing methods neglect the importance of Distal Phalanx Tip (TIP) and Wrist in predicting hand joints overall and often fail to account for the phenomenon of error accumulation for distal joints in gesture estimation, which can cause certain joints to incur larger errors, resulting in misalignments and artifacts in pose estimation and degrading the overall reconstruction quality. To address this challenge, we propose a novel segmented architecture for enhanced hand pose estimation (EHPE). We perform a local extraction of the TIP and wrist, thus alleviating the effect of error accumulation on the prediction of the TIP and further reduce the predictive errors for all joints on this basis. EHPE consists of two key stages: In the TIP and Wrist Joints Extraction stage (TW-stage), the positions of the TIP and wrist joints are estimated to provide an initial accurate joint configuration; In the Prior Guided Joints Estimation stage (PG-stage), a dual-branch interaction network is employed to refine the positions of the remaining joints. Extensive experiments on two widely used benchmarks demonstrate that EHPE achieves state-of-the-art performance.

Solar-induced chlorophyll fluorescence (SIF) has a high correlation with gross primary production (GPP) at various spatiotemporal scales. However, this relationship varies with the changing environmental conditions, requiring further investigation and interpretation at various scales. In this study, we investigated (i) the temperature sensitivities of the SIF/GPP ratio, (ii) their correlation in 33 evergreen needleleaf forest sites using TROPOMI SIF and flux tower datasets from 2018 to 2021, and (iii) the temperature responses of the ratio of quantum yield of fluorescence to quantum efficiency of photosystem II (ΦF/ΦPSII) using leaf measurements from 3 datasets. At the canopy scale, SIF effectively tracked the GPP during the year, and the SIF/GPP ratio was relatively stable from 8 to 18 °C, while it increased at cold (28°C). Furthermore, the SIF–GPP correlation was strongest at moderate temperatures (~25 °C). At the leaf scale, ΦF/ΦPSII also increased at low temperatures, which demonstrated the direct impact of temperature on the energy partitioning in the light reaction. This study indicated the need to consider the changing temperature and energy partitioning during the light reaction when using fluorescence to track photosynthesis, especially under extreme environments.

This work proposes a new retrospective motion correction method, termed DCGAN-MS, which employs disentangled CycleGAN based on multi-mask k-space subsampling (DCGAN-MS) to address the image domain translation challenge. The multi-mask k-space subsampling operator is utilized to decrease the complexity of motion artifacts by randomly discarding motion-affected k-space lines. The network then disentangles the subsampled, motion-corrupted images into content and artifact features using specialized encoders, and generates motion-corrected images by decoding the content features. By utilizing multi-mask k-space subsampling, motion artifact features become more sparse compared to the original image domain, enhancing the efficiency of the DCGAN-MS network. This method effectively corrects motion artifacts in clinical gadoxetic acid-enhanced human liver MRI, human brain MRI from fastMRI, and preclinical rodent brain MRI. Quantitative improvements are demonstrated with SSIM values increasing from 0.75 to 0.86 for human liver MRI with simulated motion artifacts, and from 0.72 to 0.82 for rodent brain MRI with simulated motion artifacts. Correspondingly, PSNR values increased from 26.09 to 31.09 and from 25.10 to 31.77. The method's performance was further validated on clinical and preclinical motion-corrupted MRI using the Kernel Inception Distance (KID) and Fréchet Inception Distance (FID) metrics. Additionally, ablation experiments were conducted to confirm the effectiveness of the multi-mask k-space subsampling approach.

Effective retrieval is essential for finding resources in demand handily amidst extensive data records in data warehouse. Mainstream map retrieval methods suffer from intention gap problem and are incapable to describe sophisticated user demands precisely due to the limits of low- and middle-level text or visual feature matching, resulting in unsatisfactory retrieval results. Such limitations are more marked when map retrieval demands were characterized with joint constraints of geographic concepts. To address this issue, we propose a map retrieval intention recognition method to perceive user demands with relevance feedback samples and geographic semantics guidance. Specifically, we construct a hierarchical intention expression model to describe retrieval goals and their multi-dimensional attribute constrains; incorporate geographic ontologies to provide semantic guidance and facilitate recognition; utilize the frequent itemset mining (FIM) algorithm Apriori to generate intention candidates from relevance feedback samples, and search for the optimal intention set by adopting the minimum description length (MDL) principle. The experiments verify the effectiveness of Apriori algorithm and MDL principle on intention recognition. The proposed method outperforms the FIM algorithm Gene Ontology (RuleGO) and the Decision Tree algorithm with Hierarchical Features (DTHF) with higher recognition accuracy and noise tolerance. Furthermore, through our sample augmentation strategy, the method yields promising recognition accuracy even when the feedback sample size is as low as ten, substantially reducing the feedback burden in human-computer interactions. We envision that the application of our method in spatial data infrastructures (SDIs), such as geoportals and catalogue services, could enhance the quality of service and user experience in geospatial data discovery.

Solar-induced chlorophyll fluorescence (SIF) provides a promising approach to monitoring plant photosynthesis. To this end, numerous retrieval algorithms have emerged and been developed to estimate ground, airborne, and satellite SIF; however, the accuracy of SIF retrieval methods in the red band is still somewhat limited. One potential obstacle that hinders the accuracy of retrieval of red SIF is the difficulty of modeling the true shape of the reflectance in the O2-B band. To overcome this issue, herein, an improved spectral-fitting method (SFM) using principal component analysis (PCA) data-driven reflectance reconstruction method, SFM-PCA, is proposed based on a novel SIF-free reflectance dataset to improve the accuracy of SIF retrieval in the O2-B band. In this work, the SFM-PCA method was validated using a field leaf dataset, canopy simulations, and tower-based canopy measurements. Compared to the true red SIF values at either the leaf or canopy levels, the SFM-PCA method was found to perform better than previous methods, with$R^{2}$values of 0.97 and 0.999 for leaf measurements and canopy simulations, respectively, and corresponding normalized root-mean-square error (NRMSE) values of 6.98% and 2.335%. For the tower-based measurements, the red SIF retrieved using the SFM-PCA method was also more consistent with the O2-A SIF. This indicates that it is feasible to make use of principal components (PCs) derived from SIF-free reflectance measurements to accurately model the true shape of the reflectance spectrum in the O2-B band and to improve ground-based SIF retrieval in the red band. This also has the potential to be applied to the satellite-based measurements.

Quantifying and monitoring land surface evapotranspiration (ET) is an essential task for understanding the earth’s water, energy, and carbon cycles. ET, specifically plant transpiration ($T$), is closely linked to the photosynthesis, which is coupled through stomatal function. However, the mechanistic links between sun-induced chlorophyll fluorescence (SIF) information indicating canopy photosynthetic activity and$T$are complex and difficult to derive empirically. An empirical SIF-$T$relationship at ecosystem scale was developed and coupled to the two-source energy balance model (TSEB-SIF) to estimate the ET and its components,$T$and soil evaporation,$E$. By comparing model predictions with observations from an irrigated cropland site located in a semiarid region, the TSEB-SIF model shows a slightly better performance to the TSEB model in estimating ET, especially under water deficit conditions. Moreover, the TSEB-SIF model more reliably partitioned the$T$from ET, while the TSEB model tended to overestimate the contribution of$T$to ET.

Remote sensing of solar-induced chlorophyll fluorescence (SIF) has been widely investigated with satellites/sensors covering the spectral range of SIF emission with fine spectral resolutions. The potential precision of SIF retrievals is limited by instruments’ spectral specifications. Here, the influence of spectral characteristics (spectral resolution (SR), signal-to-noise ratio (SNR), and spectral coverage) on data-driven SIF retrieval was assessed using simulations, and the SIF retrieval capability of the obsolete, in-orbit, and planned satellites was evaluated from the spectral perspective. As a result, the 757–759nm and 735–758nm fitting windows were found to be optimal for far-red SIF retrieval on satellites with fine (-2sr-1nm-1, followed by satellites with moderate SRs (0.1–0.5 nm) such as CO2M, TROPOMI and GOME-2 (RMSE*-2sr-1nm-1). The high SNR of SCIAMACHY did not improve the far-red SIF retrieval greatly (RMSE* = 0.47 mW m-2sr-1nm-1) but obtained a low RMSE* at the red band (0.43 mW m-2sr-1nm-1). The highest red SIF retrieval potential was found on TROPOMI, with an RMSE* of 0.41 mW m-2sr-1nm-1. TEMPO and FLEX gave poor performance (RMSE* > 0.9 mW m-2sr-1nm-1) with their current spectral specifications. Improvement of the spectral characteristics is still needed to obtain precise SIF retrievals.

Solar-induced chlorophyll fluorescence (SIF) is related to photosynthesis and can serve as a remote sensing proxy for estimating photosynthetic energy conversion and carbon uptake. In this paper, three key factors affecting the relationship between SIF and gross primary production (GPP) were investigated using both models and observations on winter wheat (C3 crop) and maize (C4 crop). Firstly, the bidirectional SIF emission was investigated by multi-angular spectral measurement, which was found to be similar to that of the canopy reflectance in the solar principal plane. Secondly, the wavelength dependent predictive power of SIF to estimate GPP was assessed using diurnal observations on winter wheat. As indicated by the preliminary studies, the far-red SIF may be more reliable for remote sensing of GPP than the red SIF due to the heterogeneous and diverse vegetation growth status at regional or global scale. Finally, the potential of far-red SIF to track the diurnal and seasonal variations in GPP for C3 and C4 crops was investigated, and the result show that the GPP SIF relationship is dependent on the type of photosynthesis.

Numerous observations and modeling results have shown that there is noticeable directional variation in the solar-induced chlorophyll fluorescence (SIF), and this has not been well investigated. In this paper, 16 multiangular spectral observations were carried out on winter wheat to assess the bidirectional SIF emission. First, the bidirectional SIF emission was retrieved from the spectral measurements made by a high-performance QE Pro spectrometer and an automatic multiangle observation system using the 3FLD algorithm. The bidirectional shape of the SIF emission was found to be similar to that of the canopy reflectance in the solar principal plane, with a mean correlation coefficient of 0.94 and 0.97 at the O2-B and O2-A bands, respectively. The modified Rahman-Pinty-Verstraete (MRPV) model, a semiempirical bidirectional reflectance distribution function (BRDF) model, was then employed to describe the bidirectional variation in the SIF and reflectance with a mean root-mean-square-error value of 0.036 and 0.041 mW m-2sr-1nm-1for the SIF at the O2-B and O2-A bands, respectively. Finally, both the bidirectional reflectance and SIF were BRDF corrected to nadir using the MRPV model. Most of the directional variation was successfully corrected by this method-the mean correction ratios were 87% and 81% for the reflectance at the O2-B and O2-A bands and 84% and 72% for the SIF at the O2-B and O2-A bands, respectively. Therefore, the SIF emission cannot be regarded as isotropic, and the high similarity between the bidirectional SIF and reflectance, together with the BRDF correction results, indicates that the bidirectional SIF emission can be adjusted using either the BRDF reflectance models or prior knowledge.

Methods based on Fraunhofer line discrimination (FLD) for solar-induced chlorophyll fluorescence retrieval require making assumptions or estimations about true reflectance, which is a large source of error, particularly at the O2-B band. This letter presents an alternative solution, which is named the pFLD method, based on principal components analysis to reconstruct the reflectance spectra. The principal components were generated with simulated reflectance spectra covering the most common conditions of vegetation. The pFLD method has been tested with both simulated and field experiment data sets. Compared with the widely used three-band FLD and improved FLD methods, pFLD showed better performance at the O2-B band, particularly when the spectral resolution (SR) or the signal-to-noise ratio was relatively low. The pFLD method can be also successfully applied to field measurements with credible accuracy even at low SR.