Solar-induced chlorophyll fluorescence (SIF), an emission of light that occurs simultaneously with plant photosynthesis, serves as an effective probe for photosynthetic activity. In recent years, satellite-retrieved SIF data have gained extensive attentions across ecological, hydrological, and climate change studies. However, these applications are largely limited by inconsistencies in retrieval methods, instrumental characteristics, overpass times and viewing-illumination geometries of a single satellite platform. Moreover, the spatiotemporal discontinuity and low spatiotemporal resolution of SIF retrievals also restrict the application of SIF for monitoring global ecological processes. To address such issue, this study develops a framework for harmonizing SIF retrievals from SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY), Global Ozone Monitoring Experiment 2 (GOME-2), and Orbiting Carbon Observatory-2 (OCO-2) satellites with cumulative distribution function matching and machine learning algorithm to generate the harmonized SIF (HSIF) - a global daily SIF product spanning the period of 2003-2023 with a 0.05 degrees spatial resolution. Across the study period, the global mean SIF was 0.18 mW m-2 nm-1 sr-1 with a significant increasing trend of 5.7 & times; 10-4 mW m-2 nm-1 sr-1 yr- 1, resulting in a cumulative increase of 7%. Validation against 10 tower-measured SIF showed robust performance in reproducing GPP-SIF relationship, and inter-comparisons with 4 reconstructed SIF products showed HSIF shares consistent spatiotemporal patterns. The HSIF product provides a valuable data source for advancing the understanding of global photosynthetic activity, carbon fluxes, and ecosystem responses to transient vegetation dynamics.

Plant transpiration plays a critical role in global water and energy cycles, requiring better process understanding as climate change intensifies drought stress and alters plant responses. Most hydrological models such as the widely-used SWAT lack representation of plant hydraulics, the mechanistic processes controlling plant water regulation and transpiration. This study developed SWAT-PHS by integrating a plant hydraulics scheme (PHS) into SWAT hydrological model, enabling explicit simulation of root water uptake, sap flow, storage and transpiration at 30-minute timescales for watershed-scale modeling. In the Hanjiang River Basin, SWAT-PHS mitigated overestimation of runoff during the rainy season and underestimation during the dry season, reducing the overall simulation error by 29% across the entire simulation period. The model can simulate reasonable plant water dynamics, including diurnal transpiration patterns and drought responses showing declining transpiration flux, hydraulic buffering through stem water storage, and depth-dependent root water uptake strategies. Sensitivity analysis shows that SWAT-PHS captured mechanistic relationships between plant hydraulic traits and transpiration, with root distribution and stem capacitance positively affecting annual transpiration while vulnerability parameters showed negative effects. This work provides a pathway for improving hydrologic modeling and water resource management by better representing plant water regulation under climate change and expected intensifying water stress conditions.

Rising concentrations of atmospheric CO2 (ca) increase plant photosynthesis (An) and reduce stomatal conductance (gs). This increases the intrinsic water-use efficiency (iWUE = An / gs), a major proxy of tree adaptation to climate change. However, whether an increase in iWUE leads to a concomitant increase in tree growth remains in dispute, prompting interest in theoretical links between iWUE and tree productivity. Here using an optimality theory for kinetics of stomatal aperture, we establish an envelope delineating maximal relative increases in tree productivity that can be inferred/expected from relative increases in iWUE. The resulting expressions are used to interpret relations between iWUE (an observable proxy) and tree growth (the target variable), using available experimental data from manipulation experiments and tree-ring isotopes. While rising ca increases iWUE, proportional increases in tree growth are unlikely given ameliorating environmental (for example, rising atmospheric dryness) and anatomical/physiological (for example, tree height) influences.

Reliable and continuous precise global navigation satellite system (GNSS) positioning serves as a cornerstone for intelligent transportation, robotic systems, and emerging Internet of Things (IoT) applications. However, achieving such reliability in urban environments remains challenging due to frequent carrier-phase outliers. Conventional antioutlier methods are highly dependent on the accuracy of prior absolute pose from inertial navigation system (INS) or other aiding sources. Once the pose estimates degrade, outlier detection may fail or misclassify GNSS measurements, leading to further degradation in positioning results, forming a vicious cycle. To address this problem, this article proposes a novel carrier-phase outlier-resistance method that is immune to prior pose error. A two-degree-of-freedom time-differenced carrier-phase (TDCP)/vehicle dead-reckoning (VDR) model is designed to eliminate dependence on prior pose accuracy by leveraging high-precision VDR relative poses and conducting a detailed error analysis of the TDCP and VDR models. Based on this model, we construct a two-step framework that integrates an improved random sample consensus (RANSAC) approach with a multiepoch finite state machine (FSM) for reliable outlier detection. Experimental results in complex urban scenarios demonstrate that the proposed method achieves a horizontal position error (CEP95) of approximately 1 m, improving by 63.6% and 56.7% over the IGG-III and Tukey robust methods, respectively, while maintaining the maximum horizontal position error below 2 m. Ablation experiments show that the proposed method maintains strong outlier resistance even with prior absolute position disturbances up to 300 m. Cycle-slip simulations demonstrate high detection recall even when up to 75% of satellites are contaminated by outliers.

Canopy conductance (Gc) is critical for assessing forest ecosystem responses to climate change. The Hanjiang River Basin represents a typical subtropical humid region in China and serves as the water source for the middle route of the South-to-North Water Diversion Project. Studies on canopy conductance of trees in this region remains lacking, limiting the corresponding understanding of forest transpiration and hydrological cycle. In this study, we measured sap flux density and derived canopy conductance (Gc) of three representative tree species of oak, pine and poplar from 2021 to 2023. We then explored characteristics of Gc and the corresponding responses to key environmental factors. Results showed that incoming short-wave radiation (Rsi) and vapour pressure deficit (VPD) are the two major controlling factors of Gc, and a crossed hysteresis pattern exists in the diurnal response of Gc to Rsi for all the three species, whereas the response of diurnal Gc to VPD (and air temperature) exhibits a closed clockwise hysteresis. The response of Gc to VPD has a clear VPD threshold: A linear relation was found when VPD is lower than the threshold, and a logarithmic relation was found when VPD is higher than the threshold. When taking Rsi and VPD as controlling factors of Gc, a statistical model for canopy conductance was developed; the model can explain over 64% of the variation in Gc for all the three tree species. This study contributes important knowledge to understanding the canopy conductance of representative tree species in the Hanjiang River Basin.

Soil moisture is a key component of the Earth system and is important for ecosystem functioning and water resources. However, existing approaches still face limitations in soil moisture forecast skill and spatial resolution. Here, we develop an Artificial Soil Moisture Forecasting Model (ASM) with a global–regional nested framework that links global low-resolution prediction with regional high-resolution forecasting. ASM consistently outperforms representative deep learning models across forecast lead times, with ablation experiments confirming the contributions of its major architectural components. Compared with ECMWF, ASM more closely reproduces ERA5 soil moisture fields and preserves greater spatial heterogeneity at 1° resolution. At a 14 day lead time, ASM achieves an ACC of 0.612, demonstrating reliable early-subseasonal forecast skill. At the regional scale, ASM provides 0.1° soil moisture forecasts for Henan Province, China, and Southern Africa, while improving extreme drought detection relative to ECMWF-driven forecasts. Attribution analysis shows that antecedent soil moisture is the dominant predictor, accounting for 61.2% of the total attribution and highlighting the importance of soil moisture memory. Soil-moisture-only autoregressive experiments further highlight that external atmospheric forcing remains essential for maintaining forecast skill. Overall, ASM provides an scalable and interpretable framework for synoptic-to-early-subseasonal soil moisture forecasting.

Forest litter cover significantly alters the ground surface resistance, which influences the water and energy transfer processes between the land surface and atmosphere. Penman-Monteith (PM) model is widely used to estimate land surface evapotranspiration (ET). However, it was shown to overestimate ET during the period when foliar litter covers the ground surface. Therefore, incorporating the effect of foliar litter on ground surface resistance can potentially improve ET estimates. In this study, we proposed a foliar litter surface resistance model to describe the effect of litter cover on water vapor transport, then incorporated the litter surface resistance into the PM model (noted as PM-EL model). The performance of the PM-EL model was evaluated using observations from 18 deciduous broadleaf forest (DBF) flux sites across global FLUXNET2015 datasets. Results showed that: (1) both the PM model and the PM-EL model are most sensitive to ground surface resistance among resistance parameters; (2) the proposed litter resistance model is capable of describing the seasonal dynamic of litter in DBF (3) the PM-EL model, has been shown to improve ET estimates with the coefficient of determination increased by 8.5 %, the mean absolute error and relative root mean square error decreased by 22.1 % and 20.9 %, respectively. The study contributes to our understanding of the effect of litter cover on ET estimation and provides insights into forest ecohydrology and land surface mass and energy transfer processes.

Visual-inertial state estimation is widely employed in the Internet of Things, with filter-based visual-inertial odometry (VIO) being a popular algorithm due to its balance between computational efficiency and localization accuracy. However, the localization performance of the commonly used multi-state constraint Kalman filter (MSCKF)-based VIO is suffering from linearization errors in feature three-dimensional (3D) positions and delayed measurement updates. Targeting more accurate and robust localization, we incorporate the pose-only representation into the filter-based VIO and propose a pose-only representation-based Kalman filter (PO-KF) in this paper. Leveraging the decoupling of camera poses and feature positions in the pose-only representation, the proposed PO-KF explicitly eliminates feature 3D coordinates from its measurement equation. As a result, the linearization errors caused by feature positions can be removed efficiently, while immediate updates of visual measurements can be conducted. We also introduce an information matrix-derived base-frame selection algorithm to identify the most suitable base-frames for each feature. Extensive experiments on multiple datasets demonstrate that PO-KF outperforms state-of-the-art VIO systems. Notably, PO-KF achieves nearly a 50% reduction in relative pose errors compared to MSCKF-based VIO. Further experiments demonstrate that PO-KF also exhibits superior robustness while maintaining real-time performance comparable to MSCKF-based VIO.

Precise Point Positioning (PPP) is a widely used as a high-precision positioning technology due to its accuracy and base station independence. However, it cannot meet the requirements of high-precision, high-reliability positioning in challenging urban environments due to its longed convergence time and insufficient observation continuity. The 5th Generation (5G) mobile communication system has the advantages of higher frequency bands, beam-forming technology and lower latency, which can assist PPP to address the issues. Accordingly, this paper presents a tightly coupled 5G/PPP positioning approach based on the Extended Kalman Filter (EKF), which directly integrates the raw 5G observations including the angle, round-trip time (RTT) and time-difference-of-arrival (TDOA) with PPP to improve the positioning performance in urban environments. The effectiveness of the tightly-coupled 5G/PPP integration system is evaluated using diverse 5G observations in the presence of white Gaussian noise. Both static and vehicle-mounted dynamic field test results demonstrate that the 5G base station signal can markedly enhance the convergence speed and positioning accuracy of PPP. The optimal localization performance among the different 5G observation combinations is achieved by the tightly coupled integration of RTT/AOD and PPP. The convergence time can be reduced to less than 1.0 minute in the case of static experiments. In the vehicle-mounted dynamic tests, the CDF99.9 of the horizontal position error can be maintained at less than 1 m, the CDF55.6 at less than 0.1 m; and the CDF98.7 of the vertical position error at less than 1 m, the CDF34.2 at less than 0.1 m.

The loosely-coupled system is the most popular architecture in global navigation satellite system (GNSS) / inertial navigation system (INS) integration, offering a starting point for beginners entering the field of positioning and navigation. Despite well-established theoretical foundations, developing fully functional integrated navigation algorithms remains challenging, especially for early-career researchers. To meet this challenge, we have developed and open-sourced a C++-based GNSS/INS data processing software, named KF-GINS, which uses an extended Kalman filter (EKF) to implement loosely-coupled GNSS/INS integration. Accompanied by our previously released video courses, tutorial documents, and result analysis scripts, KF-GINS serves as a comprehensive learning resource and a dependable research platform for those new to GNSS/INS integration. To facilitate algorithm development and exploration, we further release KF-GINS-Matlab, a Matlab version counterpart to KF-GINS. Sharing identical architecture and core algorithms with KF-GINS, KF-GINS-Matlab effectively bridges the gap between algorithm research and engineering implementation. Experimental data processing results and analysis confirm the algorithm’s correctness of KF-GINS. Besides, a comprehensive evaluation of the navigation accuracy indicates that KF-GINS achieves comparable performance in terms of positioning accuracy with the renowned commercial software NovAtel Inertial Explorer (IE) within the loosely-coupled framework.

Rangelands provide significant environmental benefits through many ecosystem services, which may include soil organic carbon (SOC) sequestration. However, quantifying SOC stocks and monitoring carbon (C) fluxes in rangelands are challenging due to the considerable spatial and temporal variability tied to rangeland C dynamics as well as limited data availability. We developed the Rangeland Carbon Tracking and Management (RCTM) system to track long‐term changes in SOC and ecosystem C fluxes by leveraging remote sensing inputs and environmental variable data sets with algorithms representing terrestrial C‐cycle processes. Bayesian calibration was conducted using quality‐controlled C flux data sets obtained from 61 Ameriflux and NEON flux tower sites from Western and Midwestern US rangelands to parameterize the model according to dominant vegetation classes (perennial and/or annual grass, grass‐shrub mixture, and grass‐tree mixture). The resulting RCTM system produced higher model accuracy for estimating annual cumulative gross primary productivity (GPP) (R2 > 0.6, RMSE <390 g C m−2) relative to net ecosystem exchange of CO2 (NEE) (R2 > 0.4, RMSE <180 g C m−2). Model performance in estimating rangeland C fluxes varied by season and vegetation type. The RCTM captured the spatial variability of SOC stocks with R2 = 0.6 when validated against SOC measurements across 13 NEON sites. Model simulations indicated slightly enhanced SOC stocks for the flux tower sites during the past decade, which is mainly driven by an increase in precipitation. Future efforts to refine the RCTM system will benefit from long‐term network‐based monitoring of vegetation biomass, C fluxes, and SOC stocks.

Evapotranspiration (ET) over forests plays a crucial role in controlling the global hydrological cycle, regulating the global energy, water, and carbon cycle. The prevailing large-scale evapotranspiration models usually rely on remotely-sensed meteorological conditions and terrestrial properties. In recent years, Solar-Induced chlorophyll Fluorescence (SIF) has shown great potential in estimating transpiration, providing a new way for evapotranspiration estimation. In this study, we developed a transpiration model based on the water-carbon coupling theory by using remotely sensed SIF. Then we adopted different evaporation models to achieve the estimation of total ET using the two-source scheme. The performances of the proposed ET models were evaluated at 51 forest sites in FLUXNET2015 database. We found that the ET models we proposed generally perform well, but the different evaporation models largely determine the overall performance of ET models. In particular, the best-performing model-ET(SIF + PT) achieves a Root Mean Square Error (RMSE) of 17.8 W m-2 and a Bias of-4.1 W m-2 by combining SIF-based transpiration model and an improved evaporation module based on the Priestley-Taylor model. The generally good performance of the ET model we proposed underscores the potential of using SIF to estimate forest evapotranspiration.

Tree transpiration plays an important role in the hydrological cycle and largely determines the availability of watershed water resources. The Hanjiang River Basin is the source of the middle route of the south-to-north water diversion project; understanding the characteristics of tree transpiration in the basin and its key controlling factors is of great importance for sustainable water resources management of the region. In this study, we measured the sap flux density as a surrogate of transpiration for three representative tree species (oak, poplar and pine) from January 2021 to December 2023 in the Hanjiang River Basin. Results showed that incoming short-wave radiation (Rsi) and vapour pressure deficit (VPD) are the major factors controlling daytime sap flux density. The nighttime sap flux density generally correlates with the daytime sap flux density for all the tree species. A statistical model was developed for estimating daytime sap flux density based on Rsi and VPD, and the nighttime sap flux density is estimated using its dependence on daytime sap flux density. The proposed model could explain more than 85% of sap flux density variation of the three tree species. Soil water content (SWC) exhibited different impacts on sap flux density among the three tree species, with oak and pine showing clear SWC control, while poplar showed negligible SWC control. Incorporating SWC in the proposed statistical model improved the model performance for oak and pine during dry periods. This study revealed the characteristics of sap flux density of oak, pine and poplar in the Hanjiang River Basin and proposed a statistical sap flux density model for sap flux density simulations in the humid region of China.

Metro plays a vital role in managing passenger distribution at intercity railway (IR) stations, particularly during holidays when there is a surge in tourist traffic. To efficiently accommodate the high demand for intercity travel, it becomes imperative for metro agencies to optimize holiday timetables. This paper focuses on designing holiday timetables of the first service period for the metro network that connects to an IR station, aiming to enhance multimodal collaboration with IR timetables while ensuring seamless coordination among various metro lines at the network level. A bi-objective model is proposed to maximize the temporal availability of metro network and minimize transfer waiting times for IR passengers traveling in early morning. To solve the model, an improved Artificial Bee Colony algorithm is designed, incorporating adaptive neighbour search and simulated annealing techniques. The effectiveness of the model and algorithm is verified using the Shanghai Metro network with Hongqiao Railway Station. Results indicate a 9.46% increase in the temporal availability of metro network for IR passengers, coupled with a 9.68% reduction in passenger transfer waiting times. Notably, the study reveals that solely advancing operations of the IR-connected metro lines is inefficient. Instead, optimizing train timetables for the entire metro network proves to be a cost-effective approach to enhancing the overall service level of early-morning operations. Furthermore, the study emphasizes the significance of even-numbered train headways in reducing passenger transfer waiting times.

This paper focuses on synchronizing festival timetables between high-speed railway (HSR) and metro for late-night operations. A multi-objective model is proposed to optimize metro timetables of the last service period incorporating collaboration with HSR timetables. The model is designed to facilitate bi-directional transfers by maximizing spatial accessibility and temporal availability of metro service as well as minimizing transfer waiting times for HSR passengers. Our model can be efficiently solved using Gurobi, providing optimal solutions for large-scale metro networks within reasonable computational timeframes. A real-world case study is conducted to validate the effectiveness of our model. The results indicate that slight adjustments to metro timetables, without introducing additional trains, can yield significantly enhancements in late-night metro service for HSR passengers. Moreover, a smaller extension of service time across the entire metro network is proved to be more effective than a greater extension of service time on hub-connected metro lines.

There is an urgent need for high-accuracy and high-reliability navigation and positioning in life safety fields such as intelligent transportation and automotive driving, especially in complex urban environments. Although, compared with the GNSS and loosely coupled integration, a tightly coupled GNSS/INS integration can improve the positioning reliability by using raw observations, it still suffers from external challenging environments such as the multipath effect. Therefore, the fault detection algorithm is a premise and guarantee to realize quality control of GNSS/INS integration. Inspired by the application of the random sample consensus (RANSAC) algorithm in GNSS fault detection, this paper proposes a RANSAC-based fault detection and exclusion algorithm for single-difference tightly coupled GNSS/INS integration. Here, a between-receiver single-difference (BRSD) model was designed to prevent the consumption of GNSS observations and reduce the waste of effective parameters, and the global proportion statistics of faults were introduced into the typical RANSAC algorithm to further ensure detection reliability. In this study, the effect of the main parameters on the proposed detection algorithm was analyzed and verified by artificial cycle slips. Multiple filed tests, including typical urban scenarios, were conducted to verify the feasibility and effectiveness of the proposed method. The comprehensive test results show that the north and east positioning accuracy in terms of cumulative distribution function (CDF, CDF = 95%) are improved by 45% and 42% over the tightly coupled mode without the proposed detection method.

The hysteresis response of tree sap flux (SF) to its main driving factor of incoming short-wave radiation (Rsi) has been widely reported, affecting the accuracy of sap flux and transpiration estimates in forest ecosystems. The diurnal cycle of SF usually lags the Rsi cycle by certain hours, thereby generating a closed counterclockwise hysteresis pattern. However, a few studies have reported that diurnal SF cycle may advance Rsi cycle, and such a response pattern has not been fully explored. In this study, we reported a rarely seen crossed hysteresis response pattern of SF to Rsi in 1/3 trees of a young temperate pine forest. We found that the diurnal SF cycle advances Rsi cycle especially in the morning induced by the early stomatal closure, thereby generating the crossed hysteresis response of SF to Rsi. We also proposed a method to quantify the magnitude of hysteresis (Ahys) for both the crossed and closed hystereses. Our analysis suggests that a lower Ahys of two time series results in (a) a larger crossing degree of hysteresis, and (b) a stronger linear correlation between the two time series. The seasonal variation of soil water content can explain the variation in Ahys for the hysteresis response of SF to Rsi, and the crossed hysteresis of SF is more likely to occur under water stress conditions. This study contributes to advancing our understanding of forest transpiration and how forests may respond to drought stress, which are expected to become more frequent and longer under future climate change. Sap flux (SF) is widely used as a surrogate of transpiration in field studies. It is broadly reported that the diurnal cycle of SF lags its driving force-incoming short-wave radiation (Rsi). Correspondingly, a closed counterclockwise loop appears when plotting SF against Rsi on a diurnal basis. Such phenomenon is widely identified as a hysteresis response of SF to Rsi. In our study, we reported that the diurnal SF cycle can advance the Rsi cycle especially in the morning, with a crossed SF-Rsi hysteresis loop emerging. The crossed hysteresis is more complex in terms of the direction and pattern than the closed hysteresis loop; we further found the crossed hysteresis pattern contributes to reducing the magnitude of hysteresis and improving the correlation between SF and Rsi. We developed a method to quantify the magnitude for both the crossed and closed hystereses. We found that the closure of stomatal conductance drives the early decline of the diurnal SF cycle, resulting in the crossed hysteresis. We also found that soil water content plays an important role in controlling the seasonal variation of hysteresis and the crossed hysteresis is more likely to emerge in water stress conditions. A novel crossed hysteresis response of sap flux to solar radiation is reported and investigated The early stomatal closure drives the variation of the hysteresis pattern of forest transpiration Soil water stress induces the seasonal variation of the sap flux-radiation hysteresis