Vapor pressure deficit (VPD) and soil moisture (SM) are the primary atmospheric and edaphic water stress indicators for terrestrial ecosystem gross primary productivity (GPP). While the individual effects of VPD and SM on GPP have been widely discussed, their respective contributions to GPP in the ecosystem remains debating due to their complicate interactions. Most of precious studies focus on coupling between VPD and SM and their linear effects on GPP, neglecting the interactions among VPD, SM and other environmental variables, such as air temperature (Ta) and photosynthetically active radiation (PAR), as well as the indirect and nonlinear effects through these variables. To investigate the independent effects and the relative roles of VPD and SM on GPP, approaches of quantile binning, ridge regression, and structural equation modeling (SEM) are applied to quantify the direct, indirect and total effects of VPD and SM on GPP, as well as their respective roles played in affecting ecosystem GPP. We have chosen the grasslands of the semi-arid region in northern China as the study area, characterized by strong land-atmosphere coupling and frequent droughts. The result indicates that VPD exerts a negative effect on GPP in the grassland ecosystem under all SM conditions, whereas SM suppressed GPP under high VPD conditions. The negative effect of SM is largely attributed to the indirect effect of SM through Ta. SM-Ta coupling and evapotranspiration (ET) play a significant role in GPP responses to SM. In the region where ET is low and water-limited, SM influences GPP positively; in the region where ET is relatively high and energy-limited, SM has a negative effect on GPP, due to the negative SM-Ta coupling. Ta and VPD dominate the variation of GPP in the semi-arid grassland ecosystem of northern China, while SM mainly attributed to the variability of GPP through land-atmospheric coupling.

In the context of global efforts to combat land degradation, the long-term sustainability of large-scale ecological restoration projects is a critical determinant of their success. As the world's largest land restoration initiative, China's major forestry projects offer a unique, continental-scale natural experiment to assess their effectiveness. Leveraging the Google Earth Engine platform, this study employs a validated Remote Sensing Ecological Index (RSEI) framework to assess the spatiotemporal dynamics of ecological quality, long-term sustainability, and key drivers across six major forestry project areas from 2000 to 2022. The results reveal significant spatial heterogeneity in restoration outcomes: while regions like the Middle Yellow River successfully reversed severe land degradation, the ecological quality in the Upper-Middle Yangtze River area exhibited a declining trend, demonstrating that restoration success is highly contingent on regional context. However, a significant knowledge gap remains: existing evaluations often rely on short-term monitoring or static "snapshot" assessments, which fail to capture the non-linear dynamic characteristics and long-term stability of ecosystem evolution, thereby overlooking potential future reversal risks. Crucially, the Hurst exponent analysis reveals a sustainability paradox: historical greening trends do not guarantee future stability. Despite significant past gains, regions like the Liaohe River and Huaihe-Taihu basins face exceptionally high risks of future degradation (reversal risks reaching 41.56% and 68.78%, respectively), posing a severe challenge to the long-term efficacy of these interventions. The study finds that while large-scale restoration projects are effective in addressing historical degradation under low socioeconomic pressure, their capacity to sustain ecosystem integrity is limited in areas with high-intensity human activity. This underscores the necessity of integrated policies that couple ecological engineering with sustainable land-use planning to secure long-term returns on global investments in land restoration.

Accurate estimation of leaf pigments and structural parameters - such as leaf chlorophyll a (Chl-a), chlorophyll b (Chl-b), carotenoid content (Car), leaf nitrogen content (LNC), leaf carbon content (LCC), and leaf thickness (LT) - is essential for assessing plant growth status, identifying environmental stress, and predicting crop yield. Traditional approaches rely on destructive measurements or vegetation indices combined with statistical regressions, but these methods are limited by poor scalability, spectral overlap issues, and weak cross-species generalization. This study investigates the feasibility of estimating multiple leaf parameters using hyperspectral remote sensing and deep learning. We propose a multi-leaf traits estimation network (MLTNet) based on one-dimensional convolutional neural networks (1D-CNNs), integrating dilated convolutions and shallow - deep feature fusion to effectively capture spectral patterns and inter-parameter correlations. The model is trained on integrated AQGRAD (53 species) and LOPEX (45 species) datasets, covering hyperspectral reflectance (430:5:900 nm, 960:10:1300 nm, 1500:10:1700 nm, 2000:10:2400 nm) along with corresponding leaf parameters. MLTNet's performance was compared with traditional vegetation index - based regression methods, including linear regression (LR), partial least squares regression (PLSR), gradient boosting decision tree (GBDT), ridge regression (Ridge), and random forest (RF). The results demonstrate that (1) MLTNet achieves superior performance in estimating all parameters, with the total normalized root mean square error (nRMSE) ranked as follows: MLTNet (1.125) 2 = 0.606 vs. 0.473-0.518 for traditional methods). This study highlights MLTNet's advantages in handling spectral complexity and multi-parameter estimation, while also recognizing challenges related to model interpretability and data scarcity. Future work should incorporate attention mechanisms and other interpretability-oriented techniques to enhance model transparency, and validate its generalization capability on UAV or satellite platforms to support operational vegetation monitoring applications.

Photovoltaic (PV) energy is critical to the transition towards a net-zero economy and plays a vital role in meeting the Sustainable Development Goals (SDGs), particularly regarding affordable clean energy (SDG 7) and climate action (SDG 13). Timely and accurate acquisition of the spatial distribution of PV installations is critical for regional energy planning, capacity estimation, and policy adjustment. However, accurately detecting PV installations remains challenging due to their environmental complexity and structural diversity. Through multi-platform spectral analysis (including Sentinel-2, Landsat-8, and GF-2 imagery), this study identifies distinctive spectral reflectance properties of PV materials, characterized by a prominent peak in the 400-500 nm range and significantly lower reflectance in the visible to near-infrared spectrum compared to natural landscapes, while exhibiting higher reflectance than water bodies. Leveraging physics-based spectral signatures that remain consistent across diverse geographical settings, we introduce the Spectral Ratio-Normalized Difference Solar Photovoltaic Panel Index (SPPI), a universal approach for efficient PV detection using optical satellite imagery. Quantitative validation across multiple regions (urban, rural, and mountainous environments) demonstrates that SPPI achieves exceptional performance with 94.34% overall accuracy and a robust Kappa coefficient of 0.778, outperforming existing index-based methodologies while producing results comparable to more computationally intensive deep learning approaches. The SPPI methodology's distinctive advantage lies in its ability to generate precise PV polygon boundaries while maintaining computational efficiency, enabling rapid large-scale mapping without specialized hardware requirements. While installation variations and extreme viewing angles may affect performance, the physics-based nature of the index ensures consistent results under normal imaging conditions. This universal, computationally efficient approach facilitates effective PV installation monitoring and energy capacity estimation, enhancing renewable energy analytics for carbon neutrality initiatives.

Organic carbon flux entering the pedosphere through forest litterfall drives the spatiotemporal dynamics of soil respiration (RS). Synthesis of 14,912 in-situ observations across 843 sites parameterized a remote sensing-driven statistical model to map global forest litterfall production, PFL, at 500 m resolution (2000-2022). Global annual average PFL reached 30.06 Pg of dry mass (95% CI: 28.91-31.22 Pg). Production density exhibited an average increase of 8.25 +/- 1.37 & times; 10-3 t & sdot;ha-1 & sdot;yr-2, with upward trends spanning 50.64% (95% CI: 49.20%-52.15%) of global forest areas. Statistically significant rises occurred across 13.87% (95% CI: 12.50%-15.10%) of these domains, predominantly within tropical evergreen broadleaf and boreal needleleaf forests. Temperature functioned as the primary driver of global PFL variability, while localized environmental factors constrained regional dynamics. Causal decoupling via asymmetric residual analysis quantified the standardized sensitivity slope of RS to PFL at 0.016 (95% CI: 0.011-0.021). Implementation of Olson's first-order decay kinetics, modeling exponential substrate decomposition over time, revealed rapid tropical turnover contrasting with profound temperate biogeochemical inertia; this lag effect yielded a 24.62% explanatory gain at a one-year lag, persisting at 2.75% after four years. Global validation across 128 in-situ manipulation experiments demonstrated that asymmetric sensitivity index, defined as the ratio of respiratory log-responses to litterfall removal versus addition, shifted systematically from-0.151 in the tropics to-0.558 in temperate regions. This confirms a mechanistic transition from acute input-dependency to robust legacy-buffering along climatic gradients. Ultimately, these findings bridge fine-scale PFL-RS coupling gaps, providing critical physical constraints for global biogeochemical models.

Monitoring large-scale photovoltaic (PV) deployment from space is critical for asset management and regulatory compliance, yet it requires robust estimation of effective light-collecting areas amidst complex backgrounds. To address spectral confusion and sub-pixel variability in diverse layouts (e.g., rooftop, water-based, and agrivoltaic PV), we propose a physics-guided unmixing framework utilizing spaceborne hyperspectral data. First, we generate EnMAP-aligned synthetic scenes to emulate realistic sub-pixel radiative transfer conditions. Then, we introduce PTV-Mamba, a deep unmixing model that instantiates an extended linear mixing model with a per-pixel illumination scaling term and a physics-tied decoder. By explicitly disentangling illumination-driven brightness changes from material variability, the model recovers precise PV abundance fields. In controlled simulations, PTV-Mamba consistently outperforms classical and deep baselines. On real EnMAP imagery in Taiwan, it achieves a low PV abundance RMSE of 0.093, with the estimated effective PV area (11.23 ha) closely matching high-resolution references (11.16 ha). Furthermore, cross-scene application to a heterogeneous agrivoltaic region in China yields physically plausible estimates, demonstrating robust generalizability. Overall, these results highlight the potential of physics-guided informatics for deriving interpretable, engineering-grade PV area estimates from satellite spectroscopy.

The primary land cover types in desert grasslands are photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), and bare soil (BS). Although there have been some studies on the Fractional Cover of Photosynthetic Vegetation (fPV), Non-photosynthetic Vegetation (fNPV) and Bare soil (fBS) both domestically and internationally, with some achievements have been made, most of them focus on fPV,with relatively limited research on fNPV. NPV is the primary component of grassland cover during the non-growing season, significantly influencing grassland degradation and sandstorm disasters. Most NPV indices are established based on the spectral differences of NPV and BS in the short-wave infrared (SWIR), with very few applications in the visible spectrum (VIS). Multispectral images often lack a complete SWIR band, thus making it impossible to separate PV, NPV, and BS. NPV and BS exhibit highly similar spectral characteristics in the VIS band, with reflectance increasing almost linearly and displaying no obvious peaks or valleys. After repeated derivative calculations of the measured PV/NPV/BS endmember spectra using ASD TerraSpec Halo, it was found that near 550 nm, the BS derivative value peaked, the PV derivative value was minimal, and the NPV derivative value fell between them. Based on this spectral difference, five derivative indices and two original band reflectance indices were established. These seven indices were used to construct a triangular scatter plot with NDVI. In the DGSI-NDVI scatter plot, NPV was clearly clustered in the lower left corner; BS was almost clustered in the upper left corner; PV was distributed in the lower right corner. The distinct separation of these three feature types enabled the effective extraction of fPV, fNPV and fBS.

Non-photosynthetic vegetation (NPV), comprising dead branches, fallen leaves, senescent stems, and plant residues, plays a critical role in regulating terrestrial carbon dynamics. However, conventional carbon models and remote sensing products frequently misclassify NPV as bare soil (BS), resulting in systematic underestimation of organic matter transfer, soil respiration, and soil carbon sink along climatic gradients. This misclassification introduces structural biases into carbon fluxes modeling and impedes accurate quantification of vegetation-soil carbon feedbacks. Here, we generated annual maximum NPV coverage (fNPV) products for China at a 300 m spatial resolution for the period 2016–2024 by integrating Sentinel imagery with field observations. To better characterize ecosystem-level carbon fluxes, we propose a novel ecosystem carbon exchange flux (ECEF) index that integrates NPV, photosynthetic vegetation (PV), and BS. Nationally, the average annual fNPV was 0.3679, with an annual increase of 0.0014 yr−1. The highest values were observed in semi-arid to sub-humid regions (Rs), reaching 0.4235 with an annual increase of 0.0075 yr−1. Boosted regression tree analysis identified quarterly temperature and precipitation as dominant climatic drivers, explaining 19.38

Building integrated photovoltaics has received increasing attention due to its superior low-carbon characteristics. However, the surrounding environments influence the pattern of solar radiation distributed on building exteriors, posing a challenge to the installation of photovoltaic (PV) on buildings. Therefore, to optimize the use of solar resources for buildings in diverse block scenarios, a comprehensive assessment approach for building-scale solar energy potential is proposed in this research, using parametric modelling and machine learning algorithms. First, the building-scale solar energy potential is assessed under thousands of surrounding environments. Next, contributions of building factors to solar potential are analyzed. Finally, machine learning algorithms are employed to forecast the PV installation and generation for roofs and facades within different surrounding environments. Results show that for target buildings higher than 24 m, the PV installation ratio of roof, west and south fa & ccedil;ades are 98 %, 39 % and 46 %. Correspondingly, their average PV power potentials are 46kWh/m2.y, 75kWh/m2.y and 87kWh/m2.y. Based on the priority of contributions of building parameters to solar potential, the applicability of prediction is verified to provide optimal forecasting models and input combinations for predicting PV application on roofs and facades. The findings and workflows can provide references for predicting solar applications in buildings.

Stand density is a key parameter for assessing forest structure and ecological function, and its remote sensingbased estimation is critically important for monitoring forest carbon stocks. As the primary component of forest resources in southern China, plantations are typically distributed across mountainous regions with complex terrain. The estimation of stand density using remote sensing in these areas faces numerous challenges due to factors such as topographic variation and interference from understory vegetation. Taking Shaoguan City in Guangdong Province as a case study, this research focuses on typical plantation areas dominated by Eucalyptus, Cunninghamia lanceolata, and Pinus massoniana, and proposes an optimized estimation method that integrates multi-source remote sensing data. Several improvements were made upon traditional approaches, including: (1) the Enhanced Vegetation Index (EVI) was utilized to reduce interference from understory vegetation and improve the accuracy of canopy cover estimation for standing trees; (2) the Modified Green-Red Vegetation Index (MGRVI) was introduced to improve the accuracy of individual tree canopy cover estimation; (3) the SCS+C topographic correction method was employed to mitigate the effects of terrain factors-specifically slope and aspect-on the accuracy of surface reflectance derived from remote sensing data; (4) a comparative experiment across spatial resolutions of 10 m, 30 m, 60 m, and 90 m was conducted, and 30 m was identified as the optimal scale for stand density estimation, offering a balance between accuracy and regional adaptability. The study demonstrates that tree species classification using the Random Forest algorithm achieved an accuracy of 93.22 %. Stand density estimation attained the highest performance at a 30 m x 30 m spatial resolution, with an R2 of 0.85 and an RMSE of fewer than 40 trees per hectare. These results highlight the effectiveness of the proposed method for accurately and efficiently estimating stand density in mountainous plantation forests, offering practical support for regional forest resource inventories and ecological assessments.

The coordinated development between ecological environmental quality and the urbanization process is key in enhancing regional sustainability. This study established a comprehensive regional spatiotemporal analysis and assessment framework that includes ecological environmental quality (EEQ) assessment, urbanization level (UL) evaluation, spatial correlation analysis, coupling coordination degree analysis, and impact factor analysis. Henan Province, China, was selected as the research area to comprehensively explore the spatiotemporal differentiation and influencing factors of the UL and EEQ from 2000 to 2020. The results indicate that during 2000-2020, the overall EEQ in Henan Province gradually improved with the average RSEI values increasing from 0.446 to 0.599, while the differences gradually diminished. During 2000-2020, the overall UL in the counties of Henan Province exhibited an upward trend. During 2000-2020, the coupling coordination degree values between EEQ and UL in the counties of Henan Province increasing from 0.469 to 0.580, approximately 89% of counties are exhibiting basic coordination or higher by 2020. The influencing factors of the coupling coordination degree between EEQ and UL in the counties of Henan Province varied significantly at different periods, with the demographic factor consistently acting as the prevailing determinant over the past two decades, its significance has been steadily increasing. And the economic factor shows a strengthening trend in its interaction with other factors. This research offers fresh perspectives and enhanced understanding for the comprehensive analysis of the coupled mechanisms between the regional ecological environment and urbanization.

Remote sensing observations of green vegetation (GV), impervious surface (IS), and bare soil (BS) fractional cover are essential for understanding climate change, characterizing ecosystem functions, monitoring urbanization process. As an important indicator of urbanization, the continuous increase of impervious surfaces alters the radiative transfer process at the surface, causing a series of environmental problems. Therefore, timely and accurate monitoring of the spatial and temporal changes in impervious surfaces and their impact on the ecological environment is of great significance for a comprehensive understanding of the process of urbanization as well as for the planning and construction of future cities. This study aims to propose a generalized method for the accurate estimation of GV, IS, and BS coverage. In this study, the visible impervious surface index (VISI), (Br-Bg)/(Br+Bg), was developed using measured spectral data of GV, IS, and BS, and analyzing their spectral characteristics to determine the spectral bands where they can be distinguished. Furthermore, the VISI combined with the NDVI was utilized to establish a triangular space for linear unmixing of the satellite image data to estimate the coverage of its GV, IS, and BS. Finally, the generalizability of this method was verified using UAV and satellite image data, with pearson correlation coefficient > 0.69. The results demonstrate that the VISI index proposed in this study is feasible for long-term series of multispectral imagery and large-scale coverage estimation.

The issue of global warming has become increasingly prevalent in recent years.Concurrently,there exists a considerable prevalence of extreme meteorological occurrences in urban environment,exemplified by the intense heat that characterizes the summer season.The urban heat environment has become a research focus under the background of global warming and rapid urbanization.At present,Local Climate Zone(LCZ)represents the principal method of classification employed in the field of urban thermal environment research.In comparison with the traditional urban-rural dichotomy,this approach entails a further subdivision of the city on the basis of the physical characteristics of the buildings and the natural ground cover features.Based on the LCZ system,this paper investigated the summer thermal environment characteristics of the main urban area of Nanjing from two perspectives:interclass and intraclass,using Landsat image inversion of surface temperature.The classification of LCZs divided the study area into 12 categories,of which 8 were designated for building types and 4 were designated for surface cover types.The proportion of building types within the study area was greater than that of ground cover types.The building types exhibited a high proportion of open high-rise(LCZ 4)and dense mid-rise(LCZ 2),which were predominantly concentrated in the central urban areas.The largest surface cover type was bare soil and sand(LCZ F).Results indicated that the thermal environments among LCZ classes showed large differences.Higher building densities had higher mean LSTs.The mean LSTs tended to rise gradually as building height decreased.The time-series trend of the mean temperature for the various LCZ types was highly consistent with the overall mean temperature trend observed in the study area.Large low-rise(LCZ 8)consistently presented high average surface temperatures during the summer months,reaching a maximum of 53.2 ℃.Moreover,the average surface temperature for each building type was higher than the average surface temperature for the study area as a whole,and the average surface temperature for each natural ground cover type except bare soil or sand was lower than the average surface temperature for the study area as a whole.The mean surface temperature of compact mid-rise(LCZ 2),compact low-rise(LCZ 3),large low-rise(LCZ 8),and heavy industry(LCZ 10)were higher than the overall mean temperature of the study area.Furthermore,this study presented intraclass analysis of different LCZ types using relative rates of change in LST.An increased sensitivity to temperature fluctuations may have adverse effects on human well-being and economic productivity.Another important finding was that the intra-LCZ thermal environment analyses indicated a heightened sensitivity to temperature fluctuations in the following categories:compact mid-rise(LCZ 2),compact low-rise(LCZ 3),heavy industry(LCZ 10),and bare soil and sand(LCZ F).The findings of this study can serve as a valuable reference point and provide insights for further research in the fields of urban planning,the mitigation of the urban heat island effect,and the enhancement of the urban heat environment.

Atmospheric windows allow satellite sensors to efficiently collect surface spectral data with minimal atmospheric interference. In particular, water vapor exhibits weak absorption in the solar spectrum at the edge of the infrared atmospheric window. It is highly sensitive to variations in the moisture content of surface vegetation, soil, and other land cover features. Equivalent water thickness (EWT) is an essential biophysical parameter that reflects vegetation water content and indicates vegetation health. This study aims to develop a generalized spectral index within the infrared atmospheric window edge to accurately estimate vegetation water content. The PROSPECT is utilized to simulate the impact of various biophysical parameters on leaf reflectance. This study primarily focused on examining the relationship between spectral reflectance and the EWT of fresh leaves across different vegetation species at the leaf scale. In addition, atmospheric transmittance and radiance at 400-2500 nm were simulated using MODTRAN to determine the atmospheric window region. The results show that the 1276-1342 nm spectral range (at the edge of the infrared atmospheric window) is more sensitive to EWT estimation. Based on this sensitive band, the infrared atmospheric window index (IAWI) is proposed. In comparison to commonly utilized spectral indices, the IAWI introduced in this study exhibited a robust correlation. At the canopy scale, the validity of IAWI-estimated vegetation EWT under varying LAI and VZA conditions was analyzed using the PROSAIL model, with R-2 > 0.79. This study proposes a novel spectral index for estimating vegetation water content using the reflectance of the infrared atmospheric window region, providing a theoretical basis for vegetation water content estimation by satellite remote sensing. These results will aid in tracking critical properties and processes within vegetation and broader ecosystems.

Building integrated photovoltaics is an important measure to promote low-carbon urban growth. However, the solar utilization performance of buildings in a block is greatly influenced by the shadings from surrounding buildings with di-verse layouts and morphologies. Therefore, this study evaluates the effect of urban morphology on solar energy potential for buildings in diverse urban environments using the parametric modelling and deep learning approaches. By con-trolling urban morphology parameters in different ranges, thousands of block models are randomly generated for residential buildings based on a parametric approach. Then, the solar energy potential, including the solar radiation and photovoltaics power potential, can be evaluated for building roofs and facades. The contribution of morphological parameters to building solar energy potential are prioritized using Global Sensitivity Analysis. The different input combinations of morphology parameters can be obtained based on the total contribution of each parameter. These input combinations are inserted into deep learning models to explore different prediction performances on the target values. Correspondingly, the influential parameters that can well predict the solar performance of building roofs and facades are selected. The proposed approach and findings are expected to offer inspirations for solar design and utilization in urban buildings.

Accurate information on the soil moisture content in croplands is essential for monitoring crop growth conditions. This study aimed to enhance soil moisture monitoring by employing laboratory-based soil spectral measurements and radiative transfer models. This study comprised three main components: (1) Utilizing laboratory-measured soil spectra to investigate the influence of soil moisture content on soil spectral properties (n = 178), and describing the impact of canopy coverage on the mixed spectra of wheat and soil in croplands using a radiative transfer model (RTM) (n = 144, 180); (2) employing a deep learning model trained on extensive simulated datasets to estimate soil moisture beneath the canopy from wheat-soil mixed spectra (n = 200); and (3) comparing the performance of deep learning model with statistical regression techniques based on soil moisture spectral index (SI) for estimating wheat fractional vegetation cover (FVC) and relative soil moisture content (RMC) under medium to low canopy coverage. The conclusions of this study were as follows: (1) Compared with the conventional statistical regression approaches, the deep learning model exhibited superior accuracy in estimating RMC across all levels of normalized difference vegetation index (NDVI). (2) By combining laboratory soil spectral measurements with an RTM, a pretrained dataset can be created. When combined with transfer learning techniques (FVC: R2 = 0.782, RMSE = 0.107, and RMC: R2 = 0.825, RMSE = 0.130), this approach enhanced the accuracy of estimating wheat FVC and RMC. Future research should expand experiments to include additional regions and crop types to verify the accuracy and generalizability of this method for estimating FVC and RMC under various remote sensing conditions.

The Enhanced Vegetation Index(EVI)combines factors such as atmospheric,soil,and saturation conditions and effectively correlates these data with vegetation biomass,leaf area index,and photosynthetically active radiation.Although the performance of the EVI is better than that of the Normalized Difference Vegetation Index(NDVI),the low temporal resolution of EVI products and the presence of cloud cover often result in a large number of missing pixels.In this study,we propose a daily resolution EVI reconstruction method that combines the Maximum Value Composite(MVC)and harmonic analysis of time series(HANTS)algorithms based on MODIS daily surface reflectance products. Given the spectral response differences of varying sensors carried by different satellites,the comparability of the EVIs calculated based on the Terra and Aqua satellites was analyzed prior to conducting the MVC operation.The analysis revealed a strong spatial linear correlation between the two variables,with R2 and RMSE values ranging from 0.9796-0.9935 and 0.0116-0.0297,respectively.The annual mean R2 and RMSE values were 0.9883 and 0.0196,respectively.The fitted parameters a and b had value ranges of 0.9447 to 1.0420 and-0.0065 to-0.0072,respectively,with annual mean values of 0.9910 and 0.0012.Despite spectral differences,the calculated EVIs based on the two satellite datasets exhibit minimal differences and thus are suitable for further processing via the MVC algorithm. This method was applied to reconstruct daily resolution EVI time series data for the North China Plain in 2021.The proposed EVI reconstruction algorithm is effective for large-scale and long-term reconstructions of daily resolution EVI time series data.The reconstructed EVI yields a rich texture,fills in the missing pixels,removes noise from the original EVI data,and follows the changing patterns of various land cover types.The HANTS method offers three advantages over the S-G filtering algorithm.First,compared with the original EVI,the HANTS method better preserved the spatial distribution patterns of the original EVI during reconstruction;by contrast,the S-G algorithm exhibited larger changes in spatial distribution in the reconstructed EVI.Second,the EVI curves reconstructed using the HANTS algorithm are smoother with minimal noise for typical land cover types;by contrast,the EVI curves reconstructed using the S-G algorithm have more local noise and nondifferentiable points,which hinders the extraction of vegetation phenological characteristics.Third,in terms of fidelity evaluation against high-quality reference EVI pixels,the HANTS algorithm demonstrated a strong linear correlation with the reference EVI pixels.The R2 and RMSE values ranged from 0.91 to 0.97 and from 0.017 to 0.032 across the months,with the strongest and weakest correlations occurring in September and June,respectively.By contrast,the S-G algorithm showed a weaker linear correlation with the reference EVI pixels.The R2 and RMSE values ranged from 0.38 to 0.91 and from 0.055 to 0.206 across the months,with the strongest and weakest correlations occurring in May and August,respectively.Overall,the HANTS method consistently outperformed the S-G method in terms of fidelity,with higher R2 values and lower RMSE values across all months.The proposed daily resolution EVI reconstruction method offers new guidelines and technical approaches for generating high-temporal resolution EVI data.