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Remote sensing observations and artificial intelligence algorithms have emerged as key components for crop yield estimation at various scales during the past decades. However, the utilization of multi-source satellite data and machine learning for estimating aggregated crop yield at the regional level in Europe has been only scarcely explored. Our study aims to bridge this research gap by focusing on the district-level spring barley yield estimation in southern Sweden from 2017 to 2022. We developed an estimation method with the random forest (RF) approach using four satellite-derived products along with two climate variables. These variables were used individually and in combinations as inputs for the RF approach. The results showed that vegetation indices (VIs) outperformed solar-induced chlorophyll fluorescence (SIF) in barley yield estimation, while combining VIs and SIF variables achieved the highest model performance (R2 = 0.77, RMSE = 488 kg/ha). The inclusion of climate variables generally had little added contributions to the model performance. Importantly, barley yield prediction could be achieved two months prior to harvest, using monthly VIs and SIF data from April and May. Our study demonstrated the feasibility of using freely accessible satellite data and the machine learning approach for estimating crop yield at the pan-European regional level. We expect that our proposed methodology can be extended to different crop types and regional-scale crop yield estimation in Europe, benefiting national and local authorities in making agricultural productivity decisions.

This study compared two different methods, the satellite altimetry-based and DEM (digital elevation model)-based, for estimating lake water volume changes. We focused on 34 lakes in China as the testing sites to compare the two methods for lake water volume changes from 2005 to 2020. The satellite altimetry-based method used water levels provided by the DAHITI (Database for Hydrological Time Series of Inland Waters) data and surface areas derived from Landsat imagery. The DEM-based method used the SRTM DEM data in combination with Landsat-derived lake extents. Our results showed a high degree of consistency in lake water volume changes estimated between the two methods (R2 > 0.90), but each method has its limitations. In terms of temporal coverage, the satellite altimetry-based method with the DAHITI data is limited by missing water level data in certain periods. The performance of the DEM-based method in extracting lake shore boundaries in regions with flat terrains (slope

Abstract Hydrological modeling realism is a central research question in hydrological studies. However, it is still a common practice to calibrate hydrological models using streamflow as a single hydrological variable, which can lead to large parameter uncertainty in hydrological simulations. To address this issue, this study employed a multi-variable calibration framework to reduce parameter uncertainty in a glacierized catchment. The current study employed multi-variable calibration using three different calibration schemes to calibrate a glacio-hydrological model (namely the FLEXG) in northern Sweden. The schemes included using only gauged streamflow data (scheme 1), using satellite snow cover area (SCA) derived from MODIS data (scheme 2), and using both gauged streamflow data and satellite SCA data as references for calibration (scheme 3) of the FLEXG model. This study integrated the objective functions of satellite-derived SCA and gauged streamflow into one criterion for the FLEXG model calibration using a weight-based approach. Our results showed that calibrating the FLEXG model based on solely satellite SCA data (from MODIS) produced an accurate simulation of SCA but poor simulation of streamflow. In contrast, calibrating the FLEXG model based on the measured streamflow data resulted in minimum error for streamflow simulation but high error for SCA simulation. The promising results were achieved for glacio-hydrological simulation with acceptable accuracy for simulation of both streamflow and SCA, when both streamflow and SCA data were used for calibration of FLEXG. Therefore, multi-variable calibration in a glacierized basin could provide more realistic hydrological modeling in terms of multiple glacio-hydrological variables.

In this article, we present the first demonstration of the FengYun-3E (FY3E) Global Navigation Satellite System Occultation Sounder II-Reflectometry (GNOS-R) payload's capacity to detect near-surface soil freeze/thaw (F/T) states. This study offers an initial analysis of the F/T retrieval algorithm applied to data collected from the Arctic Circle, underscoring the GNOS-R's potential to deliver long-term near-surface soil F/T products. Data for the period extending from the launch day of GNOS-R (Day of Year (DOY) 179, 2021) to DOY 270 in 2022 were analyzed using the surface reflectivity (SR) ratio factor to discriminate F/T variations. Comparisons were made with soil moisture active passive (SMAP) F/T products, serving as an auxiliary analysis. We found a strong consistency between SR ratio factor and SMAP F/T values, with the accuracy of the F/T retrieval algorithm exceeding 60%. These findings corroborate the efficacy of the GNOS-R payload aboard FY3E in monitoring F/T patterns at higher latitudes, specifically, the Arctic Circle. The outcomes of this study will be beneficial for future F/T detection efforts using spaceborne Global Navigation Satellite System-Reflectometry payloads.

Over the years, the field of landslide disaster research has amassed a wealth of data and specialized knowledge. However, these resources originate from a wide array of sources and often feature complex data structures, highlighting a persistent lack of methods to integrate multi-source, heterogeneous data. Traditional landslide monitoring methods typically focus on singular monitoring targets and data sources, which limits a comprehensive understanding of the complex processes involved in landslides. This paper introduces a landslide monitoring model based on a knowledge graph. This model employs P-Tuning to fine-tune ChatGLM2 for the extraction of triples. Differential InSAR (D-InSAR) is utilized to extract ground deformation data, which is then integrated with the knowledge graph for landslide monitoring and analysis. This study focuses on the co-seismic landslide in Jishishan, Gansu, China. By analyzing the landslide knowledge graph and the spatiotemporal deformation map, the results are as follows: (1) For this event, 106 entities and attributes were constructed, along with two recommended calculation routes. (2) The deformation at the earthquake’s central region reached up to 8.784 cm, with a slightly smaller deformation zone to the northwest peaking at 9.662 cm. Significant unilateral subsidence was observed in the mountain range to the southwest. (3) The area affected by the co-seismic landslide primarily includes farmland and villages, covering an area of 0.3408 square kilometers. (4) Analysis based on the knowledge graph indicates that this landslide was primarily caused by the rapid liquefaction of water-saturated soil layers due to the earthquake, resulting in instability. This study contributes to the analysis of post-disaster losses, attribution, and impacts.

In recent years, remote sensing data have revealed considerable potential in unraveling crucial information regarding water balance dynamics due to their unique spatiotemporal distribution characteristics, thereby advancing multi-objective optimization algorithms in hydrological model parameter calibration. However, existing optimization frameworks based on the Soil and Water Assessment Tool (SWAT) primarily focus on single-objective or multiple-objective (i.e., two or three objective functions), lacking an open, efficient, and flexible framework to integrate many-objective (i.e., four or more objective functions) optimization algorithms to satisfy the growing demands of complex hydrological systems. This study addresses this gap by designing and implementing a multi-objective optimization framework, Py-SWAT-U-NSGA-III, which integrates the Unified Non-dominated Sorting Genetic Algorithm III (U-NSGA-III). Built on the SWAT model, this framework supports a broad range of optimization problems, from single- to many-objective. Developed within a Python environment, the SWAT model modules are integrated with the Pymoo library to construct a U-NSGA-III algorithm-based optimization framework. This framework accommodates various calibration schemes, including multi-site, multi-variable, and multi-objective functions. Additionally, it incorporates sensitivity analysis and post-processing modules to shed insights into model behavior and evaluate optimization results. The framework supports multi-core parallel processing to enhance efficiency. The framework was tested in the Meijiang River Basin in southern China, using daily streamflow data and Penman–Monteith–Leuning Version 2 (PML-V2(China)) remote sensing evapotranspiration (ET) data for sensitivity analysis and parallel efficiency evaluation. Three case studies demonstrated its effectiveness in optimizing complex hydrological models, with multi-core processing achieving a speedup of up to 8.95 despite I/O bottlenecks. Py-SWAT-U-NSGA-III provides an open, efficient, and flexible tool for the hydrological community that strives to facilitate the application and advancement of multi-objective optimization in hydrological modeling.

The traditional remote sensing ecological index (RSEI), based on principal component analysis (PCA) to integrate four evaluation indexes: greenness (NDVI), humidity (WET), dryness (NDBSI), and heat (LST), is insufficient to comprehensively consider the influence of each eco-environment evaluation index on eco-environment quality (EEQ). In this research, a new comprehensive remote sensing ecological index (CRSEI) based on the quaternion Copula function is proposed to comprehensively characterize EEQ responded by integrating four eco-environment evaluation indexes. Additionally, the spatiotemporal variation of EEQ in Henan Province is evaluated using monthly CRSEI data from 2001 to 2020. The results show that: (1) The applicability and monitoring accuracy of CRSEI are better than that of RSEI, which can be used to assess the EEQ. (2) The EEQ of Henan Province declined between 2001 and 2010 but significantly improved and rebounded from 2011 to 2020. During this period, CRSEI values were higher in West and South Henan and lowest in central Henan, with West Henan consistently showing the highest values across all seasons. (3) The EEQ in Henan Province exhibited a tendency of deterioration from the central cities outward, followed by improvement from the outer areas back towards the central cities. In 2010, regions with poor EEQ made up 68.3% of the total area, whereas by 2020, regions with excellent EEQ accounted for 74% of the total area. (4) The EEQ was significantly negatively correlated with human activities, while it was positively correlated with precipitation. The research provides a reference and guidance for the scientific assessment of the regional eco-environment.

Abstract An Extended Triple Collocation for maximized Correlation (ETCC) method was proposed with a unique correlation function, the purpose of which is to maximize the correlation between the merged product and unknown truth. The method was tested over quasi‐global land by combining three independent precipitation products. The performance of the ETCC‐merged product was then evaluated against three reference data sets and compared with the existing Triple Collocation (TC) merging. The merged product was found to be generally superior to each contributor. Moreover, the ETCC method is better able to improve the correlation of merged product compared with the TC approach. Other improvements are also shown in the absolute difference of the ETCC‐merged product, such as regional validation for central North America and mainland China. These demonstrate the effectiveness of the ETCC method, and accordingly, it can provide a promising solution for maximizing the correlation of merged product without the truth.

The Qinghai-Tibet Plateau (QTP) serves as the source of many large rivers in Asia, and also serves as a natural habitat for rare wildlife and a genetic reservoir for plateau species. The exceptional ecological environment of the QTP has fostered diverse fish species. However, due to climate change and human activities, fish species in this area are facing serious threats, while large spatial scale investigation is a big challenge. Environmental DNA (eDNA) metabarcoding, as an innovative monitoring technology, offers a simple, fast, and environmentally friendly approach to explore fish communities on the QTP. This study utilized eDNA technology to assess the diversity, composition, and potential environmental influences of riverine fish communities across the QTP. A total of 90 fish species were identified belonging to 79 genera, 34 families, and 18 orders. The dominant species were Schizothorax chongi, Gymnocypris przewalskii, Carassius carassius, and Botia striata. PCoA and ADONIS indicated significant differences among fish community in different river basins of the Qinghai-Tibet Plateau. Canonical correspondence analysis (CCA) revealed that the ratio of SRP to TP, average NDVI, average elevation, and proportion of bare land significantly influenced the distribution of fish community. This study provided a novel monitoring method and insight for fish communities and implied potential species invasion on the QTP.

Remote sensing precipitation data have the characteristics of wide coverage and revealing spatiotemporal information, but their spatial resolution is low. The accuracy of the data is obviously different in different study areas and hydrometeorological conditions. This study evaluated four precipitation products in the Yellow River basin from 2001 to 2019, constructed the optimal combined product, conducted downscaling with various machine algorithms, and performed corrections using meteorological station precipitation data to analyze the spatiotemporal trends of precipitation. The results showed that (1) GPM and MSWEP had the best four evaluation indicators, with R2 values of 0.93 and 0.90, respectively, and the smallest FSE and RMSE, with a BIAS close to 0. A high-precision mixed precipitation dataset, GPM-MSWEP, was constructed. (2) Among the three methods, the downscaling results of DFNN showed higher accuracy. (3) The results, after correction with GWR, could more effectively enhance the accuracy of the data. (4) Precipitation in the Yellow River Basin showed a decreasing trend in January, September, and December, while it exhibited an increasing trend in other months and seasons, with 2002 and 2016 being points of abrupt change. This study provides a reference for the production of high-precision satellite precipitation products in the Yellow River basin.

Resumo Este trabalho apresenta uma avaliação do déficit de precipitação na Bacia Hidrográfica do Rio São Francisco (SFRB) com base em análises temporais, sazonais e regionais de duas décadas de produtos de dados de precipitação derivados de missões de satélite lançadas desde 1997. As análises temporais foram realizadas por meio de três Série temporal SPI de 3 meses, composta por 250 valores cada, derivados de estimativas TRMM, CHIRPS e PERSIANN-CDR. Análises sazonais e regionais foram feitas com diferenças nas quantidades médias de precipitação entre a última década, abrangendo de janeiro de 2008 a dezembro de 2018, e a década anterior, abrangendo de janeiro de 1998 a dezembro de 2007, para cada célula da grade no domínio do SFRB. O resultado da análise temporal mostra que o SFRB passou por períodos mais longos e mais secos na década 2008-2018. Os resultados das análises sazonais e regionais sugerem que o défice de precipitação foi mais forte durante o verão; e que a parte superior do SFRB foi a mais impactada pelo déficit de precipitação ocorrido nas últimas duas décadas.

The SM2RAIN (soil moisture to rain) model has been widely used for rainfall estimation worldwide. However, due to the lack of sufficient ground observation, the SM2RAIN model driven by different passive microwave soil moisture products over the Tibetan Plateau has not been fully validated. In this article, four widely used satellite microwave soil moisture products (including SMAP, ASCAT, SMOS, and AMSR2) were used as input data for rainfall estimation. Rainfall data from eight ground observation stations during 2016–2018 were used to evaluate the overall performance of the SM2RAIN algorithm under various soil moisture products at different time aggregation scales. In addition, different satellite soil moisture products were merged to evaluate whether the combined soil moisture products could improve the performance of the SM2RAIN model. Finally, the rainfall estimates with different soil moisture data were further evaluated and compared with two benchmark rainfall products (IMERG and ERA5). Results indicate that: 1) Overall, SM2RAIN-SMAP has the highest rainfall estimation accuracy, but with the time aggregation scale up to 30 days, the mean R of the four rainfall estimates could reach above 0.8 and the mean value of Kling–Gupta efficiency could reach above 0.8. 2) Combined satellite soil moisture products can significantly improve the rainfall estimates. The SM2RAIN model performed the best when SMAP and ASCAT soil moisture products were combined. 3) Using the SMAP product or combined soil moisture products yielded more accurate rainfall estimates than the two benchmark rainfall products (IMERG and ERA5).

Remote sensing is an effective means for lake water level monitoring on the Tibetan Plateau (TP). The purpose of this study is to estimate water levels of lakes on the TP using the Global Ecosystem Dynamics Investigation (GEDI) and Cloud and Land Elevation Satellite-2 (ICESat-2), evaluate the performance of ICESat-2 and GEDI in estimating water levels, and analyze the differences of water level obtained by the two altimeters. The results showed that the average coefficient of determination (R2) values between the estimated water levels (GEDI and ICESat-2) and the datasets (DAHITI and Hydroweb) were greater than 0.80, respectively. The water level of DAHITI and Hydroweb are mainly from radar nadir altimeters. The average root mean square error (RMSE) between GEDI and DAHITI was 0.54 m, between GEDI and Hydroweb was 0.38 m for Qinghai Lake. The average RMSE of Qinghai Lake between ICESat-2 and DAHITI was 0.50 m, and between ICESat-2 and Hydroweb was 0.28 m. The comparison results showed that the accuracy of GEDI seems to be slightly lower than that of ICESate-2. The main impact indicators of the difference between the GEDI and ICESat-2 in lake level estimations were the viewing angles (VAs), solar elevation, air temperature, and wind. From 2019 to 2021, GEDI covered 770 more lakes than ICESat-2, and the lake level fluctuation mainly occurred in the Inner Plateau and Yangtze basins. The GEDI can effectively estimate lake levels, which provides more water levels for lakes and lays a foundation for future research on the TP.

Study region: In the Yangtze River basin of China. Study focus: We applied a recently popular deep learning (DL) algorithm, Transformer (TSF), and two commonly used DL methods, Long-Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), to evaluate the performance of TSF in predicting runoff in the Yangtze River basin. We also add the main structure of TSF, Self-Attention (SA), to the LSTM and GRU models, namely LSTM-SA and GRU-SA, to investigate whether the inclusion of the SA mechanism can improve the prediction capability. Seven climatic observations (mean temperature, maximum temperature, precipitation, etc.) are the input data in our study. The whole dataset was divided into training, validation and test datasets. In addition, we investigated the relationship between model performance and input time steps. New hydrological insights for the region: Our experimental results show that the GRU has the best performance with the fewest parameters while the TSF has the worst performance due to the lack of sufficient data. GRU and the LSTM models are better than TSF for runoff prediction when the training samples are limited (such as the model parameters being ten times larger than the samples). Furthermore, the SA mechanism improves the prediction accuracy when added to the LSTM and the GRU structures. Different input time steps (5 d, 10 d, 15 d, 20 d, 25 d and 30 d) are used to train the DL models with different prediction lengths to understand their relationship with model performance, showing that an appropriate input time step can significantly improve the model performance.

Drought is a natural disaster that occurs globally and can damage the environment, disrupt agricultural production and cause large economic losses. The accurate prediction of drought can effectively reduce the impacts of droughts. Deep learning methods have shown promise in drought prediction, with convolutional neural networks (CNNs) being particularly effective in handling spatial information. In this study, we employed a deep learning approach to predict drought in the Fenhe River (FHR) basin, taking into account the meteorological conditions of surrounding regions. We used the daily SAPEI (Standardized Antecedent Precipitation Evapotranspiration Index) as the drought evaluation index. Our results demonstrate the effectiveness of the CNN model in predicting drought events 1~10 days in advance. We evaluated the predictions made by the model; the average Nash–Sutcliffe efficiency (NSE) between the predicted and true values for the next 10 days was 0.71. While the prediction accuracy slightly decreased with longer prediction lengths, the model remained stable and effective in predicting heavy drought events that are typically difficult to predict. Additionally, key meteorological variables for drought predictions were identified, and we found that training the CNN model with these key variables led to higher prediction accuracy than training it with all variables. This study approves an effective deep learning approach for daily drought prediction, particularly when considering the meteorological conditions of surrounding regions.

Under global warming, flooding has become one of the most destructive natural disasters along the China–Pakistan Economic Corridor (CPEC), which significantly jeopardizes the construction and ongoing stability of the CPEC. The assessment of regional flood potential is, therefore, crucial for effective flood prevention and relief measures. In light of this, our study applied MIKE 11 hydrodynamic model for the Indus River Basin of Pakistan to achieve a comprehensive analysis of the flood-affected locations and depths under typical scenarios. The flood risk zones along the CPEC were evaluated using the indicator system method in conjunction with the combination weighting method. The results show that the hydrodynamic model has a Nash–Sutcliffe efficiency of 0.86, allowing for the investigation of floods at more precise temporal and spatial scales. Punjab, Sindh, and Balochistan Provinces are the main inundation areas under a 100-year flood scenario, with inundation depths ranging from 1 to 4 m. The coastal regions of Sindh and Hafizabad in Punjab witnessed the most severe floods, with maximum inundation depths exceeding 8 m. Flooding predominantly impacts the southeastern region of the CPEC. The medium- to high-risk zones comprise 25.56% of the region, while high-risk areas constitute 4.18%. Particularly, the eastern and southern regions of Punjab, along with the central and southern regions of Sindh, have been pinpointed as high-risk areas, primarily due to their dense population and riverine characteristics. Overall, our findings provide a scientific basis for informed decision making pertaining to disaster reduction and flood prevention.

Abstract This study appraised and compared the performance of process-based hydrological SWAT (soil and water assessment tool) with a machine learning-based multi-layer perceptron (MLP) models for simulating streamflow in the Upper Indus Basin. The study period ranges from 1998 to 2013, where SWAT and MLP models were calibrated/trained and validated/tested for multiple sites during 1998–2005 and 2006–2013, respectively. The performance of both models was evaluated using nash–sutcliffe efficiency (NSE), coefficient of determination (R 2), Percent BIAS (PBIAS), and mean absolute percentage error (MAPE). Results illustrated the relatively poor performance of the SWAT model as compared with the MLP model. NSE, PBIAS, R 2, and MAPE for SWAT (MLP) models during calibration ranged from the minimum of 0.81 (0.90), 3.49 (0.02), 0.80 (0.25) and 7.61 (0.01) to the maximum of 0.86 (0.99), 9.84 (0.12), 0.87 (0.99), and 15.71 (0.267), respectively. The poor performance of SWAT compared with MLP might be influenced by several factors, including the selection of sensitive parameters, selection of snow specific sensitive parameters that might not represent actual snow conditions, potential limitations of the SCS-CN method used to simulate streamflow, and lack of SWAT ability to capture the hydropeaking in Indus River sub-basins (at Shatial bridge and Bisham Qila). Based on the robust performance of the MLP model, the current study recommends to develop and assess machine learning models and merging the SWAT model with machine learning models.

Accurate monitoring of the spatiotemporal variations in the gross primary productivity (GPP) in coastal wetlands is essential for blue carbon quantification. However, the currently available moderate-spatial-resolution GPP algorithms and products may contain large uncertainties and may rarely meet the demands of tidal wetland research. In this paper, we examine two statistical methods, namely, simple linear regression and random forest regression, to capture the high spatial resolution GPP variations in the salt marshes in the Yangtze Estuary by integrating multi-source data, including eddy covariance, Landsat, and Moderate-resolution Imaging Spectroradiometer (MODIS) satellite data. The satellite-derived photosynthetically active radiation (PAR), enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), and normalized difference water index (NDWI) are used individually and in different combinations to drive the two statistical methods and investigate their performances in estimating the GPP. The results show that together with the PAR, the EVI generally has the greatest potential for GPP estimation (R2 > 0.75, RMSE

The optimal selection of characteristic bands and retrieval models for the hyperspectral retrieval of soil heavy metal concentrations poses a significant challenge. Additionally, satellite-based hyperspectral retrieval encounters several issues, including atmospheric effects, limitations in temporal and radiometric resolution, and data acquisition, among others. Given this, the retrieval performance of the soil arsenic (As) concentration in Pingtan Island, the largest island in Fujian Province and the fifth largest in China, is currently unclear. This study aimed to elucidate this issue by identifying optimal characteristic bands from the full spectrum from both statistical and physical perspectives. We tested three linear models, namely Multiple Linear Regression (MLR), Partial Least Squares Regression (PLSR) and Geographically Weighted Regression (GWR), as well as three nonlinear machine learning models, including Back Propagation Neural Network (BP), Support Vector Machine Regression (SVR) and Random Forest Regression (RFR). We then retrieved soil arsenic content using ground-based soil full spectrum data on Pingtan Island. Our results indicate that the RFR model consistently outperformed all others when using both original and optimal characteristic bands. This superior performance suggests a complex, nonlinear relationship between soil arsenic concentration and spectral variables, influenced by diverse landscape factors. The GWR model, which considers spatial non-stationarity and heterogeneity, outperformed traditional models such as BP and SVR. This finding underscores the potential of incorporating spatial characteristics to enhance traditional machine learning models in geospatial studies. When evaluating retrieval model accuracy based on optimal characteristic bands, the RFR model maintained its top performance, and linear models (MLR, PLSR and GWR) showed notable improvement. Specifically, the GWR model achieved the highest r value for the validation data, indicating that selecting optimal characteristic bands based on high Pearson’s correlation coefficients (e.g., abs(Pearson’s correlation coefficient) ≥0.45) and high sensitivity to soil active materials successfully mitigates uncertainties linked to characteristic band selection solely based on Pearson’s correlation coefficients. Consequently, two effective retrieval models were generated: the best-performing RFR model and the improved GWR model. Our study on Pingtan Island provides theoretical and technical support for monitoring and evaluating soil arsenic concentrations using satellite-based spectroscopy in densely populated, relatively independent island towns in China and worldwide.

Soil moisture is an essential component for the planetary balance between land surface water and energy. Obtaining long-term global soil moisture data is important for understanding the water cycle changes in the warming climate. To date several satellite soil moisture products are being developed with varying retrieval algorithms, however with considerable missing values. To resolve the data gaps, here we have constructed two global satellite soil moisture products, i.e., the CCI (Climate Change Initiative soil moisture, 1989–2021; CCIori hereafter) and the CM (Correlation Merging soil moisture, 2006–2019; CMori hereafter) products separately using a Convolutional Neural Network (CNN) with autoencoding approach, which considers soil moisture variability in both time and space. The reconstructed datasets, namely CCIrec and CMrec, are cross-evaluated with artificial missing values, and further againt in-situ observations from 12 networks including 485 stations globally, with multiple error metrics of correlation coefficients (R), bias, root mean square errors (RMSE) and unbiased root mean square error (ubRMSE) respectively. The cross-validation results show that the reconstructed missing values have high R (0.987 and 0.974, respectively) and low RMSE (0.015 and 0.032 m3/m3, respectively) with the original ones. The in-situ validation shows that the global mean R between CCIrec (CCIori) and in-situ observations is 0.590 (0.581), RMSE is 0.093 (0.093) m3/m3, ubRMSE is 0.059 (0.058) m3/m3, bias is 0.032 (0.037) m3/m3 respectively; CMrec (CMori) shows quite similar results. The added value of this study is to provide long-term gap-free satellite soil moisture products globally, which helps studies in the fields of hydrology, meteorology, ecology and climate sciences.

Introduction: The Public-Private Partnership (PPP) model has the advantages of enhancing the efficiency of social infrastructure construction and reducing the risk of government liabilities, and has been widely used and developed in China. In the current securitization of PPP project assets, the question of how to price them is key to whether the Asset-backed Securities (ABS) products can be issued successfully.Methods: This study analyzes the pricing process and influencing factors of PPP project ABS in the context of PPP projects in China by collating the relevant literature on PPP project ABS products and collating the advantages and disadvantages of the static discounted cash flow (SCF), static spread (SS), and option-adjusted spread (OAS) methods. The study finds that the SCF and SS methods, primarily used in pricing PPP project ABS products, lack consideration of interest rate changes, early repayment rates, and multiple interest rate path options, leading to biased results. This study introduces the Monte Carlo simulation pricing method (MCSM) and conducts an empirical analysis based on the case of the Heating income right ABS of Huaxia Happiness PPP Project ABS.Results: It is concluded that the Monte Carlo simulation method can more flexibly and accurately price PPP project ABS products with revenue rights as the underlying assets.Discussion: This study has reference significance for the pricing of ABS products in future PPP projects and the construction of PPP projects in integrated energy service industrial parks, and can promote the development of new integrated energy service industrial parks, thereby helping to improve the energy consumption structure of enterprises in industrial parks, improve energy use efficiency, ensure the safety of energy use by enterprises, and further enhance the application of new energy by enterprises, thereby reducing their Greenhouse gas emissions.

Study region: Two hyper-arid regions (Atbara and Kassala stations) in Sudan. Study focus: The study aims to evaluate the potential of the D-vine Copula-based quantile regression (DVQR) model for estimating daily ETo during 2000–2015 based on various input structures. Further, the DVQR model was compared with Multivariate Linear quantile regression (MLQR), Bayesians Model Averaging quantile regression (BMAQR), Empirical Models (EMMs), and Classical Machine Learning (CML). Besides, the CML models including Random Forest (RF), Support Vector Machine (SVM), Extreme Learning Machine (ELM), Extreme Gradient Boosting (XGBoost), and M5 Model Tree (M5Tree) were employed. New hydrological insights for the region: The original EMMs showed poor performance, which improved after calibration techniques. The DVQR, MLQR, and BMAQR models showed better performance than the calibrated EMMs. However, the DVQR model exhibited the highest accuracy than the MLQR and BMAQR models over two study sites. The M5Tree, SVM, and XGBoost models perfumed better than ELM and RF models at both study sites. The DVQR and XGBoost models showed equivalent performance (R2, NSE, and WIA > 0.99, MAE, and RMSE < 0.2) to the M5Tree and SVM models, but they had significantly more accuracy than the calibrated EMMs, MLQR, BMAQR, ELM, and RF models in two hyper-arid regions. Overall, the high dimensional DVQR model is recommended as a promising alternative technique for estimating daily ETo in hyper-arid climate conditions around the world.

Precipitation is a major component of the water cycle. Accurate and reliable estimation of precipitation is essential for various applications. Generally, there are three main types of precipitation products: satellite based, reanalysis, and ground measurements from rain gauge stations. Each type has its advantages and disadvantages. Recent efforts have been made to develop various merging methods to improve precipitation estimates by combining multiple precipitation products. This study evaluated for the first time the performance of the random forest-based merging procedure (RF-MEP) method in enhancing the accuracy of daily precipitation estimates in Chongqing city, China with a complex terrain and sparse observational data. The RF-MEP method was used to merge three widely used gridded precipitation products (CHIRPS, ERA5-Land, and GPM IMERG) with ground measurements from a limited number of rain gauge stations to produce the merged precipitation dataset. Eight stations (approximately 70% of the available stations) were used to train the RF-MEP approach, while four stations (30%) were used for independent testing. Various statistical metrics were employed to assess the performance of the merged precipitation dataset and the three existing precipitation products against the ground measurements. Our results demonstrated that the RF-MEP approach significantly enhances the accuracy of daily precipitation estimates, surpassing the performance of the individual precipitation products and two other merging methods (the simple linear regression model and the simple averaging). Among the three existing products, ERA5-Land exhibited the best performance in capturing daily precipitation, followed by GPM IMERG, while CHIRPS performed the worst. Regarding precipitation intensity, all three existing products and the RF-MEP merged dataset performed well in capturing light precipitation events with an intensity of less than 1 mm/day, which accounts for the majority (more than 70%) of occurrences. However, all datasets showed rather poor capability in capturing precipitation events beyond 1 mm/day, with the worst performance observed for extreme heavy precipitation events exceeding 50 mm/day. The RF-MEP approach significantly improves the detection ability for all precipitation intensities, except for the most extreme intensity (>50 mm/day), where only marginal improvement is observed. Analysis of the spatial pattern of precipitation estimates and the temporal bias of daily precipitation estimates further confirms the superior performance of the RF-MEP merged precipitation dataset over the three existing products.

Information relating to errors in evapotranspiration (ET) products, including satellite-derived ET products, is critical to their application but often challenging to obtain, with a limited number of flux towers available for the sufficient validation of measurements. Triple collocation (TC) methods can assess the inherent uncertainties of the above ET products using just three independent variables as a triplet input. However, both the severity with which the variables in the triplet violate the assumptions of zero error correlations and the corresponding impact on the error estimation are unknown. This study proposed a cross-correlation analysis approach to discover the optimal triplet of satellite-derived ET products with regard to providing the most reliable error estimation. All possible triple collocation solutions for the same product were first evaluated by the extended triple collocation (ETC), among which the optimum was selected based on the correlation between ETC-based and in-situ-based error metrics, and correspondingly, a statistic experiment based on ranked triplets demonstrated how the optimal triplet was valid for all pixels of the product. Six popular products (MOD16, PML_V2, GLASS, SSEBop, ERA5, and GLEAM) that were produced between 2003 to 2018 and which cover China’s mainland were chosen for the experiment, in which the error estimates were compared with measurements from 23 in-situ flux towers. The findings suggest that (1) there exists an optimal triplet in which a product as an input of TC with other collocating inputs together violate TC assumptions the least; (2) the error characteristics of the six ET products varied significantly across China, with GLASS performing the best (median error: 0.1 mm/day), followed by GLEAM, ERA5, and MOD16 (median errors below 0.2 mm/day), while PML_V2 and SSEBop had slightly higher median errors (0.24 mm/day and 0.27 mm/day, respectively); and (3) removing seasonal variations in ET signals has a substantial impact on enhancing the accuracy of error estimations.

The crop drought risk assessment is an important basis for mitigating the effects of drought on crops. The study of drought using crop growth models is an integral part of agricultural drought risk research. The current Decision Support System for Agrotechnology Transfer (DSSAT) model is not sufficiently sensitive to moisture parameters when performing simulations, and most studies that conduct different scenario simulations to assess crop drought vulnerability are based on the site-scale. In this paper, we improved the moisture sensitivity of the Crop Environment Resource Synthesis System (CERES)-Wheat to improve the simulation accuracy of winter wheat under water stress, and then we assessed the drought intensity in the Beijing-Tianjin-Hebei region and constructed a gridded vulnerability curve. The grid vulnerability curves (1 km × 1 km) were quantitatively characterized using key points, and the drought risk distribution and zoning of winter wheat were evaluated under different return periods. The results show that the stress mechanism of coupled water and photosynthetic behavior improved the CERES-Wheat model. The accuracy of the modified model improved in terms of the above-ground biomass and yield compared with that before the modification, with increases of 20.39% and 11.45% in accuracy, respectively. The drought hazard intensity index of winter wheat in the study area from 1970 to 2019 exhibited a trend of high in the southwest and low in the southeast. The range of the multi-year average drought hazard intensity across the region was 0.29–0.61. There were some differences in the shape and characteristic covariates of the drought vulnerability curves among the different sub-zones. In terms of the cumulative loss rates, almost the entire region had a cumulative drought loss rate of 49.00–54.00%. Overall, the drought risk index decreased from west to east and from north to south under different return periods. This quantitative evaluation of the drought hazard intensity index provides a reference for agricultural drought risk evaluation.

The mantle beneath coastal SE China evolved from enriched to depleted between the Cretaceous and the Neogene, although the precise timing of this change remains unclear. Here, we focus on a newly discovered Eocene mafic volcanic neck that contains peridotite xenoliths in the Fuding area of Fujian Province, and present new whole-rock Ar–Ar data which indicate that flood basalts formed during the Eocene (ages of 38.5 ± 1.2 and 42.3 ± 2 Ma). The basalt, gabbro, and diabase in the Fuding area are geochemically similar to ocean island basalt (OIB) and have SrI values that range from 0.703794 to 0.703911 (average of 0.703865) and εNd(t) values from 3.05 to 4.56 (average of 3.90). These samples yield two-stage Nd model (TDM2) ages of 0.61–0.73 Ga (average of 0.67 Ga). These data indicate that all of these units formed from magmas derived from an OIB-type mantle source, with both the gabbro and diabase units recording minor amounts of crustal contamination. Its OIB type geochemical characteristics may be inherited from crustal materials with similar characteristics. Peridotite xenoliths within the Fuding basalts provide evidence of the nature of the Eocene mantle in this area, especially the post late-Mesozoic evolution of the mantle beneath coastal SE China. The mantle beneath coastal SE China evolved from enriched in the Cretaceous to depleted in the Neogene, with this change occurring during the Eocene.

Abstract Signals of Opportunity Reflectometry (SoOp-R) employs the communication system, GNSS (Global Navigation Satellite System) constellation and other potential Signals of Opportunity (SoOp) as the transmitters. In recent years, it has gained increased interests. Several experiments have been carried out, however it is still in the initial development stage. Theoretical predictions of SoOp Reflectometry for land surface parameters detection, such as soil moisture and vegetation biomass, should be carried out simultaneously. Meanwhile, at present less works are paid attention to the polarization study of the polarizations. The first-order radiative transfer equation models are employed here and they are developed according to the wave synthesis technique to get the various polarization combinations. Using the two models as analysis tools, we simulate the bistatic scattering at all potential SoOp Reflectometry bands, i.e., P-, L-, C- and X-band for circular polarizations and linear polarizations. While the original commonly used microwave scattering models are linear polarizations, here we compare the difference. Although the models can simulate bistatic scattering at any incident angles and scattering angles. Four special observation geometry are taken into considerations during the analysis. Using the developed models as tools, the developed models establish the relationship between the land surface parameters (such as soil moisture, soil roughness and vegetation water content, diameters et al.) and bistatic radar cross section. The forward scattering models developed here enables the understanding of the effects of different geophysical parameters and transmitter–receiver observation scenarios on the bisatic scattering at any polarization combinations for any potential SoOP reflectometry bands. Robust retrieval methods for soil moisture and vegetation biomass can benefit from the forward scattering models.

Modeling inland water quality by remote sensing has already demonstrated its capacity to make accurate predictions. However, limitations still exist for applicability in diverse regions, as well as to retrieve non-optically active parameters (nOAC). Models are usually trained only with water samples from individual or local groups of waterbodies, which limits their capacity and accuracy in predicting parameters across diverse regions. This study aims to increase data availability to understand the performance of models trained with heterogeneous databases from both remote sensing and field measurement sources to improve machine learning training. This paper seeks to build a dataset with worldwide lake characteristics using data from water monitoring programs around the world paired with harmonized data of Landsat-8 and Sentinel-2. Additional feature engineering is also examined. The dataset is then used for model training and prediction of water quality at the global scale, time series analysis and water quality maps for lakes in different continents. Additionally, the modeling performance of nOACs are also investigated. The results show that trained models achieve moderately high correlations for SDD, TURB and BOD (R2 = 0.68) but lower performances for TSM and NO3-N (R2 = 0.43). The extreme learning machine (ELM) and the random forest regression (RFR) demonstrate better performance. The results indicate that ML algorithms can process remote sensing data and additional features to model water quality at the global scale and contribute to address the limitations of transferring and retrieving nOAC. However, significant limitations need to be considered, such as calibrated harmonization of water data and atmospheric correction procedures. Moreover, further understanding of the mechanisms that facilitate nOAC prediction is necessary. We highlight the need for international contributions to global water quality datasets capable of providing extensive water data for the improvement of global water monitoring.

Both flood magnitude and frequency might change under the changing environment. In this study, a procedure combining statistical methods, flood frequency analysis and attribution analysis was proposed to investigate the response of floods to urbanization and precipitation change in the Qinhuai River Basin, an urbanized basin located in Southeast China, over the period from 1986 to 2013. The Mann–Kendall test was employed to detect the gradual trend of the annual maximum streamflow and the peaks over threshold series. The frequency analysis was applied to estimate the changes in the magnitude and frequency of floods between the baseline period (1986–2001) and urbanization period (2002–2013). An attribution analysis was proposed to separate the effects of precipitation change and urbanization on flood sizes between the two periods. Results showed that: (1) there are significant increasing trends in medium and small flood series according to the Mann–Kendall test; (2) the mean and threshold values of flood series in the urbanization period were larger than those in the baseline period, while the standard deviation, coefficient of variation and coefficient of skewness of flood series were both higher during the baseline period than those during the urbanization period; (3) the flood magnitude was higher during the urbanization period than that during the baseline period at the same return period. The relative changes in magnitude were larger for small floods than for big floods from the baseline period to the urbanization period; (4) the contributions of urbanization on floods appeared to amplify with the decreasing return period, while the effects of precipitation diminish. The procedure presented in this study could be useful to detect the changes of floods in the changing environment and conduct the attribution analysis of flood series. The findings of this study are beneficial to further understanding interactions between flood behavior and the drivers, thereby improving flood management in urbanized basins.

Solar radiation plays a pivotal role in the energy balance at the Earth's surface, evaporation, snow melting, water requirements of plants, and hydrological control of catchments. In this work, performance of ERA-Interim (a reanalysis dataset) was examined to estimate solar radiation at Ahvaz, BandarAbbas, and Kermanshah weather stations representing the even spatial distribution over Iran using eight empirical models and an artificial intelligence-based model (SVM: Support Vector Machine). In the calibration set, SVM exhibited the best performance with RMSEs of 249, 299 and 437 J.cm−2.day−1 at the aforementioned stations, respectively. In validation set, SVM reduced the errors in the estimates of solar radiation by 2.5 and 7.3 percent compared to the best empirical model at Ahvaz station (Abdallah model, RMSE = 242 J.cm−2.day−1) and Kermanshah station (Angstrom-Prescott model, RMSE = 315 J.cm−2.day−1), respectively. During the validation at BandarAbbas station, Bahel and Abdallah model (RMSE = 309 J.cm−2.day−1), Angstrom-Prescott model (RMSE = 310 J.cm−2.day−1) and SVM (RMSE = 312 J.cm−2.day−1) showed a relatively similar performance. The results also showed that the ERA-Interim dataset can be a comparatively suitable alternative to some of the empirical models, where radiation or the input parameters of empirical models are not directly measured, with RMSEs ​​of 382.81, 320.82 and 414.1 J.cm−2.day−1 at Ahvaz, BandarAbbas, and Kermanshah stations, respectively (in validation phase); although its error rates are significant compared with the SVM model, and substituting it for artificial intelligence-based models is not recommended.

The lakes in the arid or semi-arid regions of western China are more sensitive to climate changes, and lake levels are considered as a direct indicator of regional climate variability. In this study, we combined satellite altimetry data from ICESat-1 with a smaller footprint and higher accuracy (compared to radar altimetry) and CryoSat-2 with a higher resolution in the along-track direction to monitor lake levels in western China and their trends over a long time period from 2003 to 2021. Our satellite altimetry derived lake levels were well-validated by comparing them against in situ measurements for a lake and independent altimetry-derived product from the DAHITI database for the common lakes. Furthermore, the commonly used linear model was applied to our derived lake level time-series to estimate the overall change trends in 67 typical lake levels over western China. Our results showed that 55 (82%) of these lakes displayed an increasing tendency in water levels, and the remaining 12 (18%) lakes showed a decreasing trend. Overall, the mean water level changing rate in western China was +0.15 m/yr (−1.40 to +0.58 m/yr) during the studied time period. The spatial patterns of the lake level variations can be grouped into three subregions: lake level changes between 2003 and 2021 showed general rising lake levels for the central–northern TP (Tibetan Plateau) endorheic region and Xinjiang, but declining levels for the southern TP exorheic region. The seasonal characteristic of lake level changes showed a significant increase during the summer monsoon season, followed by decreases during the non-monsoon season. The precipitation variations play a leading role in the lake level changes in the context of warm and humid climate states. There were good correspondences between the monthly variations in the lake level and monthly mean precipitation. Additionally, the lake levels also showed a relationship with the air temperature change, in particular, the lake level increase showed a small degree of hysteresis behavior compared with the rising temperatures. Geographically, the precipitation increase in the westerlies regions led to widespread lake expansion in the central–northern TP and Xinjiang. Conversely, precipitation decrease in the Indian monsoon regions caused lake shrinkage in the exorheic region of the southern TP. This study helps us achieve a better understanding of the spatial-temporal patterns of lake level changes in the arid or semi-arid region of western China.

Atmospheric levels of atomic mercury pollution were measured using spectroscopic techniques in the city of Guangzhou, Guangdong Province, China. Assessments were mainly performed at ground level using a portable (vehicle mounted or hand carried) Zeeman modulation correlation spectrometer (Lumex RA-915M), and the results are given in easily comprehensible diagrams. Measurements were made with continuous recording in car traverses along major roads which cross the city, but also at selected spots, such as at a university campus with laboratory buildings. Further, pollution levels at different locations were recorded when walking through a major and a small hospital. While concentrations in the city in the range 3–10 ng/m3 were typical, and strongly dependent on the traffic situation, very high concentrations (up to 1300 ng/m3) were found at certain indoor hospital locations, again drawing attention to the fact that high mercury levels due to inadequate handling routines can remain undetected but could readily be eliminated by adequate measurements and subsequent sanitation.

Accurate streamflow simulation is crucial for many applications, such as optimal reservoir operation and irrigation. Conceptual techniques employ physical ideas and are suitable for representing the physics of the hydrologic model, but they might fail in competition with their more advanced counterparts. In contrast, deep learning (DL) approaches provide a great computational capability for streamflow simulation, but they rely on data characteristics and the physics of the issue cannot be fully understood. To overcome these limitations, the current study provided a novel framework based on a combination of conceptual and DL techniques for enhancing the accuracy of streamflow simulation in a snow-covered basin. In this regard, the current study simulated daily streamflow in the Kalixälven river basin in northern Sweden by integrating a snow-based conceptual hydrological model (MISD) with a DL model. Daily precipitation, air temperature (average, minimum, and maximum), dew point temperature, evapotranspiration, relative humidity, sunshine duration, global solar radiation, and atmospheric pressure data were used as inputs for the DL model to examine the effect of each meteorological variable on the streamflow simulation. Results proved that adding meteorological variables to the conceptual hydrological model underframe of parallel settings can improve the accuracy of streamflow simulating by the DL model. The MISD model simulated streamflow had an MAE = 8.33 (cms), r = 0.88, and NSE = 0.77 for the validation phase. The proposed deep-conceptual learning-based framework also performed better than the standalone MISD model; the DL method had an MAE = 7.89 (cms), r = 0.90, and NSE = 0.80 for the validation phase when meteorological variables and MISD results were combined as inputs for the DL model. The integrated rainfall-runoff model proposed in this research is a new concept in rainfall-runoff modeling which can be used for accurate streamflow simulations.

Scarce water resources present a major hindrance to ensuring food security. Crop water productivity (WP), embraced as one of the Sustainable Development Goals (SDGs), is playing an integral role in the performance-based evaluation of agricultural systems and securing sustainable food production. This study aims at developing a cloud-based model within the Google Earth Engine (GEE) based on Landsat -7 and -8 satellite imagery to facilitate WP mapping at regional scales (30-m resolution) and analyzing the state of the water use efficiency and productivity of the agricultural sector as a means of benchmarking its WP and defining local gaps and targets at spatiotemporal scales. The model was tested in three major agricultural districts in the Lake Urmia Basin (LUB) with respect to five crop types, including irrigated wheat, rainfed wheat, apples, grapes, alfalfa, and sugar beets as the major grown crops. The actual evapotranspiration (ET) was estimated using geeSEBAL based on the Surface Energy Balance Algorithm for Land (SEBAL) methodology, while for crop yield estimations Monteith’s Light Use Efficiency model (LUE) was employed. The results indicate that the WP in the LUB is below its optimum targets, revealing that there is a significant degree of work necessary to ameliorate the WP in the LUB. The WP varies between 0.49–0.55 (kg/m3) for irrigated wheat, 0.27–0.34 for rainfed wheat, 1.7–2.2 for apples, 1.2–1.7 for grapes, 5.5–6.2 for sugar beets, and 0.67–1.08 for alfalfa, which could be potentially increased up to 80%, 150%, 76%, 83%, 55%, and 48%, respectively. The spatial variation of the WP and crop yield makes it feasible to detect the areas with the best and poorest on-farm practices, thereby facilitating the better targeting of resources to bridge the WP gap through water management practices. This study provides important insights into the status and potential of WP with possible worldwide applications at both farm and government levels for policymakers, practitioners, and growers to adopt effective policy guidelines and improve on-farm practices.

Understanding the interactions of climate-vegetation and human-vegetation has been a critical issue and increasingly attracting attention from scientific community in the field of global change research. This study investigated the heterogeneous impacts of climate change and anthropogenic activities on vegetation change by applying the trend analysis and Geodetector approach. The spatial and temporal patterns of MODIS NDVI and LAI during 2003–2017 were firstly examined in China. We then quantified the contribution and interactions effects of climatic factors (temperature and precipitation) and anthropogenic factors (population, gross domestic product and other four categories of forestry investment) on vegetation change in five typical areas of China. Both NDVI and LAI across China demonstrated a significant increasing trend over the past two decades. However, the eastern developmental regions such as Beijing-Tianjin-Hebei Region and Yangtze River Delta exhibited a decreasing trend due to fast urbanization. Socio-economical inputs (the explanatory power of forestry investment > 40%, the range of explanatory power is 0 to 100%) were identified as the dominant driving forces of vegetation change for the most of study areas. Precipitation was the most important natural influencing factor of vegetation change. We also found that the interactions between forestry investment and other factors presented much greater explanatory power on vegetation change than a single factor. Our research highlights that the afforestation program in China during the past several decades plays an important role in contributing to vegetation greening across the country. Greening and degradation, however, are largely related to landscape context, which could be due to natural change and anthropogenic impact. To maintain high levels of forests, conserving the vegetation is more important than increasing the economic development.

Using dense soil moisture (SM) measurements in the upper Huai River basin of China, this study evaluated the spatial patterns of L-band satellite-based SM products, including Soil Moisture Active Passive (SMAP) L3, Soil Moisture and Ocean Salinity (SMOS) L3 and the European Space Agency’s Climate Change Initiative (ESA CCI) SM products. The mean difference (MD), root mean squared error (RMSE), unbiased root mean square error (ubRMSE) and Pearson correlation coefficient (R), were used in the evaluation. The evaluation results presented that SMAP and ESA CCI products can well capture the temporal variation of SM at single points quite well, with average R values of 0.51 and 0.46, respectively. And SMAP had the highest overall accuracy among the three satellite-based products in study area. We also analyzed the correlations between the four accuracy indexes and six environmental factors including the proportions of five land use/land cover types (i.e. water bodies, paddy fields, construction land, dryland and forest) and the average NDVI (Normalized Difference Vegetation Index) in 2016 in each grid. Analysis showed that the proportions of paddy fields and water bodies in each grid had significant positive correlations with MD, RMSE and ubRMSE, while NDVI, and the proportions of dryland and construction land had significant negative correlations with these three indexes. The significant correlations between the accuracy of SMAP, SMOS and ESA CCI SM products and environmental factors indicate that there exist systematic biases in these products, which can provide valuable insights into algorithm improvements.

Precipitation, as an important component of the Earth’s water cycle, plays a determinant role in various socio-economic practices. Consequently, having access to high-quality and reliable precipitation datasets is highly demanded. Although Gridded Precipitation Products (GPPs) have been widely employed in different applications, the lack of quantitative assessment of GPPs is a critical concern that should be addressed. This is because the inherent errors in GPPs would propagate into any models in which precipitation values are incorporated, introducing uncertainties into the final results. This paper aims to quantify the capability of six well-known GPPs (TMPA, CHIRPS, PERSIANN, GSMaP, IMERG, and ERA5) at multiple time scales (daily, monthly, and yearly) using in situ observations (over 1.7 million) throughout Iran over the past two decades (2000–2020). Both continuous and categorical metrics were implemented for precipitation intensity and occurrence assessment based on the point-to-pixel comparison approach. Although all metrics did not support the superior performance of any specific GPP, taking all investigations into account, the findings suggested the better performance of the Global Satellite Mapping of Precipitation (GSMaP) in estimating daily precipitation (CC = 0.599, RMSE = 3.48 mm/day, and CSI = 0.454). Based on the obtained continuous metrics, all the GPPs had better performances in dry months, while this did not hold for the categorical metrics. The validation at the station level was also carried out to present the spatial characteristics of errors throughout Iran, indicating higher overestimation/underestimation in regions with higher precipitation rates. The validation analysis over the last two decades illustrated that the GPPs had stable performances, and no improvement was seen, except for the GSMaP, in which its bias error was significantly reduced. The comparisons on monthly and yearly time scales suggested the higher accuracy of monthly and yearly averaged precipitation values than accumulated values. Our study provides valuable guidance to the selection and application of GPPs in Iran and also offers beneficial feedback for further improving these products.

Permafrost extends 40% of the Qinghai-Tibet Plateau (QTP), a region which contains the headwaters of numerous major rivers in Asia. As an aquiclude, permafrost substantially controls surface runoff and its hydraulic connection with groundwater. The freeze–thaw cycle in the active layer significantly impacts soil water movement direction, velocity, storage capacity, and hydraulic conductivity. Under the accelerating warming on the QTP, permafrost degradation is drastically altering regional and even continental hydrological regimes, attracting the attention of hydrologists, climatologists, ecologists, engineers, and decision-makers. A systematic review of permafrost hydrological processes and modeling on the QTP is still lacking, however, leaving a number of knowledge gaps. In this review, we summarize the current understanding of permafrost hydrological processes and applications of some permafrost hydrological models of varying complexity at different scales on the QTP. We then discuss the current challenges and future opportunities, including observations and data, the understanding of processes, and model realism. The goal of this review is to provide a clear picture of where we are now and to describe future challenges and opportunities. We concluded that more efforts are needed to conduct long-term field measurements, employ more advanced observation technologies, and develop flexible and modular models to deepen our understanding of permafrost hydrological processes and to improve our ability to predict the future responses of permafrost hydrology to climate changes.

Spatial downscaling is an effective way to obtain precipitation with sufficient spatial details. The performance of downscaling is typically determined by the empirical statistical relationships between precipitation and the used auxiliary variables. In this study, we conducted a comprehensive comparison of five empirical statistical methods for spatial downscaling of GPM IMERG V06B monthly and annual precipitation with a relatively long time series from 2001 to 2015 over a typical semi-arid to arid area (Gansu province, China). These methods included two parametric regression methods (univariate regression, or UR; multivariate regression, or MR) and three machine learning methods (artificial neural network, or ANN; support vector machine, or SVM; random forests, or RF), which were used to downscale the satellite precipitation from 0.1° (∼10 km) to 1 km spatial resolution. Five commonly used indices which were normalized differential vegetation index (NDVI), elevation, land surface temperature (LST), and latitude and longitude were selected as auxiliary variables. The downscaled results were validated using a total of 80 rain gauge station data during 2001–2015. Results showed that latitude had the overall largest correlation with IMERG annual precipitation, also evidenced by feature importance measurements in RF. The downscaled results at monthly scale were overall consistent with the results at annual scale. The machine learning-based methods had better predictive ability of the original IMERG precipitation than parametric regression methods, with larger coefficient of determination (R2) and smaller root-mean-square error (RMSE) as well as relative root-mean-square error (RRMSE). The downscaled 1 km IMERG precipitation by parametric regression methods had obvious underestimations (positive residual errors) in the south and east of Gansu province and overestimations (negative residual errors) in the west. In addition, the validation results of parametric regression downscaling methods showed large improvements after residual correction, while the improvements were small in the machine learning-based methods. However, the interpolation algorithm included in residual correction can cause certain errors in the downscaled results due to the ignorance of precipitation spatial heterogeneity. The machine learning-based RF downscaling had the smallest residual errors and the overall best validation results, showing great potentials to provide accurate precipitation with high spatial resolution.

We use a 785 nm shifted excitation Raman difference (SERDS) technique to measure the Raman spectra of the conidia of 10 mold species of especial toxicological, medical, and industrial importance, including Stachybotrys chartarum, Penicillium chrysogenum, Aspergillus fumigatus, Aspergillus flavus, Aspergillus oryzae, Aspergillus niger, and others. We find that both the pure Raman and fluorescence signals support the hypothesis that for an excitation wavelength of 785 nm the Raman signal originates from the melanin pigments bound within the cell wall of the conidium. In addition, the major features of the pure Raman spectra group into profiles that we hypothesize may be due to differences in the complex melanin biosynthesis pathways. We then combine the Raman spectral data with neural network models to predict species classification with an accuracy above 99%. Finally, the Raman spectral data of all species investigated is made freely available for download and use.

ABSTRACT: Numerous dark enclaves with different shapes are found in Jiuling Neoproterozoic granitoids. Precise LA-ICP-MS U-Pb dating was conducted on zircons extracted from two microcrystalline enclave samples, yielding crystallization ages of 822.6±5.8 Ma and 822.2±6.2 Ma, respectively. The consistent ages within analytical errors with the host granitoids suggested that they were the products of the same magmatism. The microcrystalline-dioritic enclaves commonly show plastic forms and contain similar plagioclase megacrysts to the host rocks, and both of the enclaves and host granitoids showed a complex composition and structural imbalance in plagioclases. Furthermore, the apatites with a euhedral acicular shape occurred widely in the microcrystalline-dioritic enclaves. All of these petrographic features above imply magma mixing is involved in their diagenesis. The enclaves and host granitoids show a marked zircon trace element difference and Hf isotopic signatures without correlation in zircon trace element pairs but form their own system between enclaves and host granitoids. Additionally, most of the zircons show extremely high εHf (t) with εHf (t) =3.54–11.94 from the southern samples, and εHf (t) =1.0–9.09 from the central region. Some zircons with the higher εHf (t) are similar to the zircons from the juvenile island arc in the eastern segment of Jiangnan Orogen. Integrated geological and Hf isotopic characteristics suggest microcrystalline-dioritic enclaves were derived from the partial melting process of the Mesoproterozoic crust which enriched juvenile island arc materials and mixed with the granitic magma that remelted from the Mesoproterozoic continental crust which relatively enriched ancient sediments and mixed with the host granitoid in diagenesis.

Proper irrigation scheduling and agricultural water management require a precise estimation of crop water requirement. In practice, reference evapotranspiration (ETo) is firstly estimated, and used further to calculate the evapotranspiration of each crop. In this study, two new coupled models were developed for estimating daily ETo. Two optimization algorithms, the shuffled frog-leaping algorithm (SFLA) and invasive weed optimization (IWO), were coupled on an adaptive neuro-fuzzy inference system (ANFIS) to develop and implement the two novel hybrid models (ANFIS-SFLA and ANFIS-IWO). Additionally, four empirical models with varying complexities, including Hargreaves–Samani, Romanenko, Priestley–Taylor, and Valiantzas, were used and compared with the developed hybrid models. The performance of all investigated models was evaluated using the ETo estimates with the FAO-56 recommended method as a benchmark, as well as multiple statistical indicators including root-mean-square error (RMSE), relative RMSE (RRMSE), mean absolute error (MAE), coefficient of determination (R2), and Nash–Sutcliffe efficiency (NSE). All models were tested in Tabriz and Shiraz, Iran as the two studied sites. Evaluation results showed that the developed coupled models yielded better results than the classic ANFIS, with the ANFIS-SFLA outperforming the ANFIS-IWO. Among empirical models, generally the Valiantzas model in its original and calibrated versions presented the best performance. In terms of model complexity (the number of predictors), the model performance was obviously enhanced by an increasing number of predictors. The most accurate estimates of the daily ETo for the study sites were achieved via the hybrid ANFIS-SFLA models using full predictors, with RMSE within 0.15 mm day−1, RRMSE within 4%, MAE within 0.11 mm day−1, and both a high R2 and NSE of 0.99 in the test phase at the two studied sites.

The accurate evaluation of shifts in vegetation phenology is essential for understanding of vegetation responses to climate change. Remote-sensing vegetation index (VI) products with multi-day scales have been widely used for phenology trend estimation. VI composites should be interpolated into a daily scale for extracting phenological metrics, which may not fully capture daily vegetation growth, and how this process affects phenology trend estimation remains unclear. In this study, we chose 120 sites over four vegetation types in the mid-high latitudes of the northern hemisphere, and then a Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A4 daily surface reflectance data was used to generate a daily normalized difference vegetation index (NDVI) dataset in addition to an 8-day and a 16-day NDVI composite datasets from 2001 to 2019. Five different time interpolation methods (piecewise logistic function, asymmetric Gaussian function, polynomial curve function, linear interpolation, and spline interpolation) and three phenology extraction methods were applied to extract data from the start of the growing season and the end of the growing season. We compared the trends estimated from daily NDVI data with those from NDVI composites among (1) different interpolation methods; (2) different vegetation types; and (3) different combinations of time interpolation methods and phenology extraction methods. We also analyzed the differences between the trends estimated from the 8-day and 16-day composite datasets. Our results indicated that none of the interpolation methods had significant effects on trend estimation over all sites, but the discrepancies caused by time interpolation could not be ignored. Among vegetation types with apparent seasonal changes such as deciduous broadleaf forest, time interpolation had significant effects on phenology trend estimation but almost had no significant effects among vegetation types with weak seasonal changes such as evergreen needleleaf forests. In addition, trends that were estimated based on the same interpolation method but different extraction methods were not consistent in showing significant (insignificant) differences, implying that the selection of extraction methods also affected trend estimation. Compared with other vegetation types, there were generally fewer discrepancies between trends estimated from the 8-day and 16-day dataset in evergreen needleleaf forest and open shrubland, which indicated that the dataset with a lower temporal resolution (16-day) can be applied. These findings could be conducive for analyzing the uncertainties of monitoring vegetation phenology changes.

It has been suggested that the magnetic properties of metal layers using reversible redox reactions could form the basis of memory devices but this requires fast electric control to be practical. Here the authors demonstrate this on sub-millisecond timescales in a metal–oxide heterostructure.

Remote Sensing, as a driver for water management decisions, needs further integration with monitoring water quality programs, especially in developing countries. Moreover, usage of remote sensing approaches has not been broadly applied in monitoring routines. Therefore, it is necessary to assess the efficacy of available sensors to complement the often limited field measurements from such programs and build models that support monitoring tasks. Here, we integrate field measurements (2013–2019) from the Mexican national water quality monitoring system (RNMCA) with data from Landsat-8 OLI, Sentinel-3 OLCI, and Sentinel-2 MSI to train an extreme learning machine (ELM), a support vector regression (SVR) and a linear regression (LR) for estimating Chlorophyll-a (Chl-a), Turbidity, Total Suspended Matter (TSM) and Secchi Disk Depth (SDD). Additionally, OLCI Level-2 Products for Chl-a and TSM are compared against the RNMCA data. We observed that OLCI Level-2 Products are poorly correlated with the RNMCA data and it is not feasible to rely only on them to support monitoring operations. However, OLCI atmospherically corrected data is useful to develop accurate models using an ELM, particularly for Turbidity (R2 = 0.7). We conclude that remote sensing is useful to support monitoring systems tasks, and its progressive integration will improve the quality of water quality monitoring programs.

Nitrogen is one of the most important nutrient indicators for the growth of crops, and is closely related to the chlorophyll content of leaves and thus influences the photosynthetic ability of the crops. In this study, five hybrid rice varieties were cultivated during one entire growing period in one experimental field supplied with six nitrogen fertilizer levels. Visible and near infrared (vis/NIR) reflectance spectroscopy combined with multivariate analysis was used to identify hybrid rice varieties and nitrogen fertilizer levels, as well as to detect chlorophyll content associated with nitrogen levels. The support vector machine (SVM) algorithm was applied to identify five varieties of hybrid rice and six levels of nitrogen fertilizer. The results demonstrated that different varieties of hybrid rice for each nitrogen level can be well distinguished except for the highest nitrogen level, and no nitrogen level for each rice variety can be completely identified from the other five nitrogen levels. Further, 12 spectral indices combined with partial least square (PLS) analysis were applied for estimating chlorophyll content of rice leaves from plants subjected to different nitrogen levels, and a root mean square error of cross-validation (RMSECV) of 0.506, a coefficient of determination (R2) of 97.8% and a ratio of performance to deviation (RPD) of 4.6 for all rice varieties indicated this as a preferable procedure. This study demonstrates that Vis/NIR spectroscopy can have a great potential for identification of rice varieties and evaluation of nitrogen fertilizer levels.

Amongst the impacts of climate change, those arising from extreme hydrological events are expected to cause the greatest impacts. To assess the changes in temperature and precipitation and their impacts on the discharge in the upper Yangtze Basin from pre-industrial to the end of 21st century, four hydrological models were integrated with four global climate models. Results indicated that mean discharge was simulated to increase slightly for all hydrological models forced by all global climate models during 1771–1800 and 1871–1900 relative to the 1971–2000 reference period, whereas the change directions in mean discharge were not consistent among the four global climate models during 2070–2099, with increases from HadGEM2-ES and MIROC5, and decreases from GFDL-ESM2M and IPSL-CM5A-LR. Additionally, our results indicated that decreases in precipitation may always result in the decrease in mean discharge, but increases in precipitation did not always lead to increases in discharge due to high temperature rise. The changes in extreme flood events with different return intervals were also explored. These extreme events were projected to become more intense and frequent in the future, which could have potential devastating impacts on the society and ecosystem in this region.

The rapid expansion of oil palm is a major driver of deforestation and other associated damage to the climate and ecosystem in tropical regions, especially Southeast Asia. It is therefore necessary to precisely detect and monitor oil palm plantations to safeguard the ecosystem services and biodiversity of tropical forests. Compared with optical data, which are vulnerable to cloud cover, the Sentinel-1 dual-polarization C-band synthetic aperture radar (SAR) acquires global observations under all weather conditions and times of day and shows good performance for oil palm detection in the humid tropics. However, because accurately distinguishing mature and young oil palm trees by using optical and SAR data is difficult and considering the strong dependence on the input parameter values when detecting oil palm plantations by employing existing classification algorithms, we propose an innovative method to improve the accuracy of classifying the oil palm type (mature or young) and detecting the oil palm planting area in Sumatra by fusing Landsat-8 and Sentinel-1 images. We extract multitemporal spectral characteristics, SAR backscattering values, vegetation indices, and texture features to establish different feature combinations. Then, we use the random forest algorithm based on improved grid search optimization (IGSO-RF) and select optimal feature subsets to establish a classification model and detect oil palm plantations. Based on the IGSO-RF classifier and optimal features, our method improved the oil palm detection accuracy and obtained the best model performance (OA = 96.08% and kappa = 0.9462). Moreover, the contributions of different features to oil palm detection are different; nevertheless, the optimal feature subset performed the best and demonstrated good potential for the detection of oil palm plantations.

Measurements of the atmospheric concentration of polluting atomic mercury were performed using the differential absorption lidar (DIAL) technique in the Lintong district, about 35 km northeast of Xi’an, the capital of the Shaanxi province, China. Concentrations ranging from 2 to 13 ng/m3 were observed. As uniquely enabled by the lidar technique, representative average concentrations, integrated over a considerable volume, were recorded and put in relation to weather conditions, and vertical concentration profiles were measured. Considerable local non-uniformities were also observed, which may indicate the presence of localized hot-spots in the area, possibly related to ancient tombs.