Search Results (18)

This study takes the Danjiangkou reservoir basin, which is the water source area of the South-to-North Water Diversion Project, one of the largest water diversion projects in the world, as the research area. Three different types of evapotranspiration (ET) datasets are adopted, including the Global Land Evaporation Amsterdam Model (GLEAM), European Centre for Medium-Range Weather Forecasts ERA5—Land Component (ERA5Land), and Complementary Relationship (CR) datasets. These datasets are analyzed for spatiotemporal evolution and data fusion using Mann–Kendall analysis, Sen’s Slope analysis, and Extended Triple Collocation (ETC). The aim is to improve the accuracy of evapotranspiration estimation in the watershed of the water source area. The results show the following: (1) All three sets of evapotranspiration data indicate an increasing trend in the watershed, with rates of 0.78 mm/year, 0.14 mm/year, and 2.56 mm/year, respectively. Additionally, the seasonal variation in evapotranspiration is significant, with the rate of change being summer > spring > autumn > winter. (2) The data fusion results indicate that ERA5Land performs best in the water source area watershed, with the smallest root mean square error (RMSE) value. In the fused data, ERA5Land’s evapotranspiration data account for the largest proportion at 59.93%, GLEAM ET data account for 39.96%, and CR’s evapotranspiration data account for the smallest proportion at only 0.11%. (3) The spatial distribution shows that the fused data fully exploits the advantages of different evapotranspiration data, inherits the advantages of ERA5Land and GLEAM ET products, and achieves effective fusion of multi-source data, thereby forming a more accurate dataset. These research findings provide scientific references for the construction of digital twin watersheds, intelligent water resource allocation, and effective responses to climate change in the water source area of the South-to-North Water Diversion Project. Full article

Haiyang 2B (HY-2B), the second Chinese ocean dynamic environment monitoring satellite, has been operational for nearly six years. The scanning microwave radiometer (SMR) onboard HY-2B provides global sea surface temperature (SST) observations. Comprehensive validation of these data is essential before they can be effectively applied. This study evaluates the operational SST product from the SMR, covering the period from 1 January 2019 to 31 August 2024, using direct comparison and extended triple collocation (ETC) methods. The direct comparison assesses bias and root mean square error (RMSE), while ETC analysis estimates the random error of the SST measurement systems and evaluates their ability to detect SST variations. Additionally, the spatial and temporal variations in error characteristics, as well as the crosstalk effects of sea surface wind speed, columnar water vapor, and columnar cloud liquid water, are analyzed. Compared with iQuam SST, the total RMSE of SMR SST for ascending and descending passes are 0.88 °C and 0.85 °C, with total biases of 0.1 °C and −0.08 °C, respectively. ETC analysis indicates that the random errors for ascending and descending passes are 0.87 °C and 0.80 °C, respectively. The SMR’s ability to detect SST variations decreases significantly at high latitudes and near 10°N latitude. Error analysis reveals that the uncertainty in SMR SSTs has increased over time, and the presence of crosstalk effects in SMR SST retrieval has been confirmed. Full article

Modern hydrological modeling frequently incorporates global remote sensing or reanalysis products for multivariate calibration. Although these datasets significantly contribute to model accuracy, the inherent uncertainties in the datasets and multivariate calibration present challenges in the modeling process. To address this issue, this study introduces an adaptive, process-wise fitting framework for the iterative multivariate calibration of hydrological models using global remote sensing and reanalysis products. A distinctive feature is the “kinship” concept, which defines the relationship between model parameters and hydrological processes, highlighting their impacts and connectivity within a directed graph. The framework subsequently develops an enhanced particle swarm optimization (PSO) algorithm for stepwise calibration of hydrological processes. This algorithm introduces a learning rate that reflects the parameter’s kinship to the calibrated hydrological process, facilitating efficient exploration in search of suitable parameter values. This approach maximizes the performance of the calibrated process while ensuring a balance with other processes. To ease the impact of inherent uncertainties in the datasets, the Extended Triple Collocation (ETC) method, operating independently of ground truth data, is integrated into the framework to assess the simulation of the calibrated process using remote sensing products with inherent data uncertainty. This proposed approach was implemented with the SWAT model in both arid and humid basins. Five calibration schemes were designed and evaluated through a comprehensive comparison of their performance in three repeated experiments. The results highlight that this approach not only improved the accuracy of ET simulation across sub-basins but also enhanced the precision of streamflow at gauge stations, concurrently reducing parameter uncertainty. This approach significantly advances our understanding of hydrological processes, demonstrating the potential for both theoretical and practical applications in hydrology. Full article

Accurate estimation of evapotranspiration (ET) is critical for understanding land-atmospheric interactions. Despite the advancement in ET measurement, a single ET estimate still suffers from inherent uncertainties. Data fusion provides a viable option for improving ET estimation by leveraging the strengths of individual ET products, especially the triple collocation (TC) method, which has a prominent advantage in not relying on the availability of “ground truth” data. In this work, we proposed a framework for uncertainty analysis and data fusion based on the extended TC (ETC) and multiple TC (MTC) variants. Three different sources of ET products, i.e., the Global Land Evaporation and Amsterdam Model (GLEAM), the fifth generation of European Reanalysis-Land (ERA5-Land), and the complementary relationship model (CR), were selected as the TC triplet. The analyses were conducted based on different climate zones and land cover types across China. Results show that ETC presents outstanding performance as most areas conform to the zero-error correlations assumption, while nearly half of the areas violate this assumption when using MTC. In addition, the ETC method derives a lower root mean square error (RMSE) and higher correlation coefficient (Corr) than the MTC one over most climate zones and land cover types. Among the ET products, GLEAM performs the best, while CR performs the worst. The merged ET estimates from both ETC and MTC methods are generally superior to the original triplets at the site scale. The findings indicate that the TC-based method could be a reliable tool for uncertainty analysis and data fusion. Full article

Accurate and reliable estimation of actual evapotranspiration (AET) is essential for various hydrological studies, including drought prediction, water resource management, and the analysis of atmospheric–terrestrial carbon exchanges. Gridded AET products offer potential for application in ungauged areas, but their uncertainties may be significant, making it difficult to identify the best products for specific regions. While in situ data directly estimate gridded ET products, their applicability is limited in ungauged areas that require FLUXNET data. This paper employs an Extended Triple Collocation (ETC) method to estimate the uncertainty of Global Land Evaporation Amsterdam Model (GLEAM), Famine Early Warning Systems Network (FLDAS), and Maximum Entropy Production (MEP) AET product without requiring prior information. Subsequently, a merged ET product is generated by combining ET estimates from three original products. Furthermore, the study quantifies the uncertainty of each individual product across different vegetation covers and then compares three original products and the Merged ET with data from 645 in situ sites. The results indicate that GLEAM covers the largest area, accounting for 39.1% based on the correlation coefficient criterion and 39.9% based on the error variation criterion. Meanwhile, FLDAS and MEP exhibit similar performance characteristics. The merged ET derived from the ETC method demonstrates the ability to mitigate uncertainty in ET estimates in North American (NA) and European (EU) regions, as well as tundra, forest, grassland, and shrubland areas. This merged ET could be effectively utilized to reduce uncertainty in AET estimates from multiple products for ungauged areas. Full article

Droughts present substantial challenges to agriculture, food security, and water resources. Employing a drought index based on soil moisture dynamics is a common and effective approach for agricultural drought monitoring. However, the precision of a drought index heavily relies on accurate soil moisture and soil hydraulic parameters. This study leverages remote sensing soil moisture data from the Climate Change Initiative (CCI) series products and model-generated soil moisture data from the Variable Infiltration Capacity (VIC) model. The extended triple collocation (ETC) method was applied to merge these datasets from 1992 to 2018, resulting in enhanced accuracy by 28% and 15% compared to the CCI and VIC soil moisture, respectively. Furthermore, this research establishes field capacity and a wilting point map using multiple soil datasets and pedotransfer functions, facilitating the development of an enhanced Soil Water Deficit Index (SWDI) based on merged soil moisture, field capacity, and wilting points. The findings reveal that the proposed enhanced SWDI achieves a higher accuracy in detecting agricultural drought events (probability of detection = 0.98) and quantifying their severity (matching index = 0.33) compared to an SWDI based on other soil moisture products. Moreover, the enhanced SWDI exhibits superior performance in representing drought-affected crop areas (correlation coefficient = 0.88), outperforming traditional drought indexes such as the Standardized Precipitation Index (correlation coefficient = 0.51), the Soil Moisture Anomaly Percent Index (correlation coefficient = 0.81), and the Soil Moisture Index (correlation coefficient = 0.83). The enhanced SWDI effectively captures the spatiotemporal dynamics of a drought, supporting more accurate agricultural drought monitoring and management strategies. Full article

Evaluating sea surface temperature (SST) products is essential before their application in marine environmental monitoring and related studies. SSTs from the in situ SST Quality Monitor (iQuam) system, Advanced Microwave Scanning Radiometer 2 (AMSR2) aboard the Global Change Observation Mission 1st-Water, and the Microwave Radiation Imager (MWRI) aboard the Chinese Fengyun-3D satellite are intercompared utilizing extended triple collocation (ETC) and direct comparison methods. Additionally, error characteristic variations with respect to time, latitude, SST, sea surface wind speed, columnar water vapor, and columnar cloud liquid water are analyzed comprehensively. In contrast to the prevailing focus on SST validation accuracy, the random errors and the capability to detect SST variations are also evaluated in this study. The result of ETC analysis indicates that iQuam SST from ships exhibits the highest random error, above 0.83 °C, whereas tropical mooring SST displays the lowest random error, below 0.28 °C. SST measurements from drifters, tropical moorings, Argo floats, and high-resolution drifters, which possess random errors of less than 0.35 °C, are recommended for validating remotely sensed SST. The ability of iQuam, AMSR2, and MWRI to detect SST variations diminishes significantly in ocean areas between 0°N and 20°N latitude and latitudes greater than 50°N and 50°S. AMSR2 and iQuam demonstrate similar random errors and capabilities for detecting SST variations, whereas MWRI shows a high random error and weak capability. In comparison to iQuam SST, AMSR2 exhibits a root-mean-square error (RMSE) of about 0.51 °C with a bias of −0.05 °C, while MWRI shows an RMSE of about 1.26 °C with a bias of −0.14 °C. Full article

Currently, satellite-based soil moisture (SM) products and land surface model assimilation techniques are widely utilized. However, the presence of systematic errors in the observation process, algorithmic discrepancies between products, and variations in spatial and temporal scales result in diverse accuracy characteristics and applicability. This study evaluates three prominent SM products in China, namely, the Essential Climate Variable Soil Moisture (ECV), the European Centre for Medium-Range Weather Forecasts’ Fifth-Generation Land Surface Reanalysis Data (ERA5-Land), and the Global Land Surface Data Assimilation System (GLDAS). The evaluation was conducted using extended triple collocation (ETC) analysis and in situ validation methods at a monthly scale from 2000 to 2020. The ETC analysis results show that among the three products, GLDAS exhibits the highest correlation coefficient (CC) and the lowest standard deviation of error (ESD), indicating its superior performance in China. ECV and ERA5-Land follow, with slightly lower performance. In the in situ validation results, ERA5-Land displays the highest correlation, capturing the temporal trend of the ground SM well. Comparatively, in terms of overall accuracy, ECV performs the best, with a slightly smaller mean error (ME) and root mean square error (RMSE) than GLDAS, and ERA5-Land has the lowest accuracy. The discrepancy between the in situ validation results and ETC analysis can be attributed to the limited number of sites and their representativeness errors. Notably, ERA5-Land exhibits a highly consistent trend of interannual fluctuations between ESD and precipitation. Furthermore, a strong association is observed between the ME and RMSE of ECV and GLDAS and precipitation. These findings serve as valuable references for future SM studies in China. Full article

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. Full article

Global land cover (GLC) data are an indispensable resource for understanding the relationship between human activities and the natural environment. Estimating their classification accuracy is significant for studying environmental change and sustainable development. With the rapid emergence of various GLC products, the lack of high-quality reference data poses a severe risk to traditional accuracy estimation methods, in which reference data are always required. Thus, meeting the needs of large-scale, fast evaluation for GLC products becomes challenging. The triple collocation approach (TCCA) is originally applied to assess classification accuracy in earthquake damage mapping when ground truth is unavailable. TCCA can provide unbiased accuracy estimation of three classification systems when their errors are conditionally independent. In this study, we extend the idea of TCCA and test its performance in the accuracy estimation of GLC data without ground reference data. Firstly, to generate two additional classification systems besides the original GLC data, a k-order neighbourhood is defined for each assessment unit (i.e., geographic tiles), and a local classification strategy is implemented to train two classifiers based on local samples and features from remote sensing images. Secondly, to reduce the uncertainty from complex classification schemes, the multi-class problem in GLC is transformed into multiple binary-class problems when estimating the accuracy of each land class. Building upon over 15 million sample points with remote sensing features retrieved from Google Earth Engine, we demonstrate the performance of our method on WorldCover 2020, and the experiment shows that screening reliable sample points during training local classifiers can significantly improve the overall estimation with a relative error of less than 4% at the continent level. This study proves the feasibility of estimating GLC accuracy using the existing land information and remote sensing data, reducing the demand for costly reference data in GLC assessment and enriching the assessment approaches for large-scale land cover data. Full article

The Chinese new marine dynamic environment satellite HY-2D was launched on 19 May 2021, carrying a Ku-band scatterometer (named HSCAT-D). In this study, wind products observed by the HSCAT-D were validated by comparing with wind data from the U.S. National Data Buoy Center (NDBC) buoys and European Centre for Medium-Range Weather Forecasts (ECMWF) model. The statistical results show that the HSCAT-D winds have a good agreement with the buoys’ wind measurements: in comparison with buoy winds, the wind speed standard deviation (STD) and root-mean-squared errors (RMSE) of direction were 0.78 m/s and 14.10°, respectively. Other scatterometers’ wind data are also employed for comparisons, including the HY-2B scatterometer (HSCAT-B), HY-2C scatterometer (HSCAT-C), and MetOp-B scatterometer (ASCAT-B) winds. The statistical results indicate that errors for HSCAT-D winds are smaller than HSCAT-C but a little bit larger than HSCAT-B. The spectral analysis shows that the HSCAT-D wind products contain less small-scale information than ASCAT-B. Moreover, the Extended Triple Collocation (ETC) results show that the HSCAT-D wind product is of good quality and well-calibrated. We believe that the HSCAT-D wind products will be helpful for the scientific community, as shown by the encouraging validation results. Full article

Traditional polarization-sensitive sensors involve a triplet of spatially collocated, orthogonally oriented, and diversely polarized electric dipoles. However, this kind of sensor has the drawback of severe mutual coupling among the three dipoles due to the characteristic of collocation, as well as low radiation efficiency because of the short length of the dipoles. Based on this problem, in this study we designed a new array structure called a ‘triple coprime array (TCA)’, equipped with long electric dipoles to obtain higher radiation efficiency. In this structure, the dipoles within different subarrays have orthogonal polarization modes, leading to mutual coupling isolation. The dipole interval of the subarrays is enlarged by means of a pairwise coprime relationship, which further weakens the mutual coupling effect and extends the array aperture. Simultaneously, a stable direction-of-arrival (DOA) and polarization estimation method is proposed. DOA information is accurately refined from the three subarrays without ambiguity problems, with the triple coprime characteristic improving the estimation results. Subsequently, polarization estimates can be obtained using the reconstructed model matrix and the least squares method. Numerous theoretical analyses were conducted and extensive simulation results verified the advantages of the TCA structure in mutual coupling, along with the superiority of the proposed joint DOA and polarization estimation algorithm in terms of estimation accuracy. Full article

Wind speed reconciliation across different wind sources is critically needed for extending available satellite wind records in Tropical Cyclones. The deviations between wind references of extremes, such as the moored buoy data and dropsonde wind estimates for guidance on geophysical model function development, are one of the main causes of wind speed differences for wind products, for instance, the overestimation of Synthetic Aperture Radars (SARs) relative to ASCAT winds. The study proposes a new wind speed adjustment to achieve mutual adjustment between ASCAT CMOD7 winds and simultaneous SAR wind speeds. The so-called CMOD7D-v2 adjustment is constructed based on the statistical analysis of SAR and ASCAT Tropical Cyclone acquisitions between 2016 and 2021, showing a satisfactory performance in wind speed reconciliation for winds with speeds higher than 14 m/s. Furthermore, the error characteristics of the CMOD7D-v2 adjustment for Tropical Cyclone winds are analyzed using the Triple Collocation analysis technique. The analysis results show that the proposed wind adjustment can reduce ASCAT wind errors by around 16.0% when adjusting ASCAT winds to SAR wind speeds. In particular, when downscaling SAR winds, the improvement in ASCAT wind errors can be up to 42.3%, effectively alleviating wind speed differences across wind sources. Furthermore, to avoid the impacts of large footprints by ASCAT sensors, wind speeds retrieved from SAR VV signals (acting as a substitute for ASCAT winds) are adjusted accordingly and compared against SAR dual-polarized winds and collocated Stepped Frequency Microwave Radiometer (SFMR) observations. We find that the bias values of adjusted winds are lower than products from other adjustment schemes by around 5 m/s at the most extreme values. These promising results verify the plausibility of the CMOD7D-v2 adjustment, which is conducive to SAR and ASCAT wind speed comparisons and extreme wind analysis in Tropical Cyclone cases. Full article

Soil moisture plays an essential role in the land-atmosphere interface. It has become necessary to develop quality large-scale soil moisture data from satellite observations for relevant applications in climate, hydrology, agriculture, etc. Specifically, microwave-based observations provide more consistent land surface records because they are unhindered by cloud conditions. The recent microwave radiometers onboard FY-3B, FY-3C and FY-3D satellites launched by China’s Meteorological Administration (CMA) extend the number of available microwave observations, covering late 2011 up until the present. These microwave observations have the potential to provide consistent global soil moisture records to date, filling the data gaps where soil moisture estimates are missing in the existing records. Along these lines, we studied the FY-3C to understand its added value due to its unique time of observation in a day (ascending: 22:15, descending: 10:15) absent from the existing satellite soil moisture records. Here, we used the triple collocation technique to optimize a benchmark retrieval model of land surface temperature (LST) tailored to the observation time of FY3C, by evaluating various soil moisture scenarios obtained with different bias-imposed LSTs from 2014 to 2016. The globally optimized LST was used as an input for the land parameter retrieval model (LPRM) algorithm to obtain optimized global soil moisture estimates. The obtained FY-3C soil moisture observations were evaluated with global in situ and reanalysis datasets relative to FY3B soil moisture products to understand their differences and consistencies. We found that the RMSEs of their anomalies were mostly concentrated between 0.05 and 0.15 m³ m⁻³, and correlation coefficients were between 0.4 and 0.7. The results showed that the FY-3C ascending data could better capture soil moisture dynamics than the FY-3B estimates. Both products were found to consistently complement the skill of each other over space and time globally. Finally, a linear combination approach that maximizes temporal correlations merged the ascending and descending soil moisture observations separately. The results indicated that superior soil moisture estimates are obtained from the combined product, which provides more reliable global soil moisture records both day and night. Therefore, this study aims to show that there is merit to the combined usage of the two FY-3 products, which will be extended to the FY-3D, to fill the gap in existing long-term global satellite soil moisture records. Full article

It is an effective measure to estimate groundwater storage anomalies (GWSA) by combining Gravity Recovery and Climate Experiment (GRACE) data and hydrological models. However, GWSA results based on a single hydrological model and GRACE data may have greater uncertainties, and it is difficult to verify in some regions where in situ groundwater-level measurements are limited. First, to solve this problem, a groundwater weighted fusion model (GWFM) is presented, based on the extended triple collocation (ETC) method. Second, the Shiyang River Basin (SYRB) is taken as an example, and in situ groundwater-level measurements are used to evaluate the performance of the GWFM. The comparison indicates that the correlation coefficient (CC) and Nash-Sutcliffe efficiency coefficient (NSE) are increased by 9–40% and 23–657%, respectively, relative to the original results. Moreover, the root mean squared error (RMSE) is reduced by 9–28%, which verifies the superiority of the GWFM. Third, the spatiotemporal distribution and influencing factors of GWSA in the Hexi Corridor (HC) are comprehensively analyzed during the period between 2003 and 2016. The results show that GWSA decline, with a trend of −2.37 ± 0.38 mm/yr from 2003 to 2010, and the downward trend after 2011 (−0.46 ± 1.35 mm/yr) slow down significantly compared to 2003–2010. The spatial distribution obtained by the GWFM is more reliable compared to the arithmetic average results, and GWFM-based GWSA fully retain the advantages of different models, especially in the southeastern part of the SYRB. Additionally, a simple index is used to evaluate the contributions of climatic factors and human factors to groundwater storage (GWS) in the HC and its different subregions. The index indicates that climate factors occupy a dominant position in the SLRB and SYRB, while human factors have a significant impact on GWS in the Heihe River Basin (HRB). This study can provide suggestions for the management and assessments of groundwater resources in some arid regions. Full article