Global LAnd Surface Satellite Research Articles

Dense flux observations reveal the incapability of evapotranspiration products to capture the heterogeneity of evapotranspiration

Accurate terrestrial evapotranspiration (ET) estimation over complex surfaces with spatial heterogeneity is crucial for local, regional, and global applications. Currently, a variety of global ET products with different spatiotemporal resolutions have been developed and evaluated. However, little is known about their performance in capturing the heterogeneity of ET over complex surfaces. Focusing on the Heihe River Basin (HRB), a typical arid and semi-arid region that is hydrologically vulnerable, this study compared ET from eleven global ET products (six remotely- sensed ET, two land surface model ET, one hydrological model-based ET, one reanalysis ET, and one synthesis ET) and one regional ET dataset against dense eddy covariance observations in terms of the magnitude, seasonal cycle, and spatial pattern. In general, the remotely-sensed ET and synthesis ET outperformed other categories, with the operational Simplified Surface Energy Balance (SSEBop), Penman-Monteith–Leuning (PML), Moderate Resolution Imaging Spectroradiometer (MOD16) and Global LAnd Surface Satellite (GLASS) performing relatively better (root mean square error ranging from 1.22 to 1.57 mm d-1). Across all the land cover types, ET products reproduced a relatively feasible ET over the desert steppe, meadow, and barren, but substantially underestimated ET over ecosystems with high ET values but a low land area fraction over HRB (cropland, wetland, and forest). This highlights the importance of accurately representing the heterogeneity and local climates in the complex land surfaces for ET quantification in regional water resource management.

Responses of ecosystem water use efficiency to drought in the Lancang–Mekong River Basin

The ecosystem water use efficiency (WUE), a crucial indicator of how climate change will affect terrestrial ecosystems, depicts the coupling of the carbon gain and water loss in terrestrial ecosystems. In this study, the spatiotemporal variations in the WUE and its responses to drought in the Lancang–Mekong River Basin (LMRB) from 1982 to 2018 were investigated using the gross primary productivity (GPP) and evapotranspiration (ET) data acquired from the Global Land Surface Satellite (GLASS) products. The analyses revealed that: (1) the mean yearly WUE for the LMRB was 1.63 g C kg−1 H2O, with comparatively higher values in forests and warm temperate climatic types. The interaction of temperature and leaf area index was the main factor affecting the spatial distribution of WUE. The yearly WUE for the entire region exhibited a decreasing trend with a rate of −0.0009 g C kg −1 H2O·yr−1, and the spatially significantly decreasing area accounted for 41.67% of the total area. (2) The annual WUE was positively correlated with drought in the humid regions, accounting for 66.55% of the total area, while a negative relationship mainly occurred in the high-altitude cold region. (3) The ecosystem WUE lagged behind the drought by 3 months in most regions. The lag effect was more apparent in the grassland-dominated upstream region and the cropland-dominated Mekong Delta. (4) The resilience analysis revealed that the ecosystems in forests and temperate climate types were strongly resistant to drought, while the grassland and high-altitude regions with a dry and cold climate had relatively poor resilience. The results of this study shed light on how the WUE responds to drought across diverse land use types, climate types, and elevation gradients, uncovering fresh insights into the potential mechanisms behind the impact of drought on water and carbon cycles within ecosystems.

Generation of global 1 km daily soil moisture product from 2000 to 2020 using ensemble learning

Abstract. Motivated by the lack of long-term global soil moisture products with both high spatial and temporal resolutions, a global 1 km daily spatiotemporally continuous soil moisture product (GLASS SM) was generated from 2000 to 2020 using an ensemble learning model (eXtreme Gradient Boosting – XGBoost). The model was developed by integrating multiple datasets, including albedo, land surface temperature, and leaf area index products from the Global Land Surface Satellite (GLASS) product suite, as well as the European reanalysis (ERA5-Land) soil moisture product, in situ soil moisture dataset from the International Soil Moisture Network (ISMN), and auxiliary datasets (Multi-Error-Removed Improved-Terrain (MERIT) DEM and Global gridded soil information (SoilGrids)). Given the relatively large-scale differences between point-scale in situ measurements and other datasets, the triple collocation (TC) method was adopted to select the representative soil moisture stations and their measurements for creating the training samples. To fully evaluate the model performance, three validation strategies were explored: random, site independent, and year independent. Results showed that although the XGBoost model achieved the highest accuracy on the random test samples, it was clearly a result of model overfitting. Meanwhile, training the model with representative stations selected by the TC method could considerably improve its performance for site- or year-independent test samples. The overall validation accuracy of the model trained using representative stations on the site-independent test samples, which was least likely to be overfitted, was a correlation coefficient (R) of 0.715 and root mean square error (RMSE) of 0.079 m3 m−3. Moreover, compared to the model developed without station filtering, the validation accuracies of the model trained with representative stations improved significantly for most stations, with the median R and unbiased RMSE (ubRMSE) of the model for each station increasing from 0.64 to 0.74 and decreasing from 0.055 to 0.052 m3 m−3, respectively. Further validation of the GLASS SM product across four independent soil moisture networks revealed its ability to capture the temporal dynamics of measured soil moisture (R=0.69–0.89; ubRMSE = 0.033–0.048 m3 m−3). Lastly, the intercomparison between the GLASS SM product and two global microwave soil moisture datasets – the 1 km Soil Moisture Active Passive/Sentinel-1 L2 Radiometer/Radar soil moisture product and the European Space Agency Climate Change Initiative combined soil moisture product at 0.25∘ – indicated that the derived product maintained a more complete spatial coverage and exhibited high spatiotemporal consistency with those two soil moisture products. The annual average GLASS SM dataset from 2000 to 2020 can be freely downloaded from https://doi.org/10.5281/zenodo.7172664 (Zhang et al., 2022a), and the complete product at daily scale is available at http://glass.umd.edu/soil_moisture/ (last access: 12 May 2023).

Impacts of land cover changes on biogenic emission and its contribution to ozone and secondary organic aerosol in China

Abstract. The greening impacts on China from 2000 to 2017 led to an increase in vegetated areas and thus enhanced biogenic volatile organic compound (BVOC) emissions. BVOCs are regarded as important precursors for ozone (O3) and secondary organic aerosol (SOA). As a result, accurate estimation of BVOC emissions is critical to understand their impacts on air quality. In this study, the Model of Emissions of Gases and Aerosols from Nature (MEGAN) v2.1 was used to investigate the impact of different leaf area index (LAI) and land cover (LC) datasets on BVOC emissions in China in 2016, and the effects on O3 and SOA were evaluated based on the Community Multiscale Air Quality (CMAQ) modeling system. Three LAI satellite datasets of the Global LAnd Surface Satellite (GLASS), the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD15A2H version 6 (MOD15), and the Copernicus Global Land Service (CGLS), as well as three LC satellite datasets of the MODIS MCD12Q1 LC products, the Copernicus Climate Change Service (C3S) LC products, and the CGLS LC products, were used in five parallel experiments (cases: C1–C5). Results show that changing LAI and LC datasets of the model input has an impact on BVOC estimations. BVOC emissions in China ranged from 25.42 to 37.39 Tg in 2016 and were mainly concentrated in central and southeastern China. Changing the LC inputs for the MEGAN model has a more significant difference in BVOC estimates than using different LAI datasets. The combination of C3S LC and GLASS LAI performs better in the CMAQ model, indicating that it is the better choice for BVOC estimations in China. The highest contribution of BVOCs to O3 and SOA can reach 12 ppb and 9.8 µg m−3, respectively. Changing the MEGAN inputs further impacts the concentrations of O3 and SOA, especially changing LC datasets. The relative difference between MCD12Q1 LC and C3S LC is over 52 % and 140 % in O3 and biogenic SOA (BSOA) in central and eastern China. The BSOA difference is mainly attributed to the isoprene SOA (ISOA), a major contributor to BSOA. The relative differences in ISOA between different cases are up to 160 % in eastern China. Therefore, our results suggest that the uncertainties in MEGAN inputs should be fully considered in future O3 and SOA simulations.

Shortwave radiation balance modulates potential evapotranspiration over China

ABSTRACT Shortwave radiation is an influential driver of global hydrological cycle, as its variation will alter evapotranspiration (ET). While climate change discussion extensively examined ET response to temperature, wind speed and land use/cover variations, little is known about the contribution of shortwave radiation balance on ET partly because of data availability. In this study, we applied the newly-released Global LAnd Surface Satellite (GLASS) products to detect changes in shortwave radiation and albedo for 2003–2018 over China, and quantified their contributions on potential evapotranspiration (PET) which is the upper limit of ET and generally the basis to estimate it. The results showed that downward shortwave radiation declined, together with a slight decrease in albedo, causing a decrease of net shortwave radiation. While climate change affected PET, the decreased net shortwave radiation reduced PET during the period. We identified a hotspot area in the north China plain that is one of the main agricultural production regions, as the radiation balance induced a contrasting effect on PET for the warm and the cold seasons. Our study indicates that it is important to consider shortwave radiation balance when estimating PET and ET to make reliable evaluation of climate change on hydrological cycle and ecosystem response.

Triple collocation-based merging of multi-source gridded evapotranspiration data in the Nordic Region

Accurate evapotranspiration (ET) data are required for many hydro-meteorological applications. Compared with the traditional evaluation that requires in-situ measurements, the triple collocation (TC) technique estimates geophysical product errors without the need for ground truth, which is especially suitable over large areas lacking a dense in-situ network. However, violations of the zero-error cross-correlation (ECC) assumption are found to be the dominant sources of impairing the TC robustness. This study presents the first application of a TC-based merging framework that optimally considers ECC to merge multi-source gridded ET products in the Nordic Region during 2003–2018. The ECC estimates of each ET dataset pair calculated by the quadruple collocation approach are used to select the qualified triplets from four products, including FLUXCOM, Global Land Surface Satellite (GLASS), Global Land Evaporation and Amsterdam Model (GLEAM), and Penman-Monteith-Leuning Version 2 (PML-V2). Then the ET merged datasets are generated by weighting TC-based rescaled error variances of the parent datasets through least square merging. Finally, the accuracy of both the parent and the merged datasets are assessed with the Integrated Carbon Observation System (ICOS) flux data in the Nordic Region based on multiple statistical metrics. Results demonstrate that the ECC values provide intuitive evidence for filtering unqualified TC triplets. Both the absolute and relative error variances (signal-to-noise ratio) are considered for ET dataset evaluation. Overall PML-V2 has the best performance among the evaluated four products. Two merged ET datasets with the reference climatology of FLUXCOM outperform all parent products with the lowest errors by using ICOS data as reference among all sites – indicating the feasibility of TC technique for improving ET accuracy in the Nordic Region. This study also analyses the impacts of reference climatology selection on the TC merged results.

Climate Change and Anthropogenic Activity Co-Driven Vegetation Coverage Increase in the Three-North Shelter Forest Region of China

The Three-North Shelter Forest (TNSF) program is a significant ecological safety barrier in northern China, where both climate change and anthropogenic activity contribute to the increase in vegetation coverage observed. However, comprehensive effects of these factors on vegetation have not been accurately quantified yet. This study utilized the Global Land Surface Satellite (GLASS) Advanced Very-High-Resolution Radiometer (AVHRR) Fractional Vegetation Cover (FVC) data, meteorological data, and spatial distribution of ecological engineering to analyze spatiotemporal variation of FVC and climate in the TNSF program region in China during the period 1982–2018. A partial correlation analysis and residual analysis were performed to determine the relative contribution of climate change and anthropogenic activity to the FVC and the overall effect of ecological governance. Results showed that since 1982, the average FVC in the TNSF program region was 0.201–0.253, with an average growth rate of 0.01·(10a)−1. The FVC showed a significant increase in 66.45% of the TNSF region, and will continue to increase, while only 7.02% showed a significant decrease. The coefficient of variation showed a large spatial variation, with 30.86% being in very low stability regions, mainly distributed in Inner Mongolia and the Loess Plateau. A warm and wet climate is more conducive to increasing the FVC than the warm and dry climate, and ecological engineering has the largest impact on areas with an annual accumulated precipitation greater than 300 mm. A quantitative analysis revealed that climate change and anthropogenic activity contributed to the significant increase in the FVC in 15.58% and 46.81% of the TNSF region, respectively. Therefore, ecological governance projects, such as the TNSF program, play a crucial role in enhancing the FVC in this region.

The Hi-GLASS all-wave daily net radiation product: Algorithm and product validation

The surface net radiation (Rn) represents the balance of the radiative budget on the land surface and drives many physical and biological processes. An accurate and long-term product for global daily coverage of Rn at a high spatial resolution is needed for a variety of applications at regional and local scales. This study proposes two algorithms, called the downward shortwave radiation (DSR)-based algorithm and the top-of-atmosphere (TOA)-based algorithm, to estimate Rn by using Landsat data. The DSR-based algorithm consists of three conditional models, and was developed based on the analysis of the relationship between Rn and shortwave radiation as well as ancillary information from ground measurements and various datasets. The TOA-based algorithm was developed by linking Rn to TOA observations from Landsat sensors and ancillary information. The two algorithms were developed by using the random forest method. The results of their validation against ground measurements showed that the DSR-based algorithm outperformed the TOA-based algorithm in terms of accuracy, with a determination coefficient (R2) of 0.93, root-mean-squared error (RMSE) of 17.58 Wm−2, and bias of −4.27 Wm−2. It was stable under various conditions. We then applied the DSR-based algorithm to generate a product of the global daily Rn, called the High-resolution (Hi)- Global LAnd Surface Satellite (GLASS), from 2013 to 2018 at a spatial resolution of 30 m under a clear sky based on remotely sensed products, including the DSR from GLASS, the normalized difference vegetation index (NDVI) obtained from Landsat, surface broadband albedo from Hi-GLASS, and meteorological factors based on reanalysis data from MERRA2. Following its validation using in-situ observations from 2013 to 2018, the overall accuracy of the daily Rn acquired by Hi-GLASS under clear sky was found to be satisfactory, with a value of R2 of 0.90 and an RMSE of 25.03 Wm−2. Moreover, compared with the daily Rn obtained from the GLASS product at a spatial resolution of 5 km, that obtained by Hi-GLASS can better characterize the surface by providing more details and capturing the variations in the measurements, especially large and small values. However, due to limitations of the available datasets and the algorithm, the data on Rn for most regions lacked information on cloudy skies and areas at high latitudes. This information thus cannot be provided by Hi-GLASS yet. Moreover, the influence of the topography on values of Rn has not been thoroughly considered. Nonetheless, values of Rn under clear sky obtained from Hi-GLASS offer promise for use in a wide range of areas, and efforts are underway to improve this product.

Time-series variation and attribution analysis of downward shortwave radiation over the Yunnan-Kweichow plateau from 1984 to 2018

ABSTRACT The downward shortwave radiation (DSR) is a key input parameter for land surface models and climate models. Based on the daily averaged Global Land Surface Satellite downward shortwave radiation (GLASS-DSR) dataset over the Yunnan-Kweichow Plateau (YKP) from 1984 to 2018, this paper analyzes variation trend and breakpoints of DSR. The results show that: annual averaged DSR decreases at a decreasing rate of −1.84 W·m−2·decade−1 over the YKP from 1984 to 2018; the overall distribution of interannual averaged DSR shows higher in the mid-west, and gradually decreasing from west to northeast over the YKP; the estimated averaged DSR is larger in spring than in summer due to the influence of the monsoon; monthly averaged DSR reaches its maximum in May and its minimum in December; breakpoints are found in the seasonal and trend components of daily averaged DSR. Eleven driving factors are examined for their effects on DSR variation, including annual average temperature, precipitation, 10 m wind speed, aerosol optical thickness (AOT), total cloud cover, elevation, slope, aspect, longitude, latitude, and climate zones. According to the findings, AOT predominates in the spatio-temporal distribution of DSR over the YKP. This study will contribute to studies related to climate change and highland radiation.

An end-to-end satellite-based GPP estimation model devoid of meteorological and land cover data

Vegetation gross primary productivity (GPP) plays a key role in the terrestrial carbon cycle, and remote sensing models are one of the main tools for GPP estimation at regional and global scales. Almost all existing remote sensing models (e.g., regression models, parametric models, process models and machine learning models) rely on plant functional type (PFT)-based parameter settings, multiple data sources (e.g., meteorological data) and key indices (e.g., leaf area index, LAI), limiting their estimation accuracy and spatial generalization capability. Therefore, we developed an End-To-End Satellite-based model (ETES) to improve GPP estimation. ETES only utilizes input variables from original satellite observations and Global Land Surface Satellite (GLASS) downward shortwave radiation data. It replaces the traditional vegetation types data with a set of numeric variables (named as Seasonal Characteristics of Vegetation Types and Growth, SCVTG) derived from the curve of vegetation index time series within each growing cycle. The multi-layer perceptron method was applied to model the end-to-end relationship between GPP and input variables. Taking the flux data from FLUXNET 2015 as the benchmark, the GPP estimation accuracy of ETES was higher than that of similar GPP products (i.e., MOD17, GOSIF, GPP-NIRv and FLUXCOM RS), with an average 27.89% reduction in RMSE (ΔRMSE, -0.96 ∼ -0.6 g C m−2 day−1) and a 28.86% increase in R2 (ΔR2, 0.09 ∼ 0.22) at the monthly scale. In short, ETES can effectively improve GPP estimation in data availability, spatial generalization capability and estimation accuracy. Meanwhile, SCVTG, as a new proxy of real vegetation types and phenology, would benefit the design of terrestrial carbon flux estimation models.

Improved cloudy-sky snow albedo estimates using passive microwave and VIIRS data

Land surface albedo (LSA) is an essential component of the surface radiation budget, and has been retrieved extensively as a basic remote sensing product; however, daily LSA products suffer from extensive data gaps primarily caused by cloud cover. Accordingly, several gap-filling methods were developed (e.g., spatiotemporal interpolation and data fusion with albedo climatology), although the traditional methods are limited by cloud scale and surface heterogeneity. Further, as the largest varying surface landscape feature, seasonal snow cover substantially influences LSA and represents a major uncertainty factor of gap recovery because previous studies failed to employ actual surface signals to capture such ephemeral but intense albedo changes under cloud cover. To address this issue, a three-step framework was proposed for estimating 1 km cloudy-sky LSA using passive microwave (PMW) data, albedo climatology, and Visible Infrared Imaging Radiometer Suite (VIIRS) clear-sky albedo: (1) All-sky snow albedo was estimated from PMW brightness temperatures using a statistical model, (2) Continuous albedo dynamics were generated by combining the all-sky snow albedo with snow-free climatological albedo, and (3) The 1 km cloudy-sky LSA was predicted after filtering 1 km VIIRS clear-sky LSA by the albedo dynamic series. PMW-derived snow albedo was assessed over the Contiguous US (CONUS), and the final 1 km cloudy-sky LSA was validated across 10 sites from SURFRAD and Core AmeriFlux in 2013. Based on the comparison with high-quality MODIS pixels, the estimated snow albedo yielded an overall RMSE of 0.064 over CONUS, with a bias of −0.010 (R2 = 0.845). The recovered 1 km cloudy-sky LSA produced RMSEs of 0.074 (0.137) for all (snow) samples, a significant improvement over the Global Land Surface Satellite (GLASS) gap-free albedo products especially on snow cases (p-value = 0.027). Corresponding RMSE in calculating surface net radiation was also decreased by 38.91 W·m−2; and anomalous snow samples were corrected as well. The temporal analysis and all-sky LSA mapping suggest that the recovered LSA has satisfactory spatiotemporal continuity, and successfully captured details of spatiotemporal variability, especially for ephemeral snow events. This study provides an innovative solution to recover gaps in LSA data, and considerably improves the LSA accuracy under cloud cover, which can inform snow melting modeling, hazard forecasting, and irrigation management.

高寒内陆河流域植被覆盖增加对地下水补给的影响

PDF HTML阅读 XML下载 导出引用 引用提醒 高寒内陆河流域植被覆盖增加对地下水补给的影响 DOI: 10.5846/stxb202207212098 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金项目（42161020）；青海省科技厅应用基础研究项目（2021-ZJ-705） Impacts of the increasing vegetation coverage on groundwater recharge in an alpine and arid endorheic river watershed Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:地下水是干旱区内陆河流域的主要基础性资源，对流域生态安全、可持续发展等具有重要意义。干旱/半干旱区的地下水补给比湿润地区更易受到地表覆盖条件的影响。为揭示干旱区内陆河流域植被覆盖增加对地下水补给的影响，以巴音河中下游为例，针对土壤和水评价工具（SWAT）模型未有效考虑降水、地形等因素对植被覆盖影响的缺陷，改进SWAT模型，采用全球地表卫星叶面积指数（GLASS LAI）数据代替其LAI计算模块，再结合SWAT土地利用更新模块，准确刻画区域植被覆盖变化。将改进后的SWAT模型与模块化有限拆分地下水流耦合（MODFLOW）模型耦合，准确模拟并分析植被覆盖增加对流域地下水补给的影响。结果表明：基于植被动态变化的土壤和水评价工具与模块化有限拆分地下水流耦合模型（DVSWAT-MODFLOW）模型的月蒸散发及月地下水位模拟效果较好；巴音河中下游2019年林地及草地面积以及LAI较2001年明显增加；2019年植被覆盖情况对应的年际及月际尺度地下水补给量较2001年分别减少了6.1-26.52 mm以及0-15.03 mm；植被覆盖增加对年际尺度地下水补给量的影响强弱在一定程度上取决于年降水量，对月际尺度地下水补给量影响较强的月份集中于植物生长旺盛且降水较多时期。 Abstract:The groundwater is one of the most important basic resources in arid land. The groundwater recharge is easier to be affected by vegetation coverage in the arid and semi-arid areas than the wet areas. Bayin River Basin, a typical alpine and arid endorheic river watershed is located in the northeast of the Qaidam Basin. In the past 20 years, because of the warmer and more humid climate and human activities, the vegetation coverage condition of the watershed was becoming better. Calculation of the groundwater recharge in endorheic watershed is difficult. Soil and Water Assessment Tool (SWAT) is one of the most important tools that can simulate the watershed hydrological processes and its impact factors. However, it has considerable limitations in vegetation coverage and groundwater processes simulation. For the vegetation coverage simulation (leaf area index, LAI), it neglects the precipitation and terrain, which is important in arid land; For the groundwater simulation, it only is based on a simple linear mathematical formula without consideration of the physical processes. To better reveal the impacts of the vegetation coverage increasing on groundwater recharge in arid inland river, this research modified the SWAT model by replacing the LAI module with Global Land Surface Satellite (GLASS) based on LAI. The land use/cover change of the Middle and Lower Reaches of the Bayin River was further considered to better simulate the vegetation dynamics. After that, the modified SWAT model (named Dynamic Vegetation SWAT, DVSWAT) was coupled with the MODFLOW (MODular finite difference groundwater FLOW model), a professional groundwater processes simulation model. The latest GLEAM (Global Land Evaporation Amsterdam Model) v3 based on monthly evapotranspiration data and the observed groundwater level data were used to calibrate the DVSWAT-MODFLOW. We further used the variable-controlling approach to analyze the impacts of the vegetation coverage increasing on groundwater recharge based on the simulation results of the calibrated DVSWAT-MODFLOW. The results showed that the performance of the DVSWAT-MODFLOW in modelling the monthly evapotranspiration was good, with the R2 value of over 0.83, NSE value of over 0.68, and PBIAS value within -22%-22% for each subbasin. The performance of the DVSWAT-MODFLOW in modelling the monthly groundwater level was good as well, with the R2 value of over 0.95 and absolute bias of below 1 m for each groundwater level observation well. The area of the forest land and grassland in the study area increased 5.41 times and 98.96%, respectively, in 2019 compared to 2001. In addition, the annually average LAI of the study in 2019 increased 28.83% than 2001. The yearly and monthly groundwater recharge reduced about 6.1-26.52 mm and 0-15.03 mm, respectively, under the impacts of the increased vegetation coverage. The influence of the increasing vegetation coverage on the annual groundwater recharge depends on the annual precipitation to a certain extent. The months with strong influence on the monthly groundwater recharge are concentrated in the period of vigorous plant growth and more precipitation. 参考文献 相似文献 引证文献

The Improved Winter Wheat Yield Estimation by Assimilating GLASS LAI Into a Crop Growth Model With the Proposed Bayesian Posterior-Based Ensemble Kalman Filter

Data assimilation has been demonstrated as the potential crop yield estimation approach. Accurate quantification of model and observation errors is the key to determining the success of a data assimilation system. However, the crop growth model error is not fully taken into account in most of the previous studies. The objective of this study is to better quantify the model uncertainty in the data assimilation system. Firstly, we calibrated a crop growth model and inferred its posterior uncertainty based on the Global LAnd Surface Satellite (GLASS) 250-m LAI product, regional statistical data, station observations, and field measurements with a Markov chain Monte Carlo (MCMC) method. Secondly, the model posterior uncertainty was used in the Ensemble Kalman Filter (EnKF) algorithm to better characterize the ensemble distribution of model errors. Our results indicated the proposed Bayesian posterior-based EnKF can improve the accuracy of winter wheat yield estimation at both the point scale (the coefficient of determination R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> value increasing from 0.06 to 0.41, the mean absolute percentage error MAPE value decreasing from 12.65% to 7.82%, and the root mean square error RMSE value decreasing from 987 to 688 kg∙ha <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> ) and the regional scale (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> value from 0.30 to 0.57, MAPE value from 19.67% to 10.13%, and RMSE value from 1275 to 695 kg∙ha <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> ) compared with the open-loop estimation. Our analysis also indicated that the Bayesian posterior-based EnKF can perform better compared to the standard Gaussian perturbation-based EnKF. The proposed framework provides an important reference for crop yield estimation at the regional scale in similar agricultural landscapes worldwide.

Mapping Spatially Seamless Fractional Vegetation Cover over China at a 30-m Resolution and Semimonthly Intervals in 2010–2020 Based on Google Earth Engine

Fractional vegetation cover (FVC) is a critical biophysical parameter that characterizes the status of terrestrial ecosystems. The spatial resolutions of most existing FVC products are still at the kilometer level. However, there is growing demand for FVC products with high spatial and temporal resolutions in remote sensing applications. This study developed an operational method to generate 30-m/15-day FVC products over China. Landsat datasets were employed to generate a continuous normalized difference vegetation index (NDVI) time series based on the Google Earth Engine platform from 2010 to 2020. The NDVI was transformed to FVC using an improved vegetation index (VI)-based mixture model, which quantitatively calculated the pixelwise coefficients to transform the NDVI to FVC. A comparison between the generated FVC, the Global LAnd Surface Satellite (GLASS) FVC, and a global FVC product (GEOV3 FVC) indicated consistent spatial patterns and temporal profiles, with a root mean square deviation (RMSD) value near 0.1 and an R 2 value of approximately 0.8. Direct validation was conducted using ground measurements from croplands at the Huailai site and forests at the Saihanba site. Additionally, validation was performed with the FVC time series data observed at 151 plots in 22 small watersheds. The generated FVC showed a reasonable accuracy (RMSD values of less than 0.10 for the Huailai and Saihanba sites) and temporal trajectories that were similar to the field-measured FVC (RMSD values below 0.1 and R 2 values of approximately 0.9 for most small watersheds). The proposed method outperformed the traditional VI-based mixture model and had the practicability and flexibility to generate the FVC at different resolutions and at a large scale.

Reponses of Land Surface Albedo to Global Vegetation Greening: An Analysis Using GLASS Data

Global vegetation greening during recent decades has been observed from various remote sensing data. The global and regional climate can be altered by an increase in carbon storage, as well as changes in land surface albedo (LSA) and evaporation. However, the LSA changes induced by global vegetation greening are still not clear, and contrasting responses of LSA to vegetation changes were reported in previous studies. In this study, we analyzed the LSA in response to global vegetation greening using the Global Land Surface Satellite (GLASS) data and a vegetation-induced LSA change model. The results showed that vegetation greening trends could be observed worldwide, which resulted in contrasting LSA responses at regional scales (LSA increased as leaf area index (LAI) increased, or LSA decreased as LAI increased). Moreover, these contrasting LSA responses to global vegetation greening were effectively explained by the albedo difference between a vegetation and soil background. The results provide new insights into the relationship between LSA changes and global vegetation dynamics, and can support recommendations for policies of vegetation protection, and large-scale afforestation and deforestation.

Global land surface 250 m 8 d fraction of absorbed photosynthetically active radiation (FAPAR) product from 2000 to 2021

Abstract. The fraction of absorbed photosynthetically active radiation (FAPAR) is a critical land surface variable for carbon cycle modeling and ecological monitoring. Several global FAPAR products have been released and have become widely used; however, spatiotemporal inconsistency remains a large issue for the current products, and their spatial resolutions and accuracies can hardly meet the user requirements. An effective solution to improve the spatiotemporal continuity and accuracy of FAPAR products is to take better advantage of the temporal information in the satellite data using deep learning approaches. In this study, the latest version (V6) of the FAPAR product with a 250 m resolution was generated from Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data and other information, as part of the Global LAnd Surface Satellite (GLASS) product suite. In addition, it was aggregated to multiple coarser resolutions (up to 0.25∘ and monthly). Three existing global FAPAR products (MODIS Collection 6; GLASS V5; and PRoject for On-Board Autonomy–Vegetation, PROBA-V, V1) were used to generate the time-series training samples, which were used to develop a bidirectional long short-term memory (Bi-LSTM) model. Direct validation using high-resolution FAPAR maps from the Validation of Land European Remote sensing Instrument (VALERI) and ImagineS networks revealed that the GLASS V6 FAPAR product has a higher accuracy than PROBA-V, MODIS, and GLASS V5, with an R2 value of 0.80 and root-mean-square errors (RMSEs) of 0.10–0.11 at the 250 m, 500 m, and 3 km scales, and a higher percentage (72 %) of retrievals for meeting the accuracy requirement of 0.1. Global spatial evaluation and temporal comparison at the AmeriFlux and National Ecological Observatory Network (NEON) sites revealed that the GLASS V6 FAPAR has a greater spatiotemporal continuity and reflects the variations in the vegetation better than the GLASS V5 FAPAR. The higher quality of the GLASS V6 FAPAR is attributed to the ability of the Bi-LSTM model, which involves high-quality training samples and combines the strengths of the existing FAPAR products, as well as the temporal and spectral information from the MODIS surface reflectance data and other information. The 250 m 8 d GLASS V6 FAPAR product for 2020 is freely available at https://doi.org/10.5281/zenodo.6405564 and https://doi.org/10.5281/zenodo.6430925 (Ma, 2022a, b) as well as at the University of Maryland for 2000–2021 (http://glass.umd.edu/FAPAR/MODIS/250m, last access 1 November 2022).

Evaluation of remotely sensed global evapotranspiration data from inland river basins

AbstractRemotely sensed (RS) evapotranspiration (ET) data sets have been widely evaluated and applied at regional scale. However, the research is relatively insufficient on inland basins. RS global ET data sets were evaluated in 19 major inland basins around the world in this study. Monthly ET was estimated by using water balance (ET_WB), as well as multi‐source precipitation and total water storage anomaly data sets from each basin. The main attributes of the ET changes were identified in each basin. The accuracy of 10 ET products was evaluated by monthly ET_WB series over the entire study region and at the basin scales. Results showed that precipitation changes were the main cause of ET changes in most basins (i.e., 11/19). The impacts of human activities, mainly because of increased irrigation diversion, dominated the ET changes in the five basins located in the western and northern regions of the Eurasian inland basin. Accurate estimated ET series in inland basins with a regular unimodal or U distribution of mean monthly ET were also obtained; this was not the case for basins with irregular seasonal ET variations. In general, ET products exhibited accurate monthly ET estimates in most inland basins, but their accuracy was reduced at the Helmand River, Iranian inland rivers, Qaidam, and Turpan Basins. All products tended to underestimate ET, except for the Penman–Monteith–Leuning product. Overall, the global land surface satellite and global land evaporation Amsterdam model products performed the best in inland basins, while the moderate resolution imaging spectroradiometer (MODIS; MOD16A2) and surface energy balance system (SEBS) products performed relatively poorly. In addition, all products except SEBS and MOD16A2 could capture the spatial ET distribution in inland basins, and the spatial correlation coefficient ranged between 0.83 and 0.93. In general, an ensemble of optimized ET products could improve the accuracy of simulated monthly ET and reduce the uncertainty of the simulated series over the entire study region and at the basin scales.

Ecological Response to Climate Change Across China From Combined Soil Temperature and Moisture Changes

AbstractA coupled soil temperature (ST) and moisture (SM) balance reflects a synthetic climate regime, having huge ecological impacts. This paper used ST and SM data from the European Center for Medium‐Range Weather Forecasts climate reanalysis‐Land and the Coupled Model Intercomparison Project Phase 6 and leaf area index (LAI) data from the Global Land Surface Satellite Product Suite. The focus was on understanding joint ST‐SM changes and the resulting ecological response across China. The results show that during 2000–2020, 24.5% of the land area in China experienced a warming‐drying trend resulting in a 9.7% LAI decrease, while 6.4% of the area experienced a warming‐wetting trend leading to an 8.6% LAI increase. During 2015–2100, 30.6% of the land area in China will be warmer and drier, while 55.2% of the area will be warmer but wetter across three shared socioeconomic pathways (SSP126, 245, and 585). Superimposed on the long‐term trends, there are also significant spatiotemporal variabilities in ST and SM on annual to decadal timescales. The LAI also showed substantial short‐term fluctuations in both typical regions and ecosystems despite consistent long‐term increases. Our findings suggest that ecosystems could be impaired on annual to decadal scales by adverse soil conditions in the twenty‐first century, but in terms of long‐term trends, ecosystems may be resilient partly because of the compensating effects of global warming and regional hydrological changes. Impact studies should thus focus more on annual to decadal soil‐ecosystem anomalous events.

Simulation of forest carbon fluxes by integrating remote sensing data into biome-BGC model

The accurate quantification and monitoring of intra- and inter-annual variability of long-term forest carbon fluxes are imperative to understand the changes in the forest ecosystems in response to the changing climate. The present study aims to simulate forest carbon fluxes in two major plant functional types (PFTs) of northwest Himalayan (NWH) foothills of India by integrating time-series remote sensing (RS) data into Biome-Biogeochemical Cycle (Biome-BGC), a process-based model, and to study the spatio-temporal variability of carbon fluxes. The parameterization of the Biome-BGC model was carried out using the data from two Eddy covariance (EC) flux tower sites located in the NWH foothills of India. An analysis was carried out to identify the sensitive parameters and their calibration was performed. The calibrated Biome-BGC model with a higher coefficient of determination (R2) and%RMSE for moist deciduous (R2 = 0.80, %RMSE = 13.24) and dry deciduous (R2 = 0.79, %RMSE = 13.85) PFTs was used to estimate the gross primary productivity (GPP) from 2001 to 2018. However, the range of model-simulated leaf area index (LAI) was found to be lower than the field observed LAI. Integrating satellite-based Global Land Surface Satellite (GLASS) LAI into the Biome-BGC model led to substantial improvement in GPP estimates of moist deciduous (R2 = 0.87, %RMSE = 11.12) and dry deciduous PFTs (R2 = 0.86, %RMSE = 10.38) when compared to EC tower-based GPP for the year 2018. Based upon the accuracy, GLASS LAI integrated Biome-BGC model was used to map the spatio-temporal variability of forest carbon fluxes. The study highlighted that integration of RS data into calibrated process-based model increased the accuracy of model-simulated forest carbon fluxes.

An R package of maximum entropy production model to estimate 41 years of global evapotranspiration

An accurate estimation of evapotranspiration (ET) is vital for understanding the global hydrological cycle. However, large uncertainties in the present global ET products originate from the distinct model structures, assumptions, and inputs. The maximum entropy production (MEP) model provides a novel method for modeling ET based on parsimonious inputs and energy conservation. In this study, an R package for MEP (RMEP) was presented to facilitate MEP model implementation. Based on RMEP, a global ET analysis was conducted using inputs from the Global Land Data Assimilation System (GLDAS) and Global Land Surface Satellite (GLASS) products during 1978–2018, and the Mann-Kendall and Theil–Sen’s methods were employed to detect the ET trends. The MEP-estimated average annual global land ET was 517 mm yr−1 during 1978–2018, and showed a close agreement with eddy-covariance (EC) measurements from 475 flux sites, with a correlation coefficient of 0.74 and root-mean-square error of 26.99 mm mon−1. The overall performance of MEP was evaluated across various land covers, and a higher ET accuracy was revealed for forestlands, wetlands, and cropland land covers. The MEP-derived ET trend corresponded well with the EC-observed ET trend, and the results indicated that the global land ET declined continuously during 1999–2018. Overall, the MEP model provided an accurate ET estimate with parsimonious inputs, which outperformed the GLDAS-Noah ET product and can serve as a global analytical method for the hydrological cycle and climate change.