Land Cover Fractions Research Articles

Intercomparison of global foliar trait maps reveals fundamental differences and limitations of upscaling approaches

Foliar traits such as specific leaf area (SLA), leaf nitrogen (N), and phosphorus (P) concentrations play important roles in plant economic strategies and ecosystem functioning. Various global maps of these foliar traits have been generated using statistical upscaling approaches based on in-situ trait observations. Here, we intercompare such global upscaled foliar trait maps at 0.5° spatial resolution (six maps for SLA, five for N, three for P), categorize the upscaling approaches used to generate them, and evaluate the maps with trait estimates from a global database of vegetation plots (sPlotOpen). We disentangled the contributions from different plant functional types (PFTs) to the upscaled maps and quantified the impacts of using different plot-level trait metrics on the evaluation with sPlotOpen: community weighted mean (CWM) and top-of-canopy weighted mean (TWM). We found that the global foliar trait maps of SLA and N differ drastically and fall into two groups that are almost uncorrelated (for P only maps from one group were available). The primary factor explaining the differences between these groups is the use of PFT information combined with remote sensing-derived land cover products in one group while the other group mostly relied on environmental predictors alone. The maps that used PFT and corresponding land cover information exhibit considerable similarities in spatial patterns that are strongly driven by land cover. The maps not using PFTs show a lower level of similarity and tend to be strongly driven by individual environmental variables. Upscaled maps of both groups were moderately correlated to sPlotOpen data aggregated to the grid-cell level (R = 0.2–0.6) when processing sPlotOpen in a way that is consistent with the respective trait upscaling approaches, including the plot-level trait metric (CWM or TWM) and the scaling to the grid cells with or without accounting for fractional land cover. The impact of using TWM or CWM was relevant, but considerably smaller than that of the PFT and land cover information. The maps using PFT and land cover information better reproduce the between-PFT trait differences of sPlotOpen data, while the two groups performed similarly in capturing within-PFT trait variation.Our findings highlight the importance of explicitly accounting for within-grid-cell trait variation, which has important implications for applications using existing maps and future upscaling efforts. Remote sensing information has great potential to reduce uncertainties related to scaling from in-situ observations to grid cells and the regression-based mapping steps involved in the upscaling.

Land cover fraction mapping across global biomes with Landsat data, spatially generalized regression models and spectral-temporal metrics

Mapping land cover in highly heterogeneous landscapes is challenging, and classifications have inherent limitations where the spatial resolution of remotely sensed data exceeds the size of small objects. For example, classifications based on 30-m Landsat data do not capture urban or other heterogeneous environments well. This limitation may be overcome by quantifying the subpixel fractions of different land cover types. However, the selection process and transferability of models designed for subpixel land cover mapping across biomes is yet challenging. We asked to what extent (a) locally trained models can be used for sub-pixel land cover fraction estimates in other biomes, and (b) training data from different regions can be combined into spatially generalized models to quantify fractions across global biomes. We applied machine learning regression-based fraction mapping to quantify land cover fractions of 18 regions in five biomes using Landsat data from 2022. We used spectral-temporal metrics to incorporate intra-annual temporal information and compared the performance of local, spatially transferred, and spatially generalized models. Local models performed best when applied to their respective sites (average mean absolute error, MAE, 9–18%), and also well when transferred to other sites within the same biome, but not consistently so for out-of-biome sites. However, spatially generalized models that combined input data from many sites worked very well when analyzing sites in many different biomes, and their MAE values were only slightly higher than those of the respective local models. A weighted training data selection approach, preferring training data with a lower spectral distance to the image data to be predicted, further enhanced the performance of generalized models. Our results suggest that spatially generalized regression-based fraction models can support multi-class sub-pixel fraction estimates based on medium-resolution satellite images globally. Such products would have great value for environmental monitoring in heterogeneous environments and where land cover varies along spatial or temporal gradients.

Multi-decadal trends and variability in burned area from the fifth version of the Global Fire Emissions Database (GFED5)

Abstract. Long-term records of burned area are needed to understand wildfire dynamics, assess fire impacts on ecosystems and air quality, and improve fire forecasts. Here, we fuse multiple streams of remote sensing data to create a 24 year (1997–2020) dataset of monthly burned area as a component of the fifth version of the Global Fire Emissions Database (GFED5). During 2001–2020, we use the Moderate Resolution Imaging Spectroradiometer (MODIS) MCD64A1 burned area product and adjust for the errors of commission and omission. Adjustment factors are estimated based on region, land cover, and tree cover fraction, using spatiotemporally aligned burned area from Landsat or Sentinel-2. Burned area in croplands, peatlands, and deforestation regions is estimated from MODIS active fire detections. Along-Track Scanning Radiometer (ATSR) and Visible and Infrared Scanner (VIRS) active fire data are used to extend the time series back to 1997. The global annual burned area during 2001–2020 is estimated to be 774 ± 63 Mha yr−1 or 5.9 ± 0.5 % of ice-free land. Burned area declined by 1.21 ± 0.66 % yr−1, a cumulative decrease of 24.2 ± 13.2 % over 20 years. The global reduction is primarily driven by a decrease in fires in savannas, grasslands, and croplands. Forest, peat, and deforestation fires did not exhibit significant long-term trends. The GFED5 global burned area is 93 % higher than MCD64A1, 61 % higher than GFED4s, and in closer agreement with products from higher-resolution satellite sensors. These data may reduce discrepancies between fire emission estimates from activity-based and atmospheric-based approaches, and improve our understanding of global fire impacts on the carbon cycle and climate system. The GFED5 global burned area product is freely accessible at https://doi.org/10.5281/zenodo.7668423 (Chen et al., 2023).

Increased sensitivity of vegetation to soil moisture and its key mechanisms in the Loess Plateau, China

AbstractSoil moisture (SM) plays a crucial role in connecting various ecological processes in terrestrial ecosystems, as vegetation growth heavily relies on SM availability. Despite many studies evaluating the impacts of SM on vegetation activity, changes in vegetation sensitivity to SM are still poorly understood. In this study, we evaluated the spatiotemporal changes of vegetation sensitivity to SM across the Loess Plateau (LP) region from 1982 to 2010 using satellite‐based normalised difference vegetation index (NDVI) and reanalysis SM data. Our results showed a significant increase in NDVI (slope = 0.0015/year, p < 0.001) and a slight decrease in SM (slope = −0.0001 m3/m3/year, p = 0.7) over the past 29 years, indicating a greening trend in the LP. By establishing a multiple linear model, we found that the sensitivity of vegetation dynamics to SM has significantly increased during the same period (slope = 0.029, p < 0.05). Spatially, vegetation in over half (51.2%) of the total area showed enhanced sensitivity to SM. Further analysis using the Lindeman–Merenda–Gold method showed that atmospheric CO2 was the major contributor (27.20%) to the sensitivity changes, followed by nitrogen deposition (23.70%) and air temperature (20.11%). Ecosystem structure changes (represented by non‐tree land cover fractions) and precipitation shifts contributed 16.13% and 12.83%, respectively, to sensitivity changes. These findings provide a better understanding of the spatiotemporal pattern and underlying mechanisms of ecosystem sensitivity to SM in the greening and warming LP.

River reach-level machine learning estimation of nutrient concentrations in Great Britain

Nitrogen (N) and phosphorus (P) are essential nutrients necessary for plant growth and support life in aquatic ecosystems. However, excessive N and P can lead to algal blooms that deplete oxygen and lead to fish death and the release of toxins that are harmful to humans. Estimates of N and P levels in rivers are typically calculated at station or grid (>1 km) scale; therefore, it is difficult to visualise the evolution of water quality as water travels downstream. Using a high-resolution reach-scale river network and associating each reach with land cover fractions and catchment descriptors, we trained random forest models on aggregated data (2010–2020) from the Environmental Agency Open Water Quality Data Archive for 2,343 stations to predict long-term nitrate and orthophosphate concentrations at each river reach in Great Britain (GB). We separated the model training and predictions for different seasons to investigate the potential difference in feature importance. Our model predicted concentrations with an average testing coefficient of determination (R2) of 0.71 for nitrate and 0.58 for orthophosphate using 5-fold cross-validation. Our model showed slightly better performance for higher Strahler stream orders, highlighting the challenges of making predictions in small streams. Our results revealed that arable and horticultural land use is the strongest and most reliable predictor for nitrate, while floodplain extents and standard percentage runoff are stronger predictors for orthophosphate. Nationally, higher orthophosphate concentrations were observed in urbanised areas. This study shows how combining a river network model with machine learning can easily provide a river network understanding of the spatial distribution of water quality levels.

Increasing contribution of urban greenery to residential real estate valuation over time

Cities can experience losing greenery to urban development, while also pursuing greening efforts. However, whether and how long-term changes in land-use and policies affect ecosystem services and greenery preferences is not well-known, due to the paucity of longitudinal data. We created a novel land cover fraction map in Singapore from 1990 to 2020 using high-resolution satellite imagery, finding an overall decline in greenery area within public housing towns and evidence of greenery intensification in the remaining greenery area. Hedonic pricing analysis suggested positive and increasing marginal effects of greenery area on real estate prices; for each apartment unit this was S$39,130 ± S$15,951 (9.2% ± 3.8% of the average real estate price) during the 1990s; S$46,049 ± S$14,124 (10.9% ± 3.3%) during the 2000s; and S$50,048 ± S$17,079 (11.7% ± 4.0%) during the 2010s. An increased preference for tree cover and decreased preference for grass cover was observed, reflecting long-term changes in urban land-use and greening efforts. Our results suggest that urban greenery has become increasingly valuable to home owners and society in Singapore, occurring alongside decreasing quantity and increasing quality of urban greenery, illustrating how combining careful development with greening efforts can lead to an increase in urban ecosystem services.

Constraining the uncertainty of urbanization effect on surface air temperature change over the Beijing–Tianjin–Hebei region in China

AbstractQuantitative assessment of urbanization effect on surface air temperature (SAT) change provides crucial basis for formal detection and attribution analyses of climate change. However, debates about urbanization‐related warming bias in documented regional SAT trend still persist, mainly due to different determination of rural stations. Here the urbanization effect on SAT change over the Beijing–Tianjin–Hebei region in China during 1980–2019 is estimated through three kinds of ways (i.e., comparisons between urban and rural stations [arithmetically station‐averaged], urban‐dominated and rural‐dominated patches [patch‐weighted mean], and realistic urban and rural areas [area‐weighted mean]). The last method explicitly takes urban and rural land cover fractions into account when calculating urban/rural and regional mean SAT trends. Urbanization‐induced warming in the annual mean SAT change of urban stations (areas) through the three ways are estimated as 0.159°C, 0.195°C, and 0.138°C per decade, respectively. And urbanization effect on regional averaged annual mean SAT calculated by patch‐weighted and area‐weighted methods are 0.113°C and 0.050°C per decade, respectively, which account for 33.8% and 14.8% of the total regional warming. The urbanization effect on observed SAT change estimated by considering realistic urban/rural land cover proportions is much lower than traditional station‐unweighted way.

HiTIC-Monthly: a monthly high spatial resolution (1 km) human thermal index collection over China during 2003–2020

Abstract. Human-perceived thermal comfort (known as human-perceived temperature) measures the combined effects of multiple meteorological factors (e.g., temperature, humidity, and wind speed) and can be aggravated under the influences of global warming and local human activities. With the most rapid urbanization and the largest population, China is being severely threatened by aggravating human thermal stress. However, the variations of thermal stress in China at a fine scale have not been fully understood. This gap is mainly due to the lack of a high-resolution gridded dataset of human thermal indices. Here, we generated the first high spatial resolution (1 km) dataset of monthly human thermal index collection (HiTIC-Monthly) over China during 2003–2020. In this collection, 12 commonly used thermal indices were generated by the Light Gradient Boosting Machine (LightGBM) learning algorithm from multi-source data, including land surface temperature, topography, land cover, population density, and impervious surface fraction. Their accuracies were comprehensively assessed based on the observations at 2419 weather stations across the mainland of China. The results show that our dataset has desirable accuracies, with the mean R2, root mean square error, and mean absolute error of 0.996, 0.693 ∘C, and 0.512 ∘C, respectively, by averaging the 12 indices. Moreover, the data exhibit high agreements with the observations across spatial and temporal dimensions, demonstrating the broad applicability of our dataset. A comparison with two existing datasets also suggests that our high-resolution dataset can describe a more explicit spatial distribution of the thermal information, showing great potentials in fine-scale (e.g., intra-urban) studies. Further investigation reveals that nearly all thermal indices exhibit increasing trends in most parts of China during 2003–2020. The increase is especially significant in North China, Southwest China, the Tibetan Plateau, and parts of Northwest China, during spring and summer. The HiTIC-Monthly dataset is publicly available from Zenodo at https://doi.org/10.5281/zenodo.6895533 (Zhang et al., 2022a).

Spectral Characteristics of the Dynamic World Land Cover Classification

The Dynamic World product is a discrete land cover classification of Sentinel 2 reflectance imagery that is global in extent, retrospective to 2015, and updated continuously in near real time. The classifier is trained on a stratified random sample of 20,000 hand-labeled 5 × 5 km Sentinel 2 tiles spanning 14 biomes globally. Since the training data are based on visual interpretation of image composites by both expert and non-expert annotators, without explicit spectral properties specified in the class definitions, the spectral characteristics of the classes are not obvious. The objective of this study is to quantify the physical distinctions among the land cover classes by characterizing the spectral properties of the range of reflectance present within each of the Dynamic World classes over a variety of landscapes. This is achieved by comparing both the eight-class probability feature space (excluding snow) and the maximum probability class assignment (label) distributions to continuous land cover fraction estimates derived from a globally standardized spectral mixture model. Standardized substrate, vegetation, and dark (SVD) endmembers are used to unmix nine Sentinel 2 reflectance tiles from nine spectral diversity hotspots for comparison between the SVD land cover fraction continua and the Dynamic World class probability continua and class assignments. The variance partition for the class probability feature spaces indicates that eight of these nine hotspots are effectively five-dimensional to 95% of variance. Class probability feature spaces of the hotspots all show a tetrahedral form with probability continua spanning multiple classes. Comparison of SVD land cover fraction distributions with maximum probability class assignments (labels) and probability feature space distributions reveal a clear distinction between (1) physically and spectrally heterogeneous biomes characterized by continuous gradations in vegetation density, substrate albedo, and structural shadow fractions, and (2) more homogeneous biomes characterized by closed canopy vegetation (forest) or negligible vegetation cover (e.g., desert, water). Due to the ubiquity of spectrally heterogeneous biomes worldwide, the class probability feature space adds considerable value to the Dynamic World maximum probability class labels by offering users the opportunity to depict inherently gradational heterogeneous landscapes otherwise not generally offered with other discrete thematic classifications.

Spectral–Spatial Generative Adversarial Network for Super-Resolution Land Cover Mapping With Multispectral Remotely Sensed Imagery

Super-resolution mapping (SRM) can effectively predict the spatial distribution of land cover classes within mixed pixels at a higher spatial resolution than the original remotely sensed imagery. The uncertainty of land cover fraction errors within mixed pixels is one of the most important factors affecting SRM accuracy. Studies have shown that SRM methods using deep learning techniques have significantly improved land cover mapping accuracy but have not coped well with spectral–spatial errors. This study proposes an end-to-end SRM model using a spectral–spatial generative adversarial network (SGS) with the direct input of multispectral remotely sensed imagery, which deals with spectral–spatial error. The proposed SGS comprises the following three parts: first, cube-based convolution for spectral unmixing is adopted to generate land cover fraction images. Second, a residual-in-residual dense block fully and jointly considers spectral and spatial information and reduces spectral errors. Third, a relativistic average GAN is designed as a backbone to further improve the super-resolution performance and reduce spectral–spatial errors. SGS was tested in one synthetic and two realistic experiments with multi/hyperspectral remotely sensed imagery as the input, comparing the results with those of hard classification and several classic SRM methods. The results showed that SGS performed well at reducing land cover fraction errors, reconstructing spatial details, removing unpleasant and unrealistic land cover artifacts, and eliminating false recognition.

Linear spectral mixture analysis of land cover for assessment level sub-pixel using PlanetScope imagery in Dieng Plateau

Information on the land cover is essential as thematic information for planning, controlling, and spatial planning to create sustainable development. Remote sensing is a tool used to solve a problem related to space, environment, and zoning. One of the obstacles in using remote sensing data is the presence of mixed pixels. This research was conducted in the Dieng Plateau. This research aims to calculate accuracy assessment and land cover map fraction using PlanetScope imagery in Dieng Plateau. The remote sensing image is PlanetScope, recorded November 19, 2020. Topographic correction is carried out using the cosine correction method. The Linear Spectral Mixture Analysis (LSMA) method determines each endmember percentage land cover fraction. The field survey was conducted with 74 samples for accuracy testing. Each sample plot measures 10 m x 10 m. The topographic correction results can significantly affect land cover classification results. The result of the LSMA method classification used PlanetScope images to determine the percentage land cover map fraction of bare soil, impervious surface, vegetation, and water in one pixel with an accuracy of MAE 99.76%.

Harmonized gap-filled datasets from 20 urban flux tower sites

Abstract. A total of 20 urban neighbourhood-scale eddy covariance flux tower datasets are made openly available after being harmonized to create a 50 site–year collection with broad diversity in climate and urban surface characteristics. Variables needed as inputs for land surface models (incoming radiation, temperature, humidity, air pressure, wind and precipitation) are quality controlled, gap-filled and prepended with 10 years of reanalysis-derived local data, enabling an extended spin up to equilibrate models with local climate conditions. For both gap filling and spin up, ERA5 reanalysis meteorological data are bias corrected using tower-based observations, accounting for diurnal, seasonal and local urban effects not modelled in ERA5. The bias correction methods developed perform well compared to methods used in other datasets (e.g. WFDE5 or FLUXNET2015). Other variables (turbulent and upwelling radiation fluxes) are harmonized and quality controlled without gap filling. Site description metadata include local land cover fractions (buildings, roads, trees, grass etc.), building height and morphology, aerodynamic roughness estimates, population density and satellite imagery. This open collection can help extend our understanding of urban environmental processes through observational synthesis studies or in the evaluation of land surface environmental models in a wide range of urban settings. These data can be accessed from https://doi.org/10.5281/zenodo.7104984 (Lipson et al., 2022).

Analisis Probability Distribution Function Terhadap Perubahan Tutupan Lahan Dan Fraksi Awan Di Indonesia

Land use exceeds the standard of suitability and availability over Indonesia, which causes uncontrolled land acquisitions every year. Changes in land use can be controlled by monitoring changes in land cover and the effect of land changes on hydrological cycle components such as cloud fraction. The land cover and cloud fraction distribution were identified using the NDVI index and cloud fraction parameters. The NDVI index value in Indonesia is 0.73 to 0.81, representing land cover in Indonesia, which is relatively high. Meanwhile, the cloud fraction showed the lowest value between 0.6 to 0.7 in summer and experienced the highest cloud cover at the peak of the rainy season. The analysis of the distribution of both land cover and cloud cover shows the consistency of value stability in the rainy season starting from November to May from 2003 to 2016. The correlation value based on the spatial analysis between the NDVI anomaly and the cloud fraction parameter anomaly has a value range of around -0. 8 to 0.8. The correlation between the NDVI anomaly and the cloud parameter anomaly has a negative correlation in Indonesia, including Sumatra, Kalimantan, Java and Bali, Sulawesi, Nusa Tenggara, Maluku, and Papua. Sumatra, Kalimantan, and Papua islands have a prominent role with a negative correlation between the NDVI anomaly and the cloud fraction parameter anomaly. It is feared to be caused by changes in land use to deforestation of natural area conservation areas into oil palm plantations and mining.

Wetland spectral unmixing using multispectral satellite images

Wetlands are of great importance to the diversity of biota and ecology, thereby to humans. Monitoring such valuable areas is essential for sustainable development. When the sizes, geographic distribution, and total coverage of wetlands across the earth are taken into account, remote sensing shines out as the most economically and technically feasible method to realise the monitoring task. Concerning the utilisation of medium resolution satellite images as the input, the pixel-level approach falls short of understanding the wetland dynamics since vast amounts of pixels in such areas have mixed content. This study proposes a framework for determining the extent of wetlands and extracting their ground characteristics at the sub-pixel level. In the extent determination part, Tasselled Cap Water Index (TCWI) values are calculated on time series, and their variations throughout the year are modelled by fitting a double-sided sigmoid function. This information is coupled with Digital Terrain Model (DTM) thresholding to extract the final extent. A sub-pixel analysis is proposed for the latter part, which includes adopting a systematic approach using a three-element (soil, vegetation, water) scheme for establishing wetland ontology and implementing supervised spectral unmixing enhanced by band weight optimisation. Balıkdamı, one of the most impressive wetlands of Turkey, is chosen as the test area. Open-access optical satellite data acquired by the Sentinel-2 constellation are utilised as the primary data input. Since the abundance values of land cover classes in each Sentinel-2 pixel are estimated, reference abundance data with a 10 m ground sampling distance (GSD) are generated using four-band aerial images having a 30 cm GSD for the verification stage. A new method entitled ‘Abundance Confusion Matrix’ is introduced for comparison and detailed assessment of fractional land cover. Experimental results demonstrate that the extent determination is addressed with a precision of 99.21% and a miss rate of 5.75%. In addition, the abundance values of land cover classes are identified with an overall accuracy of 66.17% after the optimisation step. The proposed method proves to be a valuable tool for the detailed monitoring of wetlands.

Shifts in Salt Marsh Vegetation Landcover after Debris Flow Deposition

On 9 January 2018, Carpinteria Salt Marsh Reserve received a large quantity of sediment following debris flows in Montecito, California. Because disturbances potentially impact the ecosystem services and functions that wetlands provide, an understanding of how the ecosystem responded to the debris flows is important for the management of salt marsh systems. However, a lack of field data before and after this disturbance makes this task impossible to complete by field methods alone. To address this gap, we used Sentinel-2 satellite imagery to calculate landcover fractions and spectral indices to produce maps of landcover before, during, and after the debris flow using a random forest classifier. Change detection showed that vegetation extent in November 2020 approached pre-debris flow conditions. While total vegetated area experienced little net change (0.15% decrease), there was a measurable change in the areal extent of vegetation type, with high marsh vegetation transitioning to mid marsh vegetation in regions that initially showed an increase in bare soil cover. These results are uniquely quantifiable using remote sensing techniques and show that disturbance due to debris flows may affect ecosystem function via plant community change. These impacts will need to be taken into consideration when managing wetlands prone to depositional events.

Improving estimation of urban land cover fractions with rigorous spatial endmember modeling

The spatial information has been widely utilized in Spectral Mixture Analysis (SMA) studies over the years. However, the spatial autocorrelation, as a prerequisite of these studies, was seldomly examined. In the complex urban systems, it remains largely unknown whether and how land cover spectra at different locations are spatially autocorrelated and to which extent spatial autocorrelation can explain the great spectral variability. This study proposed an SMA method with Spatial Endmember Modeling (SEMSMA) to improve the fraction estimation of major urban land cover types. SEMSMA used the Incremental Spatial Autocorrelation (ISA) analysis to quantify spatial autocorrelation patterns in broadband reflectance. With the patterns, SEMSMA predicted endmember signatures that incorporated both spatial (location-oriented) and non-spatial spectral variability via the Restricted Maximum Likelihood-Empirical Best Linear Unbiased Predictor (REML-EBLUP) and Monte Carlo sampling. The spectral variabilities were then addressed in pixel unmixing by an iterative mixture analysis method. Results of ISA demonstrated diverse spatial autocorrelation patterns in reflectance of urban land covers. These patterns can be related to the pathways that spatial processes acted on land cover properties. The predicted endmember signatures explained most of the spectral variability for all land cover types. Subpixel fraction estimates showed that SEMSMA outperformed multiple endmember SMA (MESMA) and spatially adaptive SMA (SASMA), based on the root mean square error (0.04–0.15), the mean absolute error (0.01–0.09), and the Systematic Error (SE) (−80.00–0.01). The low SE of SEMSMA revealed unbiased estimation of land cover fractions. Results of this study underscored the necessity of spatial autocorrelation examination and the advantage of rigorous spatial endmember modeling when incorporating spatial information into SMA.

Global spatiotemporal estimation of daily high-resolution surface carbon monoxide concentrations using Deep Forest

Surface carbon monoxide (CO) is a ubiquitous atmospheric pollutant, which is risky to human health and sustainable developments. To date, data-driven fusion methods have been proposed as new approaches to estimate surface CO concentrations. However, existing related works only focus on national scales or local areas, which cannot contribute to global-scale researches on surface CO. To solve this problem, this study develops a brand-new framework to estimate daily globally distributed surface CO concentrations (2019–2020) at a high spatial resolution (0.05°) through data-driven fusion. Considering the complex relationships between surface CO and multi-source data, an advanced ensemble learning technique, i.e., Deep Forest (DF), is exploited to train the estimation model. Evaluation results (historical) show that the proposed framework presents a desired estimation accuracy over the globe, with the Rs/RMSEs of 0.73/0.273 ppm and 0.77/0.215 ppm at daily and monthly scales, respectively. Meanwhile, the model performance of DF exceeds some frequently-used machine learning techniques, e.g., Light Gradient Boosting Machine and Deep Neural Network. Compared to GEOS Composition Forecasting replay CO product, the proposed framework also performs distinctly better. In addition, seasonal evaluation and uncertainty analyses based on land cover fractions are adopted to fully evaluate the estimated surface CO concentrations. As for the spatial patterns, the estimated results accurately provide the seasonal changes of global high-spatial-resolution surface CO concentrations. At last, the health-related application of ambient CO exposure is discussed in our study. It is discovered that the population-weighted surface CO concentrations of Asia are the most severe in the world, with the annual averaged value ± standard deviation of 0.58 ± 0.13 ppm. The daily surface CO concentrations (0.05°) from our study can help acquire the quantification of global ambient CO exposure and support relevant sustainable researches (e.g., CO-related health effects). The produced global surface CO dataset is available at https://doi.org/10.5281/zenodo.5558611.

M-CORE: A Novel Approach for Land Cover Fraction Mapping Using Multisite Spectral Libraries

The concept of a generic spectral library (GSL), i.e., a structured collection of image spectra sampled from various optical remote sensing images covering different sites and points in time, has been suggested in previous studies to facilitate the tedious process of producing training data for remote sensing-based land cover (LC) mapping. When using multisource libraries collected over different sites, library pruning approaches are needed to extract an apt set of labeled spectra to perform mapping on a specific area. Library pruning mainly aims to discard image-irrelevant and redundant spectra, while limiting spectral confusion during the mapping process. Most library pruning approaches focus only on one of these aspects, which has been shown to negatively affect mapping accuracies. This article emphasizes the need for a multistep approach to optimize a GSL for site-specific mapping. We propose a new library pruning method called M-CORE, specifically designed to facilitate LC fraction mapping. The method extends multiple signal classification (MUSIC) with a confusion reduction (CORE) component. Vegetation-impervious-soil (VIS) fraction mapping experiments on a Sentinel-2 image of Brussels, using a GSL with and without local spectra, show the added value of M-CORE over previously proposed library pruning methods and demonstrate the feasibility of GSL-based mapping in an urban context.

Regional differentiation in the ecological effects of land cover change in China

AbstractLand cover changes driven by either land degradation or land development have caused distinct alterations of the global environment. China has experienced a large area of land degradation and development, while the environmental responses to the national land cover changes are still vague. Based on remote‐sensed datasets, we analyzed the multiple ecological effects (temperature, biomass formation, and observed greenness) of land cover changes in China and their regional differences through neighbourhood analysis and Geogdetector, aiming to provide a theoretical basis for mitigating land degradation and sustainable land development. The results showed that the conversion of cultivated land to grassland and shrubland led to reductions in the greenness effect (−0.017) and biomass formation effect (−0.015 kg C m‐2), while the conversion of forest to grassland and shrubland led to a reduction in greenness (−0.01) and an increase in temperature (0.079°C). As the amplitude of the change in the land cover fraction increased, these trends became more obvious and irreversible. The same land cover change could have either positive or negative ecological effects in different climatic regions and at different elevation levels. In particular, the conversion of forests to grassland and shrubland had a warming effect on all climatic regions and elevation levels. The results can provide a regionally differentiated basis for improving China's current land use policy regarding the balance between cultivated land and natural land.

A Sensitivity Assessment of COSMO-CLM to Different Land Cover Schemes in Convection-Permitting Climate Simulations over Europe

The question of how sensitive the regional and local climates are to different land cover maps and fractions is important, as land cover affects the atmospheric circulation via its influence on heat, moisture, and momentum transfer, as well as the chemical composition of the atmosphere. In this study, we used three independent land cover data sets, GlobCover 2009, GLC2000 and ESACCI-LC, as the lower boundary of the regional climate model COSMO-CLM (Consortium for Small Scale Modeling in Climate Mode, v5.0-clm15) to perform convection-permitting regional climate simulations over the large part of Europe covering the years 1999 and 2000 at a 0.0275° horizontal resolution. We studied how the sensitivity of the impacts on regional and local climates is represented by different land cover maps and fractions, especially between warm (summer) and cold (winter) seasons. We show that the simulated regional climate is sensitive to different land cover maps and fractions. The simulated temperature and observational data are generally in good agreement, though with differences between the seasons. In comparison to winter, the summer simulations are more heterogeneous across the study region. The largest deviation is found for the alpine area (−3 to +3 °C), which might be among different reasons due to different classification systems in land cover maps and orographical aspects in the COSMO-CLM model. The leaf area index and plant cover also showed different responses based on various land cover types, especially over the area with high vegetation coverage. While relating the differences of land cover fractions and the COSMO-CLM simulation results (the leaf area index, and plant coverage) respectively, the differences in land cover fractions did not necessarily lead to corresponding bias in the simulation results. We finally provide a comparative analysis of how sensitive the simulation outputs (temperature, leaf area index, plant cover) are related to different land cover maps and fractions. The different regional representations of COSMO-CLM indicate that the soil moisture, atmospheric circulation, evaporative demand, elevation, and snow cover schemes need to be considered in the regional climate simulation with a high horizontal resolution.