Creation of New Global Land Cover Map with Map Integration

We present here a new approach to the development of a global land cover map. We combined three existing global land cover maps (MOD12, GLC2000, and UMD) based on the principle that the majority view prevails and validated the resulting map by using information collected as part of the Degree Confluence Project (DCP). We used field survey information gathered by DCP volunteers from 4211 worldwide locations to validate the new land cover map, as well as the three existing land cover maps that were combined to create it. Agreement between the DCP-derived information and the land cover maps was 61.3% for our new land cover map, 60.3% for MOD12, 58.9% for GLC2000, and 55.2% for UMD. Although some of the improvements we achieved were not statistically significant, this project has shown that an improved land cover map can be developed and well-validated globally using our method.

Integrated Use of Existing Global Land Cover Datasets for Producing a New Global Land Cover Dataset with a Higher Accuracy: A Case Study in Eurasia

It has been commonly acknowledged that the current global mapping projects have encountered the accuracy challenge. By conducting a comparison among the four existing global land cover datasets (MODIS LC, GLC2000, GLCNMO and GLOBCOVER), it has been identified that certain areas’ accuracy has dragged down the overall accuracy of these global land cover datasets. In this paper, those areas have been defined as the “unreliable area”. This study has recollected the training data from the “unreliable area” within the above four mentioned datasets and reclassified the “unreliable area” by using two supervised classifications. The final result has shown that compared with any existing datasets, a relatively higher accuracy has been able to achieve.

Development of Global Cropland Agreement Level Analysis by Integrating Pixel Similarity of Recent Global Land Cover Datasets

Global cropland monitoring is important when considering tactical strategies for achieving food sustainability. Different global land cover (GLC) datasets providing cropland information have already been published and they are used in many applications. The different data input methods, classification techniques, class definitions and production years among the different GLC datasets make them all independently useful sources of information. This study attempted to produce a cropland agreement level (CAL) analysis based on the integration of several cropland datasets to more accurately estimate cropland area distribution. Estimating cropland area and how it has changed on a national level was done by converting the level of cropland agreement into percentages with an existing cropland fraction map. A pre-analysis showed that the four GLC datasets used in the 2005 and 2010 groups had similar year input data acquisitions. Therefore, we placed these four datasets (GlobCover, MODIS LC, GLCNMO and ESACCI LC) into 2005 and 2010 year-groups and selected them to process dataset integration through a CRISP approach. The results of this process proposed four agreement levels for this CAL analysis, and the model correlation was converted into percentage values. The cropland estimate results from the CAL analysis were observed along with FAO data statistics and showed the highest accuracy, with a 0.70 and 0.71 regression value for 2005 and 2010 respectively. In the cropland area change analysis, this CAL change analysis had the highest level of accuracy when describing the total size of cropland area change from 2005 and 2010 when compared to other individual original GLC datasets.

High-resolution remote sensing mapping of global land water

Land water, one of the important components of land cover, is the indispensable and important basic information for climate change studies, ecological environment assessment, macro-control analysis, etc. This article describes the overall study on land water in the program of global land cover remote sensing mapping. Through collection and processing of Landsat TM/ETM+, China's HJ-1 satellite image, etc., the program achieves an effective overlay of global multi-spectral image of 30 m resolution for two base years, namely, 2000 and 2010, with the image rectification accuracy meeting the requirements of 1:200000 mapping and the error in registration of images for the two periods being controlled within 1 pixel. The indexes were designed and selected reasonably based on spectral features and geometric shapes of water on the scale of 30 m resolution, the water information was extracted in an elaborate way by combining a simple and easy operation through pixel-based classification method with a comprehensive utilization of various rules and knowledge through the object-oriented classification method, and finally the classification results were further optimized and improved by the human-computer interaction, thus realizing high-resolution remote sensing mapping of global water. The completed global land water data results, including Global Land 30-water 2000 and Global Land 30-water 2010, are the classification results featuring the highest resolution on a global scale, and the overall accuracy of self-assessment is 96%. These data are the important basic data for developing relevant studies, such as analyzing spatial distribution pattern of global land water, revealing regional difference, studying space-time fluctuation law, and diagnosing health of ecological environment.

Preliminary analysis of spatiotemporal pattern of global land surface water

Land surface water(LSW) is one of the most important resources for human survival and development, and it is also a main component of global water recycling. A full understanding of the spatial distribution of land surface water and a continuous measuring of its dynamics can support to diagnose the global ecosystem and environment. Based on the Global Land 30-water 2000 and Global Land 30-water 2010 products, this research analyzed the spatial distribution pattern and temporal fluctuation of land surface water under scale-levels of global, latitude and longitude, continents, and climate zones. The Global Land 30-water products were corrected the temporal inconsistency of original remotely sensed data using MODIS time-series data, and then calculated the indices such as water area, water ration and coefficient of spatial variation for further analysis. Results show that total water area of land surface is about 3.68 million km2(2010), and occupies 2.73% of land area. The spatial distribution of land surface water is extremely uneven and is gathered mainly in mid- to high-latitude area of the Northern Hemisphere and tropic area. The comparison of water ratio between 2000 and 2010 indicates the overall fluctuation is small but spatially differentiated. The Global Land 30-water products and the statistics provided the fundamental information for analyzing the spatial distribution pattern and temporal fluctuation of land surface water and diagnosing the global ecosystem and environment.

A global land cover map produced through integrating multi-source datasets

In the past decades,global land cover datasets have been produced but also been criticized for their low accuracies,which have been affecting the applications of these datasets.Producing a new global dataset requires a tremendous amount of efforts;however,it is also possible to improve the accuracy of global land cover mapping by fusing the existing datasets.A decision-fuse method was developed based on fuzzy logic to quantify the consistencies and uncertainties of the existing datasets and then aggregated to provide the most certain estimation.The method was applied to produce a 1-km global land cover map(SYNLCover)by integrating five global land cover datasets and three global datasets of tree cover and croplands.Efforts were carried out to assess the quality:1)inter-comparison of the datasets revealed that the SYNLCover dataset had higher consistency than these input global land cover datasets,suggesting that the data fusion method reduced the disagreement among the input datasets;2)quality assessment using the human-interpreted reference dataset reported the highest accuracy in the fused SYNLCover dataset,which had an overall accuracy of 71.1%,in contrast to the overall accuracy between 48.6%and 68.9%for the other global land cover datasets.

Global land 1° mapping dataset of XCO_(2) from satellite observations of GOSAT and OCO-2 from 2009 to 2020

A global mapping data of atmospheric carbon dioxide(CO_(2))concen-trations can help us to better understand the spatiotemporal varia-tions of CO_(2) and the driving factors of the variations to support the actions for emissions reduction and control.Greenhouse gases satel-lites that measure atmospheric CO_(2),such as the Greenhouse Gases Observing Satellite(GOSAT)and Orbiting Carbon Observatory(OCO-2),have been providing global observations of the column averaged dry-air mole fractions of CO_(2)(XCO_(2))since 2009.However,these XCO_(2) retrievals are irregular in space and time with many gaps.In this paper,we mapped a global spatiotemporally continuous XCO_(2) data-set(Mapping-XCO_(2))using the XCO_(2) retrievals from GOSAT and OCO-2 during the period from April 2009 to December 2020 based on a geostatistical approach that fills those data gaps.The dataset covers a geographic range from 56°S to 65°N and 169°W to 180°E for a 1°grid interval in space and 3-day time interval.The uncer-tainties of the mapped XCO_(2) values are generally less than 1.5 parts per million(ppm).The spatiotemporal characteristics of global XCO_(2) that are revealed by the Mapping-XCO_(2) are similar to the model data obtained from CarbonTracker.Compared to the ground observa-tions,the overall standard bias is 1.13 ppm.The results indicate that this long-term Mapping-XCO_(2) dataset can be used to investigate the spatiotemporal variations of global atmospheric XCO_(2) and can support studies related to the carbon cycle and anthropogenic CO_(2) emissions.The dataset is available at http://www.doi.org/10.7910/DVN/4WDTD8 and https://www.scidb.cn/en/detail?dataSetId=c2c3111b421043fc8d9b163c39e6f56e.

Evaluation of global land cover maps for cropland area estimation in the conterminous United States

Global land cover data could provide continuously updated cropland acreage and distribution information,which is essential to a wide range of applications over large geographical regions.Cropland area estimates were evaluated in the conterminous USA from four recent global land cover products:MODIS land cover(MODISLC)at 500-m resolution in 2010,GlobCover at 300-m resolution in 2009,FROM-GLC and FROM-GLC-agg at 30-m resolution based on Landsat imagery circa 2010 against the US Department of Agriculture survey data.Ratio estimators derived from the 30-m resolution Cropland Data Layer were applied to MODIS and GlobCover land cover products,which greatly improved the estimation accuracy of MODISLC by enhancing the correlation and decreasing mean deviation(MDev)and RMSE,but were less effective on GlobCover product.We found that,in the USA,the CDL adjusted MODISLC was more suitable for applications that concern about the aggregated county cropland acreage,while FROM-GLC-agg gave the least deviation from the survey at the state level.Correlation between land cover map estimates and survey estimates is significant,but stronger at the state level than at the county level.In regions where most mismatches happen at the county level,MODIS tends to underestimate,whereas MERIS and Landsat images incline to overestimate.Those uncertainties should be taken into consideration in relevant applications.Excluding interannual and seasonal effects,R 2 of the FROM-GLC regression model increased from 0.1 to 0.4,and the slope is much closer to one.Our analysis shows that images acquired in growing season are most suitable for Landsat-based cropland mapping in the conterminous USA.

Accuracies of Global Land Cover Maps Checked against Fluxnet Sites

Global land cover data products are key sources of information in understanding the complex interactions between human activities and global change. They play a critical role in improving performances of ecosystem, hydrological and atmospheric models. Three freely available global land cover products developed in the United States are popularly used by the scientific community. These include two global maps developed separately by the United States Geological Survey （USGS） and the University of Maryland （UMD） with NOAA Advanced Very High Resolution Radiometer （ AVHRR ） data, and one developed by Boston University with the EOS Moderate Resolution Imaging Spectroradiometer （ MODIS） data. They are compared with known land cover types at 250 available Fluxnet sites around the world. The overall accuracies are 37%, 36% and 42%, respectively for the USGS, UMD and Boston global land cover maps, Some future global land cover mapping strategies are suggested.

Characterizing and monitoring global landscapes using GlobeLand30 datasets:the first decade of the twenty-first century

Global land cover maps are important sources of information for a wide range of studies including land change analysis and climate change research.While the global land cover maps attempt to present a consistent and homogenous data in terms of the production process,the existing datasets offer coarse resolution data,e.g.1000 m for IGBP DISCover and 300 m for GlobeCover 2009 that is oftentimes challenging.Recently,GlobeLand30 data based on Landsat archive for two timestamps of 2000 and 2010 has been released.It presents a finer spatial resolution of 30 m,which provides numerous opportunities for a wide range of studies.The main objective of this study is to use this dataset for characterizing global land cover patterns,monitoring,and identifying extreme land change cases with their types and magnitude.The findings reveal massive land change patterns including deforestation,desertification,shrinkage of water bodies,and urbanization across the globe.The results and discussions of this research can help policy-makers,environmental planners,ecosystem services providers and climate change researchers to gain finer insights about the forms of global land change.Future research calls for further investigation of the underlying causes of the massive changes and their consequences on our ecosystems and human populations.

Identifying and Quantifying Pixel-Level Uncertainty among Major Satellite Derived Global Land Cover Products

Accurate global land cover(GLC), as a key input for scientific communities, is important for a wide variety of applications. In order to understand the current suitability and limitation of GLC products, the discrepancy and pixellevel uncertainty in major GLC products in three epochs are assessed in this study by using an integrated uncertainty index(IUI) that combines the thematic uncertainty and local classification accuracy uncertainty. The results show that the overall spatial agreements(Ao values) between GLC products are lower than 58%, and the total areas of forests are very consistent in major GLC products, but significant differences are found in different forest classes.The misclassification among different forest classes and mosaic types can account for about 20% of the total disagreements. The mean IUI almost reaches 0.5, and high uncertainty mostly occurs in transition zones and heterogeneous areas across the world. Further efforts are needed to make in the land cover classifications in areas with high uncertainty. Designing a classification scheme for climate models, with explicit definitions of land cover classes in the threshold of common attributes, is urgently needed. Information of the pixel-level uncertainty in major GLC products not only give important implications for the specific application, but also provide a quite important basis for land cover fusion.

Integrating global land cover datasets for deriving user-specific maps

Global scale land cover(LC)mapping has interested many researchers over the last two decades as it is an input data source for various applications.Current global land cover(GLC)maps often do not meet the accuracy and thematic requirements of specific users.This study aimed to create an improved GLC map by integrating available GLC maps and reference datasets.We also address the thematic requirements of multiple users by demonstrating a concept of producing GLC maps with user-specific legends.We used a regression kriging method to integrate Globcover-2009,LC-CCI-2010,MODIS-2010 and Globeland30 maps and several publicly available GLC reference datasets.Overall correspondence of the integrated GLC map with reference LC was 80%based on 10-fold crossvalidation using 24,681 sample sites.This is globally 10%and regionally 6–13%higher than the input map correspondences.Based on LC class presence probability maps,expected LC proportion maps at coarser resolution were created and used for characterizing mosaic classes for land system modelling and biodiversity assessments.Since more reference datasets are becoming freely accessible,GLC mapping can be further improved by using the pool of all available reference datasets.LC proportion information allow tuning LC products to specific user needs.

Satellite detection of increases in global land surface evapotranspiration during 1984-2007

As a key component of digital earth,remotely sensed data provides the compelling evidence that the amount of water vapour transferred from the entire global surface to the atmosphere increased from 1984 to 2007.The validation results from the earlier evapotranspiration(ET)estimation algorithm based on net radiation(Rn),Normalised Difference Vegetation Index(NDVI),air temperature and diurnal air temperature range(DTaR)showed good agreement between estimated monthly ET and ground-measured ET from 20 flux towers.Our analysis indicates that the estimated actual ET has increased on average over the entire global land surface except for Antarctica during 1984
2007.However,this increasing trend disappears after 2000 and the reason may be that the decline in net radiation and NDVI during this period depleted surface soil moisture.Moreover,the good correspondence between the precipitation trend and the change in ET in arid and semi-arid regions indicated that surface moisture linked to precipitation affects ET.The input parameters Rn,Tair,NDVI and DTaR show substantial spatio-temporal variability that is almost consistent with that of actual ET from 1984 to 2007 and contribute most significantly to the variation in actual ET.

Estimating global land surface broadband thermal-infrared emissivityusing advanced very high resolution radiometer optical data

An algorithm for retrieving global eight-day 5 km broadband emissivity (BBE)from advanced very high resolution radiometer (AVHRR) visible and nearinfrared data from 1981 through 1999 was presented. Land surface was dividedinto three types according to its normalized difference vegetation index (NDVI)values: bare soil, vegetated area, and transition zone. For each type, BBE at813.5 mm was formulated as a nonlinear function of AVHRR reflectance forChannels 1 and 2. Given difficulties in validating coarse emissivity products withground measurements, the algorithm was cross-validated by comparing retrievedBBE with BBE derived through different methods. Retrieved BBE was initiallycompared with BBE derived from moderate-resolution imaging spectroradiometer (MODIS) albedos. Respective absolute bias and root-mean-square errorwere less than 0.003 and 0.014 for bare soil, less than 0.002 and 0.011 fortransition zones, and 0.002 and 0.005 for vegetated areas. Retrieved BBE wasalso compared with BBE obtained through the NDVI threshold method. Theproposed algorithm was better than the NDVI threshold method, particularly forbare soil. Finally, retrieved BBE and BBE derived from MODIS data wereconsistent, as were the two BBE values.

Impacts of ENSO on rainfall of global land and China

Based on the analysis of X^2 test of global land rainfall time series, it is found that the mean global land annual rainfall reduce significantly when El Nio events occur, and increase evidently in La Nia years. The impacts of ENSO on the winter and autumn precipitation over eastern China are also notable. Usually, the rainfall of winter and autumn over southern China increases, and that over northern China decreases in El Nio years. The effects of ENSO on summer rainfall are not so significant as on autumn and winter rainfall in China. The summer precipitation of area to the north of the Yellow River often decreases in El Nio years. No evident relationship is found between ENSO and spring rainfall in China.

A 10-Yr Global Land Surface Reanalysis Interim Dataset(CRA-Interim/Land):Implementation and Preliminary Evaluation

A land surface reanalysis dataset covering the most recent decades is able to provide temporally consistent initial conditions for weather and climate models,and thus is crucial to verifying/improving numerical weather/climate forecasts/predictions.In this paper,we report the development of a 10-yr China Meteorological Administration(CMA)global Land surface ReAnalysis Interim dataset(CRA-Interim/Land;2007–2016,6-h intervals,approximately 34-km horizontal resolution).The dataset was produced and evaluated by using the Global Land Data Assimilation System(GLDAS)and NCEP Climate Forecast System Reanalysis(CFSR)global land surface reanalysis datasets,as well as in situ observations in China.The results show that the global spatial patterns and monthly variations of the CRA-Interim/Land,GLDAS,and CFSR climatology are highly consistent,while the soil moisture and temperature values of the CRA-Interim/Land dataset are in between those of the GLDAS and CFSR datasets.Compared with ground observations in China,CRA-Interim/Land soil moisture is comparable to or better than that of GLDAS and CFSR datasets for the 0–10-cm soil layer and has higher correlations and slightly lower root mean square errors(RMSE)for the 10–40-cm soil layer.However,CRA-Interim/Land shows negative biases in 10–40-cm soil moisture in Northeast China and north of central China.For ground temperature and the soil temperature in different layers,CRA-Interim/Land behaves better than the CFSR,especially in East and central China.CRA-Interim/Land has added value over the land components of CRA-Interim due to the introduction of global precipitation observations and improved soil/vegetation parameters.Therefore,this dataset is potentially a critical supplement to the CRA-Interim.Further evaluation of the CRA-Interim/Land,assimilation of near-surface atmospheric forcing variables,and extension of the current dataset to 40 yr(1979–2018)are in progress.

Impact of Spin-up Forcing on Vegetation States Simulated by a Dynamic Global Vegetation Model Coupled with a Land Surface Model

A dynamic global vegetation model （DGVM） coupled with a land surface model （LSM） is generally initialized using a spin-up process to derive a physically-consistent initial condition. Spin-up forcing, which is the atmospheric forcing used to drive the coupled model to equilibrium solutions in the spin-up process, varies across earlier studies. In the present study, the impact of the spin-up forcing in the initialization stage on the fractional coverages （FCs） of plant functional type （PFT） in the subsequent simulation stage are assessed in seven classic climate regions by a modified Community Land Model’s Dynamic Global Vegetation Model （CLM-DGVM）. Results show that the impact of spin-up forcing is considerable in all regions except the tropical rainforest climate region （TR） and the wet temperate climate region （WM）. In the tropical monsoon climate region （TM）, the TR and TM transition region （TR-TM）, the dry temperate climate region （DM）, the highland climate region （H）, and the boreal forest climate region （BF）, where FCs are affected by climate non-negligibly, the discrepancies in initial FCs, which represent long-term cumulative response of vegetation to different climate anomalies, are large. Moreover, the large discrepancies in initial FCs usually decay slowly because there are trees or shrubs in the five regions. The intrinsic growth timescales of FCs for tree PFTs and shrub PFTs are long, and the variation of FCs of tree PFTs or shrub PFTs can affect that of grass PFTs.

Regional and Global Land Data Assimilation Systems: Innovations,Challenges, and Prospects

Since the North American and Global Land Data Assimilation Systems(NLDAS and GLDAS) were established in2004, significant progress has been made in development of regional and global LDASs. National, regional, projectbased, and global LDASs are widely developed across the world. This paper summarizes and overviews the development, current status, applications, challenges, and future prospects of these LDASs. We first introduce various regional and global LDASs including their development history and innovations, and then discuss the evaluation, validation, and applications(from numerical model prediction to water resources management) of these LDASs. More importantly, we document in detail some specific challenges that the LDASs are facing: quality of the in-situ observations, satellite retrievals, reanalysis data, surface meteorological forcing data, and soil and vegetation databases; land surface model physical process treatment and parameter calibration; land data assimilation difficulties; and spatial scale incompatibility problems. Finally, some prospects such as the use of land information system software, the unified global LDAS system with nesting concept and hyper-resolution, and uncertainty estimates for model structure,parameters, and forcing are discussed.

How Could Agricultural Land Systems Contribute to Raise Food Production Under Global Change?

To feed the increasing world population, more food needs to be produced from agricultural land systems. Solutions to produce more food with fewer resources while minimizing adverse environmental and ecological consequences require sustainable agricultural land use practices as supplementary to advanced biotechnology and agronomy. This review paper, from a land system perspective, systematically proposed and analyzed three interactive strategies that could possibly raise future food production under global change. By reviewing the current literatures, we suggest that cropland expansion is less possible amid iferce land competition, and it is likely to do less in increasing food production. Moreover, properly allocating crops in space and time is a practical way to ensure food production. Climate change, dietary shifts, and other socio-economic drivers, which would shape the demand and supply side of food systems, should be taken into consideration during the decision-making on rational land management in respect of sustainable crop choice and allocation. And ifnally, crop-speciifc agricultural intensiifcation would play a bigger role in raising future food production either by increasing the yield per unit area of individual crops or by increasing the number of crops sown on a particular area of land. Yet, only when it is done sustainably is this a much more effective strategy to maximize food production by closing yield and harvest gaps.

Response of Land Use Planning in Less Developed Areas to Economic Globalization

Under the background of economic globalization, the development mechanisms of various regions face potential deep transformation, and the effective participation of less developed areas in China in economic globalization is of great significance to the sustainable development of Chinese economy and society. In this study, we summarized the characteristics and influences of economic globalization from the aspects of industrial recombination and transfer, competition, economic relevance and development modes, and analysed the opportunities and challenges of land use in less developed areas brought by economic globalization. Afterwards, based on the major problems of land use planning management in the middle of Jiangsu Province, we put forward some suggestion including management of planning process, balanced development of ecology and economy, strengtheningn planing use zoning, spatial agglomeration and protecting cultural diversity to response to economic globalization.