An Ensemble-Based Three-Dimensional Variational Assimilation Method for Land Data Assimilation

Land surface models are often highly nonlinear with model physics that contain parameterized discontinuities. These model attributes severely limit the application of advanced variational data assimilation methods into land data assimilation. The ensemble Kalman filter (EnKF) has been widely employed for land data assimilation because of its simple conceptual formulation and relative ease of implementation. An updated ensemble-based three-dimensional variational assimilation (En3-DVar) method is proposed for land data assimilation.This new method incorporates Monte Carlo sampling strategies into the 3-D variational data assimilation framework. The proper orthogonal decomposition (POD) technique is used to efficiently approximate a forecast ensemble produced by the Monte Carlo method in a 3-D space that uses a set of base vectors that span the ensemble. The data assimilation process is thus significantly simplified. Our assimilation experiments indicate that this new En3-DVar method considerably outperforms the EnKF method by increasing assimilation precision. Furthermore, computational costs for the new En3-DVar method are much lower than for the EnKF method.

Inventory of Atmospheric Pollutant Emissions from Burning of Crop Residues in China Based on Satellite-retrieved Farmland Data

The burning of crop residues emits large quantities of atmospheric aerosols.Published studies have developed inventories of emissions from crop residue burning based on statistical data.In contrast,this study used satellite-retrieved land-cover data(1 km×1 km)as activity data to compile an inventory of atmospheric pollutants emitted from the burning of crop residues in China in 2015.The emissions of PM10,PM2.5,VOCs,NOx,SO2,CO,and NH3 from burning crop straw on nonirrigated farmland in China in 2015 were 610.5,598.4,584.4,230.6,35.4,3329.3,and 36.1 Gg(1 Gg=109 g),respectively;the corresponding emissions from burning paddy rice residues were 234.1,229.7,342.3,57.5,57.5,1122.1,and 21.5 Gg,respectively.The emissions from crop residue burning showed large spatial and temporal variations.The emissions of particulate matter and gaseous pollutants from crop residue burning in nonirrigated farmland were highest in east China,particularly in Shandong,Henan,Anhui,and Sichuan provinces.Emissions from burning paddy rice residue were highest in east and central China,with particularly high levels in Shandong,Jiangsu,Zhejiang,and Hunan provinces.The monthly variations in atmospheric pollutant emissions were similar among different regions,with the highest levels observed in October in north,northeast,northwest,east,and southwest China and in June and July in central and south China.The developed inventory of emissions from crop residue burning is expected to help improve air quality models by providing high-resolution spatial and temporal data.

ERA5和ERA5-Land地面风速资料在中国陆地区域的适用性对比初步研究

利用全国40个地面台站的观测资料对ERA5及ERA5-Land两种不同空间分辨率的再分析资料开展了地面风速误差评估研究,结果表明:ERA5和ERA5-Land资料多年平均风速偏差的平均值分别为0.08 m s^(−1)、-0.06 m s^(−1),偏差的最大值分别为0.46 m s^(−1)、-0.19 m s^(−1),相对偏差的平均值为4.4%、-2.0%,相对偏差的最大值分别为33.0%、-10.1%;月平均风速线性拟合方程的斜率分别为0.93、0.97,截距分别为0.29 m s^(−1)、0.02 m s^(−1),相关系数分别为0.98、0.99;月平均风速均方根误差的平均值分别为0.17 m s^(−1)、0.14 m s^(−1),均方根误差的最大值分别为0.49 m s^(−1)、0.22 m s^(−1),相对均方根误差的平均值为7.4%、5.7%,相对均方根误差的最大值分别为35.2%、13.3%。ERA5-Land高分辨率资料地面风速误差相对较低,有利于提高风能资源评估的准确性。

基于ERA5-Land产品的黄河流域蒸散时空变化特征

近年来,黄河流域气候发生明显变化,对流域地表水文、生态等过程产生显著影响。研究黄河流域蒸散时空变化特征,对加深陆-气相互作用认识及区域水资源管理有重要意义。本文分别在黄河源区、河套地区以及黄河下游地区选取一个代表性站点[海北、SACOL(Semi-Arid Climate and Environ⁃ment Observatory of Lanzhou University)和禹城站]的观测资料,检验欧洲中期天气预报中心ERA5-Land产品中蒸散资料在黄河流域的适用性。在此基础上,利用ERA5-Land资料中1980—2021年逐月潜热通量资料,结合经验正交函数分解(Empirical Orthogonal Function,EOF)、功率谱和回归分析研究近42 a黄河流域蒸散时空变化特征。结果表明:ERA5-Land资料能够反映海北、SACOL和禹城站蒸散变化特征,与观测资料的相关性较好,偏差和均方根误差较小,适用于黄河流域蒸散时空变化特征分析。黄河流域不同区域蒸散存在多时间尺度变化,显著振荡周期主要为5、15 a,有明显的年际和年代际变化。黄河流域不同区域年蒸散EOF分解第1模态表现出同位相变化,在2004年前后由增大转为减少趋势;第2模态为偶极子分布,空间分布表现反向变化特征。近42 a黄河流域年蒸散为明显减少趋势,不同区域减幅不同,下游减少速率最快,为-3.74 mm·a^(-1),河套地区为-2.82 mm·a^(-1),黄河源区减少速率相对平缓。夏季蒸散变率最大,河套和下游减少速率较大;冬季蒸散变率较小,黄河源区减少速率最大,为-0.48 mm·a^(-1)。

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.

Assimilation of Feng-Yun-3B Satellite Microwave Humidity Sounder Data over Land

The ECMWF has been assimilating Feng-Yun-3B （FY-3B） satellite microwave humidity sounder （MWHS） data over ocean in an operational forecasting system since 24 September 2014, It is more difficult, however, to assimilate microwave observations over land and sea ice than over the open ocean due to higher uncertainties in land surface temperature, surface emissivity and less effective cloud screening. We compare approaches in which the emissivity is retrieved dynamically from MWHS channel l [150 GHz （vertical polarization）] with the use of an evolving emissivity atlas from 89 GHz observations from the MWHS onboard NOAA and EUMETSAT satellites. The assimilation of the additional data over land improves the fit of short-range forecasts to other observations, notably ATMS （Advanced Technology Microwave Sounder） humidity channels, and the forecast impacts are mainly neutral to slightly positive over the first five days. The forecast impacts are better in boreal summer and the Southern Hemisphere. These results suggest that the techniques tested allow for effective assimilation of MWHS/FY-3B data over land.

Underestimation of the Warming Trend over the Tibetan Plateau during 1998–2013 by Global Land Data Assimilation Systems and Atmospheric Reanalyses

Accurate surface air temperature(T2m)data are key to investigating eco-hydrological responses to global warming.Because of sparse in-situ observations,T2m datasets from atmospheric reanalysis or multi-source observation-based land data assimilation system(LDAS)are widely used in research over alpine regions such as the Tibetan Plateau(TP).It has been found that the warming rate of T2m over the TP accelerates during the global warming slowdown period of 1998–2013,which raises the question of whether the reanalysis or LDAS datasets can capture the warming feature.By evaluating two global LDASs,five global atmospheric reanalysis datasets,and a high-resolution dynamical downscaling simulation driven by one of the global reanalysis,we demonstrate that the LDASs and reanalysis datasets underestimate the warming trend over the TP by 27%–86%during 1998–2013.This is mainly caused by the underestimations of the increasing trends of surface downward radiation and nighttime total cloud amount over the southern and northern TP,respectively.Although GLDAS2.0,ERA5,and MERRA2 reduce biases of T2m simulation from their previous versions by 12%–94%,they do not show significant improvements in capturing the warming trend.The WRF dynamical downscaling dataset driven by ERA-Interim shows a great improvement,as it corrects the cooling trend in ERA-Interim to an observation-like warming trend over the southern TP.Our results indicate that more efforts are needed to reasonably simulate the warming features over the TP during the global warming slowdown period,and the WRF dynamical downscaling dataset provides more accurate T2m estimations than its driven global reanalysis dataset ERA-Interim for producing LDAS products over the TP.

DEVELOPMENT OF A DATA MINING METHOD FOR LAND CONTROL

Land resources are facing crises of being misused,especially for an intersection area between town and country,and land control has to be enforced.This paper presents a development of data mining method for land control.A vector_match method for the prerequisite of data mining i.e., data cleaning is proposed,which deals with both character and numeric data via vectorizing character_string and matching number.A minimal decision algorithm of rough set is used to discover the knowledge hidden in the data warehouse.In order to monitor land use dynamically and accurately,it is suggested to set up a real_time land control system based on GPS,digital photogrammetry and online data mining.Finally,the means is applied in the intersection area between town and country of Wuhan city,and a set of knowledge about land control is discovered.

Land Surface Albedo Variations in Sanjiang Plain from 1982 to 2015: Assessing with GLASS Data

As a key parameter for indicating the fraction of surface-reflected solar incident radiation, land surface albedo plays an important role in the Earth’s surface energy budget(SEB). Since the Sanjiang Plain has been severely affected by human activities(e.g., reclamation and shrinking of wetlands), it is important to assess the spatiotemporal variations of surface albedo in this region using a long-term remote sensing dataset. In order to investigate the surface albedo climatology, trends, and mechanisms of change, we evaluated the surface albedo variations in the Sanjiang Plain, China from 1982 to 2015 using the Global LAnd Surface Satellite(GLASS) broadband surface albedo product. The results showed that: 1) an increasing annual trend(+0.000 58/yr) of surface albedo was discovered in the Sanjiang Plain based on the GLASS albedo dataset, with a much stronger increasing trend(+0.001 26/yr) occurring during the winter. Most of the increasing trends occurred over the cultivated land, unused land, and land use conversion types located in the northeastern Sanjiang Plain. 2) The increasing trend of land surface albedo in Sanjiang Plain can be largely explained by the changes of both snow cover extent and land use. The surface albedo in winter is highly correlated with the snow cover extent in the Sanjiang Plain, and the increasing trend of surface albedo can be further enhanced by the land use changes.

Land Cover Classification with Multi-source Data Using Evidential Reasoning Approach

Land cover classification is the core of converting satellite imagery to available geographic data.However,spectral signatures do not always provide enough information in classification decisions.Thus,the application of multi-source data becomes necessary.This paper presents an evidential reasoning (ER) approach to incorporate Landsat TM imagery,altitude and slope data.Results show that multi-source data contribute to the classification accuracy achieved by the ER method,whereas play a negative role to that derived by maximum likelihood classifier (MLC).In comparison to the results derived based on TM imagery alone,the overall accuracy rate of the ER method increases by 7.66% and that of the MLC method decreases by 8.35% when all data sources (TM plus altitude and slope) are accessible.The ER method is regarded as a better approach for multi-source image classification.In addition,the method produces not only an accurate classification result,but also the uncertainty which presents the inherent difficulty in classification decisions.The uncertainty associated to the ER classification image is evaluated and proved to be useful for improved classification accuracy.

Detection of landuse/landcover changes using remotely-sensed data

We evaluated the use of spatial sampling and satellite images to identify deforested areas in Wonju, South Korea. The changes in land cover were identified using a grid of sample points overlaid onto medium and high-resolution remote sensing （RS） satellite images. Deforestation identified in this way （hereafter, RSD） was compared to administrative data on deforestation. We also compared high-resolution satellite images （HR-RSD） and actual deforestation based on categories which were Intergovernmental Panel on Climate Change data. RSD generated by medium-resolution satellite images overesti- mated the amount of deforested area by 1.5-2.4 times the actual deforested area, whereas RSD generated by HR- RSD underestimated the amount of deforested area by 0.4-0.9 times the actual area. The highest degree of matching （90 %） was found in HR-RSD with a grid interval of 500 m and the accuracy of HR-RSD was the highest, at 67 %. The results also revealed that the largest cause of deforestation was the establishment of settlements followed by conversion to cropland and grassland. We conclude that for the identification of deforestation using satellite images, HR-RSD with a grid interval of 500 m is most suitable.

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.

HRCLDAS与ERA5-Land气温日统计数据在山东地区的对比评估

基于山东省2021年3月—2022年2月1519个气象观测站2 m气温观测数据,对中国气象局高分辨率陆面数据同化系统(High Resolution China Meteorological Administration Land Data Assimilation System,HRCLDAS)和欧洲中期天气预报中心第五代陆面再分析数据集(ERA5-Land)逐小时2 m气温分析的日统计数据(平均气温、最高气温、最低气温)进行对比评估。结果显示:(1)HRCLDAS/ERA5-Land日统计平均气温、最高气温、最低气温的均方根误差分别为0.1/1.2℃、0.6/1.9℃、0.4/1.7℃,表明HRCLDAS具有更高的精度,且在不同地理区域、不同海拔高度的表现均优于ERA5-Land,大部地区的偏差(-0.5~0.5℃)远低于ERA5-Land(-2.0~2.0℃)。(2)两套数据对高温及寒潮过程的监测能力对比评估表明,HRCLDAS能够捕捉到大部分的高温以及寒潮过程,其与观测的高温日数及寒潮日数空间分布较为相似,但对影响范围存在一定的低估;ERA5-Land则只能监测到部分高温及寒潮过程,并对高温日数与寒潮日数存在严重的低估。

Establishing evaluation index system for desertification of Keerqin sandy land with remote sensing data

Keerqin 沙土地位于在向东北平凡、内部的蒙古之间的过渡地面(42 ° 4 1 ′ - 45 ° 1 5 ′ N， 118 ° 3 5 ′ - 123 ° 3 0 ′ E ) 在东北 Chinaand，它被沙漠化严重影响。根据地球表面的配置和生态环境，植被，赤裸的砂土的职业比率和土壤质地的范围被使用地调查数据作为评估索引选择。Keerqin 沙的沙漠化的评估索引系统被使用遥感数据建立，并且赤裸的砂土的职业比率被混合光谱模型获得。这个索引系统被域调查数据验证，结果显示它对 Keerqin 的 thedesertification 评估合适。

Landsat新型热红外地表温度产品与MODIS地表温度产品的交互对比

美国地质调查局(USGS)2020年12月正式发布了基于Landsat热红外光谱数据生产的2级地表温度产品(Landsat Collection 2 Level-2 surface temperature,LC2L2ST),但目前还鲜有该地表温度产品的相关研究报道。由于美国地质调查局已宣布自2022年开始将不提供除该产品之外的其他地表温度数据,因此有必要对该产品进行适时的评估。在同类遥感卫星产品中,MODIS地表温度产品的质量最被广大用户认可,应用也最为广泛,因此首次将Landsat新型热红外地表温度产品与MODIS地表温度产品进行交互对比,以评估新产品与MODIS地表温度产品的一致性。分别选取了我国的不同地区(福州、太湖、银川、敦煌)作为试验区,以20对同日过空的LC2L2ST与MODIS地表温度影像为数据源进行交互对比。影像涵盖植被、水体、建筑、荒漠等地物以及不同的季节。在试验区影像上选取一系列均质样区(ROI),通过各样区的地表温度均值散点进行拟合回归分析,研究二者地表温度间的差异及定量关系,并提出彼此转换的模型。结果表明,Landsat地表温度新产品与MODIS地表温度产品具有很高的相关性,4个试验区的决定系数(R^(2))皆大于0.98,集成4个试验区的总R^(2)也接近0.98;但LC2L2ST比MODIS的地表温度平均高0.90℃(RMSE=2.29℃)。分析发现,二者地表温度数据间的差异与其在空间分辨率、观测角度、地物类型和季节的不同有关。从不同地物和季节来看,LC2L2ST在晚秋和冬季略低于MODIS的地表温度,而在夏季极端高温的城镇、荒漠地区则明显高于MODIS的地表温度,且偏差可近7℃。总的看来,Landsat新型地表温度产品与MODIS同类产品的相关性显著,但在夏季的城市和沙漠地区的差异较大,因此,LC2L2ST新产品在夏季高温季节的适用性仍有待进一步基于地面实测温度的验证。鉴于两种地表温度数据产品仍存在着一定差距,因此二者如要协同使用,需要进行转换。本研究基于4个试验区的560个ROI样区构建了二者地表温度间的转换方程,并通过验证发现,经转换后的两数据差异性得到大幅缩小。因此,必要的数据转换有利于二者数据的协同使用,可为长时间序列的地表温度变化监测提供连续的遥感数据。

The effects of land market development on economic growth: an empirical analysis based on Chinese panel data,1999-2005

Optimized land resources allocation is important for economic growth because land is one of the basic ele ments for economic development. And urban land resources allocation has had an increasingly important influence since the Chinese socialist market economy system was established. This paper estimates the production function of both the secondary and the tertiary industries of China's 31 provinces, autonomous regions and municipalities directly under the central government through an analysis of the panel data of the total output value of the secondary and the tertiary industries, invested capital, invested labor forces and the land market-featured management of the above-mentioned regions during the period of 1999-2005, and examines the positive influence of the above- mentioned factors on regional economic output. This study concludes that urban economic output is positively related with the level of urban land resources market-featured management, since the rate of economic growth of those regions approximates 14.7% under the condition of urban land market running during the period of 1999-2005.

Land Cover Map Delineation, for Agriculture Development, Case Study in North Sinai, Egypt Using SPOT4 Data and Geographic Information System

Land cover map for a part of North Sinai was produced using the FAO—Land Cover Classification System (LCCS) of 2004. The standard FAO classification scheme provides a standardized system of classification that can be used to analyze spatial and temporal land cover variability in the study area. This approach also has the advantage of facilitating the integration of Sinai land cover mapping products to be included with the regional and global land cover datasets. The total study area is 7450 km2 (1,773,842) feddans. The landscape classification was performed on SPOT4 data acquired in 2011 using combined multi-spectral bands of 20 meter spatial resolution. Geographic Information System (GIS) was used to edit the classification result in order to reach the maximum possible accuracy. GIS was also used to include all necessary information. The identified vegetative land cover classes of the study area are irrigated herbaceous crops, irrigated tree crops and rain fed tree crops. The non-vegetated land covers in the study area include: bare rock, bare soil, bare soil stony, bare soil very stony, bare soil salt crusts, loose and shifting sands and sand dunes. The water bodies were classified as artificial perennial water bodies (fish ponds and irrigated canals) and natural perennial water bodies as lakes (standing) and rivers (flowing). Artificial surfaces in the study area include linear and non-linear. The produced maps and the statistics of the different land covers are included in the following sub-sections.

High Resolution Land Cover Datasets Integration and Application Based on Landsat and GlobCover Data from 1975 to 2010 in Siberia

Land cover is recognized as one of the fundamental terrestrial datasets required in land system change and other ecosystem related researches across the globe. The regional differentiation and spatial-temporal variation of land cover has significant impact on regional natural environment and socio-economic sustainable development. Under this context, we reconstructed the history land cover data in Siberia to provide a comparable datasets to the land cover datasets in China and abroad. In this paper, the European Space Agency(ESA) Global Land Cover Map(GlobCover), Landsat Thematic Mapper(TM), Enhanced Thematic Mapper(ETM), Multispectral Scanner(MSS) images, Google Earth images and other additional data were used to produce the land cover datasets in 1975 and 2010 in Siberia. Data evaluation show that the total user′s accuracy of land cover data in 2010 was 86.96%, which was higher than ESA GlobCover data in Siberia. The analysis on the land cover changes found that there were no big land cover changes in Siberia from 1975 to 2010 with only a few conversions between different natural forest types. The mainly changes are the conversion from deciduous needleleaf forest to deciduous broadleaf forest, deciduous needleleaf forest to mixed forest, savannas to deciduous needleleaf forest etc., indicating that the dominant driving factor of land cover changes in Siberia was natural element rather than human activities at some extent, which was very different from China. However, our purpose was not just to produce the land cover datasets at two time period or explore the driving factors of land cover changes in Siberia, we also paid attention on the significance and application of the datasets in various fields such as global climate change, geopolitics, cross-border cooperation and so on.

CLDAS陆面同化资料和ERA5-Land再分析资料气温产品在江西省的适用性评估

以江西省376个气象自动观测站的逐小时气温数据为基准,采用偏差、相关性和平均绝对误差等评价指标,对比分析2017—2022年CLDAS陆面同化和ERA5-Land再分析气温资料在江西省的适用性。结果表明:1) ERA5-Land、CLDAS资料均能很好反映大部分站点的气温变化,CLDAS资料与观测资料的相关系数为0.99,相关系数区间分布较为集中;ERA5-Land资料与观测资料的相关系数为0.97,分布较为分散。2)相较于观测站点多年平均气温,CLDAS资料较为接近,ERA5-Land资料则偏离较大。3) CLDAS资料的平均绝对误差明显低于ERA5-Land资料,二者均存在平原、盆地部分站点平均绝对误差较小而局部高海拔山区站点异常偏大的空间特征,以及秋季最大而冬季最小的季节特征。4) ERA5-Land资料偏差的日变化范围为-0.65—0.39℃,整体呈现单谷形分布;CLDAS资料偏差日变化范围为-0.05—0.05℃,波动幅度较小,没有明显的变化特征。5)两种格点资料均能较好反映大部分站点的低温日数变化,但对于高温日数变化,ERA5-Land资料偏差较大,CLDAS资料偏差较小。