Land surface temperature andcharacteristics for a raingarden insoil moisture distributionFitzroy Gardens, Melbourne

To better understand the cooling effect of raingarden in Fitzroy Gardens, Melbourne, as well as it benefits for an urban microclimate, two rounds of 36-h microclimate monitoring at the raingarden were conducted.Land surface temperature and soil moisture were analyzed according to monitoring data. The results showa clea raingarden cooling effect in summer. The largest difference o land surface temperatures inside and outside the raingarden can reach 23. 6 ℃, and the diurnal variation in temperature insid the raingarden was much less than that outside the raingarden.The soil moisture increased rapidly after irrigation, with th increase in the volumetric water content( VWC) of 2% to3. 6%. The soil moistures of adjacent irrigated garden bed and grass were higher than those inside the raingarden.Monitoring soil moisture helps guide raingarden irrigation.

Evaluating the Capabilities of Soil Enthalpy, Soil Moisture and Soil Temperature in Predicting Seasonal Precipitation

Soil enthalpy （H） contains the combined effects of both soil moisture （w） and soil temperature （T） in the land surface hydrothermal process. In this study, the sensitivities of H to w and T are investigated using the multi-linear regression method. Results indicate that T generally makes positive contributions to H, while w exhibits different （positive or negative） impacts due to soil ice effects. For example, w negatively contributes to H if soil contains more ice; however, after soil ice melts, w exerts positive contributions. In particular, due to lower w interannual variabilities in the deep soil layer （i.e., the fifth layer）, H is more sensitive to T than to w. Moreover, to compare the potential capabilities of H, w and T in precipitation （P） prediction, the Huanghe-Huaihe Basin （HHB） and Southeast China （SEC）, with similar sensitivities of H to w and T, are selected. Analyses show that, despite similar spatial distributions of H-P and T-P correlation coefficients, the former values are always higher than the latter ones. Furthermore, H provides the most effective signals for P prediction over HHB and SEC, i.e., a significant leading correlation between May H and early summer （June） P. In summary, H, which integrates the effects of T and w as an independent variable, has greater capabilities in monitoring land surface heating and improving seasonal P prediction relative to individual land surface factors （e.g., T and w）.

Regional Features and Seasonality of Land–Atmosphere Coupling over Eastern China

Land-atmosphere coupling is a key process of the climate system, and various coupling mechanisms have been proposed before based on observational and numerical analyses. The impact of soil moisture（SM） on evapotranspiration（ET） and further surface temperature（ST） is an important aspect of such coupling. Using ERA-Interim data and CLM4.0 offline simulation results, this study further explores the relationships between SM/ST and ET to better understand the complex nature of the land-atmosphere coupling（i.e., spatial and seasonal variations） in eastern China, a typical monsoon area. It is found that two diagnostics of land-atmosphere coupling（i.e., SM-ET correlation and ST-ET correlation） are highly dependent on the climatology of SM and ST. By combining the SM-ET and ST-ET relationships, two ＂hot spots＂ of land-atmosphere coupling over eastern China are identified： Southwest China and North China. In Southwest China, ST is relatively high throughout the year, but SM is lowest in spring, resulting in a strong coupling in spring. However, in North China, SM is relatively low throughout the year, but ST is highest in summer, which leads to the strongest coupling in summer. Our results emphasize the dependence of land-atmosphere coupling on the seasonal evolution of climatic conditions and have implications for future studies related to land surface feedbacks.

Subdaily to Seasonal Change of Surface Energy and Water Flux of the Haihe River Basin in China: Noah and Noah-MP Assessment

The land surface processes of the Noah-MP and Noah models are evaluated over four typical landscapes in the Haihe River Basin(HRB) using in-situ observations. The simulated soil temperature and moisture in the two land surface models(LSMs) is consistent with the observation, especially in the rainy season. The models reproduce the mean values and seasonality of the energy fluxes of the croplands, despite the obvious underestimated total evaporation. Noah shows the lower deep soil temperature. The net radiation is well simulated for the diurnal time scale. The daytime latent heat fluxes are always underestimated, while the sensible heat fluxes are overestimated to some degree. Compared with Noah, Noah-MP has improved daily average soil heat flux with diurnal variations. Generally, Noah-MP performs fairly well for different landscapes of the HRB. The simulated cold bias in soil temperature is possibly linked with the parameterized partition of the energy into surface fluxes. Thus, further improvement of these LSMs remains a major challenge.

基于Landsat数据地表特征参数与地表温度关系

以成都市为研究区,定量分析了各地表特征参数与地表温度之间的线性关系。通过对地表温度与归一化植被指数(NDVI)、归一化建筑指数(NDBI)、归一化水汽指数(NDMI)进行局部区域逐像元分析和总体区域统计分析,结果表明NDVI,NDMI,NDBI与地表温度间都存在明显的线性关系,可用于说明地表温度的动态变化,在3月份,NDMI与地温的相关性更优于NDVI。对传统城市热现象研究中,NDMI与NDBI能够用来以NDVI作为分析地表温度随季节而变化的互补的度量标准。

Applications of a Surface Runoff Model with Horton and Dunne Runoff for VIC

Surface runoff is mainly generated by two mechanisms, infiltration excess (Horton) runoff and saturation excess (Dunne) runoff; and the spatial variability of soil properties, antecedent soil moisture, topography, and rainfall will result in different surface runoff generation mechanisms. For a large area (e.g., a model grid size of a regional climate model or a general circulation model), these runoff generation mechanisms are commonly present at different portions of a grid cell simultaneously. Missing one of the two major runoff generation mechanisms and failing to consider spatial soil variability can result in significant under/over estimation of surface runoff which can directly introduce large errors in soil moisture states over each model grid cell. Therefore, proper modeling of surface runoff is essential to a reasonable representation of feedbacks in a land-atmosphere system. This paper presents a new surface runoff parameterization with the Philip infiltration formulation that dynamically represents both the Horton and Dunne runoff generation mechanisms within a model grid cell. The parameterization takes into account the effects of soil heterogeneity on Horton and Dunne runoff. The new parameterization is implemented into the current version of the hydrologically based Variable Infiltration Capacity (VIC) land surface model and tested over one watershed in Pennsylvania, USA and over the Shiguanhe Basin in the Huaihe Watershed in China. Results show that the new parameterization plays a very important role in partitioning the water budget between surface runoff and soil moisture in the atmosphere-land coupling system, and has potential applications on large hydrological simulations and land-atmospheric interactions. It is further found that the Horton runoff mechanism should be considered within the context of subgrid-scale spatial variability of soil properties and precipitation.

基于Landsat8遥感图像的土壤含水量提取研究

土壤含水量是影响植被生长的一个主要因素,也是研究气候、水温、生态、农业等领域的重要参数。遥感能够快速方便地获取较大区域的地表信息,因此使用遥感技术对土壤含水量进行提取具有重要的意义。以2015年8月15日获取的唐山市中北部地区Landsat 8影像为研究对象,通过计算植被归一化指数和反演地表温度,利用植被供水指数法得到该区域内的土壤含水量分布情况。研究结果表明,利用植被供水指数法可对Landsat 8遥感影像进行土壤含水量信息的提取,由此也扩充了Landsat 8影像的应用范围。

基于Landsat 8遥感影像的峁底矿区土壤含水量反演研究

为研究适用于峁底矿区土壤含水量的反演方法,以峁底矿所在的奥家湾乡为研究区域,以2013—2021年的Landsat 8 OLI_TIRS为数据源,MODIS地表温度产品为精度评定数据,选用植被供水指数法(VSWI)反演研究区9年内的土壤含水量,并分析研究区土壤含水量的时空分布特征及矿体开采对土壤含水量的影响。结果表明:1)基于Landsat 8遥感影像数据,植被供水指数法可用于反演峁底矿区及周边环境的土壤含水量。2)2013—2021年,研究区土壤含水量的整体时间变化呈波动降低的趋势,空间上以“北部高、东部较高、西部低”分布为主。3)2013—2021年,研究区土壤含水量的类型发生了明显变化,土壤含水量的整体变化趋势较差。4)矿体开采过程中排水等一系列操作使得土壤含水量有短暂的升高,但长期开采导致土壤含水量降低。通过研究土壤含水量在煤矿开采的影响下产生的变化情况,研究结果可为该地区土壤含水量的实时监测、动态预估以及改善生态环境提供一定的科学依据。

Assessing Drought Conditions in Cloudy Regions Using Reconstructed Land Surface Temperature

Temperature vegetation dryness index(TVDI)in a triangular or trapezoidal feature space can be calculated from the land surface temperature(LST)and normalized difference vegetation index(NDVI),and has been widely applied to regional drought monitoring.However,thermal infrared sensors cannot penetrate clouds to detect surface information of sub-cloud pixels.In cloudy areas,LST data include a large number of cloudy pixels,seriously degrading the spatial and temporal continuity of drought monitoring.In this paper,the Remotely Sensed Daily Land Surface Temperature Reconstruction model(RSDAST)is combined with the LST reconstructed(RLST)by the RSDAST and applied to drought monitoring in a cloudy area.The drought monitoring capability of the reconstructed temperature vegetation drought index(RTVDI)under cloudy conditions is evaluated by comparing the correlation between land surface observations for soil moisture and the TVDI before and after surface temperature reconstruction.Results show that the effective duration and area of the RTVDI in the study area were larger than those of the original TVDI(OTVDI)in 2011.In addition,RLST/NDVI scatter plots cover a wide range of values,with the fitted dry–wet boundaries more representative of real soil moisture conditions.Under continuously cloudy conditions,the OTVDI inverted from the original LST(OLST)loses its drought monitoring capability,whereas RTVDI can completely and accurately reconstruct surface moisture conditions across the entire study area.The correlation between TVDI and soil moisture is stronger for RTVDI(R=-0.45)than that for OTVDI(R=-0.33).In terms of the spatial and temporal distributions,the R value for correlation between RTVDI and soil moisture was higher than that for OTVDI.Hence,in continuously cloudy areas,RTVDI not only expands drought monitoring capability in time and space,but also improves the accuracy of surface soil moisture monitoring and enhances the applicability and reliability of thermal infrared data under extreme conditions.

Digitizing the thermal and hydrological parameters of land surface in subtropical China using AMSR-E brightness temperatures

Digitizing the land surface temperature(T_(s))and surface soil moisture(m _(v))is essential for developing the intelligent Digital Earth.Here,we developed a two parameter physical-based passive microwave remote sensing model for jointly retrieving T_(s) and m_(v) using the dual-polarized T_(b) of Aqua satellite advanced microwave scanning radiometer(AMSR-E)C-band(6.9 GHz)based on the simplified radiative transfer equation.Validation using in situ T_(s) and m_(v) in southern China showed the average root mean square errors(RMSE)of T s and m_(v) retrievals reach 2.42 K(R^(2)=0.61,n=351)and 0.025 g cm^(−3)(R^(2)=0.68,n=663),respectively.The results were also validated using global in situ T_(s)(n=2362)and m_(v)(n=1657)of International Soil Moisture Network.The corresponding RMSE are 3.44 k(R 2=0.86)and 0.039 g cm^(−3)(R^(2)=0.83),respectively.The monthly variations of model-derived Ts and mv are highly consistent with those of the Moderate Resolution Imaging Spectroradiometer T_(s)(R^(2)=0.57;RMSE=2.91 k)and ECV_SM m_(v)(R^(2)=0.51;RMSE=0.045 g cm^(−3)),respectively.Overall,this paper indicates an effective way to jointly modeling T_(s) and m_(v) using passive microwave remote sensing.

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.

Progresses on microwave remote sensing of land surface parameters

Highly accurate observations at various scales on the land surface are urgently needed for the studies of many areas,such as hydrology,meteorology,and agriculture.With the rapid development of remote sensing techniques,remote sensing has had the capacity of monitoring many factors of the Earth's land surface.Especially,the space-borne microwave remote sensing systems have been widely used in the quantitative monitoring of global snow,soil moisture,and vegetation parameters with their all-weather,all-time observation capabilities and their sensitivities to the characteristics of land surface factors.Based on the electromagnetic theories and microwave radiative transfer equations,researchers have achieved great successes in the microwave remote sensing studies for different sensors in recent years.This article has systematically reviewed the progresses on five research areas including microwave theoretical modeling,microwave inversion on soil moisture,snow,vegetation and land surface temperatures.Through the further enrichment of remote sensing datasets and the development of remote sensing theories and inversion techniques,remote sensing including microwave remote sensing will play a more important role in the studies and applications of the Earth systems.

A Forest Fire Risk Assessment Using ASTER Images in Peninsular Malaysia

Based on the physical concept of heat energy of pre-ignition,a new fire susceptibility index (FSI) is used to estimate forest fire risk. This physical basis allows calculation of ignition probabilities and comparisons of fire risk across eco-regions. The computation of the index requires inputs of fuel temperature and fuel moisture content (FMC),both of which can be estimated using remote sensing data. While ASTER data for land surface temperatures (LST) was used as proxys for fuel temperatures,fuel moisture content is estimated by regression technique utilizing the ratio NDVI/LST of ASTER data. FSIs are computed in peninsular Malaysia for nine days before the fires of 2004 and 2005 and validated with fire occurrence data. Results show that the FSI increases as the day approaches the fire day. This trend can be observed clearly about four days before the day of fire. It suggests that FSI can be a good estimator of fire risk. The physical basis provides a more meaningful FSI,allows calculation of ignition probabilities and facilitates the development of a future class of fire risk models. FSI can be used to compare fire risk across different eco-regions and time periods. FSI retains the flexibility to be localized to a vegetation type or eco-regions for improved performance.

北极涛动驱动的冬春欧亚陆面变量异常及其机理

北极涛动(Arctic Oscillation,AO)作为北半球冬季中高纬度大气环流年际变率的主模态,是气候可预测性的重要来源之一。然而,AO对中高纬度地表温度、土壤湿度、积雪等陆面特征变量的影响缺乏系统研究。本文基于ERA5和ERA5-Land再分析资料,通过回归分析和物理过程诊断,揭示了AO对冬春欧亚陆面变量的影响规律和机理。结果发现:伴随正位相的AO,欧亚地表温度呈现南北偶极型分布,即欧亚北部地表异常增暖,而欧亚南部地表异常变冷;而积雪和土壤湿度异常则总体呈现南北向三极型空间分布,即中纬度地区积雪覆盖和雪深异常减小、土壤异常干燥,而高纬度地区雪深异常增加、土壤异常湿润,低纬度地区积雪覆盖和雪深异常增加、土壤异常湿润。AO相关的大气环流异常主导的异常水汽输送驱动了上述欧亚陆面变量异常的形成。欧亚北部(南部)水汽的异常辐合(辐散)引起整层水汽和云量增加(减少),导致向下长波辐射增加(减少),使得北部(南部)地区地表异常增暖(变冷)。同时,异常水汽输送引起的降雪和降水异常导致了积雪和土壤湿度异常空间分布的形成。水汽的异常辐散(辐合)引起降雪或降水减少(增加),导致了欧亚中纬度地区(高纬和低纬度)积雪覆盖、雪深、融雪和土壤湿度异常减少(增加)。此外,AO驱动的积雪覆盖异常通过冰雪反照率反馈加剧了青藏高原地表温度异常。

北京园林树木秋色盛期的空间异质性及其对热环境差异的响应

【目的】城市化进程影响下,城市内部热环境的空间分布不均导致植物物候的空间异质性突出。秋色盛期作为落叶树种生长季的终点,其空间异质性对于城市植被的年周期固碳量及整个城市生态系统的初级生产力具有重要影响,同时会引起秋季季相景观的空间变化,是监测城市生态及景观动态的一个关键角度,值得深入探究。本研究以此为切入点,旨在揭示北京城市环境中秋色盛期的空间异质性特征及其对下垫面热环境的响应。【方法】本研究以北京主城区西北城郊梯度上9处公园绿地中的5种秋色叶树种为研究对象,基于地面物候观测对2017—2019年的秋色盛期数据进行采集,基于MODIS地温反演对样地热环境数据进行采集,对秋色盛期空间差异及其与秋季热环境的相关性进行分析。【结果】(1)北京主城区各树种的秋色盛期整体发生于10月中旬至12月上旬、集中于11月上中旬,由早到晚依次为洋白蜡、元宝枫、银杏、水杉、旱柳,且银杏雌株的秋色盛期显著早于雄株。(2)各树种秋色盛期整体上由二环—三环—五环—五环外逐渐提前,城郊物候天数差异达(10.1±0.3) d;样地间物候期整体差异显著,尤其四环外样地的秋色盛期显著早于三环内。(3)各树种秋色盛期与样地秋季平均地温(LST_(a))呈显著正相关(P <0.01),表明北京主城区内秋季地表热量的大量积累会导致秋色盛期延后;各树种秋色盛期对LST_(a)空间差异的响应敏感度平均为(4.11±0.83) d/℃,以洋白蜡和水杉响应最为敏感。【结论】北京主城区的秋色盛期表现出对城市秋季热环境空间差异的显著响应,城市热岛效应是未来气候变化的一个缩影,城市环境中物候期对热环境空间差异的响应可反映未来气候变化对植物物候的潜在影响,即具有“空间代替时间”的研究价值。

CLM5.0陆面模式不同土壤分层方案对黄河源区玛多站土壤温湿的数值模拟

选取2015年6月—2018年8月玛多站观测资料作为驱动CLM5.0(Community Land Model)模式的强迫场数据,应用CLM5.0模式中不同土壤分层方案,对这一时段玛多站土壤温湿变化特征进行模拟,并检验了模拟效果。结果表明:(1)对于土壤温度,CLM5.0模式的4种土壤分层方案均能很好地模拟出一年中玛多站不同深度土壤温度的季节变化趋势,浅层土壤温度模拟值与观测值相关性更高,深层土壤温度模拟值的变化幅度相对较小且曲线较光滑。4种分层方案中,20层方案对土壤温度的模拟效果最好,平均相关系数为0.942。(2)对于土壤湿度,4种土壤分层方案均能较好地模拟出各层土壤湿度的季节变化和日变化趋势,但较观测值都有不同程度的偏差。20层方案对土壤湿度的模拟效果更好,平均相关系数为0.730。

土壤水分的遥感监测方法概述

回顾了目前国内外土壤水分的遥感监测方法,介绍了反射率法、植被指数法、地表温度法、温度-植被指数法、作物水分胁迫指数法、热惯量法和微波法,并对各方法的优缺点进行了详细比对;在总结国内外土壤水分遥感监测研究方法的基础上,对目前该研究领域的重点、难点和未来的发展方向进行了评价。认为:热惯量法和植被温度指数法是较为成熟的方法;微波遥感因其独特的优越性,将是该领域的重点研究方向。

农业旱灾监测中的地表温度遥感反演方法——以MODIS数据为例

以目前农业旱灾监测中应用较广泛的多波段MOD IS卫星遥感数据为例,探讨农业旱灾遥感监测中所需要的地表温度反演问题,尤其是反演算法的选择、基本参数的估计和具体反演中的工作流程,为快速地进行农业旱灾监测中的水热遥感参数估计提供方法选择。虽然MOD IS有8个热红外波段用来监测地表热量变化,但波段31和32特别适用于农业旱灾监测中所需要的地表温度遥感反演。因此,在算法上,我们将选择计算过程相对简便但反演精度又很高的两因素分裂窗算法。详细地讨论了如何快速地估计分裂窗算法中大气透过率和地表比辐射率这两个基本参数。从实际应用来看,本文所提出的方法能快速地用来反演我国农业旱灾监测中所需要的农田地表温度参数,并获得很好的反演结果。

喀斯特峰丛洼地区坡地不同土地利用方式下土壤水分的时空变异特征

基于典型喀斯特峰丛洼地坡面土地利用方式试验火烧、刈割、刈割除根、封育、种植桂牧1号、种植玉米(面积分别为20m×70m)控制性试验建设,通过网格法(5 m×5 m)采样,用经典统计学和地统计学方法,分析了6种土地利用方式下(火烧、刈割、刈割除根、封育、种植桂牧1号、种植玉米)表层土壤水分在不同季节的空间变异特征。结果表明:喀斯特峰丛洼地土壤含水量均很高,雨季显著大于旱季,雨季为火烧>封育、刈割除根>玉米、桂牧1号>刈割,旱季为刈割、火烧、刈割除根>桂牧1号、封育>玉米,均呈中等至强度变异,且含水量越低变异越大;不同土地利用方式土壤水分的自相关函数均呈由正向负方向发展的相同趋势,但拐点不同,且旱季大于雨季,不同土地利用方式旱季、雨季土壤水分的最佳拟合模型不同,但均呈中等或强烈的空间相关性,变程为6.8—213 m,且旱季大于雨季;同一土地利用方式旱季、雨季表层土壤水空间格局相似,不同土地利用方式空间格局则不同,因此在该区域进行植被恢复和生态重建时应采取不同的水资源利用策略。

基于地表温度和植被指数的农业干旱遥感监测方法

农业旱灾是人类面临的最主要自然灾害之一,对我国农业生产影响非常大。土壤含水量是农业干旱监测的重要指标,通过遥感地表温度(LST)与植被指数(NDVI)结合,可以估算土壤湿度,监测农作物旱情。重点研究了LST/NDVI特征空间中干湿边的提取方法,通过14年全国NOAA/AVHRR的8km合成数据集分析发现,在LST/NDVI特征空间中,曲线斜率与实测土壤湿度显著相关(R=0.78,P<0.01),干边的截距和斜率与湿边有比较稳定的关系,但干湿边存在较大的空间和时间变异性。将全国分为6个区,分别确定LST/NDVI特征空间,根据特征空间干湿边参数反演土壤湿度,在土壤湿度分布图和全国耕地分布图基础上,进行旱情分级,得到全国耕地旱情分布图,可以为农业抗旱救灾提供快速宏观的信息服务。