scholarly journals Discrepancies in the Simulated Global Terrestrial Latent Heat Flux from GLASS and MERRA-2 Surface Net Radiation Products

2020 ◽  
Vol 12 (17) ◽  
pp. 2763
Author(s):  
Xiaozheng Guo ◽  
Yunjun Yao ◽  
Yuhu Zhang ◽  
Yi Lin ◽  
Bo Jiang ◽  
...  
Keyword(s):  
Land Cover ◽  
Latent Heat ◽  
Land Surface ◽  
Net Radiation ◽  
Land Cover Types ◽  
Daily Scale ◽  
Modern Era ◽  
Globally Distributed ◽  
Beta Version

Surface all-wave net radiation (Rn) is a crucial variable driving many terrestrial latent heat (LE) models that estimate global LE. However, the differences between different Rn products and their impact on global LE estimates still remain unclear. In this study, we evaluated two Rn products, Global LAnd Surface Satellite (GLASS) beta version Rn and Modern-Era Retrospective Analysis for Research and Applications-version 2 (MERRA-2) Rn, from 2007–2017 using ground-measured data from 240 globally distributed in-situ radiation measurements provided by FLUXNET projects. The GLASS Rn product had higher accuracy (R2 increased by 0.04–0.26, and RMSE decreased by 2–13.3 W/m2) than the MERRA-2 Rn product for all land cover types on a daily scale, and the two Rn products differed greatly in spatial distribution and variations. We then determined the resulting discrepancies in simulated annual global LE using a simple averaging model by merging five diagnostic LE models: RS-PM model, SW model, PT-JPL model, MS-PT model, and SIM model. The validation results showed that the estimated LE from the GLASS Rn had higher accuracy (R2 increased by 0.04–0.14, and RMSE decreased by 3–8.4 W/m2) than that from the MERRA-2 Rn for different land cover types at daily scale. Importantly, the mean annual global terrestrial LE from GLASS Rn was 2.1% lower than that from the MERRA-2 Rn. Our study showed that large differences in satellite and reanalysis Rn products could lead to substantial uncertainties in estimating global terrestrial LE.

scholarly journals Simplified Priestley–Taylor Model to Estimate Land-Surface Latent Heat of Evapotranspiration from Incident Shortwave Radiation, Satellite Vegetation Index, and Air Relative Humidity

2021 ◽  
Vol 13 (5) ◽  
pp. 902
Author(s):  
Yunjun Yao ◽  
Zhenhua Di ◽  
Zijing Xie ◽  
Zhiqiang Xiao ◽  
Kun Jia ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Land Cover ◽  
Latent Heat ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Mainland China ◽  
Shortwave Radiation ◽  
Land Cover Types

An operational and accurate model for estimating global or regional terrestrial latent heat of evapotranspiration (ET) across different land-cover types from satellite data is crucial. Here, a simplified Priestley–Taylor (SPT) model was developed without surface net radiation (Rn) by combining incident shortwave radiation (Rs), satellite vegetation index, and air relative humidity (RH). Ground-measured ET for 2000–2009 collected by 100 global FLUXNET eddy covariance (EC) sites was used to calibrate and evaluate the SPT model. A series of cross-validations demonstrated the reasonable performance of the SPT model to estimate seasonal and spatial ET variability. The coefficients of determination (R2) of the estimated versus observed daily (monthly) ET ranged from 0.42 (0.58) (p < 0.01) at shrubland (SHR) flux sites to 0.81 (0.86) (p < 0.01) at evergreen broadleaf forest (EBF) flux sites. The SPT model was applied to estimate agricultural ET at high spatial resolution (16 m) from Chinese Gaofen (GF)-1 data and monitor long-term (1982–2018) ET variations in the Three-River Headwaters Region (TRHR) of mainland China using the Global LAnd-Surface Satellite (GLASS) normalized difference vegetation index (NDVI) product. The proposed SPT model without Rn provides an alternative model for estimating regional terrestrial ET across different land-cover types.

scholarly journals Reconstruction of the Daily MODIS Land Surface Temperature Product Using the Two-Step Improved Similar Pixels Method

2021 ◽  
Vol 13 (9) ◽  
pp. 1671
Author(s):  
Junlei Tan ◽  
Tao Che ◽  
Jian Wang ◽  
Ji Liang ◽  
Yang Zhang ◽  
...  
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Water Cycle ◽  
Ancillary Data ◽  
Land Cover Types ◽  
Modis Land Surface Temperature ◽  
Yearly Average

The MODIS land surface temperature (LST) product is one of the most widely used data sources to study the climate and energy-water cycle at a global scale. However, the large number of invalid values caused by cloud cover limits the wide application of the MODIS LST. In this study, a two-step improved similar pixels (TISP) method was proposed for cloudy sky LST reconstruction. The TISP method was validated using a temperature-based method over various land cover types. The ground measurements were collected at fifteen stations from 2013 to 2018 during the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) field campaign in China. The estimated theoretical clear-sky temperature indicates that clouds cool the land surface during the daytime and warm it at nighttime. For bare land, the surface temperature shows a clear seasonal trend and very similar across stations, with a cooling amplitude of 4.14 K in the daytime and a warming amplitude of 3.99 K at nighttime, as a yearly average. The validation result showed that the reconstructed LST is highly consistent with in situ measurements and comparable with MODIS LST validation accuracy, with a mean bias of 0.15 K at night (−0.43 K in the day), mean RMSE of 2.91 K at night (4.41 K in the day), and mean R2 of 0.93 at night (0.90 in the day). The developed method maximizes the potential of obtaining quality MODIS LST retrievals, ancillary data, and in situ observations, and the results show high accuracy for most land cover types.

scholarly journals Modelling Key Parameters Characterising Land Surface in 1D Space using the SimSphere SVAT model: Findings from its use at European Ecosystems

2019 ◽  
Author(s):  
Swaiti Suman ◽  
Matthew North ◽  
George Petropoulos ◽  
Prashant K. Srivastava ◽  
Jon McCalmont ◽  
...  
Keyword(s):  
Latent Heat ◽  
Model Evaluation ◽  
Land Surface ◽  
Surface Interactions ◽  
Climatic Conditions ◽  
In Situ Measurements ◽  
Sensible Heat ◽  
Net Radiation ◽  
Svat Model

The present study investigates the ability of SimSphere, a Soil Vegetation Atmosphere Transfer (SVAT) model, to predict key parameters in characterising land Surface interactions. In particular, the model’s performance in predicting Net Radiation (Rnet), Latent Heat (LE), and Sensible Heat (H) was examined. For this purpose, concurrent in-situ measurements of the corresponding parameters for a total of 70 days of the year 2011 from 7 CarboEurope network sites were acquired, incorporating a variety of environmental biomes and climatic conditions in the model evaluation.

scholarly journals Cooling Effect of Different Land Cover Types: A Case Study in Xi’an and Xianyang, China

2021 ◽  
Vol 13 (3) ◽  
pp. 1099
Author(s):  
Yuhe Ma ◽  
Mudan Zhao ◽  
Jianbo Li ◽  
Jian Wang ◽  
Lifa Hu
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Urban Heat Island ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Cooling Effect ◽  
Heat Island ◽  
Land Cover Types ◽  
Island Effect ◽  
Urban Heat

One of the climate problems caused by rapid urbanization is the urban heat island effect, which directly threatens the human survival environment. In general, some land cover types, such as vegetation and water, are generally considered to alleviate the urban heat island effect, because these landscapes can significantly reduce the temperature of the surrounding environment, known as the cold island effect. However, this phenomenon varies over different geographical locations, climates, and other environmental factors. Therefore, how to reasonably configure these land cover types with the cooling effect from the perspective of urban planning is a great challenge, and it is necessary to find the regularity of this effect by designing experiments in more cities. In this study, land cover (LC) classification and land surface temperature (LST) of Xi’an, Xianyang and its surrounding areas were obtained by Landsat-8 images. The land types with cooling effect were identified and their ideal configuration was discussed through grid analysis, distance analysis, landscape index analysis and correlation analysis. The results showed that an obvious cooling effect occurred in both woodland and water at different spatial scales. The cooling distance of woodland is 330 m, much more than that of water (180 m), but the land surface temperature around water decreased more than that around the woodland within the cooling distance. In the specific urban planning cases, woodland can be designed with a complex shape, high tree planting density and large planting areas while water bodies with large patch areas to cool the densely built-up areas. The results of this study have utility for researchers, urban planners and urban designers seeking how to efficiently and reasonably rearrange landscapes with cooling effect and in urban land design, which is of great significance to improve urban heat island problem.

Plot-scale retrieval of land surface temperature and emissivity estimation

2021 ◽  
Author(s):  
Gitanjali Thakur ◽  
Stan Schymanski ◽  
Kaniska Mallick ◽  
Ivonne Trebs
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Satellite Data ◽  
Land Surface ◽  
Longwave Radiation ◽  
Landscape Scale ◽  
The Earth ◽  
Downwelling Longwave Radiation

&lt;p&gt;The surface energy balance (SEB) is defined as the balance between incoming energy from the sun and outgoing energy from the Earth&amp;#8217;s surface. All components of the SEB depend on land surface temperature (LST). Therefore, LST is an important state variable that controls the energy and water exchange between the Earth&amp;#8217;s surface and the atmosphere. LST can be estimated radiometrically, based on the infrared radiance emanating from the surface. At the landscape scale, LST is derived from thermal radiation measured using&amp;#160; satellites.&amp;#160; At the plot scale, eddy covariance flux towers commonly record downwelling and upwelling longwave radiation, which can be inverted to retrieve LST&amp;#160; using the grey body equation :&lt;br&gt;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160; R&lt;sub&gt;lup&lt;/sub&gt; = &amp;#949;&amp;#963; T&lt;sub&gt;s&lt;/sub&gt;&lt;sup&gt;4&lt;/sup&gt; + (1 &amp;#8722; &amp;#949;) R&lt;sub&gt; ldw&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160; &lt;/sub&gt;(1)&lt;br&gt;where R&lt;sub&gt;lup&lt;/sub&gt; is the upwelling longwave radiation, R&lt;sub&gt;ldw&lt;/sub&gt; is the downwelling longwave radiation, &amp;#949; is the surface emissivity, &lt;em&gt;T&lt;sub&gt;s&lt;/sub&gt;&amp;#160; &lt;/em&gt;is the surface temperature and &amp;#963;&amp;#160; is the Stefan-Boltzmann constant. The first term is the temperature-dependent part, while the second represents reflected longwave radiation. Since in the past downwelling longwave radiation was not measured routinely using flux towers, it is an established practice to only use upwelling longwave radiation for the retrieval of plot-scale LST, essentially neglecting the reflected part and shortening Eq. 1 to:&lt;br&gt;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160; R&lt;sub&gt;lup&lt;/sub&gt; = &amp;#949;&amp;#963; T&lt;sub&gt;s&lt;/sub&gt;&lt;sup&gt;4 &lt;/sup&gt;&amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160;&amp;#160; (2)&lt;br&gt;Despite&amp;#160; widespread availability of downwelling longwave radiation measurements, it is still common to use the short equation (Eq. 2) for in-situ LST retrieval. This prompts the question if ignoring the downwelling longwave radiation introduces a bias in LST estimations from tower measurements. Another associated question is how to obtain the correct &amp;#949; needed for in-situ LST retrievals using tower-based measurements.&lt;br&gt;The current work addresses these two important science questions using observed fluxes at eddy covariance towers for different land cover types. Additionally, uncertainty in retrieved LST and emissivity due to uncertainty in input fluxes was quantified using SOBOL-based uncertainty analysis (SALib). Using landscape-scale emissivity obtained from satellite data (MODIS), we found that the LST&amp;#160; obtained using the complete equation (Eq. 1) is 0.5 to 1.5 K lower than the short equation (Eq. 2). Also, plot-scale emissivity was estimated using observed sensible heat flux and surface-air temperature differences. Plot-scale emissivity obtained using the complete equation was generally between 0.8 to 0.98 while the short equation gave values between 0.9 to 0.98, for all land cover types. Despite additional input data for the complete equation, the uncertainty in plot-scale LST was not greater than if the short equation was used. Landscape-scale daytime LST obtained from satellite data (MODIS TERRA) were strongly correlated with our plot-scale estimates, but on average higher by 0.5 to 9 K, regardless of the equation used. However, for most sites, the correspondence between MODIS TERRA LST and retrieved plot-scale LST estimates increased significantly if plot-scale emissivity was used instead of the landscape-scale emissivity obtained from satellite data.&lt;/p&gt;

scholarly journals Continuous and consistent land use/cover change estimates using socio-ecological data

2016 ◽  
Author(s):  
Michael Marshall ◽  
Michael Norton-Griffiths ◽  
Harvey Herr ◽  
Richard Lamprey ◽  
Justin Sheffield ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Surface ◽  
Natural Vegetation ◽  
Additive Models ◽  
Sub Saharan Africa ◽  
Land Cover Types ◽  
Remote Sensing Classification ◽  
Functional Relationships ◽  
Macro Scale

Abstract. A growing body of research shows the importance of land use/cover change (LULCC) on modifying the earth system. Land surface models are used to stimulate land-atmosphere dynamics at the macro- (regional to global) scale, but bias and uncertainty remain that need to be addressed, before the importance of LULCC is fully realized. In this study, we propose a method of improving LULCC estimates for land surface modelling exercises. The method yields continuous (annual) long-term (30-year) estimates of LULCC driven by socio-ecological geospatial predictors available seamlessly across sub-Saharan Africa that can be used for both retrospective and prospective analyses. The method was developed with 2252 5 × 5 km2 sample frames of the proportion of several land cover types in Kenya over multiple years. Forty-three socio-ecological predictors were evaluated for model development. Machine learning was used for data reduction and simple (functional) relationships defined by generalized additive models were constructed on a subset of the highest ranked predictors (p ≤ 10) to estimate LULCC. The predictors explained 62 % and 65 % of the variance in the proportion of agriculture and natural vegetation, respectively, but were less successful at estimating more descriptive land cover types. In each case, population density on an annual basis was the highest ranked predictor. The approach was compared to a commonly used remote sensing classification procedure, given the wide use of such techniques for macro-scale LULCC detection, and out-performed it for each land cover type. The approach was used to demonstrate significant trends in expanding (declining) agricultural (natural vegetation) land cover in Kenya from 1983–2012, with the largest increases (declines) occurring in densely populated high agricultural production zones.

scholarly journals Land Surface Temperature Retrieval from LANDSAT-8 Thermal Infrared Sensor Data and Validation with Infrared Thermometer Camera

2018 ◽  
Vol 7 (4.20) ◽  
pp. 608 ◽  
Author(s):  
Muhammad Mejbel Salih ◽  
Oday Zakariya Jasim ◽  
Khalid I. Hassoon ◽  
Aysar Jameel Abdalkadhum
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Thermal Infrared ◽  
Landsat 8 ◽  
Infrared Sensor ◽  
Infrared Thermometer ◽  
Ground Truth Data ◽  
Land Cover Types

This paper illustrates a proposed method for the retrieval of land surface temperature (LST) from the two thermal bands of the LANDSAT-8 data. LANDSAT-8, the latest satellite from Landsat series, launched on 11 February 2013, using LANDSAT-8 Operational Line Imager and Thermal Infrared Sensor (OLI & TIRS) satellite data. LANDSAT-8 medium spatial resolution multispectral imagery presents particular interest in extracting land cover, because of the fine spectral resolution, the radiometric quantization of 12 bits. In this search a trial has been made to estimate LST over Al-Hashimiya district, south of Babylon province, middle of Iraq. Two dates images acquired on 2nd &18th of March 2018 to retrieve LST and compare them with ground truth data from infrared thermometer camera (all the measurements contacted with target by using type-k thermocouple) at the same time of images capture. The results showed that the rivers had a higher LST which is different to the other land cover types, of less than 3.47 C ◦, and the LST different for vegetation and residential area were less than 0.4 C ◦ with correlation coefficient of the two bands 10 and 11 Rbnad10= 0.70, Rband11 = 0.89 respectively, for the imaged acquired on the 2nd of march 2018 and Rband10= 0.70 and Rband11 = 0.72 on the 18th of march 2018. These results confirm that the proposed approach is effective for the retrieval of LST from the LANDSAT-8 Thermal bands, and the IR thermometer camera data which is an effective way to validate and improve the performance of LST retrieval. Generally the results show that the closer measurement taken from the scene center time, a better quality to classify the land cover. The purpose of this study is to assess the use of LANDSAT-8 data to specify temperature differences in land cover and compare the relationship between land surface temperature and land cover types.   

scholarly journals Spatiotemporal Variability of Land Surface Albedo over the Tibet Plateau from 2001 to 2019

2020 ◽  
Vol 12 (7) ◽  
pp. 1188
Author(s):  
Xingwen Lin ◽  
Jianguang Wen ◽  
Qinhuo Liu ◽  
Dongqin You ◽  
Shengbiao Wu ◽  
...  
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Land Cover ◽  
Snow Cover ◽  
Land Surface ◽  
Radiative Forcing ◽  
Surface Albedo ◽  
Tibet Plateau ◽  
Land Cover Types ◽  
Yearly Average

As an essential climate variable (ECV), land surface albedo plays an important role in the Earth surface radiation budget and regional or global climate change. The Tibetan Plateau (TP) is a sensitive environment to climate change, and understanding its albedo seasonal and inter-annual variations is thus important to help capture the climate change rules. In this paper, we analyzed the large-scale spatial patterns, temporal trends, and seasonal variability of land surface albedo overall the TP, based on the moderate resolution imaging spectroradiometer (MODIS) MCD43 albedo products from 2001 to 2019. Specifically, we assessed the correlations between the albedo anomaly and the anomalies of normalized difference vegetation index (NDVI), the fraction of snow cover (snow cover), and land surface temperature (LST). The results show that there are larger albedo variations distributed in the mountainous terrain of the TP. Approximately 10.06% of the land surface is identified to have been influenced by the significant albedo variation from the year 2001 to 2019. The yearly averaged albedo was decreased significantly at a rate of 0.0007 (Sen’s slope) over the TP. Additionally, the yearly average snow cover was decreased at a rate of 0.0756. However, the yearly average NDVI and LST were increased with slopes of 0.0004 and 0.0253 over the TP, respectively. The relative radiative forcing (RRF) caused by the land cover change (LCC) is larger than that caused by gradual albedo variation in steady land cover types. Overall, the RRF due to gradual albedo variation varied from 0.0005 to 0.0170 W/m2, and the RRF due to LCC variation varied from 0.0037 to 0.0243 W/m2 during the years 2001 to 2019. The positive RRF caused by gradual albedo variation or the LCC can strengthen the warming effects in the TP. The impact of the gradual albedo variations occurring in the steady land cover types was very low between 2001 and 2019 because the time series was short, and it therefore cannot be neglected when examining radiative forcing for a long time series regarding climate change.

scholarly journals Mesoscale Simulations of the Land Surface Effects of Historical Logging in a Moist Continental Climate Regime

2008 ◽  
Vol 47 (8) ◽  
pp. 2166-2182 ◽  
Author(s):  
Nicholas P. Klingaman ◽  
Jason Butke ◽  
Daniel J. Leathers ◽  
Kevin R. Brinson ◽  
Elsa Nickl
Keyword(s):  
Climate Change ◽  
Heat Flux ◽  
Land Cover ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Land Surface ◽  
Climate Regime ◽  
Clear Cut ◽  
Atmosphere System ◽  
Regional Climates

Abstract An enhanced knowledge of the feedbacks from land surface changes on regional climates is of great importance in the attribution of climate change. To explore the effects of deforestation on a midlatitude climate regime, two sets of two five-member ensembles of 28-day simulations were conducted using the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) coupled to the “Noah” land surface model. The four ensembles represented conditions in summer (August) and winter (February) across the northern mid-Atlantic United States before and after extensive late-nineteenth-century logging of hardwood forests in central and northern Pennsylvania. Prelogging ensembles prescribed a vegetative cover of an evergreen needleleaf forest; postlogging ensembles prescribed sparse vegetation and bare soil to simulate clear-cut deforestation. The results of the MM5 experiments showed a decided seasonality in the response of the land surface–atmosphere system to deforestation, with much stronger effects arising in summer. In August, deforestation caused a repartitioning of the surface energy budget, beginning with a decrease in the latent heat flux of more than 60 W m−2 across the land cover–forcing area, representing almost one-half of the latent heat flux under prelogging land cover. Concomitant with this decrease in evapotranspiration, mean 2-m air temperatures warmed by at least 1.5°C. Increases in sensible heat flux led to a 150-m mean increase in the height of the atmospheric boundary layer over the deforested area. Low-level atmospheric mixing ratios and total precipitation decreased under clear-cut conditions. Mean soil moisture increased in all model levels to 150 cm because of a decrease in vegetative uptake of water, except at the 5-cm level at which such decreases were effectively balanced by greater soil evaporation and less precipitation. A strong diurnal variation in the response to deforestation of ground and lower-atmosphere temperatures and heat fluxes was also identified for the summer season. The February simulations showed the effects of deforestation during low-insolation months to be small and variable. The strong response of the summer land surface–atmosphere system to deforestation shown here suggests that land cover changes can appreciably affect regional climates. Thus, the role of human-induced and naturally occurring land cover variability should not be ignored in the attribution of climate change.

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