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Journal articles on the topic 'Land surface'

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Author: Grafiati

Published: 4 June 2021

Last updated: 28 September 2022

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1

Shengcheng Cui, Shengcheng Cui, Zhen Wang Zhen Wang, and Shizhi Yang Shizhi Yang. "Parameterization of land surface albedo." Chinese Optics Letters 12, no. 11 (2014): 110101–5. http://dx.doi.org/10.3788/col201412.110101.

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2

Nicholson, Sharon E. "Land surface atmosphere interaction." Progress in Physical Geography: Earth and Environment 12, no. 1 (March 1988): 36–65. http://dx.doi.org/10.1177/030913338801200102.

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3

Streeter, R., and A. J. Dugmore. "Anticipating land surface change." Proceedings of the National Academy of Sciences 110, no. 15 (March 25, 2013): 5779–84. http://dx.doi.org/10.1073/pnas.1220161110.

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4

YAMAMOTO, Yuhei, and Hirohiko ISHIKAWA. "Thermal Land Surface Emissivity for Retrieving Land Surface Temperature from Himawari-8." Journal of the Meteorological Society of Japan. Ser. II 96B (2018): 43–58. http://dx.doi.org/10.2151/jmsj.2018-004.

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5

Ke, Y., L. R. Leung, M. Huang, and H. Li. "Enhancing the representation of subgrid land surface characteristics in land surface models." Geoscientific Model Development 6, no. 5 (September 27, 2013): 1609–22. http://dx.doi.org/10.5194/gmd-6-1609-2013.

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Abstract. Land surface heterogeneity has long been recognized as important to represent in the land surface models. In most existing land surface models, the spatial variability of surface cover is represented as subgrid composition of multiple surface cover types, although subgrid topography also has major controls on surface processes. In this study, we developed a new subgrid classification method (SGC) that accounts for variability of both topography and vegetation cover. Each model grid cell was represented with a variable number of elevation classes and each elevation class was further described by a variable number of vegetation types optimized for each model grid given a predetermined total number of land response units (LRUs). The subgrid structure of the Community Land Model (CLM) was used to illustrate the newly developed method in this study. Although the new method increases the computational burden in the model simulation compared to the CLM subgrid vegetation representation, it greatly reduced the variations of elevation within each subgrid class and is able to explain at least 80% of the total subgrid plant functional types (PFTs). The new method was also evaluated against two other subgrid methods (SGC1 and SGC2) that assigned fixed numbers of elevation and vegetation classes for each model grid (SGC1: M elevation bands–N PFTs method; SGC2: N PFTs–M elevation bands method). Implemented at five model resolutions (0.1°, 0.25°, 0.5°, 1.0°and 2.0°) with three maximum-allowed total number of LRUs (i.e., NLRU of 24, 18 and 12) over North America (NA), the new method yielded more computationally efficient subgrid representation compared to SGC1 and SGC2, particularly at coarser model resolutions and moderate computational intensity (NLRU = 18). It also explained the most PFTs and elevation variability that is more homogeneously distributed spatially. The SGC method will be implemented in CLM over the NA continent to assess its impacts on simulating land surface processes.

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6

Ke, Y., L. R. Leung, M. Huang, and H. Li. "Enhancing the representation of subgrid land surface characteristics in land surface models." Geoscientific Model Development Discussions 6, no. 1 (March 28, 2013): 2177–212. http://dx.doi.org/10.5194/gmdd-6-2177-2013.

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Abstract. Land surface heterogeneity has long been recognized as important to represent in the land surface models. In most existing land surface models, the spatial variability of surface cover is represented as subgrid composition of multiple surface cover types. In this study, we developed a new subgrid classification method (SGC) that accounts for the topographic variability of the vegetation cover. Each model grid cell was represented with a number of elevation classes and each elevation class was further described by a number of vegetation types. The numbers of elevation classes and vegetation types were variable and optimized for each model grid so that the spatial variability of both elevation and vegetation can be reasonably explained given a pre-determined total number of classes. The subgrid structure of the Community Land Model (CLM) was used as an example to illustrate the newly developed method in this study. With similar computational burden as the current subgrid vegetation representation in CLM, the new method is able to explain at least 80% of the total subgrid Plant Functional Types (PFTs) and greatly reduced the variations of elevation within each subgrid class compared to the baseline method where a single elevation class is assigned to each subgrid PFT. The new method was also evaluated against two other subgrid methods (SGC1 and SGC2) that assigned fixed numbers of elevation and vegetation classes for each model grid with different perspectives of surface cover classification. Implemented at five model resolutions (0.1°, 0.25°, 0.5°, 1.0° and 2.0°) with three maximum-allowed total number of classes Nclass of 24, 18 and 12 representing different computational burdens over the North America (NA) continent, the new method showed variable performances compared to the SGC1 and SGC2 methods. However, the advantage of the SGC method over the other two methods clearly emerged at coarser model resolutions and with moderate computational intensity (Nclass = 18) as it explained the most PFTs and elevation variability among the three subgrid methods. Spatially, the SGC method explained more elevation variability in topography-complex areas and more vegetation variability in flat areas. Furthermore, the variability of both elevation and vegetation explained by the new method was more spatially homogeneous regardless of the model resolutions and computational burdens. The SGC method will be implemented in CLM over the NA continent to assess its impacts on simulating land surface processes.

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7

Mihailović, Dragutin T., Borivoj Rajković, Branislava Lalić, Dušan Jović, and Ljiljana Dekić. "Partitioning the land surface water simulated by a land–air surface scheme." Journal of Hydrology 211, no. 1-4 (November 1998): 17–33. http://dx.doi.org/10.1016/s0022-1694(98)00190-5.

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8

Qiao, Zhi, Dianjun Zhang, Xinliang Xu, and Luo Liu. "Robustness of satellite-derived land surface parameters to urban land surface temperature." International Journal of Remote Sensing 40, no. 5-6 (September 26, 2018): 1858–74. http://dx.doi.org/10.1080/01431161.2018.1484962.

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9

Prigent, Catherine, William B. Rossow, and Elaine Matthews. "Global maps of microwave land surface emissivities: Potential for land surface characterization." Radio Science 33, no. 3 (May 1998): 745–51. http://dx.doi.org/10.1029/97rs02460.

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10

Debie, Ermias, Mesfin Anteneh, and Tadele Asmare. "Land Use/Cover Changes and Surface Temperature Dynamics Over Abaminus Watershed, Northwest Ethiopia." Air, Soil and Water Research 15 (January 2022): 117862212210979. http://dx.doi.org/10.1177/11786221221097917.

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The study investigates the impact of land use/cover changes on the dynamics of surface temperature in the Abaminus watershed, Northwest Ethiopia. Landsat-5 images of 1987, 1999, and 2010, and the Landsat-8 image of 2018 were used as the sources of data. The land use/cover changes were calculated using a land-use transition matrix. Data generated from household surveys were presented using percentage values to identify the driving forces of land use/cover changes. The land surface temperature (LST) result was quantified using the respective index equation. Results indicated that wetland, forest, shrublands, and grasslands declined by 96.6%, 72%, 77.7%, and 89.4% respectively over the analysis period. The encroachment of cultivation and overgrazing to marginal lands, weak institutional arrangement, sedimentation, high drainage of wetlands for crop production, and recurrent drought were the major driving forces behind the land use/cover change. Within this effect, the average land surface temperature was increased by 11.5°C, 3.22°C, and 2.02°C due to wetland loss, clearing of the forest, and decline of shrublands respectively for the last 31 years. LSTs had correspondingly decreased by 5.42°C and 3.77°C on the afforested barren surfaces and planted shrublands. Hence, there should be an improved institutional arrangement for managing open access resources through the participation of local people in the management for minimizing the increase of land surface temperature in the study watershed. Moreover, enclosure management and plantation of multipurpose species on degraded communal lands shall be scaled-up to significantly reduce land surface temperatures.

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11

Steyaert, Louis T., Thomas R. Loveland, and William J. Parton. "Land Cover Characterization and Land Surface Parameterization Research." Ecological Applications 7, no. 1 (February 1997): 1–2. http://dx.doi.org/10.1890/1051-0761(1997)007[0001:lccals]2.0.co;2.

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12

Makumbura, Randika K., Jayanga Samarasinghe, and Upaka Rathnayake. "Multidecadal Land Use Patterns and Land Surface Temperature Variation in Sri Lanka." Applied and Environmental Soil Science 2022 (May 30, 2022): 1–11. http://dx.doi.org/10.1155/2022/2796637.

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Agricultural land conversion due to urbanization, industrialization, and many other factors is one of the significant concerns to food production. Therefore, analyzing the temporal and spatial variation of agricultural lands is an emerging topic in the research world. However, an agrarian country like Sri Lanka was given weaker attention to the temporal and spatial variation of the land use, including the agricultural lands. This study presents an extended analysis of temporal and spatial variation of land use patterns in Sri Lanka, specifically looking at the agricultural land conversion and land surface temperature (LST) change. Remote sensing techniques and geographic information system (GIS) were used for the presented work. The satellite images from three Landsat’s were analyzed for 2000, 2010, and 2020 to identify the potential land use conversions. In addition, LSTs were extracted for the same period. Significant and continuous increases can be seen in the agricultural lands from 33.94% (of total area) in 2000 to 43.2% in 2020. In contrast, the forest areas showcase a relative decrease from 38.51% to 33.82% (of total area) during the analyzed period. In addition, the rate of conversion from agriculture to settlements is higher in the latter decade (2010–2020) compared to the earlier decade (2000–2010). Only general conclusions were drafted based on the LSTs results as they were not extracted in the same months of the year due to high cloud cover. Therefore, the results and conclusions of this study can be effectively used to improve the land use policies in Sri Lanka and lead to a sustainable land use culture.

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13

Kumar, S. V., C. D. Peters-Lidard, J. Santanello, K. Harrison, Y. Liu, and M. Shaw. "Land surface Verification Toolkit (LVT) – a generalized framework for land surface model evaluation." Geoscientific Model Development 5, no. 3 (June 26, 2012): 869–86. http://dx.doi.org/10.5194/gmd-5-869-2012.

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Abstract. Model evaluation and verification are key in improving the usage and applicability of simulation models for real-world applications. In this article, the development and capabilities of a formal system for land surface model evaluation called the Land surface Verification Toolkit (LVT) is described. LVT is designed to provide an integrated environment for systematic land model evaluation and facilitates a range of verification approaches and analysis capabilities. LVT operates across multiple temporal and spatial scales and employs a large suite of in-situ, remotely sensed and other model and reanalysis datasets in their native formats. In addition to the traditional accuracy-based measures, LVT also includes uncertainty and ensemble diagnostics, information theory measures, spatial similarity metrics and scale decomposition techniques that provide novel ways for performing diagnostic model evaluations. Though LVT was originally designed to support the land surface modeling and data assimilation framework known as the Land Information System (LIS), it supports hydrological data products from non-LIS environments as well. In addition, the analysis of diagnostics from various computational subsystems of LIS including data assimilation, optimization and uncertainty estimation are supported within LVT. Together, LIS and LVT provide a robust end-to-end environment for enabling the concepts of model data fusion for hydrological applications. The evolving capabilities of LVT framework are expected to facilitate rapid model evaluation efforts and aid the definition and refinement of formal evaluation procedures for the land surface modeling community.

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14

Kumar, S. V., C. D. Peters-Lidard, J. Santanello, K. Harrison, Y. Liu, and M. Shaw. "Land surface Verification Toolkit (LVT) – a generalized framework for land surface model evaluation." Geoscientific Model Development Discussions 5, no. 1 (February 8, 2012): 229–76. http://dx.doi.org/10.5194/gmdd-5-229-2012.

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Abstract. Model evaluation and verification are key in improving the usage and applicability of simulation models for real-world applications. In this article, the development and capabilities of a formal system for land surface model evaluation called the Land surface Verification Toolkit (LVT) is described. LVT is designed to provide an integrated environment for systematic land model evaluation and facilitates a range of verification approaches and analysis capabilities. LVT operates across multiple temporal and spatial scales and employs a large suite of in-situ, remotely sensed and other model and reanalysis datasets in their native formats. In addition to the traditional accuracy-based measures, LVT also includes uncertainty and ensemble diagnostics, information theory measures, spatial similarity metrics and scale decomposition techniques that provide novel ways for performing diagnostic model evaluations. Though LVT was originally designed to support the land surface modeling and data assimilation framework known as the Land Information System (LIS), it supports hydrological data products from non-LIS environments as well. In addition, the analysis of diagnostics from various computational subsystems of LIS including data assimilation, optimization and uncertainty estimation are supported within LVT. Together, LIS and LVT provide a robust end-to-end environment for enabling the concepts of model data fusion for hydrological applications. The evolving capabilities of LVT framework are expected to facilitate rapid model evaluation efforts and aid the definition and refinement of formal evaluation procedures for the land surface modeling community.

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15

Sun, Jing, and Jing He. "Influence of Land Use and Land Cover Change on Land surface temperature." E3S Web of Conferences 283 (2021): 01038. http://dx.doi.org/10.1051/e3sconf/202128301038.

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The rapid urbanization process has recently led to significant land use and land cover (LULC) changes, thereby affecting the climate and the environment. The purpose of this study is to analyze the LULC changes in Hefei City, Anhui Province, and their relationship with land surface temperature (LST). To achieve this goal, multitemporal Landsat data were used to monitor the LULC and LST between 2005 and 2015. The study also used correlation analysis to analyze the relationship between LST, LULC, and other spectral indices (NDVI, NDBI, and NDWI). The results show that the built-up land has expanded significantly, transforming from 488.26 km2 in 2005 to 575.64 km2 in 2015. It further shows that the mean LST in Hefei city has increased from 284.0 K in 2005 to 285.86 K in 2015. The results also indicate that there is a positive correlation between LST and NDVI and NDBI, while there is a negative correlation between LST and NDWI. This means that urban expansion and reduced water bodies will lead to an increase in LST.

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16

Ibrahim, I., A. Abu Samah, R. Fauzi, and N. M. Noor. "THE LAND SURFACE TEMPERATURE IMPACT TO LAND COVER TYPES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 10, 2016): 871–76. http://dx.doi.org/10.5194/isprsarchives-xli-b3-871-2016.

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Land cover type is an important signature that is usually used to understand the interaction between the ground surfaces with the local temperature. Various land cover types such as high density built up areas, vegetation, bare land and water bodies are areas where heat signature are measured using remote sensing image. The aim of this study is to analyse the impact of land surface temperature on land cover types. The objectives are 1) to analyse the mean temperature for each land cover types and 2) to analyse the relationship of temperature variation within land cover types: built up area, green area, forest, water bodies and bare land. The method used in this research was supervised classification for land cover map and mono window algorithm for land surface temperature (LST) extraction. The statistical analysis of post hoc Tukey test was used on an image captured on five available images. A pixel-based change detection was applied to the temperature and land cover images. The result of post hoc Tukey test for the images showed that these land cover types: built up-green, built up-forest, built up-water bodies have caused significant difference in the temperature variation. However, built up-bare land did not show significant impact at p<0.05. These findings show that green areas appears to have a lower temperature difference, which is between 2° to 3° Celsius compared to urban areas. The findings also show that the average temperature and the built up percentage has a moderate correlation with R<sup>2</sup> = 0.53. The environmental implications of these interactions can provide some insights for future land use planning in the region.

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17

Ibrahim, I., A. Abu Samah, R. Fauzi, and N. M. Noor. "THE LAND SURFACE TEMPERATURE IMPACT TO LAND COVER TYPES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 10, 2016): 871–76. http://dx.doi.org/10.5194/isprs-archives-xli-b3-871-2016.

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Land cover type is an important signature that is usually used to understand the interaction between the ground surfaces with the local temperature. Various land cover types such as high density built up areas, vegetation, bare land and water bodies are areas where heat signature are measured using remote sensing image. The aim of this study is to analyse the impact of land surface temperature on land cover types. The objectives are 1) to analyse the mean temperature for each land cover types and 2) to analyse the relationship of temperature variation within land cover types: built up area, green area, forest, water bodies and bare land. The method used in this research was supervised classification for land cover map and mono window algorithm for land surface temperature (LST) extraction. The statistical analysis of post hoc Tukey test was used on an image captured on five available images. A pixel-based change detection was applied to the temperature and land cover images. The result of post hoc Tukey test for the images showed that these land cover types: built up-green, built up-forest, built up-water bodies have caused significant difference in the temperature variation. However, built up-bare land did not show significant impact at p<0.05. These findings show that green areas appears to have a lower temperature difference, which is between 2° to 3° Celsius compared to urban areas. The findings also show that the average temperature and the built up percentage has a moderate correlation with R<sup>2</sup> = 0.53. The environmental implications of these interactions can provide some insights for future land use planning in the region.

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18

Desborough, Carl E., and Andrew J. Pitman. "The BASE land surface model." Global and Planetary Change 19, no. 1-4 (December 1998): 3–18. http://dx.doi.org/10.1016/s0921-8181(98)00038-1.

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19

Rowe, Clinton M. "Global land-surface albedo modelling." International Journal of Climatology 13, no. 5 (July 1993): 473–95. http://dx.doi.org/10.1002/joc.3370130502.

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20

Garratt, J. R. "Extreme Maximum Land Surface Temperatures." Journal of Applied Meteorology 31, no. 9 (September 1992): 1096–105. http://dx.doi.org/10.1175/1520-0450(1992)031<1096:emlst>2.0.co;2.

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21

Ridder, Koen De, and Guy Schayes. "The IAGL Land Surface Model." Journal of Applied Meteorology 36, no. 2 (February 1997): 167–82. http://dx.doi.org/10.1175/1520-0450(1997)036<0167:tilsm>2.0.co;2.

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22

Furrer, R., A. Barsch, C. Olbert, and M. Schaale. "Multispectral imaging of land surface." GeoJournal 32, no. 1 (January 1994): 7–16. http://dx.doi.org/10.1007/bf00806350.

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23

Haynes, Marcus W., Frank G. Horowitz, Malcolm Sambridge, Ed J. Gerner, and Graeme R. Beardsmore. "Australian mean land-surface temperature." Geothermics 72 (March 2018): 156–62. http://dx.doi.org/10.1016/j.geothermics.2017.10.008.

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24

Blyth, Eleanor M., Vivek K. Arora, Douglas B. Clark, Simon J. Dadson, Martin G. De Kauwe, David M. Lawrence, Joe R. Melton, et al. "Advances in Land Surface Modelling." Current Climate Change Reports 7, no. 2 (May 11, 2021): 45–71. http://dx.doi.org/10.1007/s40641-021-00171-5.

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AbstractLand surface models have an increasing scope. Initially designed to capture the feedbacks between the land and the atmosphere as part of weather and climate prediction, they are now used as a critical tool in the urgent need to inform policy about land-use and water-use management in a world that is changing physically and economically. This paper outlines the way that models have evolved through this change of purpose and what might the future hold. It highlights the importance of distinguishing between advances in the science within the modelling components, with the advances of how to represent their interaction. This latter aspect of modelling is often overlooked but will increasingly manifest as an issue as the complexity of the system, the time and space scales of the system being modelled increase. These increases are due to technology, data availability and the urgency and range of the problems being studied.

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25

Benavides Pinjosovsky, Hector Simon, Sylvie Thiria, Catherine Ottlé, Julien Brajard, Fouad Badran, and Pascal Maugis. "Variational assimilation of land surface temperature within the ORCHIDEE Land Surface Model Version 1.2.6." Geoscientific Model Development 10, no. 1 (January 6, 2017): 85–104. http://dx.doi.org/10.5194/gmd-10-85-2017.

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Abstract. The SECHIBA module of the ORCHIDEE land surface model describes the exchanges of water and energy between the surface and the atmosphere. In the present paper, the adjoint semi-generator software called YAO was used as a framework to implement a 4D-VAR assimilation scheme of observations in SECHIBA. The objective was to deliver the adjoint model of SECHIBA (SECHIBA-YAO) obtained with YAO to provide an opportunity for scientists and end users to perform their own assimilation. SECHIBA-YAO allows the control of the 11 most influential internal parameters of the soil water content, by observing the land surface temperature or remote sensing data such as the brightness temperature. The paper presents the fundamental principles of the 4D-VAR assimilation, the semi-generator software YAO and a large number of experiments showing the accuracy of the adjoint code in different conditions (sites, PFTs, seasons). In addition, a distributed version is available in the case for which only the land surface temperature is observed.

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26

Li, Bin, and Roni Avissar. "The Impact of Spatial Variability of Land-Surface Characteristics on Land-Surface Heat Fluxes." Journal of Climate 7, no. 4 (April 1994): 527–37. http://dx.doi.org/10.1175/1520-0442(1994)007<0527:tiosvo>2.0.co;2.

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27

PANDA, SURAJIT, and MANISH KUMAR JAIN. "Effects of Green Space Spatial Distribution on Land Surface Temperature: Implications for Land Cover Change as Environmental Indices." INTERNATIONAL JOURNAL OF EARTH SCIENCES AND ENGINEERING 10, no. 02 (April 26, 2017): 180–84. http://dx.doi.org/10.21276/ijee.2017.10.0207.

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28

Tran, Duy X., Filiberto Pla, Pedro Latorre-Carmona, Soe W. Myint, Mario Caetano, and Hoan V. Kieu. "Characterizing the relationship between land use land cover change and land surface temperature." ISPRS Journal of Photogrammetry and Remote Sensing 124 (February 2017): 119–32. http://dx.doi.org/10.1016/j.isprsjprs.2017.01.001.

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29

Kleidon, Axel, and Maik Renner. "An explanation for the different climate sensitivities of land and ocean surfaces based on the diurnal cycle." Earth System Dynamics 8, no. 3 (September 25, 2017): 849–64. http://dx.doi.org/10.5194/esd-8-849-2017.

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Abstract. Observations and climate model simulations consistently show a higher climate sensitivity of land surfaces compared to ocean surfaces. Here we show that this difference in temperature sensitivity can be explained by the different means by which the diurnal variation in solar radiation is buffered. While ocean surfaces buffer the diurnal variations by heat storage changes below the surface, land surfaces buffer it mostly by heat storage changes above the surface in the lower atmosphere that are reflected in the diurnal growth of a convective boundary layer. Storage changes below the surface allow the ocean surface–atmosphere system to maintain turbulent fluxes over day and night, while the land surface–atmosphere system maintains turbulent fluxes only during the daytime hours, when the surface is heated by absorption of solar radiation. This shorter duration of turbulent fluxes on land results in a greater sensitivity of the land surface–atmosphere system to changes in the greenhouse forcing because nighttime temperatures are shaped by radiative exchange only, which are more sensitive to changes in greenhouse forcing. We use a simple, analytic energy balance model of the surface–atmosphere system in which turbulent fluxes are constrained by the maximum power limit to estimate the effects of these different means to buffer the diurnal cycle on the resulting temperature sensitivities. The model predicts that land surfaces have a 50 % greater climate sensitivity than ocean surfaces, and that the nighttime temperatures on land increase about twice as much as daytime temperatures because of the absence of turbulent fluxes at night. Both predictions compare very well with observations and CMIP5 climate model simulations. Hence, the greater climate sensitivity of land surfaces can be explained by its buffering of diurnal variations in solar radiation in the lower atmosphere.

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30

Djuraevna, Teshaboeva Nodira. "Surface identification methods used in land management and land cadastre." ACADEMICIA: An International Multidisciplinary Research Journal 10, no. 8 (2020): 98. http://dx.doi.org/10.5958/2249-7137.2020.00907.6.

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31

Balsamo, G., C. Albergel, A. Beljaars, S. Boussetta, E. Brun, H. Cloke, D. Dee, et al. "ERA-Interim/Land: a global land surface reanalysis data set." Hydrology and Earth System Sciences 19, no. 1 (January 21, 2015): 389–407. http://dx.doi.org/10.5194/hess-19-389-2015.

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Abstract. ERA-Interim/Land is a global land surface reanalysis data set covering the period 1979–2010. It describes the evolution of soil moisture, soil temperature and snowpack. ERA-Interim/Land is the result of a single 32-year simulation with the latest ECMWF (European Centre for Medium-Range Weather Forecasts) land surface model driven by meteorological forcing from the ERA-Interim atmospheric reanalysis and precipitation adjustments based on monthly GPCP v2.1 (Global Precipitation Climatology Project). The horizontal resolution is about 80 km and the time frequency is 3-hourly. ERA-Interim/Land includes a number of parameterization improvements in the land surface scheme with respect to the original ERA-Interim data set, which makes it more suitable for climate studies involving land water resources. The quality of ERA-Interim/Land is assessed by comparing with ground-based and remote sensing observations. In particular, estimates of soil moisture, snow depth, surface albedo, turbulent latent and sensible fluxes, and river discharges are verified against a large number of site measurements. ERA-Interim/Land provides a global integrated and coherent estimate of soil moisture and snow water equivalent, which can also be used for the initialization of numerical weather prediction and climate models.

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32

Steyaert, Louis T., Thomas R. Loveland, and William J. Parton. "Invited Feature: Land Cover Characterization and Land Surface Parameterization Research." Ecological Applications 7, no. 1 (February 1997): 1. http://dx.doi.org/10.2307/2269402.

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33

Howell, Jean. "'Subterranean land law': rights below the surface of the land." Northern Ireland Legal Quarterly 53, no. 3 (July 17, 2020): 268–87. http://dx.doi.org/10.53386/nilq.v53i3.698.

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34

Jin, Menglin, and Shunlin Liang. "An Improved Land Surface Emissivity Parameter for Land Surface Models Using Global Remote Sensing Observations." Journal of Climate 19, no. 12 (June 15, 2006): 2867–81. http://dx.doi.org/10.1175/jcli3720.1.

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Abstract Because land surface emissivity (ɛ) has not been reliably measured, global climate model (GCM) land surface schemes conventionally set this parameter as simply constant, for example, 1 as in the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP) model, and 0.96 for bare soil as in the National Center for Atmospheric Research (NCAR) Community Land Model version 2 (CLM2). This is the so-called constant-emissivity assumption. Accurate broadband emissivity data are needed as model inputs to better simulate the land surface climate. It is demonstrated in this paper that the assumption of the constant emissivity induces errors in modeling the surface energy budget, especially over large arid and semiarid areas where ɛ is far smaller than unity. One feasible solution to this problem is to apply the satellite-based broadband emissivity into land surface models. The Moderate Resolution Imaging Spectroradiometer (MODIS) instrument has routinely measured spectral emissivities (ɛλ) in six thermal infrared bands. The empirical regression equations have been developed in this study to convert these spectral emissivities to broadband emissivity (ɛ) required by land surface models. The observed emissivity data show strong seasonality and land-cover dependence. Specifically, emissivity depends on surface-cover type, soil moisture content, soil organic composition, vegetation density, and structure. For example, broadband ɛ is usually around 0.96–0.98 for densely vegetated areas [(leaf area index) LAI > 2], but it can be lower than 0.90 for bare soils (e.g., desert). To examine the impact of variable surface broadband emissivity, sensitivity studies were conducted using offline CLM2 and coupled NCAR Community Atmosphere Models, CAM2–CLM2. These sensitivity studies illustrate that large impacts of surface ɛ occur over deserts, with changes up to 1°–2°C in ground temperature, surface skin temperature, and 2-m surface air temperature, as well as evident changes in sensible and latent heat fluxes.

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Pavelin, E. G., and B. Candy. "Assimilation of surface-sensitive infrared radiances over land: Estimation of land surface temperature and emissivity." Quarterly Journal of the Royal Meteorological Society 140, no. 681 (August 7, 2013): 1198–208. http://dx.doi.org/10.1002/qj.2218.

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Santanello, Joseph A., Sujay V. Kumar, Christa D. Peters-Lidard, Ken Harrison, and Shujia Zhou. "Impact of Land Model Calibration on Coupled Land–Atmosphere Prediction." Journal of Hydrometeorology 14, no. 5 (October 1, 2013): 1373–400. http://dx.doi.org/10.1175/jhm-d-12-0127.1.

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Abstract Land–atmosphere (LA) interactions play a critical role in determining the diurnal evolution of both planetary boundary layer (PBL) and land surface heat and moisture budgets, as well as controlling feedbacks with clouds and precipitation that lead to the persistence of dry and wet regimes. In this study, the authors examine the impact of improved specification of land surface states, anomalies, and fluxes on coupled Weather Research and Forecasting Model (WRF) forecasts during the summers of extreme dry (2006) and wet (2007) land surface conditions in the U.S. southern Great Plains. The improved land initialization and surface flux parameterizations are obtained through calibration of the Noah land surface model using the new optimization and uncertainty estimation subsystems in NASA's Land Information System (LIS-OPT/LIS-UE). The impact of the calibration on the 1) spinup of the land surface used as initial conditions and 2) the simulated heat and moisture states and fluxes of the coupled WRF simulations is then assessed. In addition, the sensitivity of this approach to the period of calibration (dry, wet, or average) is investigated. Results show that the offline calibration is successful in providing improved initial conditions and land surface physics for the coupled simulations and in turn leads to systematic improvements in land–PBL fluxes and near-surface temperature and humidity forecasts. Impacts are larger during dry regimes, but calibration during either primarily wet or dry periods leads to improvements in coupled simulations due to the reduction in land surface model bias. Overall, these results provide guidance on the questions of what, how, and when to calibrate land surface models for coupled model prediction.

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Su, Z., H. Pelgrum, and M. Menenti. "Aggregation effects of surface heterogeneity in land surface processes." Hydrology and Earth System Sciences 3, no. 4 (December 31, 1999): 549–63. http://dx.doi.org/10.5194/hess-3-549-1999.

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Abstract. In order to investigate the aggregation effects of surface heterogeneity in land surface processes we have adapted a theory of aggregation. Two strategies have been adopted: 1) Aggregation of radiative fluxes. The aggregated radiative fluxes are used to derive input parameters that are then used to calculate the aerodynamic fluxes at different aggregation levels. This is equivalent to observing the same area at different resolutions using a certain remote sensor, and then calculating the aerodynamic fluxes correspondingly. 2) Aggregation of aerodynamic fluxes calculated at the original observation scale to different aggregation levels. A case study has been conducted to identify the effects of aggregation on areal estimates of sensible and latent heat fluxes. The length scales of surface variables in heterogeneous landscapes are estimated by means of wavelet analysis.

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Weng, Qihao, Dengsheng Lu, and Bingqing Liang. "Urban Surface Biophysical Descriptors and Land Surface Temperature Variations." Photogrammetric Engineering & Remote Sensing 72, no. 11 (November 1, 2006): 1275–86. http://dx.doi.org/10.14358/pers.72.11.1275.

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Inamdar, Anand K., and Andrew French. "Disaggregation of GOES land surface temperatures using surface emissivity." Geophysical Research Letters 36, no. 2 (January 2009): n/a. http://dx.doi.org/10.1029/2008gl036544.

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Grant, A. L. M. "Surface drag and turbulence over an inhomogeneous land surface." Boundary-Layer Meteorology 56, no. 4 (September 1991): 309–37. http://dx.doi.org/10.1007/bf00119210.

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Desborough, C. E. "Surface energy balance complexity in GCM land surface models." Climate Dynamics 15, no. 5 (May 4, 1999): 389–403. http://dx.doi.org/10.1007/s003820050289.

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Magidi, James, and Fethi Ahmed. "Spatio-temporal variations of land surface temperature using Landsat and MODIS: case study of the City of Tshwane, South Africa." South African Journal of Geomatics 9, no. 2 (September 7, 2022): 379–96. http://dx.doi.org/10.4314/sajg.v9i2.25.

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Urbanisation is accelerating urban land use dynamics and this has a significant impact on land surface temperature (LST). Impervious surfaces and increase in air pollution has led to the increase in land surface temperature. This study reports on the use of geospatial technologies to monitor and quantify changes in LST using remotely sensed data in the City of Tshwane. Land surface temperature was retrieved using the winter and summer Landsat datasets for 1997 and 2015 and the MODIS data from 2000 to 2015. Land surface temperature was extracted using emissivity and satellite temperature as input parameters. The spatial and temporal variations in the LST were retrieved to show the effects of land cover change on LST. Change in LST was also analysed on different land cover types using transects across the study area. The study revealed an increase in land surface temperature between the years. It also showed that impervious surfaces had a higher LST compared to the non-impervious surfaces. The results revealed variations in LST between non-cropped and cropped agricultural areas, where the former had higher LST than the latter. Temporal trends revealed a notable increase in LST in the urban areas and there were some seasonal variations in LST with high LST values in summer and low values in winter. Cross-section analysis along transects revealed spatio-temporal thermal variations across different land cover types.

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Bakar, Suzana Binti Abu, Biswajeet Pradhan, Usman Salihu Lay, and Saleh Abdullahi. "Spatial assessment of land surface temperature and land use/land cover in Langkawi Island." IOP Conference Series: Earth and Environmental Science 37 (June 2016): 012064. http://dx.doi.org/10.1088/1755-1315/37/1/012064.

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Wang, Liming, Fuqiang Tian, Xufeng Wang, Yanzheng Yang, and Zhongwang Wei. "Attribution of the land surface temperature response to land-use conversions from bare land." Global and Planetary Change 193 (October 2020): 103268. http://dx.doi.org/10.1016/j.gloplacha.2020.103268.

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Sun, Qinqin, Zhifeng Wu, and Jianjun Tan. "The relationship between land surface temperature and land use/land cover in Guangzhou, China." Environmental Earth Sciences 65, no. 6 (June 22, 2011): 1687–94. http://dx.doi.org/10.1007/s12665-011-1145-2.

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Salmamza, Y. M., S. M. Onywere, and S. C. Letema. "GEOSPATIAL DIMENSIONS OF LAND COVER TRANSITIONS AND LAND SURFACE TEMPERATURE IN ABUJA CITY, NIGERIA." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2022 (May 30, 2022): 699–704. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2022-699-2022.

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Abstract. Urbanization is often accompanied by succession of underlying land cover with impervious surfaces. Built intensification significantly alters the surface energy budget making cities warmer than their outlying suburbs, which signifies an ecological deterioration. Landsat imageries with scene covering Abuja city is processed using Google Earth Engine platform to estimate land cover and land surface temperature over the span of 30 years (1990–2020). Dimensions of land cover transitions were examined in-terms losses, gains, swaps, net-change and persistency. Thermal signature of each land cover type was estimated using land surface temperature. Urban thermal field variance index is computed from land surface temperature to evaluate the thermal conditions in the city. Results indicate that net-changes for built-up exhibited gains of 40% while agricultural land, bare-land and vegetation exhibited loss of 27%, 7% and 8% respectively. Built-up also showed the highest proportion of persistence (12%). Results shows that land surface temperature has increased by 2.01 °C from 1990 to 2020. Agricultural land, bare-land and built-up were found with the highest temperature. Lowest temperature was found in waterbody and vegetation. The ecological evaluation showed that 47% of the city is experiencing bad to worst thermal condition. These findings provide further information that can contribute towards an informed spatial planning in cities.

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Wang, Yu, and Corene J. Matyas. "Simulating the Effects of Land Surface Characteristics on Planetary Boundary Layer Parameters for a Modeled Landfalling Tropical Cyclone." Atmosphere 13, no. 1 (January 14, 2022): 138. http://dx.doi.org/10.3390/atmos13010138.

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This study examined whether varying moisture availability and roughness length for the land surface under a simulated Tropical Cyclone (TC) could affect its production of precipitation. The TC moved over the heterogeneous land surface of the southeastern U.S. in the control simulation, while the other simulations featured homogeneous land surfaces that were wet rough, wet smooth, dry rough, and dry smooth. Results suggest that the near-surface atmosphere was modified by the changes to the land surface, where the wet cases have higher latent and lower sensible heat flux values, and rough cases exhibit higher values of friction velocity. The analysis of areal-averaged rain rates and the area receiving low and high rain rates shows that simulations having a moist land surface produce higher rain rates and larger areas of low rain rates in the TC’s inner core. The dry and rough land surfaces produced a higher coverage of high rain rates in the outer regions. Key differences among the simulations happened as the TC core moved over land, while the outer rainbands produced more rain when moving over the coastline. These findings support the assertion that the modifications of the land surface can influence precipitation production within a landfalling TC.

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Bennartz, Ralf, Katja Paape, Jürgen Fischer, and Tim J. Hewison. "Comparison of observed and simulated microwave land surface emissivities over bare soil." Meteorologische Zeitschrift 11, no. 1 (March 5, 2002): 5–12. http://dx.doi.org/10.1127/0941-2948/2002/0011-0005.

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Wuyep zitta, Solomon, Diekoer Charity Damak, Hyacinth Madaki Daloeng, Hassan Buhari Arin, and Nankap Latur Binbol. "Spatio-Temporal Analysis of Urban Heat Hazard in Jos Metropolis." BOKKOS JOURNAL OF APPLIED SCIENTIFIC REPORTS 1, no. 2 (March 14, 2021): 35–60. http://dx.doi.org/10.47452/bjasrep.v1i2.24.

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The rapid urbanization has produced a remarkable effect on the surface thermal response. The effects significantly influence urban internal microclimate on a regional scale. This has led to the transformation of the natural landscape into anthropogenic surfaces in urban settlements. In this study, the surface temperature and land cover types retrieved from Landsat ETM+ and OLI images of Jos Metropolis for 2005 and 2019 were analysed. The Erdas imagine 9.2 and ArcGIS 10.1 was used for data preparation and map composition. Thermal band data was used to compute surface temperature maps for the two years and the relationship between land use land cover and surface temperature was analyzed. Results from land use land cover maps between 2005 and 2019 revealed a notable increase with an annual average rate of 5.1 %. Also, urban land development raised surface temperature by 1.360C between 2005 and 2019. Bare land exhibited the high value of surface temperature while vegetation showed low values of surface temperature. The result also shows that there is an occurrence of physiological discomfort in the environment with a very strong heat stress leading to increase in the probability of heat stroke and cardiovascular embarrassment. Focus should be given on the effect of urban growth, growing impervious surfaces and careful greening methods are recommended.

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Tahooni, A., and A. A. Kakroodi. "RELATIONSHIPS BETWEEN LAND USE/LAND COVER AND LAND SURFACE TEMPERATURE IN TABRIZ FROM 2000 TO 2017." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 19, 2019): 1041–44. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-1041-2019.

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Abstract. Urban Heat Island (UHI) refers to the development of higher urban temperatures of an urban area compared to the temperatures of surrounding suburban and rural areas. Highly reflective urban materials to solar radiation present a significantly lower surface temperature and contribute to reducing the sensible heat released in the atmosphere and mitigating the urban heat island. Many studies of the UHI effect have been based on Land Surface Temperature (LST) measurements from remote sensors. The remotely sensed UHI has been termed the surface urban heat island (SUHI) effect. This study examines Tabriz city land use/land cover (LULC) and LST changes using Landsat satellite images between 2000 and 2017. Maximum likelihood classification and single channel methods were used for LULC classification and LST retrieval respectively. Results show that impervious surface has increased 13.79% and bare soil area has decreased 16.2%. The results also revealed bare soil class LST after a constant trend become increasing. It also revealed the impervious surface LST has a decreasing trend between 2000 and 2011 and has a little change. Using materials that have low absorption and high reflectance decrease the effect of heat island considerably.

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