scholarly journals Parametric Design of a Microwave Radiometer for Land Surface Temperature Retrieval from Moon-Based Earth Observation Platform

2020 ◽  
Vol 12 (24) ◽  
pp. 4110
Author(s):  
Linan Yuan ◽  
Jingjuan Liao
Keyword(s):  
Surface Temperature ◽  
Spatial Resolution ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Microwave Radiometer ◽  
Earth Observation ◽  
Design Parameters ◽  
Observation Data ◽  
The Moon ◽  
Antenna Aperture

Increasing attention is being paid to the monitoring of global change, and remote sensing is an important means for acquiring global observation data. Due to the limitations of the orbital altitude, technological level, observation platform stability and design life of artificial satellites, spaceborne Earth observation platforms cannot quickly obtain global data. The Moon-based Earth observation (MEO) platform has unique advantages, including a wide observation range, short revisit period, large viewing angle and spatial resolution; thus, it provides a new observation method for quickly obtaining global Earth observation data. At present, the MEO platform has not yet entered the actual development stage, and the relevant parameters of the microwave sensors have not been determined. In this work, to explore whether a microwave radiometer is suitable for the MEO platform, the land surface temperature (LST) distribution at different times is estimated and the design parameters of the Moon-based microwave radiometer (MBMR) are analyzed based on the LST retrieval. Results show that the antenna aperture size of a Moon-based microwave radiometer is suitable for 120 m, and the bands include 18.7, 23.8, 36.5 and 89.0 GHz, each with horizontal and vertical polarization. Moreover, the optimal value of other parameters, such as the half-power beam width, spatial resolution, integration time of the radiometer system, temperature sensitivity, scan angle and antenna pattern simulations are also determined.

scholarly journals Technical note: A view from space on global flux towers by MODIS and Landsat: The FluxnetEO dataset

2021 ◽  
Author(s):  
Sophia Walther ◽  
Simon Besnard ◽  
Jacob A. Nelson ◽  
Tarek S. El-Madany ◽  
Mirco Migliavacca ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Eddy Covariance ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Earth Observation ◽  
Research Network ◽  
Observation System ◽  
Data Sets ◽  
Observation Data ◽  
Earth Observation Data

Abstract. The eddy-covariance technique measures carbon, water, and energy fluxes between the land surface and the atmosphere at several hundreds of sites globally. Collections of standardised and homogenised flux estimates such as the LaThuile, Fluxnet2015, National Ecological Observatory Network (NEON), Integrated Carbon Observation System (ICOS), AsiaFlux, and Terrestrial Ecosystem Research Network (TERN) / OzFlux data sets are invaluable to study land surface processes and vegetation functioning at the ecosystem scale. Space-borne measurements give complementary information on the state of the land surface in the surroundings of the towers. They aid the interpretation of the fluxes and support the training and validation of ecosystem models. However, insufficient quality, frequent and/or long gaps are recurrent problems in applying the remotely sensed data and may considerably affect the scientific conclusions drawn from them. Here, we describe a standardised procedure to extract, quality filter, and gap-fill Earth observation data from the MODIS instruments and the Landsat satellites. The methods consistently process surface reflectance in individual spectral bands, derived vegetation indices and land surface temperature. A geometrical correction estimates the magnitude of land surface temperature as if seen from nadir or 40° off-nadir. We offer to the community pre-processed Earth observation data in a radius of 2 km around 338 flux sites based on the MCD43A4/A2, MxD11A1 MODIS products and Landsat collection~1 Tier1 and Tier2 products. The data sets we provide can widely facilitate the integration of activities in the fields of eddy-covariance, remote sensing and modelling.

scholarly journals A Random Forest-Based Data Fusion Method for Obtaining All-Weather Land Surface Temperature with High Spatial Resolution

2021 ◽  
Vol 13 (11) ◽  
pp. 2211
Author(s):  
Shuo Xu ◽  
Jie Cheng ◽  
Quan Zhang
Keyword(s):  
Random Forest ◽  
Surface Temperature ◽  
Spatial Resolution ◽  
Land Surface Temperature ◽  
Land Surface ◽  
High Spatial Resolution ◽  
Learning Algorithm ◽  
Mainland China ◽  
Fusion Method ◽  
Moisture Condition

Land surface temperature (LST) is an important parameter for mirroring the water–heat exchange and balance on the Earth’s surface. Passive microwave (PMW) LST can make up for the lack of thermal infrared (TIR) LST caused by cloud contamination, but its resolution is relatively low. In this study, we developed a TIR and PWM LST fusion method on based the random forest (RF) machine learning algorithm to obtain the all-weather LST with high spatial resolution. Since LST is closely related to land cover (LC) types, terrain, vegetation conditions, moisture condition, and solar radiation, these variables were selected as candidate auxiliary variables to establish the best model to obtain the fusion results of mainland China during 2010. In general, the fusion LST had higher spatial integrity than the MODIS LST and higher accuracy than downscaled AMSR-E LST. Additionally, the magnitude of LST data in the fusion results was consistent with the general spatiotemporal variations of LST. Compared with in situ observations, the RMSE of clear-sky fused LST and cloudy-sky fused LST were 2.12–4.50 K and 3.45–4.89 K, respectively. Combining the RF method and the DINEOF method, a complete all-weather LST with a spatial resolution of 0.01° can be obtained.

scholarly journals Analysis of the Spatiotemporal Change in Land Surface Temperature for a Long-Term Sequence in Africa (2003–2017)

2020 ◽  
Vol 12 (3) ◽  
pp. 488 ◽  
Author(s):  
Nusseiba NourEldeen ◽  
Kebiao Mao ◽  
Zijin Yuan ◽  
Xinyi Shen ◽  
Tongren Xu ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Observation Data ◽  
The West ◽  
Thermal Infrared Remote Sensing ◽  
Ground Station ◽  
Station Data ◽  
Effects Of Temperature ◽  
Reconstruction Model

It is very important to understand the temporal and spatial variations of land surface temperature (LST) in Africa to determine the effects of temperature on agricultural production. Although thermal infrared remote sensing technology can quickly obtain surface temperature information, it is greatly affected by clouds and rainfall. To obtain a complete and continuous dataset on the spatiotemporal variations in LST in Africa, a reconstruction model based on the moderate resolution imaging spectroradiometer (MODIS) LST time series and ground station data was built to refactor the LST dataset (2003–2017). The first step in the reconstruction model is to filter low-quality LST pixels contaminated by clouds and then fill the pixels using observation data from ground weather stations. Then, the missing pixels are interpolated using the inverse distance weighting (IDW) method. The evaluation shows that the accuracy between reconstructed LST and ground station data is high (root mean square er–ror (RMSE) = 0.84 °C, mean absolute error (MAE) = 0.75 °C and correlation coefficient (R) = 0.91). The spatiotemporal analysis of the LST indicates that the change in the annual average LST from 2003–2017 was weak and the warming trend in Africa was remarkably uneven. Geographically, “the warming is more pronounced in the north and the west than in the south and the east”. The most significant warming occurred near the equatorial region in South Africa (slope > 0.05, R > 0.61, p < 0.05) and the central (slope = 0.08, R = 0.89, p < 0.05) regions, and a nonsignificant decreasing trend occurred in Botswana. Additionally, the mid-north region (north of Chad, north of Niger and south of Algeria) became colder (slope > −0.07, R = 0.9, p < 0.05), with a nonsignificant trend. Seasonally, significant warming was more pronounced in winter, mostly in the west, especially in Mauritania (slope > 0.09, R > 0.9, p < 0.5). The response of the different types of surface to the surface temperature has shown variability at different times, which provides important information to understand the effects of temperature changes on crop yields, which is critical for the planning of agricultural farming systems in Africa.

Downscaling sub-daily Land Surface Temperature time series for monitoring heat in urban environments

2020 ◽  
Author(s):  
Nikos Alexandris ◽  
Matteo Piccardo ◽  
Vasileios Syrris ◽  
Alessandro Cescatti ◽  
Gregory Duveiller
Keyword(s):  
Time Series ◽  
Heat Stress ◽  
Surface Temperature ◽  
Spatial Resolution ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Environments ◽  
High Temporal Resolution ◽  
Landsat 8 ◽  
Fine Spatial Resolution

&lt;p&gt;The frequency of extreme heat related events is rising. This places the ever growing number of urban dwellers at higher risk. Quantifying these phenomena is important for the development and monitoring of climate change adaptation and mitigation policies. In this context, earth observations offer increasing opportunities to assess these phenomena with an unprecedented level of accuracy and spatial reach. Satellite thermal imaging systems acquire Land Surface Temperature (LST) which is fundamental to run models that study for example hotspots and heatwaves in urban environments.&lt;/p&gt;&lt;p&gt;Current instruments include TIRS on board Landsat 8 and MODIS on board of Terra satellites. These provide LST products on a monthly basis at 100m and twice per day at 1km respectively. Other sensors on board geostationary satellites, such as MSG and GOES-R, produce sub-hourly thermal images. For example the SEVIRI instrument onboard MSG, captures images every 15 minutes. However, this is done at an even coarser spatial resolution, which is 3 to 5 km in the case of SEVIRI. Nevertheless, none of the existing systems can capture LST synchronously with fine spatial resolution at a high temporal frequency, which is a prerequisite for monitoring heat stress in urban environments.&lt;/p&gt;&lt;p&gt;Combining LST time series of high temporal resolution (i.e. sub-daily MODIS- or SEVIRI-derived data) with products of fine spatial resolution (i.e. Landsat 8 products), and potentially other related variables (i.e. reflectance, spectral indices, land cover information, terrain parameters and local climatic variables), facilitates the downscaling of LST estimations. Nonetheless, considering the complexity of how distinct surfaces within a city heat-up differently during the course of a day, such a downscaling is meaningful for practically synchronous observations (e.g. Landsat-8 and MODIS Terra&amp;#8217;s morning observations).&lt;/p&gt;&lt;p&gt;The recently launched ECOSTRESS mission provides multiple times in a day high spatial resolution thermal imagery at 70m. Albeit, recording the same locations on Earth every few days at varying times. We explore the associations between ECOSTRESS and Landsat-8 thermal data, based on the incoming radiation load and distinct surface properties characterised from other datasets. In our approach, first we upscale ECOSTRESS data to simulate Landsat-8 images at moments that coincide the acquisition times of other sensors products. In a second step, using the simulated Landsat-8 images, we downscale LST products acquired at later times, such as MODIS Aqua (ca. 13:30) or even the hourly MSG data. This composite downscaling procedure enables an enhanced LST estimation that opens the way for better diagnostics of the heat stress in urban landscapes.&lt;/p&gt;&lt;p&gt;In this study we discuss in detail the concepts of our approach and present preliminary results produced with the JEODPP, JRC's high throughput computing platform.&lt;/p&gt;

Global Land Surface Temperature from historical NOAA AVHRR datasets (1981-2000)

2020 ◽  
Author(s):  
Jin Ma ◽  
Ji Zhou ◽  
Frank-Michael Göttsche ◽  
Shaofei Wang
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Surface Temperature ◽  
Spatial Resolution ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Microwave Remote Sensing ◽  
Community Land Model ◽  
Noaa Avhrr

&lt;p&gt;As one of the most important indicators in the energy exchange between land and atmosphere, Land Surface Temperature (LST) plays an important role in the research of climate change and various land surface processes. In contrast to &lt;em&gt;in-situ&lt;/em&gt; measurements, satellite remote sensing provides a practical approach to measure global and local land surface parameters. Although passive microwave remote sensing offers all-weather observation capability, retrieving LST from thermal infra-red data is still the most common approach. To date, a variety of global LST products have been published by the scientific community, e.g. MODIS and (A)ASTR /SLSTR LST products, and used in a broad range of research fields. Several global and regional satellite retrieved LSTs are available since 1995. However, the temporal-spatial resolution before 2000 is generally considerably lower than that after 2000. According to the latest IPCC report, 1983 &amp;#8211; 2012 are the warmest 30 years for nearly 1400 years. Therefore, for global climate change research, it is meaningful to extend the time series of global LST products with a relatively higher temporal-spatial resolution to before 2000, e.g. that of NOAA AVHRR. In this study, global daily NOAA AVHRR LST products with 5-km spatial resolution were generated for 1981-2000. The LST was retrieved using an ensemble of RF-SWAs (Random Forest and Split-Window Algorithm). For a maximum uncertainty in emissivity and water vapor content of 0.04 and 1.0 g/cm&lt;sup&gt;2&lt;/sup&gt;, respectively, the training and testing with simulated datasets showed a retrieval accuracy with MBE of less than 0.1 K and STD of 1.1 K. The generated RF-SWA LST product was also evaluated against &lt;em&gt;in-situ&lt;/em&gt; measurements: for water sites of the National Data Buoy Center (NDBC) between 1981 and 2000, it showed an accuracy similar to that for the simulated data, with a small MBE of less than 0.1 K and a STD between 0.79 K and 1.02 K. For SURFRAD data collected between 1995 and 2000, the MBE is -0.03 K with a range of -1.20 K &amp;#8211; 0.54 K and a STD with a mean of 2.55 K and a range of 2.08 K &amp;#8211; 3.0 K (site dependent). As a new global historical dataset, the RF-SWA LST product can help to close the gap in long-term LST data available to climate research. Furthermore, the data can be used as input to land surface process models, e.g. the Community Land Model (CLM). In support of the scientific research community, the RF-SWA LST product will be freely available at the National Earth System Science Data Center of China (http://www.geodata.cn/).&lt;/p&gt;

scholarly journals Estimation of Diurnal Cycle of Land Surface Temperature at High Temporal and Spatial Resolution from Clear-Sky MODIS Data

2014 ◽  
Vol 6 (4) ◽  
pp. 3247-3262 ◽  
Author(s):  
Si-Bo Duan ◽  
Zhao-Liang Li ◽  
Bo-Hui Tang ◽  
Hua Wu ◽  
Ronglin Tang ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Spatial Resolution ◽  
Land Surface Temperature ◽  
Diurnal Cycle ◽  
Land Surface ◽  
Modis Data ◽  
Clear Sky ◽  
Temporal And Spatial

scholarly journals Deep Learning Convolutional Neural Network for the Retrieval of Land Surface Temperature from AMSR2 Data in China

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2987 ◽  
Author(s):  
Jiancan Tan ◽  
Nusseiba NourEldeen ◽  
Kebiao Mao ◽  
Jiancheng Shi ◽  
Zhaoliang Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Surface Temperature ◽  
Convolutional Neural Network ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Measurement Data ◽  
Retrieval Algorithm ◽  
Observation Data ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Better Than

A convolutional neural network (CNN) algorithm was developed to retrieve the land surface temperature (LST) from Advanced Microwave Scanning Radiometer 2 (AMSR2) data in China. Reference data were selected using the Moderate Resolution Imaging Spectroradiometer (MODIS) LST product to overcome the problem related to the need for synchronous ground observation data. The AMSR2 brightness temperature (TB) data and MODIS surface temperature data were randomly divided into training and test datasets, and a CNN was constructed to simulate passive microwave radiation transmission to invert the surface temperature. The twelve V/H channel combinations (7.3, 10.65, 18.7, 23.8, 36.5, 89 GHz) resulted in the most stable and accurate CNN retrieval model. Vertical polarizations performed better than horizontal polarizations; however, because CNNs rely heavily on large amounts of data, the combination of vertical and horizontal polarizations performed better than a single polarization. The retrievals in different regions indicated that the CNN accuracy was highest over large bare land areas. A comparison of the retrieval results with ground measurement data from meteorological stations yielded R2 = 0.987, RMSE = 2.69 K, and an average relative error of 2.57 K, which indicated that the accuracy of the CNN LST retrieval algorithm was high and the retrieval results can be applied to long-term LST sequence analysis in China.

scholarly journals A SIMPLE AND EFFECTIVE RETRIEVAL OF LAND SURFACE TEMPERATURE USING A NEW REFLECTANCE BASED EMISSIVITY ESTIMATION TECHNIQUE

2016 ◽  
Vol XLI-B8 ◽  
pp. 443-447
Author(s):  
Y. Nithiyanandam ◽  
J. E. Nichol
Keyword(s):  
Surface Temperature ◽  
Spatial Resolution ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Field Measurements ◽  
Measured Temperature ◽  
Reference Channel ◽  
Data Logger ◽  
Surface Temperatures ◽  
Data Loggers

Emissivity is a significant factor in determining land surface temperature (LST) retrieved from the thermal infrared (TIR) satellite images. A new simplified method (reflectance method) for emissivity correction was developed in this study while estimating emissivity values at a spatial resolution of 30 m from the radiance values of the SWIR image. This in turn enables mapping surface temperatures at a much finer spatial resolution (30 m). Temperatures so estimated are validated against surface temperatures measured in the ground by thermocouple data loggers recorded during satellite overpass time. In this study, surface emissivity values are derived directly from the AST_ L1B images. The reflectance method estimates temperature at higher spatial resolution of 30 m when compared to the 90 m spatial resolution of TES and reference channel methods. Temperature determined for the daytime image of 30<sup>th</sup> November 2007 using different emissivity techniques was compared with the temperatures measured on the field using thermocouple data loggers. It is observed that the estimates from the reflectance method are much closer to the field measurements than the TES and reference channel methods. The temperature difference values range from 0.2 to 2.3 &deg;C, 0.15 to 5.6 &deg;C, and 2.6 to 8.6 &deg;C for the reflectance method, normalization method and reference channel method, respectively. The new reflectance emissivity techniques i.e. reflectance method exhibits the least deviation from the field measured temperature values. While considering the accuracy of data logger (1 &deg;C) the reflectance method enables one to map surface temperature precisely than other two methods.

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