scholarly journals Identificación de regiones expuestas a bajas temperaturas en el Perú usando imágenes de la temperatura de la superficie del suelo procedente de sensor MODIS/Aqua

2018 ◽  
pp. 44-49
Keyword(s):  
Spatial Resolution ◽  
Air Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Low Temperatures ◽  
Earth Surface ◽  
The Earth ◽  
San Marcos ◽  
The Mean ◽  
Minimum Air Temperature

Identificación de regiones expuestas a bajas temperaturas en el Perú usando imágenes de la temperatura de la superficie del suelo procedente de sensor MODIS/Aqua Jaime Aguilar-Lome y Joel Rojas Acuña Facultad de Ciencias Físicas, Universidad Nacional Mayor de San Marcos, Lima, Perú. Recibido el 19 de junio del 2018. Aceptado el 5 de julio del 2018 Resumen La temperatura de la superficie terrestre (LST, siglas en inglés) es una variable clave en las interacciones y los flujos de energía entre la superficie de la Tierra y la atmósfera. Se ha analizado la LST MODIS nocturna a una resolución espacial de 1 km en el periodo 2003-2017 (junio-agosto) sobre el Perú, para identificar las regiones expuestas a bajas temperaturas. Nuestro resultado muestra que las regiones por debajo de los 0°C se encuentran por encima de 3500 msnm (en promedio). A demás la LST nocturna promedio mensual está correlacionado con la temperatura mínima media mensual del aire (R=0.96, N=763) y la topografía influye significativamente en la variabilidad de la LST. Descriptores: LST MODIS, Heladas Radiativas, Andes Abstract Land Surface Temperature (LST) is a key variable in the interactions and energy fluxes between the Earth surface and the atmosphere. The MODIS LST nighttime at spatial resolution of 1 km was analyzed during the period 2003-2017 (June-August) over Peru to identify regions exposed to low temperatures. Our result shows that the regions below 0°C are above 3500 masl (in average). In addition, the mean monthly nighttime LST is correlated with the mean monthly minimum air temperature (R = 0.96, N = 763) and the topography significantly influences the variability of LST. Keywords: MODIS LST, Radiation Frost, Andes

scholarly journals Identificación de regiones expuestas a bajas temperaturas en el Perú usando imágenes de la temperatura de la superficie del suelo procedente de sensor MODIS/Aqua

2018 ◽  
pp. 44-49
Keyword(s):  
Spatial Resolution ◽  
Air Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Low Temperatures ◽  
Earth Surface ◽  
The Earth ◽  
San Marcos ◽  
The Mean ◽  
Minimum Air Temperature

Identificación de regiones expuestas a bajas temperaturas en el Perú usando imágenes de la temperatura de la superficie del suelo procedente de sensor MODIS/Aqua Jaime Aguilar-Lome y Joel Rojas Acuña Facultad de Ciencias Físicas, Universidad Nacional Mayor de San Marcos, Lima, Perú. Recibido el 19 de junio del 2018. Aceptado el 5 de julio del 2018 Resumen La temperatura de la superficie terrestre (LST, siglas en inglés) es una variable clave en las interacciones y los flujos de energía entre la superficie de la Tierra y la atmósfera. Se ha analizado la LST MODIS nocturna a una resolución espacial de 1 km en el periodo 2003-2017 (junio-agosto) sobre el Perú, para identificar las regiones expuestas a bajas temperaturas. Nuestro resultado muestra que las regiones por debajo de los 0°C se encuentran por encima de 3500 msnm (en promedio). A demás la LST nocturna promedio mensual está correlacionado con la temperatura mínima media mensual del aire (R=0.96, N=763) y la topografía influye significativamente en la variabilidad de la LST. Descriptores: LST MODIS, Heladas Radiativas, Andes Abstract Land Surface Temperature (LST) is a key variable in the interactions and energy fluxes between the Earth surface and the atmosphere. The MODIS LST nighttime at spatial resolution of 1 km was analyzed during the period 2003-2017 (June-August) over Peru to identify regions exposed to low temperatures. Our result shows that the regions below 0°C are above 3500 masl (in average). In addition, the mean monthly nighttime LST is correlated with the mean monthly minimum air temperature (R = 0.96, N = 763) and the topography significantly influences the variability of LST. Keywords: MODIS LST, Radiation Frost, Andes

scholarly journals Spring Frost Damage to Tea Plants Can Be Identified with Daily Minimum Air Temperatures Estimated by MODIS Land Surface Temperature Products

2021 ◽  
Vol 13 (6) ◽  
pp. 1177
Author(s):  
Peijuan Wang ◽  
Yuping Ma ◽  
Junxian Tang ◽  
Dingrong Wu ◽  
Hui Chen ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Air Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Frost Damage ◽  
Estimation Model ◽  
Spring Frost ◽  
Tea Plants ◽  
Elevation Model ◽  
Minimum Air Temperature

Tea (Camellia sinensis) is one of the most dominant economic plants in China and plays an important role in agricultural economic benefits. Spring tea is the most popular drink due to Chinese drinking habits. Although the global temperature is generally warming, spring frost damage (SFD) to tea plants still occurs from time to time, and severely restricts the production and quality of spring tea. Therefore, monitoring and evaluating the impact of SFD to tea plants in a timely and precise manner is a significant and urgent task for scientists and tea producers in China. The region designated as the Middle and Lower Reaches of the Yangtze River (MLRYR) in China is a major tea plantation area producing small tea leaves and low shrubs. This region was selected to study SFD to tea plants using meteorological observations and remotely sensed products. Comparative analysis between minimum air temperature (Tmin) and two MODIS nighttime land surface temperature (LST) products at six pixel-window scales was used to determine the best suitable product and spatial scale. Results showed that the LST nighttime product derived from MYD11A1 data at the 3 × 3 pixel window resolution was the best proxy for daily minimum air temperature. A Tmin estimation model was established using this dataset and digital elevation model (DEM) data, employing the standard lapse rate of air temperature with elevation. Model validation with 145,210 ground-based Tmin observations showed that the accuracy of estimated Tmin was acceptable with a relatively high coefficient of determination (R2 = 0.841), low root mean square error (RMSE = 2.15 °C) and mean absolute error (MAE = 1.66 °C), and reasonable normalized RMSE (NRMSE = 25.4%) and Nash–Sutcliffe model efficiency (EF = 0.12), with significantly improved consistency of LST and Tmin estimation. Based on the Tmin estimation model, three major cooling episodes recorded in the "Yearbook of Meteorological Disasters in China" in spring 2006 were accurately identified, and several highlighted regions in the first two cooling episodes were also precisely captured. This study confirmed that estimating Tmin based on MYD11A1 nighttime products and DEM is a useful method for monitoring and evaluating SFD to tea plants in the MLRYR. Furthermore, this method precisely identified the spatial characteristics and distribution of SFD and will therefore be helpful for taking effective preventative measures to mitigate the economic losses resulting from frost damage.

scholarly journals Estimating Land Surface Temperature from Satellite Passive Microwave Observations with the Traditional Neural Network, Deep Belief Network, and Convolutional Neural Network

2020 ◽  
Vol 12 (17) ◽  
pp. 2691 ◽  
Author(s):  
Shaofei Wang ◽  
Ji Zhou ◽  
Tianjie Lei ◽  
Hua Wu ◽  
Xiaodong Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Surface Temperature ◽  
Convolutional Neural Network ◽  
Spatial Resolution ◽  
Air Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Passive Microwave ◽  
Mean Square

Neural networks, especially the latest deep learning, have exhibited good ability in estimating surface parameters from satellite remote sensing. However, thorough examinations of neural networks in the estimation of land surface temperature (LST) from satellite passive microwave (MW) observations are still lacking. Here, we examined the performances of the traditional neural network (NN), deep belief network (DBN), and convolutional neural network (CNN) in estimating LST from the AMSR-E and AMSR2 data over the Chinese landmass. The examinations were based on the same training set, validation set, and test set extracted from 2003, 2004, and 2009, respectively, for AMSR-E with a spatial resolution of 0.25°. For AMSR2, the three sets were extracted from 2013, 2014, and 2016 with a spatial resolution of 0.1°, respectively. MODIS LST played the role of “ground truth” in the training, validation, and testing. The examination results show that CNN is better than NN and DBN by 0.1–0.4 K. Different combinations of input parameters were examined to get the best combinations for the daytime and nighttime conditions. The best combinations are the brightness temperatures (BTs), NDVI, air temperature, and day of the year (DOY) for the daytime and BTs and air temperature for the nighttime. By adding three and one easily obtained parameters on the basis of BTs, the accuracies of LST estimates can be improved by 0.8 K and 0.3 K for the daytime and nighttime conditions, respectively. Compared with the MODIS LST, the CNN LST estimates yielded root-mean-square differences (RMSDs) of 2.19–3.58 K for the daytime and 1.43–2.14 K for the nighttime for diverse land cover types for AMSR-E. Validation against the in-situ LSTs showed that the CNN LSTs yielded root-mean-square errors of 2.10–4.72 K for forest and cropland sites. Further intercomparison indicated that ~50% of the CNN LSTs were closer to the MODIS LSTs than ESA’s GlobTemperature AMSR-E LSTs, and the average RMSDs of the CNN LSTs were less than 3 K over dense vegetation compared to NASA’s global land parameter data record air temperatures. This study helps better the understanding of the use of neural networks for estimating LST from satellite MW observations.

scholarly journals Estimation of Climatologies of Average Monthly Air Temperature over Mongolia Using MODIS Land Surface Temperature (LST) Time Series and Machine Learning Techniques

2019 ◽  
Vol 11 (21) ◽  
pp. 2588 ◽  
Author(s):  
Otgonbayar ◽  
Atzberger ◽  
Mattiuzzi ◽  
Erdenedalai
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Spatial Resolution ◽  
Air Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Near Surface ◽  
Monthly Average ◽  
Modis Land Surface Temperature ◽  
Weather Stations

The objective of this research was to develop a robust statistical model to estimate climatologies (2002–2017) of monthly average near-surface air temperature (Ta) over Mongolia using Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) time series products and terrain parameters. Two regression models were analyzed in this study linking automatic weather station data (Ta) with Earth observation (EO) images: partial least squares (PLS) and random forest (RF). Both models were trained to predict Ta climatologies for each of the twelve months, using up to 17 variables as predictors. The models were applied to the entire land surface of Mongolia, the eighteenth largest but most sparsely populated country in the world. Twelve of the predictor variables were derived from the LST time series products of the Terra MODIS satellite. The LST MOD11A2 (collection 6) products provided thermal information at a spatial resolution of 1 km and with 8-day temporal resolution from 2002 to 2017. Three terrain variables, namely, elevation, slope, and aspect, were extracted using a Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM), and two variables describing the geographical location of weather stations were extracted from vector data. For training, a total of 8544 meteorological data points from 63 automatic weather stations were used covering the same period as MODIS LST products. The PLS regression resulted in a coefficient of determination (R2) between 0.74 and 0.87 and a root-mean-square error (RMSE) from 1.20 °C to 2.19 °C between measured and estimated monthly Ta. The non-linear RF regression yielded even more accurate results with R2 in the range from 0.82 to 0.95 and RMSE from 0.84 °C to 1.93 °C. Using RF, the two best modeled months were July and August and the two worst months were January and February. The four most predictive variables were day/nighttime LST, elevation, and latitude. Using the developed RF models, spatial maps of the monthly average Ta at a spatial resolution of 1 km were generated for Mongolia (~1566 × 106 km2). This spatial dataset might be useful for various environmental applications. The method is transparent and relatively easy to implement.

scholarly journals A Data Fusion Modeling Framework for Retrieval of Land Surface Temperature from Landsat-8 and MODIS Data

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4337
Author(s):  
Guohui Zhao ◽  
Yaonan Zhang ◽  
Junlei Tan ◽  
Cong Li ◽  
Yanrun Ren
Keyword(s):  
Data Fusion ◽  
Environmental Change ◽  
Surface Temperature ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Landsat 8 ◽  
Modeling Framework ◽  
The Mean

Land surface temperature (LST) is a critical state variable of land surface energy equilibrium and a key indicator of environmental change such as climate change, urban heat island, and freezing-thawing hazard. The high spatial and temporal resolution datasets are urgently needed for a variety of environmental change studies, especially in remote areas with few LST observation stations. MODIS and Landsat satellites have complementary characteristics in terms of spatial and temporal resolution for LST retrieval. To make full use of their respective advantages, this paper developed a pixel-based multi-spatial resolution adaptive fusion modeling framework (called pMSRAFM). As an instance of this framework, the data fusion model for joint retrieval of LST from Landsat-8 and MODIS data was implemented to generate the synthetic LST with Landsat-like spatial resolution and MODIS temporal information. The performance of pMSRAFM was tested and validated in the Heihe River Basin located in China. The results of six experiments showed that the fused LST was high similarity to the direct Landsat-derived LST with structural similarity index (SSIM) of 0.83 and the index of agreement (d) of 0.84. The range of SSIM was 0.65–0.88, the root mean square error (RMSE) yielded a range of 1.6–3.4 °C, and the averaged bias was 0.6 °C. Furthermore, the temporal information of MODIS LST was retained and optimized in the synthetic LST. The RMSE ranged from 0.7 °C to 1.5 °C with an average value of 1.1 °C. When compared with in situ LST observations, the mean absolute error and bias were reduced after fusion with the mean absolute bias of 1.3 °C. The validation results that fused LST possesses the spatial pattern of Landsat-derived LSTs and inherits most of the temporal properties of MODIS LSTs at the same time, so it can provide more accurate and credible information. Consequently, pMSRAFM can be served as a promising and practical fusion framework to prepare a high-quality LST spatiotemporal dataset for various applications in environment studies.

Spatial Hotspot Analysis of Bucharest’s Urban Heat Island (UHI) Using Modis Data

2018 ◽  
Vol 18 (1) ◽  
pp. 14-22 ◽  
Author(s):  
Georgiana Grigoraș ◽  
Bogdan Urițescu
Keyword(s):  
Surface Temperature ◽  
Air Temperature ◽  
Land Surface Temperature ◽  
Hot Spots ◽  
Land Surface ◽  
Hot Spot ◽  
Monitoring Network ◽  
Residential Areas ◽  
Hotspot Analysis ◽  
The City

Abstract The aim of the study is to find the relationship between the land surface temperature and air temperature and to determine the hot spots in the urban area of Bucharest, the capital of Romania. The analysis was based on images from both moderate-resolution imaging spectroradiometer (MODIS), located on both Terra and Aqua platforms, as well as on data recorded by the four automatic weather stations existing in the endowment of The National Air Quality Monitoring Network, from the summer of 2017. Correlation coefficients between land surface temperature and air temperature were higher at night (0.8-0.87) and slightly lower during the day (0.71-0.77). After the validation of satellite data with in-situ temperature measurements, the hot spots in the metropolitan area of Bucharest were identified using Getis-Ord spatial statistics analysis. It has been achieved that the “very hot” areas are grouped in the center of the city and along the main traffic streets and dense residential areas. During the day the "very hot spots” represent 33.2% of the city's surface, and during the night 31.6%. The area where the mentioned spots persist, falls into the "very hot spot" category both day and night, it represents 27.1% of the city’s surface and it is mainly represented by the city center.

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.

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