scholarly journals A Machine Learning Based Downscaling Approach to Produce High Spatio-Temporal Resolution Land Surface Temperature of the Antarctic Dry Valleys from MODIS Data

2021 ◽  
Vol 13 (22) ◽  
pp. 4673
Author(s):  
Lilian-Maite Lezama Valdes ◽  
Marwan Katurji ◽  
Hanna Meyer

To monitor environmental and biological processes, Land Surface Temperature (LST) is a central variable, which is highly variable in space and time. This particularly applies to the Antarctic Dry Valleys, which host an ecosystem highly adapted to the extreme conditions in this cold desert. To predict possible climate induced changes on the Dry Valley ecosystem, high spatial and temporal resolution environmental variables are needed. Thus we enhanced the spatial resolution of the MODIS satellite LST product that is sensed sub-daily at a 1 km spatial resolution to a 30 m spatial resolution. We employed machine learning models that are trained using Landsat 8 thermal infrared data from 2013 to 2019 as a reference to predict LST at 30 m resolution. For the downscaling procedure, terrain derived variables and information on the soil type as well as the solar insolation were used as potential predictors in addition to MODIS LST. The trained model can be applied to all available MODIS scenes from 1999 onward to develop a 30 m resolution LST product of the Antarctic Dry Valleys. A spatio-temporal validation revealed an R2 of 0.78 and a RMSE of 3.32 ∘C. The downscaled LST will provide a valuable surface climate data set for various research applications, such as species distribution modeling, climate model evaluation, and the basis for the development of further relevant environmental information such as the surface moisture distribution.

2020 ◽  
Author(s):  
Maite Lezama Valdes ◽  
Marwan Katurji ◽  
Hanna Meyer

<p>Anthropogenic Climate Change is expected to take a toll on the Antarctic environment and its biodiversity, which is concentrated on the continent’s few ice-free areas, such as the McMurdo Dry Valleys (MDV). To model the current terrestrial habitat distribution and predict possible climate induced changes, high spatial and temporal resolution abiotic variables, especially land surface temperature (LST) and soil moisture are needed, but are currently unavailable.</p><p>The aim of this project is to fill this gap and create a high resolution LST dataset of the Antarctic Dry Valleys. This variable is acquired in a high temporal resolution (sub-daily) by the MODIS sensor aboard Terra and Aqua satellites. However, as LST varies greatly in space, the spatial resolution provided by this data source (1000 m) is too low to give a meaningful impression of LST and to study biodiversity patterns. Therefore, we use data from Landsat and ASTER sensors as a reference to downscale MODIS LST to a spatial resolution of 30 m. 7 year’s worth of satellite data as well as terrain-derived auxiliary variables went into the development of the model, which predicts 30 m LST for the Antarctic Dry Valleys. </p><p>To model complex relations between terrain, radiation, land cover and LST, machine learning models are used. Multiple algorithms (Random Forest, NN, SVM, Gradient Boosting) are compared to find the best approach for predicting high resolution LST based on MODIS data. Using the best performing model, a daily dataset is created that provides LST for the Antarctic Dry Valleys from 2002 on.</p>


Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4337
Author(s):  
Guohui Zhao ◽  
Yaonan Zhang ◽  
Junlei Tan ◽  
Cong Li ◽  
Yanrun Ren

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.


2020 ◽  
Author(s):  
Ferran Gascon ◽  
Anja Stromme ◽  
Michael Rast ◽  
Jens Nieke ◽  
Benjamin Koetz ◽  
...  

<p>The Copernicus EU program started in 1998 with the overarching aim to become Europe’s operational Earth Observation monitoring system providing data and information services. An essential part of the program is the Copernicus Space Component (CSC), which is managed by the European Space Agency (ESA) as responsible for the Copernicus Sentinels satellite constellations.</p><p>The presentation will include an overview of the CSC Optical Imaging Family (OIF) currently operated missions, namely Sentinel-2 and Sentinel-3, and candidate potential missions being developed, namely Copernicus Hyperspectral Imaging Mission for Environment (CHIME) and High Spatio-Temporal Resolution Land Surface Temperature Monitoring Mission (LSTM). The next generation missions are not included here.</p><p>Sentinel-2 is an Earth Observation mission developed by the European Space Agency (ESA) in the frame of the Copernicus program of the European Commission. The mission consists on a Multi-Spectral Instruments (MSI) on board a constellation of two satellites: Sentinel-2A launched in June 2015 and Sentinel-2B launched in March 2017. It covers the Earth’s land surfaces and coastal waters every five days under the same viewing conditions and every three days at mid-latitudes with high spatial resolution and a wide field of view.</p><p>5 day revisit (i.e. under same viewing conditions) is met at all latitudes of observations (not only at equator), and with the swath overlap and the S2 orbit repeat pattern (14+3/10 rev/day, i.e. a 3 day sub-cycle), 3 day geometric coverage is achieved at mid latitudes.</p><p>Sentinel-3 mission is measuring sea surface topography, sea and land surface temperature, and ocean and land surface colour with high accuracy and reliability to support ocean forecasting systems, environmental monitoring and climate monitoring. The Sentinel-3 mission is jointly operated by ESA and EUMETSAT to deliver operational ocean and land observation services.</p><p>CHIME, identified as one of the Copernicus Expansion High Priority Candidate Missions (HPCM), will provide routine observations through the Copernicus Programme for managing natural resources and assets in support of EU policy, and will complement currently flying multi-spectral missions such as Sentinel-2. Compared to multi-spectral missions, CHIME will have an increased number of narrow spectral bands (spectral resolution of 10nm with no gaps between bands) in the visible-to-shortwave infrared range (400-2500nm), which will allow for a more accurate determination of biochemical and biophysical variables.</p><p>LSTM, also identified as one of the HPCM, will provide enhanced measurements of land surface temperature with a focus responding to user requirements related to agricultural monitoring. High spatio-temporal resolution thermal infrared observations are considered fundamental to sustainable management natural resources in the context of water and food security of a global society. Operational land surface temperature (LST) measurements and derived evapotranspiration (ET) are key variables in understanding and responding to climate variability, managing water resources for agricultural production, predicting droughts but also addressing land degradation, natural hazards, coastal and inland water management as well as urban heat island issues.</p>


2021 ◽  
Vol 13 (11) ◽  
pp. 2211
Author(s):  
Shuo Xu ◽  
Jie Cheng ◽  
Quan Zhang

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.


2019 ◽  
Vol 221 ◽  
pp. 210-224 ◽  
Author(s):  
Temesgen Alemayehu Abera ◽  
Janne Heiskanen ◽  
Petri Pellikka ◽  
Miina Rautiainen ◽  
Eduardo Eiji Maeda

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