Composite Land Surface Temperature Research Articles

Prediction of MODIS land surface temperature using new hybrid models based on spatial interpolation techniques and deep learning models.

Land surface temperature (LST) prediction is of great importance for climate change, ecology, environmental and industrial studies. These studies require accurate LST map predictions considering both spatial and temporal dynamics. In this study, multilayer perceptron (MLP), long short-term memory (LSTM) and an integrated machine learning model, namely Convolutional LSTM (ConvLSTM), were utilized for one step ahead LST prediction. Data were gathered from 1-day (MYD11A1) and 8-day composite (MYD11A2) Moderate Resolution Imaging Spectroradiometer (MODIS) sensors, which have 1-km × 1-km spatial resolution. Considering the inability of MODIS sensors to provide LST data under cloudy conditions, Inverse DISTANCE WEIGHTING (IDW), natural neighbor (NN), and cubic spline (C) methods were used to overcome the missing pixel problem. The proposed methods were tested over the Northern part of Adana province, Turkey, and the performances of the models were quantitatively evaluated through performance measures, namely, Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The selected datasets range from 01 January 2017 to 01 November 2020 and from 01 January 2015 to 01 November 2020 for daily LST and 8-day composite LST, respectively. While 60% of the datasets were used as training set, the remaining 40% were used as validation (20%) and test (20%) sets. RMSE maps were generated to evaluate the pixelwise performance of the proposed method. On the other hand, the best average RMSE and MAE for the daily test set were obtained from the combination of ConvLSTM and NN (NN-ConvLSTM) as 3.62°C and 2.85°C, respectively, while they were acquired 3.57°C and 2.69oC from the combination of MLP and NN (NN-MLP) for the 8-day composite LST test set. The results revealed that the proposed hybrid models could be used for one step ahead spatiotemporal prediction of LST data.

Implications of Soil and Canopy Temperature Uncertainty in the Estimation of Surface Energy Fluxes Using TSEB2T and High-resolution Imagery in Commercial Vineyards.

Estimation of surface energy fluxes using thermal remote sensing-based energy balance models (e.g., TSEB2T) involves the use of local micrometeorological input data of air temperature, wind speed, and incoming solar radiation, as well as vegetation cover and accurate land surface temperature (LST). The physically based Two-source Energy Balance with a Dual Temperature (TSEB2T) model separates soil and canopy temperature (Ts and Tc) to estimate surface energy fluxes including Rn, H, LE, and G. The estimation of Ts and Tc components for the TSEB2T model relies on the linear relationship between the composite land surface temperature and a vegetation index, namely NDVI. While canopy and soil temperatures are controlling variables in the TSEB2T model, they are influenced by the NDVI threshold values, where the uncertainties in their estimation can degrade the accuracy of surface energy flux estimation. Therefore, in this research effort, the effect of uncertainty in Ts and Tc estimation on surface energy fluxes will be examined by applying a Monte Carlo simulation on NDVI thresholds used to define canopy and soil temperatures. The spatial information used is available from multispectral imagery acquired by the AggieAir sUAS Program at Utah State University over vineyards near Lodi, California as part of the ARS-USDA Agricultural Research Service's Grape Remote Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) project. The results indicate that LE is slightly sensitive to the uncertainty of NDVIs and NDVIc. The observed relative error of LE corresponding to NDVIs uncertainty was between -1% and 2%, while for NDVIc uncertainty, the relative error was between -2.2% and 1.2%. However, when the combined NDVIs and NDVIc uncertainties were used simultaneously, the domain of the observed relative error corresponding to the absolute values of |ΔLE| was between 0% and 4%.

Spatial Distribution of Land Surface Temperatures in Kuwait: Urban Heat and Cool Islands.

The global rise of urbanization has led to the formation of surface urban heat islands and surface urban cool islands. Urban heat islands have been shown to increase thermal discomfort, which increases heat stress and heat-related diseases. In Kuwait, a hyper-arid desert climate, most of the population lives in urban and suburban areas. In this study, we characterized the spatial distribution of land surface temperatures and investigated the presence of urban heat and cool effects in Kuwait. We used historical Moderate-Resolution Imaging Spectroradiometer (MODIS) Terra satellite 8-day composite land surface temperature (LST) from 2001 to 2017. We calculated the average LSTs of the urban/suburban governorates and compared them to the average LSTs of the rural and barren lands. We repeated the analysis for daytime and nighttime LST. During the day, the temperature difference (urban/suburban minus versus governorates) was −1.1 °C (95% CI; −1.2, −1.00, p < 0.001) indicating a daytime urban cool island. At night, the temperature difference (urban/suburban versus rural governorates) became 3.6 °C (95% CI; 3.5, 3.7, p < 0.001) indicating a nighttime urban heat island. In light of rising temperatures in Kuwait, this work can inform climate change adaptation efforts in the country including urban planning policies, but also has the potential to improve temperature exposure assessment for future population health studies.

An empirical comparison of interpolation methods for MODIS 8-day land surface temperature composites across the conterminous Unites States

Eight-day composite land surface temperature (LST) images from the Moderate Resolution Imaging Specroradiometer (MODIS) sensor are extensively utilized due to their limited number of invalid pixels and smaller file size, in comparison to daily products. Remaining invalid values (the majority caused by cloud coverage), however, still pose a challenge to researchers requiring continuous datasets. Although a number of interpolation methods have been employed, validation has been limited to provide comprehensive guidance. The goal of this analysis was to compare the performance of all methods previously used for 8-day MODIS LST images under a range of cloud cover conditions and in different seasons. These included two temporal interpolation methods: Linear Temporal and Harmonic Analysis of Time Series (HANTS); two spatial methods: Spline and Adaptive Window; and two spatiotemporal methods: Gradient and Weiss. The impact of topographic, land cover, and climatic factors on interpolation performance was also assessed. Methods were implemented on high quality test images with simulated cloud cover sampled from 101 by 101 pixel sites (1-km pixels) across the conterminous United States.These results provide strong evidence that spatial and spatiotemporal methods have a greater predictive capability than temporal methods, regardless of the time of day or season. This is true even under extremely high cloud cover (>80%). The Spline method performed best at low cloud cover (<30%) with median absolute errors (MAEs) ranging from 0.2 °C to 0.6 °C. The Weiss method generally performed best at greater cloud cover, with MAEs ranging from 0.3 °C to 1.2 °C. The regression analysis revealed spatial methods tend to perform worse in areas with steeper topographic slopes, temporal methods perform better in warmer climates, and spatiotemporal methods are influenced by both of these factors, to a lesser extent. Assessed covariates, however, explained a low portion of the overall variation in MAEs and did not appear to cause deviations from major interpolation trends at sites with extreme values. While it would be most effective to use the Weiss method for images with medium to high cloud cover, Spline could be applied under all circumstances for simplicity, considering that (i) images with <30% cloud cover represent the vast majority of 8-day LST images requiring interpolation, and (ii) Spline functions are readily available and easy to implement through several software packages. Applying a similar framework to interpolation methods for daily LST products would build on these findings and provide additional information to future researchers.

An operational method for the disaggregation of land surface temperature to estimate actual evapotranspiration in the arid region of Chile

Monitoring evapotranspiration in arid and semi-arid environments plays a key role in water irrigation scheduling for water use efficiency. This work presents an operational method for evapotranspiration retrievals based on disaggregated Land Surface Temperature (LST). The retrieved LSTs from Landsat-8 and MODIS data were merged in order to provide an 8-day composite LST product at 100×100m resolution. The method was tested in the arid region of Copiapó, Chile using data from years 2013–2014 and validated using data from years 2015–2016. In-situ measurements from agrometeorological stations such as air temperature and potential evapotranspiration (ET0) estimated at the location were used in the ET estimation method. The disaggregation method was developed by taking into account (1) the spatial relationship between Landsat-8 and MODIS LST, (2) the spatial relationship between LST and the Normalized Difference Vegetation Index (NDVI) at high spatial resolution (Landsat-8), and (3) the temporal variations along the year of both relationships aforementioned. The comparison between disaggregated LST at 100m resolution and in situ LST measurements presents a coefficient of determination (r2), in average, equal to 0.70 and a RMSE equal to 3.6K. The disaggregated LST was used in an operational model to estimate the actual evapotranspiration (ETa). The ETa shows good results in terms of seasonal variations and in comparison to the evapotranspiration estimated by using crop coefficients (kc). The comparison between remotely sensed and in situ ETa presents an overall r2 close to 0.67 and a RMSE equal to 0.6mmday−1 for both crops. These results are important for further improvements in water use sustainability in the Copiapó valley, which is currently affected by high water demand.

Crop water stress maps for an entire growing season from visible and thermal UAV imagery

Abstract. This study investigates whether a water deficit index (WDI) based on imagery from unmanned aerial vehicles (UAVs) can provide accurate crop water stress maps at different growth stages of barley and in differing weather situations. Data from both the early and late growing season are included to investigate whether the WDI has the unique potential to be applicable both when the land surface is partly composed of bare soil and when crops on the land surface are senescing. The WDI differs from the more commonly applied crop water stress index (CWSI) in that it uses both a spectral vegetation index (VI), to determine the degree of surface greenness, and the composite land surface temperature (LST) (not solely canopy temperature).Lightweight thermal and RGB (red–green–blue) cameras were mounted on a UAV on three occasions during the growing season 2014, and provided composite LST and color images, respectively. From the LST, maps of surface-air temperature differences were computed. From the color images, the normalized green–red difference index (NGRDI), constituting the indicator of surface greenness, was computed. Advantages of the WDI as an irrigation map, as compared with simpler maps of the surface-air temperature difference, are discussed, and the suitability of the NGRDI is assessed. Final WDI maps had a spatial resolution of 0.25 m.It was found that the UAV-based WDI is in agreement with measured stress values from an eddy covariance system. Further, the WDI is especially valuable in the late growing season because at this stage the remote sensing data represent crop water availability to a greater extent than they do in the early growing season, and because the WDI accounts for areas of ripe crops that no longer have the same need for irrigation. WDI maps can potentially serve as water stress maps, showing the farmer where irrigation is needed to ensure healthy growing plants, during entire growing season.

Application of remote sensing-based two-source energy balance model for mapping field surface fluxes with composite and component surface temperatures

Operational application of a remote sensing-based two source energy balance model (TSEB) to estimate evaportranspiration (ET) and the components evaporation (E), transpiration (T) at a range of space and time scales is very useful for managing water resources in arid and semiarid watersheds. The TSEB model uses composite land surface temperature as input and applies a simplified Priestley–Taylor formulation to partition this temperature into soil and vegetation component temperatures and then computes subsequent component energy fluxes. The remote sensing-based TSEB model using component temperatures of the soil and canopy has not been adequately evaluated due to a dearth of reliable observations. In this study, soil and vegetation component temperatures partitioned from visible and near infrared and thermal remote sensing data supplied by advanced scanning thermal emission and reflection radiometer (ASTER) are applied as model inputs (TSEBCT) to assess and refine the subsequent component energy fluxes estimation in TSEB scheme under heterogeneous land surface conditions in an advective environment. The model outputs including sensible heat flux (H), latent heat flux (LE), component LE from soil and canopy from the TSEBCT and original model (TSEBPT) are compared with ground measurements from eddy covariance (EC) and larger aperture scintillometers (LAS) technique, and stable isotopic method. Both model versions yield errors of about 10% with LE observations. However, the TSEBCT model output of H and LE are in closer agreement with the observations and is found to be generally more robust in component flux estimation compared to the TSEBPT using the ASTER data in this heterogeneous advective environment. Thus given accurate soil and canopy temperatures, TSEBCT may provide more reliable estimates of plant water use and values of water use efficiency at large scales for water resource management in arid and semiarid landscapes.

Developing an Effective Model for Predicting Spatially and Temporally Continuous Stream Temperatures from Remotely Sensed Land Surface Temperatures

Although water temperature is important to stream biota, it is difficult to collect in a spatially and temporally continuous fashion. We used remotely-sensed Land Surface Temperature (LST) data to estimate mean daily stream temperature for every confluence-to-confluence reach in the John Day River, OR, USA for a ten year period. Models were built at three spatial scales: site-specific, subwatershed, and basin-wide. Model quality was assessed using jackknife and cross-validation. Model metrics for linear regressions of the predicted vs. observed data across all sites and years: site-specific r2 = 0.95, Root Mean Squared Error (RMSE) = 1.25 °C; subwatershed r2 = 0.88, RMSE = 2.02 °C; and basin-wide r2 = 0.87, RMSE = 2.12 °C. Similar analyses were conducted using 2012 eight-day composite LST and eight-day mean stream temperature in five watersheds in the interior Columbia River basin. Mean model metrics across all basins: r2 = 0.91, RMSE = 1.29 °C. Sensitivity analyses indicated accurate basin-wide models can be parameterized using data from as few as four temperature logger sites. This approach generates robust estimates of stream temperature through time for broad spatial regions for which there is only spatially and temporally patchy observational data, and may be useful for managers and researchers interested in stream biota.

A satellite-based disturbance index algorithm for monitoring mitigation strategies effects on desertification change in an arid environment

This research focuses on monitoring the desertification change as a result of mitigation and adaptation strategies in arid environmental condition. Exploring environmental hazards, specifically desertification development, is important for understanding loss of productivity in dry lands. Developing a new satellite-based algorithm for monitoring desertification in an arid environment delivers information useful in protecting the environment and mitigating natural hazards. A multi-temporal remote sensing data of MODerate resolution Imaging Spectroradiometer (MODIS) were used for estimating the Soil-Adjusted Vegetation Index (SAVI) and Land Surface Temperature (LST), based on monthly data during the years 2002, 2005, 2008 and 2011. The MODIS-based disturbance index (MBDI) was improved by estimating the long-term variation in the ratio of annual maximum composite LST and SAVI on a pixel-by-pixel basis. A significant correlation (r = −0.88; P < 0.001) was found between the mean-maximum SAVI and mean-maximum LST in the dry season. The response of the MBDI to land degradation was assessed by comparing the obtained soil salinity data to the algorithm outcomes. The results showed that the proposed new satellite-based algorithm has a high potential to detect the spatial extent of prime land degradation in an arid environment. Also, this algorithm was able to recognize the difference between the natural variability and instantaneous/non-instantaneous desertification symptoms in an arid environment. The mitigation strategies in the case study decreased the desertification development and combat the land degradation in the last decade.

Testing a MODIS Global Disturbance Index across North America

Large-scale ecosystem disturbances (LSEDs) have major impacts on the global carbon cycle as large pulses of CO 2 and other trace gases from terrestrial biomass loss are emitted to the atmosphere during disturbance events. The high temporal and spatial variability of the atmospheric emissions combined with the lack of a proven methodology to monitor LSEDs at the global scale make the timing, location and extent of vegetation disturbance a significant uncertainty in understanding the global carbon cycle. The MODIS Global Disturbance Index (MGDI) algorithm is designed for large-scale, regular, disturbance mapping using Aqua/Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) and Aqua/MODIS Enhanced Vegetation Index (EVI) data. The MGDI uses annual maximum composite LST data to detect fundamental changes in land-surface energy partitioning, while avoiding the high natural variability associated with tracking LST at daily, weekly, or seasonal time frames. Here we apply the full Aqua/MODIS dataset through 2006 to the improved MGDI algorithm across the woody ecosystems of North America and test the algorithm by comparison with confirmed, historical wildfire events and the windfall areas of documented major hurricanes. The MGDI accurately detects the location and extent of wildfire throughout North America and detects high and moderate severity impacts in the windfall area of major hurricanes. We also find detections associated with clear-cut logging and land-clearing on the forest–agricultural interface. The MGDI indicates that 1.5% (195,580 km 2) of the woody ecosystems within North America was disturbed in 2005 and 0.5% (67,451 km 2) was disturbed in 2006. The interannual variability is supported by wildfire detections and official burned area statistics.

Dynamic drought monitoring in Guangxi using revised temperature vegetation dryness index

Moderate resolution imaging spectroradiometer (MODIS) data are very suitable for vast extent, long term and dynamic drought monitoring for its high temporal resolution, high spectral resolution and moderate spatial resolution. The composite Enhanced Vegetation Index (EVI) and composite land surface temperature (T s ) obtained from MODIS data MOD11A2 and MOD13A2 were used to construct the EVI-T s space. And Temperature Vegetation Dryness Index (TVDI) was calculated to evaluate the agriculture drought in Guangxi province, China in October of 2006. The results showed that the drought area in Guangxi was evidently increasing and continuously deteriorating from the middle of September to the middle of November. The TVDI, coming from the EVI-T s space, could effectively indicate the spatial distribution and temporal evolution of drought, so that it could provide a strong technical support for the forecasting agricultural drought in south China.

A NEW SATELLITE-BASED METHODOLOGY FOR CONTINENTAL-SCALE DISTURBANCE DETECTION

The timing, location, and magnitude of major disturbance events are currently major uncertainties in the global carbon cycle. Accurate information on the location, spatial extent, and duration of disturbance at the continental scale is needed to evaluate the ecosystem impacts of land cover changes due to wildfire, insect epidemics, flooding, climate change, and human-triggered land use. This paper describes an algorithm developed to serve as an automated, economical, systematic disturbance detection index for global application using Moderate Resolution Imaging Spectroradiometer (MODIS)/Aqua Land Surface Temperature (LST) and Terra/MODIS Enhanced Vegetation Index (EVI) data from 2003 to 2004. The algorithm is based on the consistent radiometric relationship between LST and EVI computed on a pixel-by-pixel basis. We used annual maximum composite LST data to detect fundamental changes in land-surface energy partitioning, while avoiding the high natural variability associated with tracking LST at daily, weekly, or seasonal time frames. Verification of potential disturbance events from our algorithm was carried out by demonstration of close association with independently confirmed, well-documented historical wildfire events throughout the study domain. We also examined the response of the disturbance index to irrigation by comparing a heavily irrigated poplar tree farm to the adjacent semiarid vegetation. Anomalous disturbance results were further examined by association with precipitation variability across areas of the study domain known for large interannual vegetation variability. The results illustrate that our algorithm is capable of detecting the location and spatial extent of wildfire with precision, is sensitive to the incremental process of recovery of disturbed landscapes, and shows strong sensitivity to irrigation. Disturbance detection in areas with high interannual variability of precipitation will benefit from a multiyear data set to better separate natural variability from true disturbance.