Remote Sensing Land Surface Temperature Research Articles

A Geostatistics‐Based Tool to Characterize Spatio‐Temporal Patterns of Remotely Sensed Land Surface Temperature Fields Over the Contiguous United States

AbstractSurface fluxes and states can recur and remain consistent across various spatial and temporal scales, forming space‐time patterns. Quantifying and understanding the observed patterns is desirable, as they provide information about the dynamics of the processes involved. This study introduces the empirical spatio‐temporal covariance function and a corresponding parametric covariance function as tools to identify and characterize spatio‐temporal patterns in remotely sensed fields. The method is demonstrated using 2 km hourly GOES‐16/17 land surface temperature (LST) data over the Contiguous United States by splitting the area into 1.0° × 1.0° domains. The summer day‐time LST ESTCFs for 2018 to 2022 are derived for each domain, and a parametric covariance model is fitted. Clustering analysis is applied to detect areas with similar spatio‐temporal LST patterns. Six main zones within CONUS are identified and characterized based on their variance and temporal and spatial characteristic length scales (i.e., scales for which the temperature variations are temporally and spatially related), respectively: (a) Eastern plains with 3 K2, ∼6 hr, and 0.15°, (b) Gulf of California with 60 K2, ∼8 hr, and 0.34°, (c) mountains and coasts transition 1 with 16 K2, ∼11 hr, and 0.25°, (d) central US, Midwest, and South cities with 5.5 K2, ∼8 hr, and ∼0.2°, (e) mountains and coasts transition 2 with ∼10 K2, ∼8 hr, and 0.2°, and (f) largest mountains and coastlines with ∼19 K2, ∼13 hr, and 0.3°. The tools introduced provide a pathway to formally identify and summarize the spatio‐temporal patterns observed in remotely sensed fields and relate those to more complex processes within the Soil‐Vegetation‐Atmosphere System.

High-precision estimation of pan-Arctic soil surface temperature from MODIS LST by incorporating multiple environment factors and monthly-based modeling

Global warming has shown an “Arctic amplification effect” in recent decades, leading to pronounced changes in pan-Arctic soil surface temperature (SST). SST plays a direct role in energy exchange between soil and atmosphere and serves as an indicator of the land–atmosphere energy balance. Remote sensing land surface temperature (LST) data is able to indicate near-surface temperature, but influences from environment factors, such as vegetation and snow, can introduce biases between LST and SST. In this study, the importances of five environment factors (vegetation, snow, surface soil composition, topography, and solar radiation) to monthly mean SST estimation from MODIS LST in pan-Arctic were analyzed. Then a method for pan-Arctic monthly mean SST estimation from MODIS LST by incorporating these environment factors and monthly-based modeling based on random forest (RF) algorithm was proposed. The results reveal that all the selected environment factors contribute to monthly-based modeling, with vegetation exerting the greatest importance from May to October and snow in March and April. The root mean square error (RMSE) of pan-Arctic monthly SST estimated by the proposed method from 2003 to 2022 ranges from 0.89 to 1.88 °C, which is a 42.95–––53.35 % reduction compared to the widely used season-based multivariate linear regression (MLR) models based solely on LST (RMSE between 1.56 and 4.03 °C). The accuracy is notably improved in areas with lower and no vegetation (grassy woodlands, grasslands, permanent wetlands, and barrens) in the cold season (September to the following April), and in higher vegetation (forests) areas in the warm season (May to August). The proposed method can contribute to producing high-precision monthly mean SST data from LST, estimating permafrost extent and active layer thickness, and understanding the land–atmosphere energy balance in pan-Arctic.

Analyzing effects of environmental indices on satellite remote sensing land surface temperature using spatial regression models

Analyzing effects of environmental indices on satellite remote sensing land surface temperature using spatial regression models

Validation of Remotely Sensed Land Surface Temperature at Lake Baikal’s Surroundings Using In Situ Observations

The accuracy of Land Surface Temperature (LST) products retrieved from satellite data in mountainous-coastal areas is not well understood. This study presents an analysis of the spatial and temporal variability of the differences between the LST and in situ observed air and surface temperatures (ISTs) for the southeastern slope of Lake Baikal’s surroundings. The IST was measured at 12 ground observation sites located on the southeastern macro-slope of the Primorskiy Ridge (Baikal, Russia) within an elevation range of 460–1656 m above sea level from 2009 to 2021. LST was estimated using 617 Landsat (7 and 8) images from 2009–2021, taking into account brightness temperature, surface emissivity and vegetation cover fraction. The comparison of the LST from satellite data and the IST from ground observation showed relatively high differences, which varied depending on the season and site type. A neural network was suggested and calibrated to improve the LST data. The corrected remote-sensed temperature was found to reproduce the IST very well, with mean differences of about 0.03 °C and linear correlation coefficients of 0.98 and 0.95 for the air and surface IST.

REMOTE SENSING ANALYSIS AND NUMERICAL MODELLING OF SURFACE TEMPERATURE ANOMALIES OVER PETROLEUM ACCUMULATIONS: A CASE STUDY OF THE ALBORZ OILFIELD, CENTRAL IRAN

Petroleum accumulations may coincide with either positive or negative temperature anomalies, which are conventionally detected using in situ temperature measurements made in shallow boreholes 1‐3 m deep. Data gathered in this way, however, can be sparse and costly, and may require intensive fieldwork over a long time period. This article explores the possibility of detecting thermal anomalies associated with petroleum entrapment using satellite‐derived land surface temperature data. For this aim, a robust correction scheme based on a physically‐based land surface model was applied to night‐time kinetic temperature data derived from NASA's ASTER instrument. The numerical model, known as SKinTES, attempts to simulate diurnal effects and to remove them from the measured temperature data to yield a residual temperature anomaly map. The performance of this methodology was tested over the Alborz oilfield located on an anticline of the same name in the Qom region of Central Iran. The study area has an arid to semi‐arid climate and the surface geology is dominated by outcrops of the Lower Miocene Upper Red Formation. The modelling approach used successfully highlighted several negative temperature anomalies over the oil‐bearing parts of the Alborz structure. In comparison to the uncorrected data, the anomalies were shown to be highly enhanced in both spatial and magnitude terms. In addition, time series analysis indicated that the temperature anomalies were consistent over time. The authenticity of the anomalies was confirmed by a suite of in situ temperature measurements made at shallow boreholes. In conclusion, a unifying framework is proposed to explain the occurrence of both negative and positive temperature anomalies over petroleum accumulations. The new modelling and correction scheme is expected to broaden the application of remote sensing land surface temperature data not only in petroleum exploration but also in other types of geothermic investigations including geothermal exploration.

Deep Interpolation of Remote Sensing Land Surface Temperature Data with Partial Convolutions.

Land Surface Temperature (LST) is an important resource for a variety of tasks. The data are mostly free of charge and combine high spatial and temporal resolution with reliable data collection over a historical timeframe. When remote sensing is used to provide LST data, such as the MODA11 product using information from the MODIS sensors attached to NASA satellites, data acquisition can be hindered by clouds or cloud shadows, occluding the sensors' view on different areas of the world. This makes it difficult to take full advantage of the high resolution of the data. A common solution to interpolating LST data is statistical interpolation methods, such as fitting polynomials or thin plate spine interpolation. These methods have difficulties in incorporating additional knowledge about the research area and learning local dependencies that can help with the interpolation process. We propose a novel approach to interpolating remote sensing LST data in a fixed research area considering local ground-site air temperature measurements. The two-step approach consists of learning the LST from air temperature measurements, where the ground-site weather stations are located, and interpolating the remaining missing values with partial convolutions within a U-Net deep learning architecture. Our approach improves the interpolation of LST for our research area by 44% in terms of RMSE, when compared to state-of-the-art statistical methods. Due to the use of air temperature, we can provide coverage of 100%, even when no valid LST measurements were available. The resulting gapless coverage of high resolution LST data will help unlock the full potential of remote sensing LST data.

Biophysical Impact of Multiple Surface Forcings on Land Surface Temperature Over Eastern China

AbstractOver the past decades, human activities have directly or indirectly driven the land surface changes in eastern China. These anthropogenic forcings could trigger biochemical feedback and alter the surface biophysical properties, thus affecting local temperature. However, the latter is recognized as the “noise” and ignored when assessing historical or future climate. Here, we adopt the “observation minus reanalysis” (OMR) method to isolate the biophysical temperature footprint of multiple surface changes in eastern China over 2001–2018, using remote sensing land surface temperature and reanalysis skin temperature. A spatial regression model was used to separate the contributions from different processes. We find the biophysical feedbacks of surface changes have an annual cooling effect of −0.072 K/decade in eastern China, and the contributions from urban expansion, agricultural development, and natural vegetation greening are 0.042, −0.042, and −0.072 K/decade, correspondingly. The Northeast Plain shows agricultural activities induced cooling of −0.040 K/decade; the Loess Plateau shows natural vegetation recovery dominated cooling of −0.145 K/decade; the Huang‐Huai‐Hai Plain demonstrates a predominant urbanization warming effect of 0.124 K/decade; the Middle‐lower Yangtze shows natural vegetation greening related cooling of −0.106 K/decade. Both the intensity of the land surface changes and the temperature sensitivities drive the large spatial variability of the temperature effect. Overall, the temperature effects of surface changes are spatially heterogeneous and show considerable magnitudes. We emphasize that vegetation changes in eastern China show a strong surface cooling effect, and may contribute to the regional climate mitigation in the context of global warming.

Prediction of apple first flowering date using daily land surface temperature spatio-temporal reconstruction and machine learning

The first flowering date (FFD) is a critical phenological parameter closely related to the apple yield, so the accurate prediction of the FFD is important for precise orchard production management. Existing methods to predict the FFD are mostly based on air temperature (Ta) measured at meteorological stations, but to great differences in meteorological variations and the ecological conditions, these methods cannot present the differences of FFD under complex meteorological conditions and provide spatially continuous FFD information at the level of a region. Therefore, we propose a method to predict spatially continuous apple FFD from remote sensing land surface temperature (LST) based on flowering prediction model. Firstly, the missing LST data were reconstructed by using spatio-temporal reconstruction (STR) approach developed. Next, new air temperature (NAT) data were generated by using the daily Ta estimation (DTE) model and the reconstructed LST. Finally, apple FFD was predicted by the NAT data and the apple flowering prediction model established based on random forest (RF) algorithm and the phenology sequential model, and the prediction accuracy was verified by comparison with the independently measured apple FFD. The LST reconstructed by using the STR approach has mean absolute error (MAE) ranging from 0.51 to 0.68 °C, and root mean square error (RMSE) ranging from 1.07 to 1.21 °C. The MAE between the NAT data and the High-Resolution Land Surface Data Assimilation System (HR-CLDAS) meteorological data ranges from 2.15 to 3.23 °C, and the RMSE ranges from 2.81 to 4.27 °C. In addition, the determination coefficient (R2) and RMSE between the predicted and measured FFD is 0.72 and 2.96 days, respectively. These results demonstrate that the developed method maximizes the potential of MODIS LST in predicting spatially continuous apple FFD, which is valuable for flower and fruit thinning, to defend against frost disasters, and in general for refined orchard production management.

Combining Spatial and Temporal Data to Create a Fine-Resolution Daily Urban Air Temperature Product from Remote Sensing Land Surface Temperature (LST) Data

Remotely sensed land surface temperature (LST) is often used as a proxy for air temperature in urban heat island studies, particularly to illustrate relative temperature differences between locations. Two sensors are used predominantly in the literature, Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS). However, each has shortcomings that currently limit its utility for many urban applications. Landsat has high spatial resolution but low temporal resolution, and may miss hot days, while MODIS has high temporal resolution but low spatial resolution, which is inadequate to represent the fine grain heterogeneity in cities. In this paper, we overcome this inadequacy by combining high spatial frequency Environmental Services (ES), Landsat-driven Normalized Difference Vegetation Index (NDVI), and MODIS low spatial frequency background LST at different spatial frequency bands (spatial spectral composition). The method is able to provide fine scale LST four times daily on any day of the year. Using data from Paris in 2019 we show that (1) daytime cooling by vegetation reaches a maximum of 30 °C, above which there is no further increase in cooling. In addition, (2) the cooling is relatively local and does not extend further than 200 m beyond the boundary of the NBS. This model can be used to quantify the benefits of NBS in providing cooling in cities.

Spatial and Temporal Assessment of Remotely Sensed Land Surface Temperature Variability in Afghanistan during 2000–2021

The dynamics of land surface temperature (LST) in Afghanistan in the period 2000–2021 were investigated, and the impact of the factors such as soil moisture, precipitation, and vegetation coverage on LST was assessed. The remotely sensed soil moisture data from Land Data Assimilation System (FLDAS), precipitation data from Climate Hazards Group Infra-Red Precipitation with Station (CHIRPS), and NDVI and LST from Moderate-Resolution Imaging Spectroradiometer (MODIS) were used. The correlations between these data were analyzed using the regression method. The result shows that the LST in Afghanistan has a slightly decreasing but insignificant trend during the study period (R = 0.2, p-value = 0.25), while vegetation coverage, precipitation, and soil moisture had an increasing trend. It was revealed that soil moisture has the highest impact on LST (R = −0.71, p-value = 0.0007), and the soil moisture, precipitation, and vegetation coverage explain almost 80% of spring (R2 = 0.73) and summer (R2 = 0.76) LST variability in Afghanistan. The LST variability analysis performed separately for Afghanistan’s river subbasins shows that the LST of the Amu Darya subbasin had an upward trend in the study period, while for the Kabul subbasin, the trend was downward.

Soil erosion as a factor of desertification of agrolandscapes in Ukraine

The description of the typical structure of agricultural landscapes of Ukraine and the most common degradation processes are given. Water and wind erosion are considered as one of the largest contributors to soil degradation in Ukraine, accompanied by declining soil fertility, moisture loss to surface runoff, air and surface water pollution, and degradation of small rivers. The sown areas of main crops for 1990–2020 are analysed per administrative oblast according to the State Statistical Service of Ukraine. A global long-term satellite remote sensing land surface temperature dataset (NOAA AVHRR) was used to analyse the dynamics of the average sum of effective temperatures for the vegetation season in 1982–2019. Sentinel-5P satellite data was used to analyse the spread and exposure of a large-scale dust storm in Polissya region in April 2020. As a result of climate change and economic factors, the area under corn and sunflower has been significantly increased. Due to the increased frequency of stormy rains and strong wind under climate change, the conditions for intensification of water and wind erosion in agricultural landscapes has been created. The local manifestation of wind erosion is typical for Polissya, mainly on overdried peat bogs and cohesive-sandy soils. But in the spring of 2020 a large-scale dust storm was observed for the first time on the territory of Ukrainian and Belarusian Polissya on the area of about 3.5 million hectares.The growing risk of soil erosion due to the climate change and current agricultural practices requires the improvement not only of the state land management system, but also the agri-environmental monitoring system, scientific methodical and information-advisory support of regional governments, landowners and land users. In order to implement state policy and coordinate the work on the rational use and protection of soils, combating their desertification and degradation, as well as adaptation of land use systems to climate change, it is proposed to establish the governing body «Monitoring, land management and soil protection» on the basis of existing specialized units of central and regional governments in the Ministry of Agrarian Policy and Food of Ukraine.

A Review of Reconstructing Remotely Sensed Land Surface Temperature under Cloudy Conditions

Land surface temperature (LST) is an important environmental parameter in climate change, urban heat islands, drought, public health, and other fields. Thermal infrared (TIR) remote sensing is the main method used to obtain LST information over large spatial scales. However, cloud cover results in many data gaps in remotely sensed LST datasets, greatly limiting their practical applications. Many studies have sought to fill these data gaps and reconstruct cloud-free LST datasets over the last few decades. This paper reviews the progress of LST reconstruction research. A bibliometric analysis is conducted to provide a brief overview of the papers published in this field. The existing reconstruction algorithms can be grouped into five categories: spatial gap-filling methods, temporal gap-filling methods, spatiotemporal gap-filling methods, multi-source fusion-based gap-filling methods, and surface energy balance-based gap-filling methods. The principles, advantages, and limitations of these methods are described and discussed. The applications of these methods are also outlined. In addition, the validation of filled LST values’ cloudy pixels is an important concern in LST reconstruction. The different validation methods applied for reconstructed LST datasets are also reviewed herein. Finally, prospects for future developments in LST reconstruction are provided.

Improving the Evapotranspiration Estimation under Cloudy Condition by Extending the Ts-VI Triangle Model

Evapotranspiration (ET) of soil-vegetation system is the main process of the water and energy exchange between the atmosphere and the land surface. Spatio-temporal continuous ET is vitally important to agriculture and ecological applications. Surface temperature and vegetation index (Ts-VI) triangle ET model based on remote sensing land surface temperature (LST) is widely used to monitor the land surface ET. However, a large number of missing data caused by the presence of clouds always reduces the availability of the main parameter LST, thus making the remote sensing-based ET estimation unavailable. In this paper, a method to improve the availability of ET estimates from Ts-VI model is proposed. Firstly, continuous LST product of the time series is obtained using a reconstruction algorithm, and then, the reconstructed LST is applied to the estimate ET using the Ts-VI model. The validation in the Heihe River Basin from 2009 to 2011 showed that the availability of ET estimates is improved from 25 days per year (d/yr) to 141 d/yr. Compared with the in situ data, a very good performance of the estimated ET is found with RMSE 1.23 mm/day and R2 0.6257 at point scale and RMSE 0.32 mm/day and R2 0.8556 at regional scale. This will improve the understanding of the water and energy exchange between the atmosphere and the land surface, especially under cloudy conditions.

Resolution Enhancement of Remotely Sensed Land Surface Temperature: Current Status and Perspectives

Remotely sensed land surface temperature (LST) distribution has played a valuable role in land surface processes studies from local to global scales. However, it is still difficult to acquire concurrently high spatiotemporal resolution LST data due to the trade-off between spatial and temporal resolutions in thermal remote sensing. To address this problem, various methods have been proposed to enhance the resolutions of LST data, and substantial progress in this field has been achieved in recent years. Therefore, this study reviewed the current status of resolution enhancement methods for LST data. First, three groups of enhancement methods—spatial resolution enhancement, temporal resolution enhancement, and simultaneous spatiotemporal resolution enhancement—were comprehensively investigated and analyzed. Then, the quality assessment strategies for LST resolution enhancement methods and their advantages and disadvantages were specifically discussed. Finally, key directions for future studies in this field were suggested, i.e., synergy between process-driven and data-driven methods, cross-comparison among different methods, and improvement in localization strategy.

Evapotranspiration estimates from an energy-water-balance model calibrated on satellite land surface temperature over the Heihe basin

A distributed hydrological energy-water-balance model (FEST-EWB) is calibrated over the Heihe Basin, a mainly desertic basin in China, employing remotely-sensed Land Surface Temperature (LST) (MODIS, 1-km resolution) as calibration variable. This approach overcomes the problem of model parameters characterization, which are usually difficult to define especially over large basins, allowing a pixel-by-pixel calibration, preserving the spatial heterogeneity. Hence, the spatial distribution of the modelled LST, but also of soil moisture (SM) and evapotranspiration (ET) is improved. The accuracy of the calibration process is documented through common statistical indexes. The modelled ET is compared locally against two eddy covariance stations in the agricultural area, while distributively against the ET estimates of the ETMonitor model and some global re-analysis products (ERA-Interim, GLDAS2, GLEAM and MERRA-2). Calibration and validation performed in this study prove that a considerable model accuracy is attainable even in extremely arid environments. An average LST bias of 2.6 °C is obtained over the basin. A good adaptation of FEST-EWB is also obtained against eddy-covariance stations ET with a little bias around −1 mm/d. On the other hand, the reanalysis products display a much worse performance, with higher absolute biases (around −3.5 mm/d), although with high variability among the models.

Detecting Tree Species Effects on Forest Canopy Temperatures with Thermal Remote Sensing: The Role of Spatial Resolution

Canopy temperatures are important for understanding tree physiology, ecology, and their cooling potential, which provides a valuable ecosystem service, especially in urban environments. Linkages between tree species composition in forest stands and air temperatures remain challenging to quantify, as the establishment and maintenance of onsite sensor networks is time-consuming and costly. Remotely-sensed land surface temperature (LST) observations can potentially acquire spatially distributed crown temperature data more efficiently. We analyzed how tree species modify canopy air temperature at an urban floodplain forest (Leipzig, Germany) site equipped with a detailed onsite sensor network, and explored whether mono-temporal thermal remote sensing observations (August, 2016) at different spatial scales could be used to model air temperatures at the tree crown level. Based on the sensor-network data, we found interspecific differences in summer air temperature to vary temporally and spatially, with mean differences between coldest and warmest tree species of 1 °C, and reaching maxima of up to 4 °C for the upper and lower canopy region. The detectability of species-specific differences in canopy surface temperature was found to be similarly feasible when comparing high-resolution airborne LST data to the airborne LST data aggregated to 30 m pixel size. To realize a spatial resolution of 30 m with regularly acquired data, we found the downscaling of Landsat 8 thermal data to be a valid alternative to airborne data, although detected between-species differences in surface temperature were less expressed. For the modeling of canopy air temperatures, all LST data up to the 30 m level were similarly appropriate. We thus conclude that satellite-derived LST products could be recommended for operational use to detect and monitor tree species effects on temperature regulation at the crown scale.

Minimum temperature mapping with spatial copula interpolation

Monitoring of variables like temperature, precipitation, and air quality is performed to determine their current situation, exhibit the presence of trends and occurrence of outliers. These variables are measured at specific locations and to obtain a full estimation map, we need to predict values at unknown locations. This study focuses on making a minimum air temperature map using copula interpolation with the spline family. Minimum temperature observations for January 2017, all months of 2017, and seasonal averages are analysed over the Euphrates Basin in Turkey. The minimum temperature observations have a high variability due to the varying topography of the area, ranging between -2 C∘ and +14 C∘ in whole of 2017. The interpolation methods incorporated the above mean sea level elevation map and remotely-sensed land surface temperature. We evaluated the accuracy of the predictions using ten-fold cross-validation and compare copula interpolation with External Drift Kriging (KED). The study shows that copulas provided more accurate predictions than KED for most of the months, and for the summer and autumn seasons, whereas KED produced high accurate predictions for January 2017. Results of this study indicate that copulas are able to detect variation in minimum temperatures accurately in areas where topography and observation values are highly variable. Further work will focus on copula-based space–time interpolation techniques for the minimum temperature mapping.

Exploring Changes in Land Surface Temperature Possibly Associated with Earthquake: Case of the April 2015 Nepal Mw 7.9 Earthquake.

Satellite thermal infrared remote sensing has received worldwide attention in the exploration for earthquake precursors; however, this method faces great controversy. Obtaining repeatable phenomena related to earthquakes is helpful to reduce this controversy. In this paper, a total of 15 or 17 years of Moderate-resolution Imaging Spectroradiometer (MODIS)/Aqua and MODIS/Terra satellite remote sensing land surface temperature (LST) products is selected to analyze the temperature changes before and after the Mw 7.9 earthquake in Nepal on 25 April 2015 and to explore possible thermal information associated with this earthquake. Major findings are given as follows: (1) from the time course, the temperature slowly cooled before the earthquake, reached a minimum at the time of the earthquake, and returned to normal after the earthquake. Since these changes were initiated before the earthquake, they may even have been precursors to the Nepal earthquake. (2) From the space distribution, the cooling areas correspond to the seismogenic structure during the earthquake. These cooling areas are distributed along the Himalayas and are approximately 1300 km long. The widths of the East and West sides are slightly different, with an average temperature decrease of 5.6 °C. For these cooling areas, the Western section is approximately 90 km wide and 500 km long; the East side is approximately 190 km wide and 800 km long. The Western side of the cooling strips appeared before the earthquake. In short, these kinds of spatial and temporal changes are tectonically related to the earthquake and may have been caused by the tectonic activity associated with the Nepal earthquake. This process began before the earthquake and therefore might even be potentially premonitory information associated with the Nepal earthquake.

A Statistical Analysis of the Remotely Sensed Land Surface Temperature–Vegetation Index Method for the Retrieval of Evaporative Fraction Over Grasslands in the Southern Great Plains

The triangle method based on the spatial relationship between remotely sensed (RS) land surface temperature and vegetation index (TVX) has been widely used for the estimation of evaporative fraction (EF). Combing moderate resolution imaging spectroradiometer and in situ EF observations, the physical constraint and theoretical accuracy of the TVX method is investigated in this paper through statistical analysis. In theory, the physical constraint of the TVX method is mathematically solvable by using EF measurements as inputs. However, our research suggests that nearly 90% of its solutions are meaningless to retrieve the spatial distribution of EF. Instead, we propose an optimization method to retrieve the spatial distribution of EF over a large heterogeneous area from a limited number of in situ EF observations. Results show that the new method using just one site for calibration has an accuracy comparable with those produced by the traditional TVX method. Its robustness is also demonstrated by its applicability under partially cloudy sky conditions. The accuracy of this optimization method increases with the number of sites used for calibration, but there is an upper limit. Besides, when all sites are used for calibration, the accuracy produced represents the upper limit of the accuracy of the TVX method. Thus, the proposed method also provides a perspective to evaluate the theoretical accuracy of RS-based EF models.

A genetic algorithm for identifying spatially-varying environmental drivers in a malaria time series model

Time series models of malaria cases can be applied to forecast epidemics and support proactive interventions. Mosquito life history and parasite development are sensitive to environmental factors such as temperature and precipitation, and these variables are often used as predictors in malaria models. However, malaria-environment relationships can vary with ecological and social context. We used a genetic algorithm to optimize a spatiotemporal malaria model by aggregating locations into clusters with similar environmental sensitivities. We tested the algorithm in the Amhara Region of Ethiopia using seven years of weekly Plasmodium falciparum data from 47 districts and remotely-sensed land surface temperature, precipitation, and spectral indices as predictors. The best model identified six clusters, and the districts in each cluster had distinctive responses to the environmental predictors. We conclude that spatial stratification can improve the fit of environmentally-driven disease models, and genetic algorithms provide a practical and effective approach for identifying these clusters.