Night Land Surface Temperature Research Articles

The contrasting trend of global urbanization-induced impacts on day and night land surface temperature from a time-series perspective

Understanding the warming effect of the impervious surface area (ISA) quantitively is crucial to alleviate the urban heat island effect. However, the quantitative analyses of urbanization-induced land surface temperature (LST) change globally remain to be determined during the global warming hiatus. Here, we first characterized the response of LST to ISA (i.e., δLST) in 369 global cities using MODIS LST product (1 km) and the global artificial impervious area dataset (30 m). We then investigated the spatiotemporal changes of δLST and its associated driving factors, including enhanced vegetation index (1 km), albedo (ALB, 500 m), population (100 m), air temperature (AT, 0.1°), and precipitation (0.1°). The analysis was conducted using linear regression models from 2000 to 2010 at the pixel level. The results illustrate that (1) the daytime δLST in urban areas shows a negative trend with –0.2971 °C/% and –0.0870 °C/% in arid and temperate regions, and the nighttime δLSTs were 0.0938 °C/% and 0.0048 °C/%, respectively. In tropical regions, the δLST remains positive throughout the day, while it exhibits an opposite trend in snow regions. (2) The magnitude of nighttime δLST is stronger compared to daytime globally, which is more significant in arid regions with the largest ΔδLST values (i.e., diurnal variations) in urban (0.3909 °C/%) and rural (0.3262 °C/%) areas. (3) In temperate regions, the response of ALB to ISA (δALB, negative effect) and the response of AT to ISA (δAT, positive effect) are dominant factors in regulating δLST during daytime and nighttime, respectively However, in arid regions, the daytime and nighttime δLSTs are negatively correlated by urban population and vegetation, respectively. These findings provide a quantitative understanding on long-term LST variations and driving factors behind at the pixel level, suggesting that urban greening and increasing surface albedo are effective strategies to mitigate the urbanization-induced surface warming.

Soil organic carbon stocks in native forest of Argentina: a useful surrogate for mitigation and conservation planning under climate variability

BackgroundThe nationally determined contribution (NDC) presented by Argentina within the framework of the Paris Agreement is aligned with the decisions made in the context of the United Nations Framework Convention on Climate Change (UNFCCC) on the reduction of emissions derived from deforestation and forest degradation, as well as forest carbon conservation (REDD+). In addition, climate change constitutes one of the greatest threats to forest biodiversity and ecosystem services. However, the soil organic carbon (SOC) stocks of native forests have not been incorporated into the Forest Reference Emission Levels calculations and for conservation planning under climate variability due to a lack of information. The objectives of this study were: (i) to model SOC stocks to 30 cm of native forests at a national scale using climatic, topographic and vegetation as predictor variables, and (ii) to relate SOC stocks with spatial–temporal remotely sensed indices to determine biodiversity conservation concerns due to threats from high inter-annual climate variability.MethodsWe used 1040 forest soil samples (0–30 cm) to generate spatially explicit estimates of SOC native forests in Argentina at a spatial resolution of approximately 200 m. We selected 52 potential predictive environmental covariates, which represent key factors for the spatial distribution of SOC. All covariate maps were uploaded to the Google Earth Engine cloud-based computing platform for subsequent modelling. To determine the biodiversity threats from high inter-annual climate variability, we employed the spatial–temporal satellite-derived indices based on Enhanced Vegetation Index (EVI) and land surface temperature (LST) images from Landsat imagery.ResultsSOC model (0–30 cm depth) prediction accounted for 69% of the variation of this soil property across the whole native forest coverage in Argentina. Total mean SOC stock reached 2.81 Pg C (2.71–2.84 Pg C with a probability of 90%) for a total area of 460,790 km2, where Chaco forests represented 58.4% of total SOC stored, followed by Andean Patagonian forests (16.7%) and Espinal forests (10.0%). SOC stock model was fitted as a function of regional climate, which greatly influenced forest ecosystems, including precipitation (annual mean precipitation and precipitation of warmest quarter) and temperature (day land surface temperature, seasonality, maximum temperature of warmest month, month of maximum temperature, night land surface temperature, and monthly minimum temperature). Biodiversity was influenced by the SOC levels and the forest regions.ConclusionsIn the framework of the Kyoto Protocol and REDD+, information derived in the present work from the estimate of SOC in native forests can be incorporated into the annual National Inventory Report of Argentina to assist forest management proposals. It also gives insight into how native forests can be more resilient to reduce the impact of biodiversity loss.

Spatio-temporal analysis of the urban green infrastructure of the city of Granada (Spain) as a heat mitigation measure using high-resolution images Sentinel 3

At present, and motivated by a substantial growth of the population, a considerable expansion of urban areas is taking place through the modification of land uses. These changes, together with global warming and extreme weather events, produce increases in the temperature of the earth's surface and a deterioration of the environment that affects people's quality of life. The green areas of cities are upheld as one of the best for adapting to such phenomena, since they help lower outdoor temperatures. In this research, using high-resolution Sentinel 3 satellite images and the TsHARP algorithm, the Land Surface Temperature (LST) and the Park Cool Island (PCI) were obtained at a resolution of 10 m over green areas in the city of Granada. The objective was to analyze the relationship between surface, PCI effect and cooling distance. In turn, for each of the eight green areas studied, the following variables were taken into account and included in a statistical analysis known as data panel: normalized difference vegetation index, vegetal proportion, sky view factor, landscape shape index, model digital elevation, wind and solar radiation. Our results report diurnal LST decreases of 1 K and night LST of 0.6 K in green areas as compared to urban areas. There is moreover a correlation between the size of the green areas, the decrease in temperature they generate, and distance of the minimizer effect.

A simple method for the estimation of minimum and maximum air temperature monthly mean maps using MODIS images in the region of Murcia, Spain

Air temperature records are acquired by networks of weather stations which may be several kilometres apart. In complex topographies the representativeness of a meteorological station may be diminished in relation to a flatter valley, and the nearest station may have no relation to a place located near it. The present study shows a simple method to estimate the spatial distribution of minimum and maximum air temperatures from MODIS land surface temperature (LST) and normalized difference vegetation index (NDVI) images. Indeed, there is a strong correlation between MODIS day and night LST products and air temperature records from meteorological stations, which is obtained by using geographically weighted regression equations, and reliable results are found. Then, the results allow to spatially interpolate the coefficients of the local regressions using altitude and NDVI as descriptor variables, to obtain maps of the whole region for minimum and maximum air temperature. Most of the meteorological stations show air temperature estimates that do not have significant differences compared to the measured values. The results showed that the regression coefficients for the selected locations are strong for the correlations between minimum temperature with LSTnight (R2 = 0.69–0.82) and maximum temperature with LSTday (R2 = 0.70–0.87) at the 47 stations. The root mean square errors (RMSE) of the statistical models are 1.0 °C and 0.8 °C for night and daytime temperatures, respectively. Furthermore, the association between each pair of data is significant at the 95% level (p<0.01).

Satellite based detection of pre-earthquake thermal anomaly, co-seismic deformation and source parameter modelling of past earthquakes

The presented work brings satellite-based detection of pre-earthquake transient thermal anomaly and coseismic deformation mechanism using InSAR (SAR Interferometry) observations of the two major earthquakes that occurred recently (the Iran-Iraq border earthquake of Nov. 12, 2017 (Mw 7.3) and the New Mirpur, Pakistan earthquake of 24th Sept. 2019 (Mw 5.4)). Near Iran-Iraq border earthquake, a rise of 4–6 ​°C from the normal temperature in both day and night Land Surface Temperature (LST) maps along the SW part of the fault is observed. In case of the New Mirpur, Pakistan earthquake, a rise of 3–5 ​°C from normal temperature in both day and night LST maps around the SSW and NW side of the epicenter is observed. Further, Sentinel-1A microwave satellite data employing InSAR technique have been used to estimate co-seismic ground deformations and fault geometry and slip distribution of both earthquakes. The results show that both events occurred due to blind reverse fault mechanism dipping NE with shallow dip. The weightage of strike slip component and dip slip component is more or less equal to each other in total slip distribution for Iran-Iraq border earthquake. Similarly, New Mirpur, Pakistan earthquake have more strike slip components than dip slip components in the weightage in total slip distribution. Considerable number of aftershocks have also occurred in the regions as estimated by coulomb static stress along the SW side of the fault geometry of Iran-Iraq border earthquake and for New Mirpur, Pakistan earthquake, along the SSW, NW, and east side of the fault geometry. The correlation between high stress regions before and after the occurrence of earthquakes using thermal anomaly map and Coulomb static stress map has been also observed.

Effects of COVID-19 Restriction Policies on Urban Heat Islands in Some European Cities: Berlin, London, Paris, Madrid, and Frankfurt.

The present study investigates the effects of policies restricting human activities during the COVID-19 epidemic on the characteristics of Night Land Surface Temperature (NLST) and Night Urban Heat Islands (NUHI) in five major European cities. In fact, the focus of this study was to explore the role of anthropogenic factors in the formation and intensity of NUHI. The effect of such factors was uncontrollable before the COVID-19 outbreak on the global scale and in a real non-laboratory environment. In this study, two indices, the concentration of Nitrogen dioxide (NO2) and Nighttime Lights (NL), were used as indicators of the number of anthropogenic activities. The data were collected before the COVID-19 outbreak and after its prevalence in 2019–2020. A Paired samples t-test and a Pearson correlation were used to examine the differences or significant relationships between the variables and indicators studied throughout the two periods. The results of the study confirmed a direct and significant relationship between NO2 and NL indices and the NUHI and NLST variables; however, using strict restrictions during the COVID-19 pandemic, the NO2 and NL indices decreased seriously, leading to significant changes in the characteristics of the NUHI and NLST in the five cities. This study has some implications for urban planners and politicians, e.g., the environmental impacts of changing the nature and level of anthropogenic activities can greatly affect the pattern and intensity of the Urban Heat Islands (UHIs) (as a serious environmental challenge).

Spatiotemporal Evolution and Relationship between Night Time Light and Land Surface Temperature: A Case Study of Beijing, China

Rapid urbanization has triggered significant changes in urban land surface temperature (LST), which in turn affects urban ecosystems and the health of residents. Therefore, exploring the interrelationship between urban development and LST can help optimize the urban thermal environment and promote sustainable development. Based on remote sensing data from 2004–2019 within the sixth ring road of Beijing, this study investigates the spatiotemporal coupling law of night time light (NTL) and LST using an overall coupling model and analyzes the degree of coordination between them using a coordination model. The spatial response law between them was also analyzed using standard deviation ellipses and bivariate spatial autocorrelation. The results show that, from the perspective of spatiotemporal evolution, the spatial distributions of NTL and LST within the sixth ring road of Beijing were closely related from 2004 to 2019, although the overall coupling of NTL and LST was initially decreased and then continuously increased. From the perspective of coordination types, the main types of coordination between NTL and LST deteriorated over time. The increase in LST lagged behind NTL from 2004 to 2009 (heating hysteresis type), while LST increased ahead of NTL from 2014 to 2019 (heating advance type). This suggests that urban development became less efficient, while LST increases became more significant. In terms of correlation, NTL and LST showed significant positive correlation and spatial positive correlation; the correlation coefficient first decreased significantly and then continued to increase. From 2004 to 2009, the temperature increase caused by urbanization was suppressed due to the 2008 Beijing Olympics and related ecological protection policies, resulting in a significant decrease in the correlation coefficient between NTL and LST. From 2009 to 2019, short-term measures taken by Beijing during the Olympic Games were no longer effective, and the opposition between urban development and related policies made the policies increasingly less effective, thereby increasing the correlation coefficient between NTL and LST, and the increase in LST was more significant. This will greatly affect the urban ecological environment and residents’ health and make the previous government investment to suppress the temperature increase all in vain. This study can provide theoretical and practical support for the development of thermal environment optimization schemes and LST mitigation strategies in Beijing and other cities.

Role of eco-climatic factors in the distribution of bluetongue in endemic areas in Tunisia.

Background:Bluetongue (BT) is an important infectious, non-contagious, OIE-listed viral disease of domestic and wild ruminants. The disease is transmitted among susceptible animals by a few species of an insect vector in the genus Culicoides. Recently, during the fall of 2020 (September and October), a Bluetongue virus-4 epizootic marked the epidemiological situation in several delegations of Tunisia with clinical cases recorded in sheep and cattle.Aim:Determine the eco-climatic variables most likely associated with delegations reporting BT cases.Methods:A logistic regression model (LRM) was used to examine which eco-climatic variables were most likely associated with delegations reporting BT cases.Results:Based on the LRM, our findings demonstrated that the key factors contributing significantly to BT cases’ distribution among delegations in Tunisia included day land surface temperatures (DLST), night land surface temperatures (NLST) and normalized difference vegetation index (NDVI). A positive correlation between sheep distribution and rainfall amounts was demonstrated. Statistical analysis focusing on the most affected delegations during the BT epidemic (the Sahel and the Centre of Tunisia) demonstrated that the epidemic situation seems to be a consequence of the combination of the following environmental parameters: NDVI with values ranging between 0.16 and 0.2, moderate rainfall 2–4-fold above the normal (10–50 mm) and DLST values between 32°C and 34°C in September.Conclusion:These findings suggest and develop a robust and efficient early warning surveillance program in risk areas based on eco-climatic risk factors.

CONTRIBUTION INDEX OF LAND COVER AND LAND SURFACE TEMPERATURE CHANGES IN UPPER HILL NAIROBI, KENYA

Abstract. Urban heat island is the difference in thermal temperature between rural and urban areas. The urbanization process alters the material type with impervious surfaces being absorbers of incoming radiation during the day and emitting it at night. The research involved the use of time-series satellite imagery from Sentinel, Landsat, ASTER and MODIS for the period 1986, 1995, 2000, 2005, 2011, 2015 and 2017 over the Upper Hill, Nairobi. Morning, afternoon and night land surface temperatures (LST) were calculated for each of these years and analyzed together with the land cover. The mean albedo was calculated to determine the relationship between each land cover and mean LST. The contribution index was calculated to determine whether a land contributed positively or negatively to the mean LST in Upper Hill. Results indicated that built-up land cover had increased from 1986 to 2017 by 0.86% per annum while forest land cover had decreased by 0.99% per annum. Sparse grassland had higher albedo and LST values of 0.81 and 27.9 °C respectively, whereas water had lower albedo and LST values of 0.09 and 25.1 °C. Water had the lowest mean LST during the day but highest mean LST in the afternoon and night in each of the years due to its high thermal capacity. Bare ground tends to have a higher contribution index compared to other land covers, while forest land cover has a negative contribution index, indicating the impact land cover types have on LST and the urban heat island effect.

Spatiotemporal and physiographic relationship between MODIS land surface temperature and air temperature over Iran

Land cover and topography influence land surface temperature (LST). The purpose of this study was to determine whether there is a correlation between Moderate Resolution Imaging Spectroradiometer (MODIS) LST and air temperature (AT) under various physiographic conditions in Iran. A novel method was developed to determine the match-up time between the LST of the Terra/Aqua overpass and the AT observation at an appropriate distance. Numerous statistical metrics were used, including the correlation coefficient (R), root mean square differences (RMSD), and relative root mean square differences (RRMSD). The findings indicate that LST and AT have a strong correlation coefficient (0.7-0.9) in Iran. Intra-annual changes in R, on the other hand, show higher values during the cold months than during the warmer months. The differences between LST and AT were lowest at the coasts and highest in the central and northwestern deserts of Iran according to the spatial distribution of the RRMSD. The RRMSD for MODIS Terra night LST (LSTTN) was lower than for MODIS Aqua day LST (LSTAD). Seasonal differences between LST and AT were observed, particularly at higher elevations and slopes. The most important factors that result in a higher correlation and a smaller difference between LST and AT are surface moisture, vegetation density, and land cover dynamics (LCD). The LST/AT correlation and difference can also be affected by the daily and intra-annual temperature cycle. The findings can be applied to further studies, e.g. when estimating AT from LST in deserts and at high elevations.

Land Surface Temperature Anomalies Detection for the Strong Earthquakes in 2018

Earthquake every year leads to human and material losses and unpredictability of it by now makes this natural disaster worsen. The objective of the current study was to determine the anomalies in land surface temperature (LST) in areas affected by earthquakes. In this research, three earthquakes (M >6) were studied. Moderate Resolution Imaging Spectroradiometer Aqua and Terra day and night LST data used from 2003 to 2018. The interquartile range (IQR) and mean ± 2σ methods utilized to select anomalies. As a result, based on the IQR method, no prior and after anomaly detected in selected cases and data. Based on mean ± 2σ, usually positive anomaly occurred during daytime. However, negative (or positive) anomaly occurred during the nighttime before the Mexico and Bolivia earthquakes. During 10 days after the earthquake, sometimes a negative anomaly detected.

A comparison between day and night land surface temperatures using acquired satellite thermal infrared data in a winter wheat field

Land surface temperature (LST) plays a crucial role in many scientific applications, including climatology, hydrology, ecology, and agriculture. Multiple studies have successfully utilized daytime thermal infrared (TIR) data obtained from satellite platforms to retrieve LST data over agricultural lands for irrigation planning and monitoring plant health status. Yet, to our knowledge, the retrieval of nighttime LST over agricultural lands has not been investigated using high-resolution satellite TIR data. This study aims to examine the spatial variation of LST over a winter wheat crop field by using day and night-acquired satellite TIR data from Landsat-8 and ASTER L1T, respectively. In parallel with the LST data, we calculated spectral indices related to vegetation health and greenness (NDVI) and water-related indices (NDWI) from multi-source satellite platforms (Landsat-8 and Sentinel-2). Also, we calculated canopy biophysical and biochemical properties such as leaf area index (LAI) and canopy water content (CWC) from Sentinel-2 data. We assessed which spectral indices and canopy properties have a strong correlation with LST during the day and night using the Pearson correlation coefficient and ANOVA analysis. Results demonstrated significant differences (p < 0.05) between day and night LST over the winter wheat field. We also found that calculated spectral indices have a weak negative correlation with the daytime LST and a moderate positive relationship with the night LST, particularly the water-related index (NDWI) and canopy biophysical and biochemical properties (LAI and CWC). The results of this study demonstrate the enormous potential of night satellite TIR data in retrieving LST data, with positive implications for agricultural practices, particularly for irrigation and plant health monitoring.

Comparing different spatio-temporal modeling methods in dengue fever data analysis in Colombia during 2012–2015

In this paper, we compare a variety of spatio-temporal conditional autoregressive models to a dengue fever dataset in Colombia, and incorporate an innovative data transformation method in the data analysis. In order to gain a better understanding on the effects of different niche variables in the epidemiological process, we explore Poisson-lognormal and binomial models with different Bayesian spatio-temporal modeling methods in this paper. Our results show that the selected model can well capture the variations of the data. The population density, elevation, daytime and night land surface temperatures are among the contributory variables to identify potential dengue outbreak regions; precipitation and vegetation variables are not significant in the selected spatio-temporal mixed effects model. The generated dengue fever probability maps from the model show a geographic distribution of risk that apparently coincides with the elevation gradient. The results in the paper provide the most benefits for future work in dengue studies.

A comparative analysis of day and night land surface temperature in two semi-arid cities using satellite images sampled in different seasons

Urban heat island (UHI) refers to the occurrence of very high temperature patches in urban areas with respect to surrounding rural areas. The behaviour of Land Surface Temperature (LST) with different land covers varies for day and night time in a given urban area. Therefore, an attempt has been made for the comparative analysis of the day and night LST in two semi-arid cities in India i.e. Ahmedabad and Gandhinagar using two Landsat 8 images (one for day and one for night time) for each city. The relations of day and night LST with three independent variables such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI) and impervious surface fraction (ISF) were established using Landsat 8 data. Based on the relation of LST with indices, the combinations of indices were used for developing the models to improve the correlation of day and night LST with the land cover. The developed models improved the correlation of day and night LST with the combinations of indices as compared to the relation of LST with individual variables in semi-arid urban landscapes with reasonably good coefficient of determination (R2) values of 0.63 in Ahmedabad and 0.79 in Gandhinagar for day time. The reasonably good values of R2 were found to be 0.81 and 0.75 for Ahmedabad and Gandhinagar, respectively, for night time. The developed model was also evaluated using 12 MODIS satellite images i.e. six images of each city for the summer, post-monsoon and winter seasons (one for day and one for night time in each season). The results of the developed models showed high capability to explain LST in heterogeneous semi-arid urban area for both day and night time in the summer, post-monsoon and winter seasons in a year. However, the relations established in this study can further be investigated for other months and years to understand its significance for other dates in semi-arid urban areas.

Extrapolation of daily air temperatures of Egypt from MODIS LST data

The limited and poor spatial distribution of meteorological stations is inadequate to reconstruct accurate regional air temperature maps, which are important for environmental planning and resources management. This study aimed to generate a gridded dataset of max/min daily surface air temperatures for Egypt based on remotely sensed data acquired from the MODIS land surface temperature (LST) product. Linear regression analysis was performed between observed air temperatures from ground weather stations and LST records from satellite images. Regression coefficients were as high as 0.8 between min air temperature and night LST data, compared to 0.77 for max air temperature and day LST. Statistical analysis reveals significant correlations (p < .01) for both regression relationships. MODIS data showed a satisfactory performance in producing regional monthly and annual as well as diurnal surface air temperatures maps for Egypt. MODIS data could also highlight the influence of topography, vegetation and lithology on air temperatures.

Evaluating Multivariable Statistical Methods for Downscaling Nighttime Land Surface Temperature in Urban Areas

For research and practice in fields such as the environment and meteorology, nighttime land surface temperature (LST) images at fine resolution provide important basic data. Since only a few satellite sensors can take fine-resolution thermal infrared images at night, such images are rather scarce. Downscaling of coarse-resolution LST images is a potential method for obtaining high-resolution LST images. However, downscaling methods have been mostly proposed for daytime LST images. This study aimed to evaluate the performance of methods that combine multiple explanatory variables and machine learning algorithms for downscaling coarse-resolution nighttime LST images in urban areas. Verification showed that the errors in the downscaling results were acceptable (mean absolute errors within 2 K). The resulting images could depict the spatial pattern of night LST in the study area in great detail. It was demonstrated that visible-near infrared images taken in the daytime could be used for downscaling of nighttime LST images, the rationality of which was deduced. It was also demonstrated that the performance of the spectral explanatory variables in nighttime LST downscaling was not lower than that of the mechanism explanatory variables. This method has high application value in many academic and practical efforts, such as land-atmosphere interface radiation budget studies, suitability assessments of human settlement, and urban environmental planning.

Pattern and Trend of Night Land Surface Temperature in Africa

This study aims to identify patterns and trends of the night land surface temperature over eight day period from 2000 to 2014 in Africa using statistical analysis. Data were obtained from the United States National Aeronautics and Space Administration satellite, comprising 99 locations of 5° by 5° latitude and longitude grid-boxes between latitudes 35° north and south of the equator and longitudes 20° west to 50° east. First, the variation in the night surface temperatures was removed. Then, the trend of seasonally adjusted night temperatures was estimated using linear regression. The correlations between adjoining regions were considered by using factor analysis to classify the temperatures into four regions. Cubic spline models were fitted to the data within these regions to investigate patterns of the temperatures. The result showed that temperatures in most regions of Africa increased. The temperatures decreased was observed in southern Africa and parts of central and eastern Africa.

Estimation of monthly surface air temperatures from MODIS LST time series data: application to the deserts in the Sultanate of Oman.

Air temperature records in remote deserts and inaccessible mountainous regions rely upon data acquired from the nearest meteorological stations, which could be at tens of kilometers apart. The present study provides a reliable approach to extract air temperatures for any distant region using thermal data of satellite images. The study postulates that if there is a strong correlation between land surface temperatures (LST) from satellite images and air temperature records from ground meteorological stations, hence, air temperatures (day/night) could be directly extrapolated from regression equations with high confidence results. Data utilized in this study were obtained from 12 meteorological stations settled and distributed upon different physiographic units of Oman. Satellite images were acquired from the Moderate Resolution Imaging Spectroradiometer (MODIS) LST product. Regression analysis of max and min air temperatures from weather stations was conducted versus day and night LST from MODIS Aqua LST (MYD11A2) images. Results showed that the regression coefficients for the selected locations are strong for the night/min (R2 = 0.81-0.94) and day/max (R2 = 0.72-0.92) correlations of the 12 stations. The root mean square errors (RMSE) of the statistical models are 0.97 and 1.98 for the night and day temperatures, respectively. Moreover, the association between each pair of the data is significant at the 99% level (p < 0.01). As MODIS data cover large geographic extents, it was possible to produce national diurnal and annual air temperature maps of accurate records with considering the variation of the physiographic setting.

Evaluation of the MODIS collections 5 and 6 for change analysis of vegetation and land surface temperature dynamics in North and South America

The latest collection (C6) of MODIS data provides several algorithmic improvements and calibration adjustments that correct for sensor degradation, theoretically making the C6 MODIS products more accurate compared to previous collections. C6 adjustments also introduce several improvements in the vegetation index (VI) retrieval algorithms. With these improvements, we expect only minor differences between data from Terra and Aqua, but significantly different results between C5 and C6. In this paper, we investigate three different MODIS products to determine the extent that improvements made to C6 influence the overall trend results for time series between 2001 and 2017. We focus on these three products specifically, both to allow for a comparison of vegetation index products—NDVI and EVI from MOD13C1, and NDVI and EVI calculated based on surface reflectance from MCD43C4—and also to gain an understanding of the improvements on an entirely different product from the same sensor, namely Land Surface Temperature (LST) from MOD11C2. For the MCD43C4 dataset, we find that 17.9% and 16.4% of EVI and NDVI pixels, respectively, display trend discordance between C5 and C6. For the MOD13C1 vegetation indices, we found comparable rates of trend discordance between C5 and C6: 18.5% and 17.4% for the EVI and NDVI pixels, respectively. For both products the greatest changes between C5 and C6 are an overall increase in pixels exhibiting a significant greening trend and an overall decline in pixels exhibiting a significant browning trend. Moreover, the largest differences between C5 and C6 for the NDVI and EVI data appear in cropland areas and in regions with relatively little human influence. In the Land Surface Temperature product (MOD11C2), the discordance between C5 and C6 is much lower: only 3.2% of day and 5.0% of night LST trends exhibited discordance between C5 and C6. We analyze the complementary results of vegetation index and land surface temperature trends and demonstrate that combining the results from different products observed at different portions of the electromagnetic spectrum—but linked through the biogeophysical processes of surface energy balance—allows us to portray change with more confidence than when relying on vegetation index data alone.

Interpolation of the Mean Anomalies for Cloud Filling in Land Surface Temperature and Normalized Difference Vegetation Index

When monitoring time series of remote sensing data, it is advisable to fill gaps, i.e., missing or distorted data, caused by atmospheric effects or technical failures. In this paper, a new method for filling these gaps called interpolation of the mean anomalies (IMA) is proposed and compared with some competitors. The method consists of: 1) defining a neighborhood for the target image from previous and subsequent images across previous and subsequent years; 2) computing the mean target image of the neighborhood; 3) estimating the anomalies in the target image by subtracting the mean image from the target image; 4) filtering the anomalies; 5) averaging the anomalies over a predefined window; 6) interpolating the averaged anomalies; and 7) adding the interpolated anomalies to the mean image. To assess the performance of the IMA method, both a real example and a simulation study are conducted with a time series of Moderate Resolution Imaging Spectroradiometer (MODIS) TERRA and MODIS AQUA images captured over the region of Navarre (Spain) from 2011 to 2013. We analyze the land surface temperature (LST) day and night, and the normalized difference vegetation index (NDVI). In the simulation study, seven sizes of artificial clouds are randomly introduced to each image in the studied time series. The square root of the mean-squared prediction error (RMSE) between the original and the filled data is chosen as an indicator of the goodness of fit. The results show that the IMA method outperforms Timesat, Hants, and Gapfill (GF) in filling small, moderate, and big cloud gaps in both the day and night LST and NDVI data, reaching RMSE reductions of up to 23%.