Spatial interpolation of climatic variables using land surface temperature and modified inverse distance weighting

Özelkan, Emre; Bagis, Serdar; Özelkan, Ertunga C.; Ustundag, Burak Berk; Yücel, Meriç; Örmeci, Cankut

doi:10.1080/01431161.2015.1007248

Cited by 55 publications

(22 citation statements)

References 49 publications

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“…However, such stations cannot provide sufficient spatial coverage due to their sparse distribution [12]. Therefore, to maintain the spatial continuity of LST data from these stations, various geostatistical interpolation approaches, such as kriging interpolation and inverse distance weighting (IDW) modified by digital elevation model (DEM) data, have been applied [13].…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Spatiotemporal Change in Land Surface Temperature for a Long-Term Sequence in Africa (2003–2017)

et al. 2020

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It is very important to understand the temporal and spatial variations of land surface temperature (LST) in Africa to determine the effects of temperature on agricultural production. Although thermal infrared remote sensing technology can quickly obtain surface temperature information, it is greatly affected by clouds and rainfall. To obtain a complete and continuous dataset on the spatiotemporal variations in LST in Africa, a reconstruction model based on the moderate resolution imaging spectroradiometer (MODIS) LST time series and ground station data was built to refactor the LST dataset (2003–2017). The first step in the reconstruction model is to filter low-quality LST pixels contaminated by clouds and then fill the pixels using observation data from ground weather stations. Then, the missing pixels are interpolated using the inverse distance weighting (IDW) method. The evaluation shows that the accuracy between reconstructed LST and ground station data is high (root mean square er–ror (RMSE) = 0.84 °C, mean absolute error (MAE) = 0.75 °C and correlation coefficient (R) = 0.91). The spatiotemporal analysis of the LST indicates that the change in the annual average LST from 2003–2017 was weak and the warming trend in Africa was remarkably uneven. Geographically, “the warming is more pronounced in the north and the west than in the south and the east”. The most significant warming occurred near the equatorial region in South Africa (slope > 0.05, R > 0.61, p < 0.05) and the central (slope = 0.08, R = 0.89, p < 0.05) regions, and a nonsignificant decreasing trend occurred in Botswana. Additionally, the mid-north region (north of Chad, north of Niger and south of Algeria) became colder (slope > −0.07, R = 0.9, p < 0.05), with a nonsignificant trend. Seasonally, significant warming was more pronounced in winter, mostly in the west, especially in Mauritania (slope > 0.09, R > 0.9, p < 0.5). The response of the different types of surface to the surface temperature has shown variability at different times, which provides important information to understand the effects of temperature changes on crop yields, which is critical for the planning of agricultural farming systems in Africa.

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Section: Introductionmentioning

confidence: 99%

Analysis of the Spatiotemporal Change in Land Surface Temperature for a Long-Term Sequence in Africa (2003–2017)

et al. 2020

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“…Inverse distance weighted interpolation estimates unknown precipitation values by calculation of weightings in relation to distance from observed values, and incorporates a power function selected by the user prior to calculation (Chen and Liu, 2012; Keblouti et al, 2012; Ly et al, 2013). A weighting factor of 2 was used in this study given its adoption elsewhere (e.g., Ozelkan et al, 2015) and also after inspection of plots of RMSE against weighting factor. All plots of RMSE against weighting factor showed a steep decline in RMSE between 1 and 2 weight factors and sometimes gave the lowest RMSE values at higher weight factors, but the latter tended to give outputs that were more jagged and irregular in shape than lower weightings.…”

Section: Methodsmentioning

confidence: 99%

Projection of future precipitation extremes across the Bangkok Metropolitan Region

Cooper

2019

Heliyon

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There is a pressing need to develop local-scale climate projection profiles for supporting climate impact assessments. This study contributes plausible future precipitation scenarios for the Bangkok Metropolitan Region (BMR), which builds on the existing evidence base that projects increasing future precipitation. Meteorological data sets from 16 stations located within the BMR and nearby provinces were used for bias correcting five regional climate model scenarios, and future extreme indices were graphed and spatially interpolated to interpret how precipitation extremes may develop to the end of the 21st century. Results indicate that over the coming century, total annual rainfall will increase, with the volume and number of days with heavy/very heavy rainfall also increasing. Total monthly and monthly heavy/very heavy rainfall are projected to increase in the late monsoon, and monthly five-day cumulative and one-day maxima project higher amounts of late monsoonal rains. Spatial interpolation of selected indices indicate substantial projected increases in extreme rainfall across the BMR, with its northern part receiving the heaviest amounts of precipitation. In comparison to the past period (1980–2009), over the long-term (2070–2098) the total monthly heavy/very heavy precipitation during October is projected to increase by 100–120% over Pathum Thani province and 80–100% over the remainder of the BMR. Together with the study's associated R and Python scripts, this study aims to provide an open and reproducible approach to deriving plausible future projections of climate variables at the city scale.

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“…The ground-measured air temperatures were spatially interpolated using a GIS with conventional spatial statistics techniques such as kriging, inverse distance weighting (IDW), spline, and so on for expanding the estimation to the polygon level. Although the development of GIS and spatial statistics have led to a drastic refinement in the interpolated result, there is a severe weakness due to the limited number of the points [60,72].…”

Section: Utility Of Modis Images As a Tool For Phenology Researchmentioning

confidence: 99%

Monitoring for Changes in Spring Phenology at Both Temporal and Spatial Scales Based on MODIS LST Data in South Korea

et al. 2020

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This study aims to monitor spatiotemporal changes of spring phenology using the green-up start dates based on the accumulated growing degree days (AGDD) and the enhanced vegetation index (EVI), which were deducted from moderate resolution imaging spectroradiometer (MODIS) land surface temperature (LST) data. The green-up start dates were extracted from the MODIS-derived AGDD and EVI for 30 Mongolian oak (Quercus mongolica Fisch.) stands throughout South Korea. The relationship between green-up day of year needed to reach the AGDD threshold (DoYAGDD) and air temperature was closely maintained in data in both MODIS image interpretation and from 93 meteorological stations. Leaf green-up dates of Mongolian oak based on the AGDD threshold obtained from the records measured at five meteorological stations during the last century showed the same trend as the result of cherry observed visibly. Extrapolating the results, the spring onset of Mongolian oak and cherry has become earlier (14.5 ± 4.3 and 10.7 ± 3.6 days, respectively) with the rise of air temperature over the last century. The temperature in urban areas was consistently higher than that in the forest and the rural areas and the result was reflected on the vegetation phenology. Our study expanded the scale of the study on spring vegetation phenology spatiotemporally by combining satellite images with meteorological data. We expect our findings could be used to predict long-term changes in ecosystems due to climate change.

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Spatial interpolation of climatic variables using land surface temperature and modified inverse distance weighting

Cited by 55 publications

References 49 publications

Analysis of the Spatiotemporal Change in Land Surface Temperature for a Long-Term Sequence in Africa (2003–2017)

Analysis of the Spatiotemporal Change in Land Surface Temperature for a Long-Term Sequence in Africa (2003–2017)

Projection of future precipitation extremes across the Bangkok Metropolitan Region

Monitoring for Changes in Spring Phenology at Both Temporal and Spatial Scales Based on MODIS LST Data in South Korea

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