Development of a methodology to fill gaps in MODIS LST data for Antarctica

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesLand Surface Temperature (LST) is an essential parameter for analyzing many environmental questions. Lack of high spatio-temporal resolution of LST data in Antarctica limits the understanding of climatological, ecological processes. The MODIS LST product is a promising source that provides daily LST data at 1 km spatial resolution, but MODIS LST data have gaps due to cloud cover. This research developed a method to fill those gaps with user-defined options to balance processing time and accuracy of MODIS LST data. The presented method combined temporal and spatial interpolation, using the nearest MODIS Aqua/Terra scene for temporal interpolation, Generalized Additive Model (GAM) using 3-dimensional spatial trend surface, elevation, and aspect as covariates. The moving window size controls the number of filled pixels and the prediction accuracy in the temporal interpolation. A large moving window filled more pixels with less accuracy but improved the overall accuracy of the method. The developed method's performance validated and compared to Local Weighted Regression (LWR) using 14 images and Thin Plate Spline (TPS) interpolation by filling different sizes of artificial gaps 3%, 10%, and 25% of valid pixels. The developed method performed better with a low percentage of cloud cover by RMSE ranged between 0.72 to 1.70 but tended to have a higher RMSE with a high percentage of cloud cover

Towards an operational model for estimating day and night instantaneous near-surface air temperature for urban heat island studies: outline and assessment

Near-surface air temperature (NSAT) is key for assessing urban heat islands, human health, and well-being. However, a widely recognized and cost- and time-effective replicable approach for estimating hourly NSAT is still urgent. In this study, we outline and validate an easy-to-replicate, yet effective, operational model, for automating the estimation of high-resolution day and night instantaneous NSAT. The model is tested on a heat wave event and for a large geographical area. The model combines remotely sensed land surface temperature and digital elevation model, with air temperature from local fixed weather station networks. Achieved NSAT has daily and hourly frequency consistent with MODIS revisiting time. A geographically weighted regression method is employed, with exponential weighting found to be highly accurate for our purpose. A robust assessment of different methods, at different time slots, both day- and night-time, and during a heatwave event, is provided based on a cross-validation protocol. Four-time periods are modelled and tested, for two consecutive days, i.e. 31st of July 2020 at 10:40 and 21:50, and 1st of August 2020 at 02:00 and 13:10 local time. High R2 was found for all time slots, ranging from 0.82 to 0.88, with a bias close to 0, RMSE ranging from 1.45 °C to 1.77 °C, and MAE from 1.15 °C to 1.36 °C. Normalized RMSE and MAE are roughly 0.05 to 0.08. Overall, if compared to other recognized regression models, higher effectiveness is allowed also in terms of spatial autocorrelation of residuals, as well as in terms of model sensitivity

High-resolution grids of daily air temperature for Peru - the new PISCOt v1.2 dataset

Gridded high-resolution climate datasets are increasingly important for a wide range of modelling applications. Here we present PISCOt (v1.2), a novel high spatial resolution (0.01°) dataset of daily air temperature for entire Peru (1981–2020). The dataset development involves four main steps: (i) quality control; (ii) gap-filling; (iii) homogenisation of weather stations, and (iv) spatial interpolation using additional data, a revised calculation sequence and an enhanced version control. This improved methodological framework enables capturing complex spatial variability of maximum and minimum air temperature at a more accurate scale compared to other existing datasets (e.g. PISCOt v1.1, ERA5-Land, TerraClimate, CHIRTS). PISCOt performs well with mean absolute errors of 1.4 °C and 1.2 °C for maximum and minimum air temperature, respectively. For the first time, PISCOt v1.2 adequately captures complex climatology at high spatiotemporal resolution and therefore provides a substantial improvement for numerous applications at local-regional level. This is particularly useful in view of data scarcity and urgently needed model-based decision making for climate change, water balance and ecosystem assessment studies in Peru

Comparison of Spatial Interpolation and Regression Analysis Models for an Estimation of Monthly Near Surface Air Temperature in China

Near surface air temperature (NSAT) is a primary descriptor of terrestrial environmental conditions. In recent decades, many efforts have been made to develop various methods for obtaining spatially continuous NSAT from gauge or station observations. This study compared three spatial interpolation (i.e., Kriging, Spline, and Inversion Distance Weighting (IDW)) and two regression analysis (i.e., Multiple Linear Regression (MLR) and Geographically Weighted Regression (GWR)) models for predicting monthly minimum, mean, and maximum NSAT in China, a domain with a large area, complex topography, and highly variable station density. This was conducted for a period of 12 months of 2010. The accuracy of the GWR model is better than the MLR model with an improvement of about 3 °C in the Root Mean Squared Error (RMSE), which indicates that the GWR model is more suitable for predicting monthly NSAT than the MLR model over a large scale. For three spatial interpolation models, the RMSEs of the predicted monthly NSAT are greater in the warmer months, and the mean RMSEs of the predicted monthly mean NSAT for 12 months in 2010 are 1.56 °C for the Kriging model, 1.74 °C for the IDW model, and 2.39 °C for the Spline model, respectively. The GWR model is better than the Kriging model in the warmer months, while the Kriging model is superior to the GWR model in the colder months. The total precision of the GWR model is slightly higher than the Kriging model. The assessment result indicated that the higher standard deviation and the lower mean of NSAT from sample data would be associated with a better performance of predicting monthly NSAT using spatial interpolation models

Integrating in-situ data with satellite-derived products to assess surface-groundwater interactions and sustainability of groundwater resources in semi-arid environment

In arid and semi-arid regions, water scarcity is nowadays a primary challenge, because of continuously increasing spatio-temporal rainfall variability and high evapotranspiration, both implying a decline of freshwater resources. Moreover, it is expected that this problem will worsen with the ongoing climate change. It is therefore important an in-depth investigations of the spatio-temporal surface-groundwater (SW-GW) interactions and sustainability of groundwater resources, which can be optimally realized through application of an integrated hydrological models (IHMs). This study proposes an approach to integrate satellite-derived products with in-situ measurements to assess the spatio-temporal SW-GW interactions and sustainability of groundwater resources in the data scarce Zamra catchment (ZC), northern Ethiopia. The research approach consists of four study chapters (chapter2-5). Chapter 2 focusses on the integration of daily satellite rainfall with in-situ rainfall. The study demonstrated that the Geographically Weighted Regression approach, could substantially reduce the daily biases between satellite and in-situ rainfall products in topographically complex areas, indicating further validation and improvement can be achieved by increasing in-situ gauge network and eventually considering more accuracy-effective explanatory variables. Chapter 3 focusses on the spatio-temporal estimate of interception loss (EI), characterized by various land use types and the overall estimated EI demonstrated high spatial and temporal variability, ranging on annual basis from zero at bare lands to 30% in forested areas. Chapter 4 focusses on derivation of PET as the product of RS-based, FAO-Penman-Monteith ETo and NDVI-based land use land cover factor (Kc). The NDVI-based Kc demonstrated high spatio-temporal variability, ranging from ~0.15 (bare land) to ~1.4 (forest), which resulted in higher PET values than the bias-corrected RS-based ETo in locations where Kc >1 and vice versa (i.e. in locations where Kc <1), which underlined substantial difference between ETo and PET. Chapter 5 focusses on IHM assessment of the spatio-temporal variability of SW-GW interactions and on sustainability of groundwater resources in the hydrologically complex ZC. RS approaches to estimate driving forces (Chapter 2-4) were applied as inputs of the MODFLOW 6 IHM (MOD6-IHM). The calibrated MOD6-IHM, showed high spatio-temporal water fluxes variability in the ZC, largely influenced by high spatio-temporal rainfall variability, in which the only source of water input. Throughout the MOD6-IHM solution, that rainfall (P) was partitioned into the two dominant sinks, evapotranspiration (ET=53.2% of P) and stream outflow (q= 46% of P). As the net recharge (1.6% of P) constrains groundwater resources sustainability, the issue of sustainability of ZC groundwater resources is crucial considering their future utilization for agricultural purposes