Estimation of the near-surface air temperature and soil moisture from satellites and numerical modelling in New Zealand

• Main Author: Sohrabinia, Mohammad
• Language: en
• Published: University of Canterbury. Geography 2013
• Subjects: MODIS LST; remote sensing; air temperature; soil moisture; numerical modelling; WRF; AMSR-E; ATI; Canterbury
• Online Access: http://hdl.handle.net/10092/8707

Satellite observations provide information on land surface processes over a large spatial extent with a frequency dependent on the satellite revisit time. These observations are not subject to the spatial limitations of the traditional point measurements and are usually collected in a global scale. With a reasonable spatial resolution and temporal frequency, the Moderate Resolution Imaging Spectroradiometer (MODIS) is one of these satellite sensors which enables the study of land-atmospheric interactions and estimation of climate variables for over a decade from remotely sensed data. This research investigated the potential of remotely sensed land surface temperature (LST) data from MODIS for air temperature (Ta) and soil moisture (SM) estimation in New Zealand and how the satellite derived parameters relate to the numerical model simulations and the in-situ ground measurements. Additionally, passive microwave SM product from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) was applied in this research. As the first step, the MODIS LST product was validated using ground measurements at two test-sites as reference. Quality of the MODIS LST product was compared with the numerical simulations from the Weather Research and Forecasting (WRF) model. Results from the first validation site, which was located in the alpine areas of the South Island, showed that the MODIS LST has less agreement with the in-situ measurements than the WRF model simulations. It turned out that the MODIS LST is subject to sources of error, such as the effects of topography and variability in atmospheric effects over alpine areas and needs a careful pre-processing for cloud effects and outliers. On the other hand, results from the second validation site, which was located on the flat lands of the Canterbury Plains, showed significantly higher agreement with the ground truth data. Therefore, ground measurements at this site were used as the main reference data for the accuracy assessment of Ta and SM estimates. Using the MODIS LST product, Ta was estimated over a period of 10 years at several sites across New Zealand. The main question in this part of the thesis was whether to use LST series from a single MODIS pixel or the series of a spatially averaged value from multiple pixels for Ta estimation. It was found that the LST series from a single pixel can be used to model Ta with an accuracy of about ±1 ºC. The modelled Ta in this way showed r ≈ 0.80 correlation with the in-situ measurements. The Ta estimation accuracy improved to about ±0.5 ºC and the correlation to r ≈ 0.85 when LST series from spatially averaged values over a window of 9x9 to 25x25 pixels were applied. It was discussed that these improvements are due to noise reduction in the spatially averaged LST series. By comparison of LST diurnal trends from MODIS with Ta diurnal trends from hourly measurements in a weather station, it was shown that the MODIS LST has a better agreement with Ta measurements at certain times of the day with changes over day and night. After estimation of Ta, the MODIS LST was applied to derive the near-surface SM using two Apparent Thermal Inertia (ATI) functions. The objective was to find out if more daily LST observations can provide a better SM derivation. It was also aimed to identify the potential of a land-atmospheric coupled model for filling the gaps in derived SM, which were due to cloud cover. The in-situ SM measurements and rainfall data from six stations were used for validation of SM derived from the two ATI functions and simulated by the WRF model. It was shown that the ATI function based on four LST observations has a better ability to derive SM temporal profiles and is better able to detect rainfall effects. Finally, the MODIS LST was applied for spatial and temporal adjustment of the near-surface SM product from AMSR-E passive microwave observations over the South Island of New Zealand. It was shown that the adjustment technique improves AMSR-E seasonal trends and leads to a better matching with rainfall events. Additionally, a clear seasonal variability was observed in the adjusted AMSR-E SM in the spatial domain. Findings of this thesis showed that the satellite observed LST has the potential for the estimation of the land surface variables, such as the near-surface Ta and SM. This potential is greatly important on remote and alpine areas where regular measurements from weather stations are not often available. According to the results from the first validation site, however, the MODIS LST needs a careful pre-processing on those areas. The concluding chapter included a discussion of the limitations of remotely sensed data due to cloud cover, dense vegetation and rugged topography. It was concluded that the satellite observed LST has the potential for SM and Ta estimations in New Zealand. It was also found that a land-atmospheric model (such as the WRF coupled with the Noah and surface model) can be applied for filling the gaps due to cloud cover in remotely sensed variables.