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Development of the 1-meter aperture Lunar Laser Ranging (LLR) telescope is underway at the Hartebeesthoek Radio Astronomy Observatory (HartRAO) which is expected to achieve sub-centimeter range precision and accuracy to the Moon, for enhanced tests of Earth-Moon system dynamics. Key to the operational performance of the telescope is thermal analysis of the telescope composite structure including the optical mirrors. This study presents a thermal analysis on the integrated component materials comprising the LLR telescope in ANSYS, with the aim of simulating its thermal behaviour in response to site-based ambient air temperature (T A ). Results show that for a full day T A profile spanning the time period 00:00 to 23:59 the resulting range of simulated thermal variations measured at 12:00 midday and 23:59 nighttime across the telescope composite structure was found to be 9.11 oC to 10.03 oC and 9.12 oC to 9.86 oC respectively. In particular, the spider assembly and outer tube surface had the largest range of thermal variations i.e. greater than absolute 1 oC and thus, could be the main areas on the telescope where most thermal variations would occur. Furthermore, validation of the outer tube thermal variations using the 64 Resistant Temperature Detector (RTD) sensors mounted onto the test tube, showed relatable overall thermal variations of about 2 oC, at wind speeds of 0 to 0.4 km/h. In overall, these findings provide a typical expectation of the LLR telescope thermal behavior in response to T A at the site; and thus could be used as a guide to develop an RTD-based thermal monitoring system for the HartRAO LLR optical telescope.

Monitoring wheat growth under different weather and ecological conditions is vital for a reliable supply of wheat yield estimations. Remote sensing techniques have been applied in the agricultural sector for monitoring crop biophysical properties and predicting crop yields. This study explored the application of Land Surface Temperature (LST)-vegetation index relationships for winter wheat in order to determine indices that are sensitive to changes in the wheat health status. The indices were derived from Landsat 8 scenes over the wheat growing area across Bloemfontein, South Africa. The vegetation abundance indices evaluated were the Normalised Difference Vegetation Index (NDVI) and the Green Normalised Difference Vegetation Index (GNDVI). The moisture indices evaluated were the Normalised Difference Water Index (NDWI) and the Normalised Difference Moisture Index (NDMI). The results demonstrated that LST exhibited an opposing trend with the vegetation abundance indices and an analogous trend with the moisture indices. Furthermore, NDVI proved to be a better index for winter wheat abundance as compared to the GNDVI. The NDWI proved to be a better index for determining water stress in winter wheat as compared to the NDMI. These results indicate that NDVI and NDWI are very sensitive to LST. These indices can be comprehensive indicators for winter wheat health status. These pilot results prove that LST-vegetation index relationships can be used for agricultural applications with a high level of accuracy.

Tropospheric delay is a major error source in positioning by Global Navigation Satellite Systems (GNSS). Many techniques are available for tropospheric delay mitigation consisting of surface meteorological models and global empirical models. Surface meteorological models need surface meteorological data to give high accuracy mitigation while the global empirical models need not. However, most GNSS stations in the African region are not equipped with a meteorological sensor for the collection of surface meteorological data during the measurement. Zenith Tropospheric Delay (ZTD) is often calculated by the various high precision GNSS software packages by utilising standard atmosphere values. Lately, researchers in the University of New Brunswick and Vienna University of Technology have both developed global models (University of New Brunswick (UNB3M) and Global Pressure and Temperature 2 wet (GPT2w) models) for tropospheric delay correction, respectively. This report represents an appraisal of the performance of the GPT2w and UNB3M models with accurate International GNSS Service (IGS)- tropospheric estimations for fifteen IGS stations over a period of 1 year on the Africa continent. Both models perform significantly better at low latitudes than higher latitudes. There was better agreement between the GPT2w model and the IGS estimate than the UNB3m at all stations. Thus, the GPT2w model is recommended as a correction model of the tropospheric error for the GNSS positioning and navigation on the African Continent.Keywords: Global Navigation Satellite Systems (GNSS), Zenith Tropospheric Delay (ZTD), Zenith Wet Delay (ZWD), Zenith Hydrostatic Delay (ZHD), International GNSS Service (IGS), Blind Tropospheric models