WINETECH Technical Yearbook 2020

SUMMARY The topography of the Western Cape is complex and changes drastically over short distances which is why the climate chang- es over short distances. For this reason increased resolution of climate data is cru- cial for effective adaptive strategies in the context of climate change. Reliable climate data can be costly and currently requires in- tensive data validation. This study aimed to find an alternative resource to quantify the climate over the spatial extent of the West- ern Cape, for possible semi-real time ap- plications. Land surface temperature maps are intrinsically spatialised, providing daily temperature values that in the past would have only been possible by spatial interpo- lation of sparse weather station networks, which could only be as accurate as the input data. The daily mean land surface tempera- ture and weather station temperature data exhibited a strong linear relationship with good prediction accuracy in the complex ter- rain of the Western Cape. The integration of land surface temperature images with field weather station temperatures will improve the mapping of temperature for improved decision making at farm and field level. This will help producers to stay economically sustainable and to make strategic decisions about future production decisions.

comparing the three sources was rela- tively accurate. The LST, standard spa- tial interpolation had a 2% difference in the average compared to the reference weather station values. CONCLUSION Temporal, spatial and thermal resolutions of mean temperature acquired from daily LST products, offers a new and powerful tool for classification of viticulture land - scapes and seasonal monitoring. A wide scope of applications can benefit from the improved remote sensing based mean WS temperature estimations presented in this study for the area of Western Cape. Two main practical aspects stand out in terms of the contribution of this work to research applications. Firstly, to provide improved spatial-temporally distributed estimations of temperature and indices maps which are particularly important in regions with low station density or with highly variable spatial patterns between stations. Secondly, a collection of remote sensing layers should be created to comple- ment and fill weather station gaps and used for integrated grapevine studies for future use in the wine industry as a key tool for decision making. Future work to quantify climate change in the Western Cape and South Africa can be complemented with the use of intrinsically spatialised remote sensing products, such as land surface temperature layers.

a temperature difference of 2.3-3.6°C compared to the daily mean LST tem- peratures. The model created from the data source comparison, accounts for the error differences between the two instruments (satellite vs weather sta- tions). Thereby providing a more accu- rate temperature mean map for the day, ensuring continuous temperature maps by supplementing WS with LST. 3. Factors and processes influencing the temperature estimation errors. The ex- treme sites far inland and near the coast tended to have had lower consisten- cy, with an over estimation of warm- er temperatures and underestimation of cooler temperatures. The daily LST temperature layers has its limitations in capturing the extremes within a day due to the fly-over time and thermal na - ture of the instrument. Secondly, cloud cover days result in no data for that image/day, which would leave a gap in the time series. We can overcome these limitations, with the use of the regres- sion equations from the entire study period. The regression equation corrects the errors in the LST layer using the actual WS data values as a calibration factor. The integration of LST and WS results in a more accurate and contin- uous temperature layer, a layer that is already intrinsically spatial in nature taking into account the terrain complex-

ity of the Western Cape. To also avoid the over and under estimation of LST temperatures, the best correlation was using the mean daily temperature from the LST layers (calculated from the min- imum and maximum for the day) rather than using the average of all four layers. Although the overall difference between the daily average LST temperature and weather station temperature is rela- tively high (difference of 2.23°C), the addition of it to the temperature in- terpolations, and ultimately the bio- climatic indices, added basically no error. As the interpolation algorithm uses the LST only as a means to ex- plain the variation in the temperature, the over- and underestimation with- in LST itself, might not influence the final interpolated surfaces. A grow- ing degree map (GDM) for the season (1 September to 31 March) was calcu- lated for LST (figure 2a) and WS with standard interpolations using elevation and continentality as covariates (figure 2b). As a continuous daily set of maps from September to March is needed for indices calculations, interpolations with only elevation and continentality were incorporated for the days where LST had no data due to cloud cover. The same calculation (as for GDM) was done at 14 weather station points and used as reference for the accuracy assessment. The accuracy assessment of the results

For more information, contact Tara Southey at tara@sun.ac.za.

WINETECH TECHNICAL YEARBOOK 2020 24