WINETECH Technical Yearbook 2020

extreme sites far inland and near the coast tended to have had lower consistency, with an over estimation of warmer 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 nature of the instrument. already intrinsically spatial in nature taking into account the terrain complexity of the Western Cape. To also avoid the over and under

reconstructed daily time series from the satellite can potenti lly be useful in m ny cases to substitute meteorological temperature observations.

estimation of LST temperatures, the best correlation was using the mean daily temperature from the LST layers (calculated from the minimum

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 regression 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 continuous temperature layer, a layer that is already intrinsically spatial in nature taking into account the terrain complexity 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 minimum and maximum for the day) rather than using the average of all four layers. 4. Although the overall difference between the daily average LST temperature and weather station temperature is relatively high (difference of 2.23°C), the addition of it to the temperature interpolations, and ultimately the bioclimatic indices, added basically no error. As the interpolation algorithm uses the LST only as a means to explain the variation in the temperature, the over- and underestimation within LST itself, might not influence the final interpolated surfaces. A growing degree map (GDM) for the season (1 September to 31 March) was calculated for LST (Figure 2a) and WS with standard interpolations using elevation and continentality as

and maximum for the day) rather than using the average of all four layers. 4. Although the overall difference between the daily average LST temperature and weather station temperature is relatively high

FIGURE 2a and 2b. Growing degree map (GDM) for a season September to March, calculated using MODIS LST map series and gap-filled using interpolated surfaces. Compared to the image on the right, GDM using only weather stations points for the interpolation using elevation and ing the temperature estimation errors. 4. Highlight the limitations and possible future developments of using LST in agri ult r . (difference of 2.23°C), the addition of it to the temperature interpolations, and ultimately the bioclimatic indices, added basically no error. As the interpolation algorithm uses the LST only as a means to explain the variation in the temperature, the over- and underestimation within LST itself, might not influence the final interpolated surfaces. A growing degree map (GDM) for the season (1 September to 31 March) was calculated for LST (Figure 2a) and WS with standard interpolations using elevation and continentality as covariates (Figure 2b). As a THE STUDY OBJECTIVES WERE TO 1. Estimate air temperature (WS) using remote sensing LST from the MODIS satellite, (LST) over a set three years (1 April 2012 to 30 April 2015). 2. Provide temperature estimations with an accuracy which will support future applications. 3. Explore factors and processes influenc- A

B

FIGURE 2A AND 2B. Example of a growing degree map (GDM) for the season (1 September to 31 March), calculated for LST (Figure 2a) and WS with stand rd int rpolations using elevation and continentality as covariates (Figure 2b).

FIGURE 2a and 2b. Growing degree map (GDM) for a season September to March, calculated using MODIS LST map series and gap-filled using interpolated surfaces. Compared to the image on the right, GDM using only weather stations points for the interpolation using elevation and continentality as covariates. RESULTS 1. The estimation of air temperature (WS) using remote sensing LST layers, had the best correlation when the daily mean LST was compared to the daily meanWS. The daily mean LST was calculated form the maximum and minimum LST layers for the day, rather than using the mean temperature averaged from all four LST layers for the day. The mean daily LST layers does sufficiently estimated the daily mean WS temperature. 2. Simple statistical methods estimated the daily mean WS temperature to have

WINETECH TECHNICAL YEARBOOK 2020 23 3. Explore factors and p ocesses influ ncing th temperature estimation er ors. 4. Highlight the limitations and possible future developments of using LST in agriculture. FIGURE 1. Western Cape extent, with the MODIS tile overlay categorised into growing season temperatures, showing distribution f weath stations selected for the study. In the top right corner is a zoomed-in version highlighting the resolution of 1 km x 1 km MODIS pixels. The study objectives were to: 1. Estimate air temperature (WS) using remote sensing LST from the MODIS satellite, (LST) over a set three years (1 April 2012 to 30 April 2015). 2. Provide temperature estimations with an accuracy which will support future applications. FIGURE 1. Example of a MODIS tile extent, categorised into growing season temperatures. Distribution of weath r stations used in th study also highlighted. In the top right corner is a zoomed-in version highlighting the MODIS tile resolution of 1 km x 1 km pixels. DAILY SATELLITE LAND SURFACE TEMPERATURES IS A POSSIBLE SOLUTION TO OVERCOME OUR WEATHER STATION LIMITATIONS IN COMPLEX TERRAINS The daily LST layers can greatly improve the estimation of temperature in spatio- temporal patterns, improving the knowl- edge of both climate and biological pro- cesses in the wine industry on regional and global spatial scales. The weather station spatial layout in the context of the Moder- ate Resolution Imaging Spectroradiometer (MODIS) LST pixel outline (1 km x 1 km) and proximity to the oce n are displayed in figure 1. The LST layers can be used as a daily series of valu s as the satellite takes an image of the entire earth four times per day. The reconstructed daily time series from the satellite can potentially be useful in many cases to substitute meteorological temperature observat ons.