Abstract
The irrigation of green areas in cities should be managed appropriately to ensure its sustainability. In large cities, not all green areas might be monitored simultaneously, and the data acquisition time can skew the gathered value. Our purpose is to evaluate which parameter has a lower hourly variation. We included soil parameters (soil temperature and moisture) and plant parameters (canopy temperature and vegetation indexes). Data were gathered at 5 different hours in 11 different experimental plots with variable irrigation and with different grass composition. The results indicate that soil moisture and Normalized Difference Vegetation Index are the sole parameters not affected by the data acquisition time. For soil moisture, the maximum difference was in experimental plot 4, with values of 21% at 10:45 AM and 27% at 8:45 AM. On the other hand, canopy temperature is the most affected parameter with a mean variation of 15 °C in the morning. The maximum variation was in experimental plot 8 with a 19 °C at 8:45 AM and 39 °C at 12:45 PM. Data acquisition time affected the correlation between soil moisture and canopy temperature. We can affirm that data acquisition time has to be included as a variability source. Finally, our conclusion indicates that it is vital to consider data acquisition time to ensure water distribution for irrigation in cities.
Highlights
We evaluate if the data acquisition time (DAT) affects the existing and well-studied correlations among addivartion, we evaluate if the DAT affects the existing and well-studied correlations among variiables
We presented a problem related to the combination of Wireless Sensor Networks (WSN) and remote sensing when monitoring large areas, and the effect of DAT on data variability
Due to the relevance and impact of data reliability, it is essential to evaluate which of the most-used irrigation management parameters is less affected by the DAT and should be prioritised to compare different green areas in large cities
Summary
Reaching sustainability and the efficient use of resources is one of the major pillars in smart cities. The use of different technologies, such as Wireless Sensor Networks (WSN) or remote sensing, to achieve these goals is a promising option. The efficient management of water is one of the Sustainable Development Goals. We can find several examples in which WSN and remote sensing benefits are reported [1,2]. We can identify different water uses, and all of them must be adequately managed to accomplish sustainability. Despite the fast growth of the above-mentioned technologies, they cannot be applied in some specific areas or monitoring certain parameters, and manual data gathering is still necessary. One example is plant stress monitoring in gardens to adjust irrigation [3]
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