Remote Sensing and Citizen science supporting irrigation monitoring in the Capitanata Irrigation Consortium (Italy)

In different ways, Citizen Science and Remote Sensing (RS) have been recently developing as innovative and inclusive ways to improve data gathering and the comprehension of many environmental biophysical processes. In this framework, the GROW Observatory has been promoting the individual farmer awareness in agriculture as a counterpart to the ever-developing frequency and accuracy of RS products.

In this analysis, 456 on-ground sensors from the GROW Observatory have been deployed in the Capitanata Irrigation Consortium (Apulia, Italy), with the aim of measuring the components of the water cycle with a dense, high-resolution pattern. The possibility of channelling these data into a high-resolution, plant-oriented Irrigation Water Need (IWN) parameter has been investigated, as a counterpart of coarser-resolution, spatially distributed monitoring powered by remote sensing and hydrological modelling.

The instruments have the possibility of measuring three main variables: Surface Soil Moisture (at a maximum depth of 5 cm), Air temperature and Solar Illuminance (measured a few centimetres above ground). The monitoring period is July-October 2019, contemplating a wide range of different cultivation regimes.

Irrigation water needs estimates has been obtained both in a point-wise (plant-oriented) and field-wise (spatial) format, in order to derive an irrigation water management tool. IWN and Surface Soil Moisture data are also employed in inferring back actual irrigation information from on-ground and RS data. These estimates have then be compared with observed data.

Intermediate measure of Surface Soil Moisture, Air Temperature and radiation (by the Solar Illuminance proxy) have also been compared both with local measurements (those of and eddy-covariance station in place) and RS products from Sentinel and Landsat. Furthermore, Solar Illuminance data have been processed to extract a Leaf Area Index (LAI) product, also comparable with satellite estimates. These comparisons have been conducted through spatial and temporal correlations between the ground-gathered and remotely-sensed data.

The potentiality and also the limitations of these low-cost instruments are presented and discussed.