# Remote Sensing for Irrigation of Horticultural Crops
## 5 Conclusions  
The advances in electronic and information technologies have developed various non-invasive sensing systems, which return accurate georeferred information on spatial variability within fields. The sensing systems utilize sensors and instruments that produce a huge amount of data at different scales (temporal and spatial) and distances from the target. Remote (satellite, airborne and UAV imageries) and proximal sensors (mounted on tractors, poles or towers, and portable specto-radiometers) are able to detect soil and crop information, such as soil status and traits, crop yield, canopy volume and biomass, crop water status, and pests (disease, weeds, and insects). These sensing technologies cane be used for precision agriculture and irrigation management. Soaring investments in R&D have spurred all branches of knowledge linked to RS, boosting the production of reliable and accurate sensors. Temporal frequency of RS imagery has also improved consistently, although it has been impaired by considerable lengthening of the average times for data processing. Data processing of images necessitates adequate hardware, specific expertise, skills and funds, which are likely to be provided only by specialized reprocessing companies. Overall, there are numerous studies with spatially distributed information on ET and plant water status. These approaches use reflectance, including thermal radiance, measurements of the surface, vegetation index-based crop coefficients, surface energy balance models, and soil water balance (although limited to the upper soil layer). On a small scale, the focus on time and space has significantly improved the monitoring of the crop water status of many crops, especially orchards and vineyards. The estimation of ET and kc, of appearance of biotic and abiotic stresses, of crop spatial variability, and of canopy volume have been improved significantly. On a field scale, the attention paid to soil-water status seems inadequate, although soil moisture monitoring is a prerequisite for professionally controlled irrigation that satisfies crop water requirements (precision irrigation) and reduces inefficient spatial allocation of irrigation water, in particular over irrigation that causes N leaching. There are current limits of RS, which can answer the question of \"_when to apply water_\", because the irrigation volume needs to be estimated based on other methods, as wisely pointed out by [PERSON] et al. (1982) These other methods, include--amongst many--the use of electronic probes measuring crop water consumption ([PERSON] et al., 2013), with the aim of defining the right water depth to apply to a field, and hopefully to each homogeneous area. Since positive correlations between crop-water status and yield vary among crops, RS studies on irrigation should pay more attention to field production, either in absolute terms (maximizing field productivity) or in relative terms (higher WUE). In the near future, it is likely that all available tools (multi-temporal images combined with models and improved sensors) will contribute to monitoring crop status for triggering irrigation. As mentioned above, doubts persist on their ability to estimate the water delivery to the field for each crop, or to different zones in the case of high crop spatial variability. A holistic approach is necessary for moving toward precision irrigation. It will be necessary to set up networks that contemplate the participation of public/private partnerships, universities, and research institutes, for producing multi-temporal images, models, sensors and data sets [94] for managing precision irrigation. Among several EU hubs of excellence, the Quantalab IAS--CSIC in Spain focuses on quantitative methods for remote sensing in agriculture and forestry, for a wide application of precision agriculture and precision irrigation, for the assessment of environmental impacts and stress detection. Other scientists are pursuing grassroots remote sensing and democratic mapping. [PERSON] et al.