# A Review of Irrigation Information Retrievals from Space and Their Utility for Users
## 6 Synthesis and Future Perspectives  
This study provided a comprehensive review of the use of remote technologies to retrieve information on irrigation, and methods to integrate this information with models. Moreover, we assessed whether this information meets the requirements of various actors working in the field of water resource management and agriculture. What emerged from the literature is synthetized below:  
1. The information of irrigation collected in situ is very limited due to the reluctance of farmers and managers to share these data [137], and the difficulty to collect data at global and regional levels. This poses a big challenge for the understanding of energy, water, and carbon cycles, climate interactions and future projections, sustainable agriculture and water management, food production, and water security. 2. Estimates of areas equipped for irrigation, derived from inventories of national and local authorities, have partially covered this gap, but being mainly based on statistics and sparsely and historically collected information, they are likely to be inaccurate and inhomogeneous. Moreover, this information is static and thus does not say when, where, and how much irrigation has been applied. Despite these limitations, these data remain a reference for many applications, including global hydrological modeling, modeling of changes in crop productivity, or climate impact assessments. 3. In the last twenty years, there has been a substantial improvement in both spectral, spatial, and temporal resolution of Earth observations, which has boosted methodological developments. A first advance was to separate the identification of irrigated areas from general land cover classification approaches. A second advance came with improvements in spatial resolution, which allowed for more accurate assessments of irrigated areas [123; 145] that do not hinge on information about the fraction of irrigated area within low-resolution pixels. A third advance was the synergetic use of various satellite, climatic, and ecoregions time series instead of vegetation index time series alone. 4. The automation of high-resolution time series processing has benefited greatly from the emergence of platforms that allow the parallel processing of big amounts of data, such as Google Earth Engine, Amazon, or the European DIAS. The technological advances have made it feasible to estimate the irrigated area at ever smaller time steps, progressing from a decadal overview of the areas equipped for irrigation toward the actual irrigated area at the beginning of the season, also thanks to the continuous development of new machine learning and classification methods which so far relied mainly upon supervised types of algorithms. The lack of the real data to guide these algorithms, especially over data-scar regions, surely demands for a more massive use of unsupervised techniques [146; 147]. 5. Microwave-based observations and their combination with optical data and models have provided new ways to map irrigated areas. Previous work using coarse-scale and disaggregated soil moisture products have shown potential for retrieving information of irrigation from space, but also have several limitations associated with: (i) the noise of these products compared to the strength of the irrigation signal, and (ii) the scale mismatch between the satellite footprint and the size of the irrigated fields. In this context, SAR data have demonstrated to be a viable way to provide information on irrigation mapping and researchers are currently exploring ways to retrieve quantitative irrigation estimates from them as well. The latter is, however, more challenging compared to simple irrigated area mapping. 6. Visible, near-infrared, and microwave-based methods have all demonstrated a certain ability to quantify volumes of applied irrigation.