Abstract
Irrigation represents one of the most impactful human interventions in the terrestrial water cycle. Knowing the distribution and extent of irrigated areas as well as the amount of water used for irrigation plays a central role in modeling irrigation water requirements and quantifying the impact of irrigation on regional climate, river discharge, and groundwater depletion. Obtaining high-quality global information about irrigation is challenging, especially in terms of quantification of the water actually used for irrigation. Here, we review existing Earth observation datasets, models, and algorithms used for irrigation mapping and quantification from the field to the global scale. The current observation capacities are confronted with the results of a survey on user requirements on satellite-observed irrigation for agricultural water resources’ management. Based on this information, we identify current shortcomings of irrigation monitoring capabilities from space and phrase guidelines for potential future satellite missions and observation strategies.
Highlights
Irrigated agriculture accounts for more than 70 percent of the water withdrawn worldwide from lakes, rivers, and aquifers [1], in many countries even for more than90 percent, being the greatest human disturbance in the terrestrial water cycle [2].Even though only 17 percent of global crop land is irrigated, these lands already produce40 percent of the world’s food [3]
We reviewed the status of research in methods for retrieving irrigation information from space by systematically reviewing relevant literature in the topic resulting from the most important databases, such as Scopus, Web of Science, and Google Scholar
High spatio-temporal resolution Normalized Difference Vegetation Index (NDVI) data (30 × 30 m) from the Chinese HJ-1A/B (HuanJing, HJ) satellite were used by Jin et al [39] to separate irrigated from rainfed areas in the semi-arid province of Shanxi in China, through a novel classification method based on a Support Vector Machine (SVM)
Summary
To cite this version: Christian Massari, Sara Modanesi, Jacopo Dari, Alexander Gruber, Gabrielle de Lannoy, et al. A. Remote Sensing, MDPI, 2021, 13 (20), pp.4112. HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Distributed under a Creative Commons Attribution| 4.0 International License remote sensing
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