Luca Zappa

TL;DR: This technical note presents the first Sentinel-2 data service platform for obtaining atmospherically-corrected images and generating the corresponding value-added products for any land surface on Earth.

TL;DR: The main scope of this paper is to inform readers about the evolution of the ISMN over the past decade, including a description of network and data set updates and quality control procedures.

TL;DR: In this paper, the authors review existing Earth observation datasets, models, and algorithms used for irrigation mapping and quantification from the field to the global scale and identify current shortcomings of irrigation monitoring capabilities from space and phrase guidelines for potential future satellite missions and observation strategies.

TL;DR: The findings highlight the suitability of using ground measurements in conjunction with machine learning to derive high spatially resolved SM maps from coarse-scale satellite products and show the potential of low-cost sensors as a practical and cost-effective solution for gathering the necessary observations.

TL;DR: In this paper, the capability of ground-based and remotely sensed observations of water cycle ECVs to consistently observe the hydrological cycle is reviewed and the authors discuss gaps in observation systems and formulate guidelines for future water cycle observation strategies.