Special Issue "Remote Sensing used in Environmental Hydrology"

A special issue of Geosciences (ISSN 2076-3263). This special issue belongs to the section "Hydrogeology".

Deadline for manuscript submissions: 31 October 2019

Special Issue Editor

Guest Editor
Dr. Antonino Maltese

Dipartimento di Ingegneria Civile, Ambientale, Aerospaziale, dei Materiali, University of Palermo, 90128 Palermo, Italy
E-Mail
Interests: remote sensing; hydrology; geomatics

Special Issue Information

Dear Colleagues,

Although Earth Observation (EO) technology has improved our ability to characterize and manage ecosystems and water resources both in space and in time, there are many emerging technologies and research areas where the potential of Earth Observation has not fully exploited.

The development of hyperspectral sensors, SAR acquiring at different frequencies, LiDAR, unmanned aerial vehicles, small micro and nano satellites, virtual constellations, represents unprecedented new opportunities; is the research community developing remote sensing to its full potential?

Research papers are sought covering progress, recent advances and future trends of the following topics: satellite rainfall estimation; snow and ice hydrology; water resource management; drought monitoring and prediction; soil moisture mapping; flooding; water quality; evapotranspiration; water securing for food and climate change, landslides; safety and security of critical infrastructures; hydrogeomatics.

Dr. Antonino Maltese
Guest Editor

Manuscript Submission Information

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Keywords

  • emerging technologies
  • securing water for food
  • soil moisture
  • unmanned aerial vehicles
  • surface energy balances
  • lidar and radar
  • hydrogeomatics

Published Papers (1 paper)

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Research

Open AccessArticle
Performance of Remotely Sensed Soil Moisture for Temporal and Spatial Analysis of Rainfall over São Francisco River Basin, Brazil
Geosciences 2019, 9(3), 144; https://doi.org/10.3390/geosciences9030144
Received: 30 January 2019 / Revised: 20 March 2019 / Accepted: 22 March 2019 / Published: 26 March 2019
Cited by 1 | PDF Full-text (4561 KB) | HTML Full-text | XML Full-text
Abstract
Variability in precipitation patterns in the northeast and southeast regions of Brazil are complex, and the combined effects of the Tropical Atlantic, Pacific Niños, and local characteristics influence the precipitation rates. This study assesses the performance of multi-satellite precipitation product SM2RAIN-Climate Change Initiative [...] Read more.
Variability in precipitation patterns in the northeast and southeast regions of Brazil are complex, and the combined effects of the Tropical Atlantic, Pacific Niños, and local characteristics influence the precipitation rates. This study assesses the performance of multi-satellite precipitation product SM2RAIN-Climate Change Initiative (SM2RAIN-CCI) for the period of 1998–2015 at monthly scale. To accomplish this aim, various statistical analyses and comparison of multi-satellite precipitation analysis products with rain gauge stations are carried out. In addition, we used three values corresponding to extreme events: The total daily precipitation (PRCPTOT) and the number of consecutive dry/wet days (CDD/CWD). Results reveal that monthly rainfall data from SM2RAIN-CCI are compatible with surface observations, showing a seasonal pattern typical of the region. Data correlate well with observations for the selected stations (r ≥ 0.85) but tend to overestimate high rainfall values (>80 mm/month) in the rainy area. There is a significant decrease in rainfall to the indices, especially in PRCPTOT during the occurrence of tropical ocean–atmosphere interactions, reflecting CWD and CDD values. Moreover, our findings also indicate a relationship, at interannual timescales, between the state of El Niño Southern-Oscillation (ENSO) and Tropical Atlantic (TA) annual precipitation variability from 1998 to 2015. The SM2RAIN-CCI could be a useful alternative for rain-gauge precipitation data in the São Francisco River basin. Full article
(This article belongs to the Special Issue Remote Sensing used in Environmental Hydrology)
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