Spectral Enhancement of Imagery for Small Inland Water Bodies Monitoring: Utilization of UAV-Based Data

Komárková, Jitka; Čermáková, Ivana; Sedlák, Pavel; Jech, J.

doi:10.29333/jisem/6346

Cited by 3 publications

(1 citation statement)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…4,5 In particular, synthetic aperture radar (SAR) imagery is widely used in water mapping applications-based on the low backscatter signal of water, 4,[6][7][8] but terrain effects 9 and the presence of high and dense inundated vegetation are known to induce errors. [10][11][12] And while the use of unmanned aerial vehicles (UAV) 13,14 could reduce the terrain effects and provide improved spatio-temporal accuracy, the high operational cost and the limited area coverage in comparison to data acquired through satellite systems, renders them unsuitable for wide-area monitoring applications (at regional or national level), or for remote areas (e.g., extensive wetlands), such as those discussed in the present research. The Sentinel-1 (S-1) A and B satellites launched by the European Space Agency (ESA) regularly provide SAR imagery (on a 6-day basis in Greece), at a spatial resolution of 10 m and with a swath of 250 km, when the ground range detected (GRD) interferometric wide (IW) mode product is considered.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of machine learning approaches for surface water monitoring using Sentinel-1 data

Pantazi

Tamouridou

Moshou

et al. 2022

J. Appl. Rem. Sens.

View full text Add to dashboard Cite

.The monitoring of mosquito breeding habitats requires the production of surface water maps on a regular basis and at a high-resolution using mapping algorithms. To map surface water, several machine learning (ML) algorithms were evaluated, taking advantage of frequently available synthetic aperture radar imagery from the Sentinel-1 mission with a 10-m spatial resolution and a large dataset of field observations of the water state (inundated/dry) in rice paddies and wetlands. One-class support vector machine, one-class self-organizing map, and multilayer perceptron with automatic relevance determination (MLP-ARD) algorithms were trained and assessed to examine their accuracy in detecting surface water. Results show the robustness of the MLP-ARD algorithm, which provides an overall accuracy of 0.974 for a single date and 0.892 for a 5-month study period from May to September. The accuracy of water detection was found to be mainly affected by the presence of dense and high vegetation in inundated fields and the presence of floating vegetation or algae.

show abstract