Remotely Sensed Monitoring of Small Reservoir Dynamics: A Bayesian Approach

Eilander, Dirk; Annor, Frank; Iannini, Lorenzo; Giesen, Nick van de

doi:10.3390/rs6021191

Cited by 43 publications

(27 citation statements)

References 29 publications

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“…In these areas, rural population mainly relies on small reservoirs for water harvesting in the rainy season [6]. As an example, in Burkina Faso, it is estimated that 1700 reservoirs are actually employed for agricultural activities, livestock watering and human consumption [7].…”

Section: Introductionmentioning

confidence: 99%

Sentinel-1 for Monitoring Reservoirs: A Performance Analysis

Amitrano¹,

Martino²,

Iodice³

et al. 2014

Remote Sensing

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Abstract:In this paper we explore the performances and the opportunities provided by the European satellite Sentinel-1 for water resource management applications in low-income countries. The analysis is supported by a synthetic aperture radar (SAR) simulator, which allowed the quantification of the expected characteristics of Sentinel-1 products in three applications: interferometric digital elevation models (DEMs) generation, land cover mapping and estimation of water volumes retained by small reservoirs. The obtained results quantitatively show that Sentinel-1 data characteristics are fully suitable for most of the application already explored in the recent SAR literature.

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Section: Introductionmentioning

confidence: 99%

Sentinel-1 for Monitoring Reservoirs: A Performance Analysis

Amitrano¹,

Martino²,

Iodice³

et al. 2014

Remote Sensing

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“…Single-sensor applications include monitoring of annual land cover changes [50][51][52][53], soil moisture [54][55][56] and lake dynamics [57]. Fusing multi-temporal Landsat and SAR data for land cover classification was demonstrated by [58,59].…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Approach to Combine Landsat and ALOS PALSAR Time Series for Near Real-Time Deforestation Detection

et al. 2015

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Abstract:To address the need for timely information on newly deforested areas at medium resolution scale, we introduce a Bayesian approach to combine SAR and optical time series for near real-time deforestation detection. Once a new image of either of the input time series is available, the conditional probability of deforestation is computed using Bayesian updating, and deforestation events are indicated. Future observations are used to update the conditional probability of deforestation and, thus, to confirm or reject an indicated deforestation event. A proof of concept was demonstrated using Landsat NDVI and ALOS PALSAR time series acquired at an evergreen forest plantation in Fiji. We emulated a near real-time scenario and assessed the deforestation detection accuracies using three-monthly reference data covering the entire study site. Spatial and temporal accuracies for the fused Landsat-PALSAR case (overall accuracy = 87.4%; mean time lag of detected deforestation = 1.3 months) were consistently higher than those of the Landsat-and PALSAR-only cases. The improvement maintained even for increasing missing data in the Landsat time series.

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“…In search for methods to overcome this data gap, the PUB initiative helped to raise interest in the development of innovative in situ gauging techniques, as well as the use of remotely sensed earth observation data. Nowadays, precipitation (TRMM, Kummerow et al, 1998;GPM, Hou et al, 2014), evaporation and water stress (Bastiaanssen et al, 1998;van Emmerik et al, 2015), topography (DEM), large scale groundwater variation (GRACE, Mulder et al, 2015), reservoir dynamics (Eilander et al, 2014) and soil moisture (SMOS, Kerr et al, 2001;SMAP, Entekhabi et al, 2010; AMSR-E, Khan et al, 2012), among others, can be estimated on a global scale, with high temporal and spatial resolution. However, in most PUB cases these data are only used as model inputs, while hydrologists still rely on discharge measurements as a calibration and validation tool.…”

Section: Pub Heritagementioning

confidence: 99%

Predicting the ungauged basin: model validation and realism assessment

et al. 2015

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The hydrological decade on Predictions in Ungauged Basins (PUB) led to many new insights in model development, calibration strategies, data acquisition and uncertainty analysis. Due to a limited amount of published studies on genuinely ungauged basins, model validation and realism assessment of model outcome has not been discussed to a great extent. With this paper we aim to contribute to the discussion on how one can determine the value and validity of a hydrological model developed for an ungauged basin. As in many cases no local, or even regional, data are available, alternative methods should be applied. Using a PUB case study in a genuinely ungauged basin in southern Cambodia, we give several examples of how one can use different types of soft data to improve model design, calibrate and validate the model, and assess the realism of the model output. A rainfall-runoff model was coupled to an irrigation reservoir, allowing the use of additional and unconventional data. The model was mainly forced with remote sensing data, and local knowledge was used to constrain the parameters. Model realism assessment was done using data from surveys. This resulted in a successful reconstruction of the reservoir dynamics, and revealed the different hydrological characteristics of the two topographical classes. This paper does not present a generic approach that can be transferred to other ungauged catchments, but it aims to show how clever model design and alternative data acquisition can result in a valuable hydrological model for an ungauged catchment.

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Remotely Sensed Monitoring of Small Reservoir Dynamics: A Bayesian Approach

Cited by 43 publications

References 29 publications

Sentinel-1 for Monitoring Reservoirs: A Performance Analysis

Sentinel-1 for Monitoring Reservoirs: A Performance Analysis

A Bayesian Approach to Combine Landsat and ALOS PALSAR Time Series for Near Real-Time Deforestation Detection

Predicting the ungauged basin: model validation and realism assessment

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