# Flood pulse monitoring in wetlands with multi-temporal Sentinel-1 interferometric coherence data: Application to the Okavango Delta (Botswana)

Louis Gaudaré, Samuel Corgne, Marc Jolivet, Olivier Dauteuil, Cécile Doubre, Piotr Wolski, Raphaël Grandin, Marie-Pierre Doin, Philippe Durand

► To cite this version:

Louis Gaudaré, Samuel Corgne, Marc Jolivet, Olivier Dauteuil, Cécile Doubre, et al.. Flood pulse monitoring in wetlands with multi-temporal Sentinel-1 interferometric coherence data: Application to the Okavango Delta (Botswana). Remote Sensing of Environment, 2026, 334, pp.115173. 10.1016/j.rse.2025.115173. hal-05399769

HAL Id: hal-05399769

https://hal.science/hal-05399769v1

Submitted on 5 Dec 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

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.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_2.png" /></figure>

# Flood pulse monitoring in wetlands with multi-temporal Sentinel-1 interferometric coherence data: Application to the Okavango Delta (Botswana)

Louis Gaudaré<sup>a, *, 1</sup>, Samuel Corgne<sup>b</sup>, Marc Jolivet<sup>a</sup>, Olivier Dauteuil<sup>a</sup>, Cécile Doubre<sup>c</sup>, Piotr Wolski<sup>d</sup>, Raphaël Grandin<sup>e</sup>, Marie-Pierre Doin<sup>f</sup>, Philippe Durand<sup>g</sup>, FLATSIM Working Group

## ARTICLE INFO

Edited by Menghua Wang

Keywords:
Wetland
Flood pulse
Coherence
InSAR
Seasonality
Okavango Delta

## ABSTRACT

Flood-pulsed wetlands are characterized by significant seasonal water fluctuations, which play a critical role in the dynamics of these sensitive ecosystems. Among the growing number of existing remote sensing products, we explore the potential of interferometric (InSAR) coherence time series, derived from Sentinel-1 synthetic-aperture radar images, to characterize the hydrological dynamics of the Okavango Delta, a vast flood-pulsed wetland. Interferometric coherence reflects changes in surface conditions, making it a powerful tool for detecting flood propagation. By fitting harmonic functions, we produce parameters that quantify the seasonality of coherence time series with short isotemporal baselines (12 days). In particular, we developed a normalized seasonal index based on the ratio between the seasonal amplitude and the root-mean-square error of the fitted harmonic function, to map the seasonality of the coherence time series. A multi-annual analysis of coherence time series reveals a strong relationship between their seasonality, land cover, and flood frequency. Unsupervised clustering applied to statistical and seasonal metrics of coherence time series yields consistent classifications that map the variability of flood frequencies across wetland areas and clearly distinguish wetlands from dry zones. Similarly, thresholds applied to normalized seasonal indices delineate the year-to-year extent of flood pulses with accuracy around 79 %. We show that coherence time series in never flooded areas exhibit a pronounced seasonal pattern driven by rainfall cycle, whereas this seasonality is disrupted by flood pulses in wetlands. Building on this, we developed a change-detection approach to map the floods by identifying the date when coherence time series diverge from their seasonal pattern. The resulting flood arrival dates achieve 74–83 % accuracy compared to a reference dataset derived from optical data. Our results highlight the potential of coherence time series as a robust indicator of seasonal variations in inundation extent in flood-pulsed wetlands.

## 1. Introduction

Wetlands provide significant climatic, ecological and societal services: they are biodiversity hotspots, carbon sinks, buffer zones for water and nutrient storage, and important resources for human societies

(Mitsch and Gosselink, 2000; Millennium Ecosystem Assessment Board, 2005). They account for 20–30 % of the total carbon stored in soils (Dean and Gorham, 1998; Lal, 2008), while covering only 4–8 % of the Earth’s land surface (Prigent et al., 2001; Mitsch et al., 2009). Monitoring their short-term and long-term dynamics has become a prominent

<footnote><sup>a</sup> Géosciences Rennes, UMR-CNRS 6118, Université de Rennes, 35042 Rennes, France</footnote>

<footnote><sup>b</sup> Institut Français de Pondichéry, UMIFRE 21, UAR CNRS 3330, India</footnote>

<footnote><sup>c</sup> Université de Strasbourg/CNRS, Institut Terre et Environnement de Strasbourg UMR7063, 5 rue René Descartes, Strasbourg 67084, France</footnote>

<footnote><sup>d</sup> Climate System Analysis Group, University of Cape Town, Private Box X3, 7701 Rondebosch, South Africa</footnote>

<footnote><sup>e</sup> Université Paris Cité, Institut de physique du globe de Paris, CNRS UMR 7154, 1 rue Jussieu 75238 Paris 05, France</footnote>

<footnote><sup>f</sup> Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, IRD, Univ. Gustave Eiffel, ISTerre, 38000 Grenoble, France</footnote>

<footnote><sup>g</sup> CNES: Centre National d'Études Spatiales, 18 Avenue Edouard Belin, Cedex 9, 31401 Toulouse, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: gaudare@ipgp.fr (L. Gaudaré).</footnote>

<footnote n="1">¹ Now at Institut de Physique du Globe de Paris, CNRS UMR 7154, 1 rue Jussieu 75238 Paris 05, France.</footnote>
research focus since the late-20th century, additionally motivated by global climate change and growing anthropogenic pressures on natural habitats and water resources (Millennium Ecosystem Assessment Board, 2005; Erwin, 2009; van Asselen et al., 2013; Jolivet, 2025). Approximately 95 % of wetlands are inland (as opposed to coastal), a category that includes freshwater marshes, swamps, and peatlands (Mitsch et al., 2009). Among these, flood-pulsed wetlands are characterized by seasonal fluctuations in water extent between dry and wet periods, which drive their ecosystem dynamics (Junk et al., 1989; Davidson et al., 2012). Accurately monitoring the spatiotemporal evolution of floods is therefore essential to their management and preservation.

Remote sensing approaches are now extensively used for wetland and flood monitoring. Among the multiple types of remote sensing data, Synthetic Aperture Radar (SAR) data have proven highly effective in characterizing wetland environments and mapping flood events. Radar waves can penetrate vegetation cover and are sensitive to vegetation structure, canopy density, phenological changes, and soil moisture, while being largely unaffected by cloud cover (Koch et al., 2012; Zhang et al., 2016; Hrysiewicz et al., 2023).

Although SAR backscattered intensity remains the most widely used observable (Tsyganskaya et al., 2018; Adeli et al., 2020), an increasing number of studies have highlighted the potential of repeat-pass Interferometric Synthetic Aperture Radar (InSAR) coherence for monitoring wetland and flood dynamics (e.g., Nico et al., 2000; Hall-Atkinson and Smith, 2001; Ramsey et al., 2006; Brisco et al., 2017; Mohammadimanesh et al., 2018; Canisius et al., 2019; Sica et al., 2019; Jacob et al., 2020; Millard et al., 2020; Tampuu et al., 2020; Amani et al., 2021). Repeat-pass differential InSAR measures the phase difference between two SAR acquisitions at different dates, to produce an interferogram. Coherence, derived from the interferogram's variance, quantifies the similarity of radar signal between acquisitions (Zebker and Villasenor, 1992). In wetland studies, it is often used as a quality proxy for the interferogram to monitor surface deformation or water level fluctuations under the canopy (e.g., Alsdorf et al., 2000, 2001; Lu and Kwoun, 2008; Wdowinski et al., 2008; Hong et al., 2010; Kim et al., 2013, 2017; Alshammari et al., 2020; Hoyt et al., 2020; Liao et al., 2020; De Zan and Gomba, 2018; Siles et al., 2020). However, because coherence is affected by changes in the dielectric properties and the distribution of scatterers, it also serves as an indicator of changes in surface conditions between acquisitions (Rignot and van Zyl, 1993; Jakowitz et al., 1996; Bouaraba et al., 2012; Yun et al., 2015; Wahl et al., 2016; Manzoni et al., 2021). Coherence values range from 0 to 1, with high coherence indicating stable surface properties and low coherence indicating changes due to various processes (e.g., important ground deformation, soil moisture, land cover, phenology, or hydrological variations), except when decorrelation is caused by large perpendicular baselines of the satellite relative to the topography (geometric decorrelation). Consequently, numerous studies have employed InSAR coherence as an independent indicator of the evolution of surface conditions in wetlands. Table S1 summarizes the existing literature on coherence analysis, highlighting the diversity of datasets, analytical approaches, and objectives associated with this technique.

In flood-pulsed wetlands, coherence values are primarily influenced by soil moisture variations, vegetation dynamics, and flooding (Mohammadimanesh et al., 2018). Variations in soil moisture changes the soil's volumetric dielectric properties, leading to reduced coherence (Zwieback et al., 2015; Hrysiewicz et al., 2023). Vegetation affects coherence through several mechanisms: wind-induced canopy motion, plant growth, and phenological cycles modify the distribution of scatterers between acquisitions (Hagberg et al., 1995; Wegmuller and Werner, 1995; Villarroya-Carpio et al., 2022). Permanently flooded vegetation can maintain high coherence values due to persistent double-bounce scattering (Lu and Kwoun, 2008; Zhang et al., 2015), whereas open water typically exhibits low coherence due to its specular properties and dynamic surface, which produce weak and variable radar backscatter (Hall-Atkinson and Smith, 2001; Siles et al., 2020). Flood

arrival on bare or vegetated ground induces an abrupt change in the dielectric properties of the surface, resulting in low coherence (Nico et al., 2000; Refice et al., 2014). Consequently, the cyclic nature of weather conditions, phenology, and flooding produces a pronounced seasonality in coherence time series over wetlands (Chul Jung and Alsdorf, 2010; Brisco et al., 2017; Liao et al., 2020; Amani et al., 2021; Hrysiewicz et al., 2023). Previous studies have typically described this seasonality through direct observation of raw time series or by averaging time series over specific land cover types (Table S1), without deriving quantitative indicators to characterize the seasonal behavior of coherence.

More quantitative approaches rely on modeling seasonality using harmonic models, which have been successfully applied to remote sensing time series other than coherence, for both land cover mapping and change detection (Eastman et al., 2009). The basic principle is to represent the observed signal as a Fourier series, i.e., as a sum of sine and cosine functions, as follows:

$$s_m(t) = A_0 + \sum_{k=1}^{N} \left( A_{s,k} \sin\left(\frac{2\pi kt}{T}\right) + A_{c,k} \cos\left(\frac{2\pi kt}{T}\right) \right) \quad (1)$$

where $s_m(t)$ is the modeled signal at time $t$, $N$ is the number of harmonics of the fitted Fourier series, $T$ is the period of the periodic signal, $A_0$ is a constant term and, $A_{s,k}$ and $A_{c,k}$ are the sine and cosine coefficients of the $k^{th}$ harmonic of the Fourier series, respectively.

This approach has been applied to optical remote sensing data, particularly for vegetation characterization, as phenological cycles induce seasonal variability in vegetation indexes (Andres et al., 1994; Moody and Johnson, 2001; Roy and Yan, 2020). The differences in seasonal dynamics between different vegetation types are reflected in the $A_{s,k}$ and $A_{c,k}$ coefficients, which can in turn be used for classification purposes (Azzali and Menenti, 2000; Evans and Geerken, 2006; Zhu and Woodcock, 2014; Wang et al., 2019; Adams et al., 2020; Yang et al., 2022). The harmonic model also enables the estimation of missing data in time series, e.g., due to cloud cover in optical imagery (Brooks et al., 2012; Ashok et al., 2021), or can be incorporated into change detection algorithms (Coppin et al., 2004; Verbesselt et al., 2010a, 2010b, 2012; Zhu and Woodcock, 2014; Yang et al., 2022; Zhou et al., 2022). In an application similar to the objective of this paper, Ashok et al. (2021) and Yang et al. (2022) used harmonic regressions on optical water indexes, showing good performance in detecting water distribution in wetland environments.

By contrast, very few studies implemented similar approaches on SAR time series. Schlaffer et al. (2015, 2017) fitted harmonic models to SAR backscatter time series to detect flood events and extents, while Schlaffer et al. (2016) characterized the seasonality of backscattering coefficient time series in a regional-scale wetland. In that study, the derived harmonic coefficients were used in RGB composites and unsupervised classification to discriminate between permanent and seasonal wetlands. To date, similar harmonic modeling approaches have not been applied to interferometric coherence time series, despite some exhibiting clear seasonal patterns (Brisco et al., 2017).

This study aims to illustrate the monitoring of flood pulses in wetlands by exploiting the seasonality of InSAR coherence through a harmonic regression approach applied to a series of 12-day Sentinel-1 coherence maps generated via an automated InSAR processing workflow. The method is demonstrated in the Okavango Delta, a large flood-pulsed inland wetland located in Botswana. The analytical approach includes three steps. First, we examine the relationship between the seasonal signature of coherence, land cover, and flood propagation. Based on this analysis, we develop a seasonal index using the coefficients estimated from a first order harmonic regression, and subsequently employ both the coefficients and the seasonal index as inputs to a classification procedure. Second, we map the inter-annual variations in flood pulse extent from seasonal coherence pattern and threshold applied to the seasonal index. Finally, we implement a change-detection
procedure that accounts for the seasonality of coherence data to monitor
flood pulse progression.

## 2. Study area and datasets

### 2.1. The Okavango Delta

The study area is the Okavango Delta in northern Botswana (Fig. 1a), a vast endorheic wetland (> 12,000 km²) characterized by an annual flood pulse that can more than double the water extent between the low flood period of February–March and the peak flood period of August–October (McCarthy et al., 1993; Gumbricht et al., 2004; Wolski et al., 2006). The Delta is located in the semi-arid region of the Kalahari Desert, which experiences strong contrasts in rainfall between the rainy season (October–April) and the dry season (May–September) (Milzow et al., 2009; Dauteuil et al., 2023). The asynchronous seasonal pattern of inundation and local rain creates an ideal setting to assess the seasonality of coherence time series in the framework of flood-pulse detection.

The Okavango Delta wetlands developed on an alluvial megafan that formed within the Okavango Graben topographic depression (McCarthy, 2013). The Okavango Delta is divided into the alluvial fan proper and the Panhandle, an upstream NW–SE broad valley (Fig. 1a). The water supply to wetlands is dominated by the annual flood pulse from the Okavango River (~9–10 km³/yr on average), with local rainfall playing lesser role (~450 mm/yr or approximately ~5 km³/yr on average) (McCarthy et al., 2003; McCarthy, 2006; Mackay et al., 2011; Wolski et al., 2012; McCarthy, 2013). The annual flood pulse magnitude varies significantly, depending on the annual precipitation in the Okavango headwaters in Angola, with annual discharges varying by a factor of 2.5 between dry and wet years (~6 to ~16 km³/yr) (Gumbricht et al., 2004; McCarthy, 2013).

The flood pulse is delayed relative to rainfall and begins at the onset of the subsequent dry season, such that the flood is nearly perfectly out of phase with rainfall (Fig. 1b). This delay is caused by the flat topography and the ~800 km distance between the headwaters of the Okavango River, which receive the highest precipitation rates, and the Delta inlet (Fig. 1c). The water level peaks around April in the Okavango River at Mohembo, at the apex of the Delta, and around July–August in its distal section, in the Thamalakane River at Maun (Fig. 1a and b).

Wetlands of the Okavango Delta (Fig. 1a) comprise permanent swamps, seasonal swamps that are inundated during annual flood pulses, and occasional swamps that are variably inundated depending on flood pulse intensity (McCarthy et al., 1998; Gumbricht et al., 2004; Davidson et al., 2012; Wolski et al., 2017). The inundated area of the Delta fluctuates between 3000 and 12,000 km² and is closely correlated to the magnitude of the flood pulse (Gumbricht et al., 2004; Wolski et al., 2006; Wolski and Murray-Hudson, 2006; Thito et al., 2016).

The Okavango hydrographic system is endorheic: most of the water arriving in the Okavango Delta exits the alluvial fan through infiltration and evapotranspiration. Only ~3 % of the water outflows from the Okavango Delta (McCarthy et al., 1998; McCarthy, 2006), mostly through the Boteti River that terminates in the arid area of the Makgadikgadi Pans and occasionally through the Selinda Spillway that connects to the Linyanti Swamps (Fig. 1a and c). The water supply of the downstream areas of the Delta is dependent on the amplitude of the flood-pulse. Thus, the water extent of Lake Ngami (Fig. 1a), to the south, oscillated between >250 km² and 0 km² (totally dry) over the last century, with a last peak at 277 km² in 2012 and a total desiccation in 2019 (Shaw, 1983; Kurugundla et al., 2018; Mokgedi et al., 2019).

### 2.2. InSAR coherence time series

InSAR coherence time series were calculated by the ForM@Ter LArge-Scale Multi-Temporal Sentinel-1 InterferoMetry Service (FLATSIM) (ForM@Ter, 2020; Thollard et al., 2021). FLATSIM automatically produces InSAR products spanning several years over large areas based

on massive VV-polarized Sentinel-1 datasets downloaded from the PEPS platform of the French Centre National d'Etudes Spatiales (Thollard et al., 2021; Text S1). Its algorithms are based on the New Small Baseline (NSBAS) processing chain (Doin et al., 2011; Grandin, 2015), which relies on the Repeat Orbit Interferometry Package (ROI_PAC) (Buckley et al., 2000; Rosen et al., 2004). The FLATSIM procedure and products are detailed in Thollard et al. (2021) and have been used in several studies, mainly for displacement time series analyses (e.g. Lemrabet et al., 2023; Neyrinck et al., 2024).

To entirely cover the Okavango Delta, we worked on two ascending Sentinel-1 tracks (tracks 14 and 87) (Fig. S1). Coherence images produced by the FLATSIM service have a 117*117 m spatial resolution (Table 1). We built a coherence time series with all the successive interferometric coherence images having a 12-day temporal baseline from October 2016 to mid-April 2021 (139 coherence images, corresponding to 140 Sentinel-1 acquisitions for track 14; 132 coherence images, corresponding to 133 SAR acquisitions for track 87). Since coherence images are calculated from two SAR acquisitions, we assigned the date of the second acquisition to the coherence value in the coherence time series, so that the coherence value at a given date reflects changes that occurred during the last 12 days. The 12-day temporal baseline corresponds to the Sentinel-1 revisiting time along each ascending track. We worked with this lowest temporal baseline only as it provided the highest temporal resolution possible for characterizing flood propagation, and also limited the temporal decorrelation induced at longer temporal baselines (Hall-Atkinson and Smith, 2001; Rocca, 2007; Sica et al., 2019).

### 2.3. Reference datasets

#### 2.3.1. Land cover

To assess the impact of land cover on coherence time series, we used the 2018 Copernicus Global Land Service - Land Cover 100 m - version 3 (CGLS-LC100m) land cover dataset (Buchhorn et al., 2020; Fig. 2a; Table 1). It has an estimated accuracy of 80 % ± 2 over Africa (Tsendbazar et al., 2021).

#### 2.3.2. Reference inundation maps

As a reference dataset for flood extents, we used a series of inundation maps (Fig. 2b; Table 1) derived from MCD43A3 Moderate-Resolution Imaging Spectroradiometer (MODIS) data, following the methodology developed in Wolski et al. (2017) and extending the dataset presented there (ORI Monitoring, 2024). After evaluating the performance of various spectral indexes for flood mapping in the Okavango Delta, Wolski et al. (2017) identified an automatic adaptive thresholding of the SWIR band as the most effective approach for delineating flooded areas. The dataset used here includes 98 images from 2017 to 2020, with pixels classified into three categories: flooded, dry, or cloud (Fig. 2b). The difference in spatial resolution between the FLATSIM coherence dataset (117 m) and the reference inundation maps (500 m) necessitated downscaling the FLATSIM data when comparing the two datasets, as described in the methodology section.

## 3. Methodology

Based on coherence time series derived from FLATSIM products (Text S1), we conducted three distinct analyses at different time scales and with different objectives (Fig. 3), as detailed in the following sections.

First, we analyzed multi-year coherence time series to characterize their long-term temporal patterns. For each pixel, temporal statistics and seasonal parameters were derived by fitting a simplified harmonic model, followed by k-means unsupervised clustering (MacQueen, 1967) of the resulting statistics and seasonal parameters. The seasonal parameters and cluster classes were then compared with reference datasets to evaluate the influence of land cover and flooding on coherence
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Study area: the Okavango Delta. a. Map of the Okavango Delta and its main tributaries (modified from Davidson et al., 2012; OpenStreetMap contributors, 2015; Okavango Data, 2024). Background: Google Earth imagery in grey level (Image © Landsat and Image © Copernicus) with topographic shading generated from Copernicus data elevation model COP-DEM DTED GLO-30 (ESA, 2019). b. Precipitation and relative water level time series at Mohembo. Monthly precipitation is derived from daily precipitation values of Integrated Multi-satellite Retrievals for GPM (IMERG) data (Huffman et al., 2019), averaged over the whole study area. The relative water level was measured at Mohembo by the Department of Water Affairs of Botswana and data distributed by the Okavango Research Institute (Okavango Data, 2024). c. Mean annual precipitation over the Okavango catchment area. Precipitation data are from IMERG (Huffman et al., 2019).</figcaption></figure>
<table><caption>**Table 1**

Characteristics of the datasets used in this study. ¹Minimum temporal resolution, as cloud cover causes acquisition gaps.</caption><thead><tr><th rowspan="3">Product</th><th colspan="3">Data source</th><th colspan="7">Final dataset</th></tr><tr><th rowspan="2">Sensor type</th><th rowspan="2">Satellite</th><th rowspan="2">Band/polarization</th><th colspan="4">Resolution</th><th rowspan="2">Data type</th><th rowspan="2">Reference</th></tr><tr><th>Spatial</th><th>Temp.</th><th>Time span</th><th>N. scenes</th></tr></thead><tbody><tr><td>InSAR coherence time series<br>CGLS-LC100m (2018)</td><td>SAR</td><td>Sentinel-1</td><td>VV</td><td>117 m</td><td>12 days</td><td>Oct. 2016 - Apr. 2021</td><td>139,132</td><td>InSAR coherence (0.0-1.0)</td><td>Thollard et al. (2021)</td></tr><tr><td>MODIS Flood Maps</td><td>Optical</td><td>POBA-V</td><td>Multispectral</td><td>100 m</td><td>N.A.</td><td>2018</td><td>1</td><td>Land cover classification (23 classes)</td><td>Buchhorn et al. (2020)</td></tr><tr><td></td><td>Optical</td><td>MODIS</td><td>SWIR</td><td>500 m</td><td>8 days<sup>1</sup></td><td>2017-2020</td><td>98</td><td>Flood classification (flood, dry, cloud)</td><td>Wolski et al. (2017)</td></tr></tbody></table>

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** The reference datasets for landcover and floods. a. CGLS-LC100m land cover map (Buchhorn et al., 2020). b. Reference inundation map (Wolski et al., 2017) and map of inundation frequency for the period 2017–2020 (the calculation of inundation frequencies is detailed in the methodology section).</figcaption></figure>

seasonality. Second, we analyzed each year’s coherence time series to assess the effects of the individual flood pulses on coherence values. Finally, a change detection approach was implemented to map flood propagation dynamics over each year. This method incorporates average seasonal parameters for the entire area to account for regional coherence fluctuations, and identifies flood arrival when the observed coherence signal departs from the expected seasonal trend.

### 3.1. Multi-annual analysis of coherence time series

#### 3.1.1. Seasonal model and temporal statistics

The temporal statistics and the seasonal parameters were calculated on a pixel-to-pixel basis. In this study, we assess whether coherence time series exhibit annual cyclicity. To this end, we applied a first-order harmonic model, hereafter referred to as the seasonal model, corresponding to the first term of the harmonic equation (N=1 in Eq. (1)), with a period T of 365.25 days. Thus, Eq. (1) becomes:

$$s_m(t) = A_0 + A_1 \cos\left(\frac{2\pi}{365.25}(t + t_{\max})\right) \quad (2)$$

where $A_1$ is the seasonal amplitude of the signal and $t_{\max}$ is the day corresponding to the maximum peak of the seasonal signal (Fig. 4).

Eq. (2) was fitted to the full coherence time series presented in Section 2.2 at each pixel using the least squares regression method, taking the January 1, 2017 as the time origin so that $t_{\max}$ values can be directly interpreted as a day of the year (note that the time of origin taken for the regression is not the first date of the coherence dataset, therefore dates before January 1, 2017 correspond to negative values of t). Then, the Root Mean Square Error (RMSE) between measured coherence data and the fitted model was calculated at each pixel as follows:

$$RMSE = \sqrt{\frac{1}{n}\sum_{t=1}^{n}\varepsilon_{t}^{2}} \text{ where } \varepsilon_{t} = \gamma(t) - s_{m}(t) \quad (3)$$
<figure><img src="image_5.png" /><figcaption>Fig. 3. Processing workflow of the seasonal analysis of coherence time series. [1] Thollard et al. (2021); [2] Buchhorn et al. (2020); [3] Wolski et al. (2017).</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Fig. 4. Schematic representation of the seasonal parameters. Annotations correspond to parameters and functions cited in the text. The grey dots are the observed coherence values (γ); the red line is the seasonal model (Eq. (2)). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

where n is the number of dates in the coherence time series, and γ(t) and s_m(t) are the measured and the modeled coherences at time t, respectively.

To characterize the seasonality at a given pixel, we built a Normalized Seasonal Indicator (NSI) based on the seasonal amplitude A₁ and the RMSE, calculated at each pixel as:

$$NSI = \frac{A_1 - RMSE}{A_1 + RMSE} \qquad (4)$$

NSI values range between -1.0 and 1.0. Values close to 1.0 indicate that the RMSE is negligible compared to the seasonal amplitude, depicting a strongly seasonal signal. Conversely, values close to -1.0 indicate that the seasonal amplitude is negligible compared to the RMSE, indicating no seasonality in the coherence time series. NSI values close to zero depict areas where the seasonal amplitude and the RMSE have similar values. It can depict a noisy seasonal signal or a very flat signal with low noise.

The temporal average coherence, the seasonal amplitude of the coherence, the RMSE, and the NSI were computed for the two FLATSIM tracks and merged into synthetic maps covering the study area.

### 3.1.2. K-means clustering into classes of seasonality

We applied unsupervised k-means clustering to the statistical and seasonal parameters (average coherence, seasonal amplitude, RMSE, and NSI). Unsupervised clustering was preferred over supervised classification because of the temporal and spatial variability of flooding in the Okavango Delta. This variability makes it challenging to assign discrete classes to wetlands, as the dynamics of water extents imply fuzzy boundaries and rapid changes in land cover, which do not conform to static categories that can be defined a-priori.
The k-means algorithm partitions a dataset into a predefined number of clusters by minimizing the variance within each cluster (MacQueen, 1967). To determine the optimal number of clusters, we computed the k-means algorithm with four to ten clusters and calculated the silhouette score and the inertia for each number of clusters (Rousseeuw, 1987). This analysis indicated that seven clusters were appropriate to minimize inertia while maximizing the silhouette score (Fig. S2). The resulting classes from the k-means algorithm were numbered from one to seven, ordered by increasing average seasonal amplitude.

### 3.1.3. Assessing the impact of land cover and flood on coherence time series

Both the output parameters of the seasonal model and the discrete k-means classes were compared to the 2018 CGLS-LC100m land cover data and the reference inundation maps. CGLS-LC100m was co-registered with the FLATSIM coherence dataset using nearest neighbor resampling, as the two datasets have closely matching spatial resolutions. Additionally, a synthetic inundation frequency product was created from the reference inundation maps by calculating the proportion of acquisitions classified as flooded relative to the total number of acquisitions during the period of interest (2017–2021), similar to the method used by Wolski et al. (2017) (Fig. 2b). Thus, reference flood frequencies are expressed as percentages. Maps derived from the coherence analysis were downsampled to match the lower resolution of the reference inundation maps. For continuous parameters, such as seasonal amplitudes, downsampling involved calculating the average value. For discrete classes, the class at the center of the pixel was sampled.

Then, we calculated the statistical distribution of the seasonal parameters in the different land cover and flood frequency classes. Additionally, we calculated the contingency table between the k-means classes, the CGLS-LC100m classes, and the inundation frequency classes to analyze the overlap and the degree of agreement between these datasets.

## 3.2. Inter-annual analysis of coherence seasonality in relation to flood-pulse extents

### 3.2.1. Calculation of yearly NSI maps

To evaluate the ability of the NSI to derive the maximum flood extent of each annual flood pulse, we generated yearly NSI maps by applying the approach described in Section 3.1.1 to one-year coherence time series. For each year, the time origin was set to January 1.

### 3.2.2. Comparison of yearly NSI and maximum flood extents and classification

To compare the annual NSI maps to flood extents, we derived a reference maximum flood extent map for each year from the reference inundation dataset. For each year, we calculated the inundation frequency as in Section 3.1.3. Following Wolski et al. (2017), each yearly maximum flood extent was defined as all pixels inundated on more than 10 % of acquisition of the year in the reference dataset. To account for the difference in spatial resolution between the NSI maps and the reference inundation maps, we calculated the mode (i.e., the most frequent value) of NSI values within each pixel of the maximum flood extent maps. We then analyzed the distribution of these NSI mode values in dry and flooded areas of the maximum inundation maps using histograms. Finally, to classify flooded areas from NSI maps, we defined a threshold value for each year based on these histograms. The threshold corresponded to the 5th percentile of NSI values in dry pixels of the reference flood extent maps for each year, with all NSI values below this threshold classified as flooded. The percentile was identified empirically from the histograms, and although the same percentile value was applied across all years, the resulting NSI threshold values varied due to differences in annual NSI distribution.

## 3.3. Flood pulse propagation from change detection based on coherence seasonality

### 3.3.1. Change detection methodology

Finally, we implemented a change detection approach on coherence time series to map the arrival dates of flood pulses. Based on the observations made in previous analyses, this method relies on the seasonality of coherence time series. To detect flood pulses, each year was analyzed independently.

The first step consisted of calculating the average seasonal model of the entire scene over a given year. For each acquisition date, we calculated the average coherence value of the scene. A seasonal model $s_{m,avg}$ (Eq. (2)) was then fitted to these average coherence values. From this model, we determined the average $t_{max, avg}$ value of the study area for the given year.

The second step consisted of fitting, at each pixel, the seasonal model that best corresponds to the average seasonal model of the scene, calculated in step one. To do so, we weighted coherence values by the normalized inverse of their distance to the average seasonal model. We then fitted the seasonal model $s_{m,w}$ (Eq. (2)) to weighted coherence values at each pixel with $t_{max}$ imposed to be $t_{max, avg}$. To summarize, the least squares regression consisted in minimizing the cost C as follows:

$$C = \sum_{t=1}^{n} \left( \frac{\sigma_{max}}{\sigma(t)} \right)^2 * (s_{m,w}(t) - s_{m,avg}(t))^2 \quad (5)$$

where,

$$\sigma(t) = |\gamma(t) - s_{m,avg}(t)| \quad (6)$$

where $s_{m,avg}(t)$ is the model fitted to average coherence values $\gamma_{avg}(t)$ of each scene, and $s_{m,w}(t)$ is the model fitted to weighted coherence values $\gamma(t)$ at a given pixel, with $t_{max}$ imposed to be $t_{max, avg}$.

From the model $s_{m,w}$, we calculated the seasonal amplitude, the RMSE, and the NSI at each pixel. Where the seasonal amplitude of $s_{m,w}$ was negative (i.e. coherence values are higher during the rainy season compared to the dry season), we set $s_{m,w}$ to $s_{m,avg}$ and NSI to -1.

The third and final step, consisted of determining when the coherence values $\gamma(t)$ diverges from the expected seasonal model $s_{m,w}(t)$. To do so, we calculated the difference $\delta(t)$ between the measured coherence $\gamma(t)$ and the seasonal model $s_{m,w}(t)$, and summed the differences over intervals where $\gamma(t)$ is lower than $s_{m,w}(t)$. We selected the interval with the highest sum of differences and defined the date of deviation from the seasonal model as the first date of this interval. A graphical description of this procedure is shown on Fig. 5.

### 3.3.2. Validation of the date of flood arrival

To validate the change detection approach, we generated maps of the flood arrival date for each year using the reference inundation maps

<figure><img src="image_7.png" /><figcaption>Fig. 5. Schematic representation of the change detection approach. Annotations are explained in the text. Orange areas correspond to intervals where coherence values (in black) are lower than the seasonal model (in red). The first date of the interval with the largest area is selected as the change date corresponding to the arrival of the flood. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
(Fig. S3). Similar to the previous section, we only considered pixels that were flooded in more than 10 % of the acquisitions over the year. Three biases had to be accounted for during the validation of coherence-derived flood arrival maps against this reference dataset.

First, the reference inundation maps and the FLATSIM coherence data have different temporal resolutions. To determine if the flood dates indicated by the two datasets were consistent, we checked whether the intervals covered by their temporal resolutions overlapped. If they overlapped, we considered the error to be zero. If their intervals did not overlap, we considered the error to be the shortest time difference between the two intervals.

Second, the reference dataset lacks acquisitions during the rainy season due to cloud cover. Thus, in areas reached by flood pulses during this period, the validation is limited to checking if the detected flood date is consistent with the first available acquisition of the reference dataset.

Third, the two datasets have different spatial resolutions. Because the FLATSIM dataset has higher resolution than the reference inundation maps, for each pixel in the reference dataset, we selected the overlapping FLATSIM pixel with the lowest error. This approach was necessary because neighboring high-resolution pixels might show very different change dates, (e.g., a flooded floodplain pixel next to a dry island pixel), which would otherwise inflate errors if all pixels were considered.

# 4. Results

## 4.1. Multi-annual seasonality of interferometric coherence time series

### 4.1.1. Per-date spatial average of the coherence over the study area

Average coherence values over the entire study area show clear seasonality (Fig. 6a). Minimum coherence occurs during the rainy

season (December to March) and the maximum coherence is reached during the dry season (July to August). The spatial standard deviation of the coherence is higher during the dry season compared to the rainy season. These observations are consistent with similarity of moisture levels in wetlands and surrounding non-wetland areas during the rainy season, leading to a decreased coherence contrast between these land cover types (Fig. 6bi and ii). During the dry season, as soil moisture levels drop outside wetlands, coherence increases and the contrast between flooded wetlands and dry areas amplifies (Fig. 6biii and iv).

### 4.1.2. Per-pixel temporal pattern of coherence time series

Per-pixel temporal statistics and seasonal parameters are mapped and compared with the 2018 CGLS-LC100m and with reference inundation maps in Figs. 7 and 8. Wetlands clearly contrast with the surrounding dry areas in average coherence, seasonal amplitude, and RMSE maps, delineating the shape of the Okavango Delta, its hydrographic tributaries, and floodplains (Fig. 7).

Lowest average coherence values correspond to the permanent wetlands of the upper part of the Delta, characterized by open water surfaces, reeds, and papyrus floodplains. Occasional wetlands located in the downstream region of the Delta have slightly higher average coherence values, consistent with the occurrence of dry periods inducing higher values in herbaceous floodplains (Fig. 7). When analyzed by land cover class, average coherence values are the lowest over the permanent open water class and the highest in shrub and herbaceous vegetation classes, while wetlands present the widest range of values (Fig. 8). There is a clear decrease in average coherence values when flood frequency increases, with the strongest decrease for the most frequently flooded areas (Fig. 8).

The seasonal amplitudes of coherence values are lower over wetland classes compared to dry areas. Permanent open waters have the lowest seasonal amplitude, followed by wetlands and undetermined closed

<figure><img src="image_8.png" /><figcaption>**Fig. 6.** Spatiotemporal distribution of coherence values. a. Seasonal model fitted to the spatial average of coherence values. Average coherence values are calculated for each date with all the pixels of the study area on track 14 (black dots) and track 87 (black crosses). Grey bars indicate the standard deviation of the coherence at each date. The red curve represents the seasonal model. Purple numbers point to average coherence values of images in b. b. Example of coherence ($\gamma$) maps. Black contours correspond to wetland areas extracted from the 2018 CGLS-LC100m land cover. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>**Fig. 7.** Maps of reference datasets and parameters of the seasonal model calculated over the period of interest (2017–2020). a. Reference land cover (legend in Fig. 2a). b. Reference inundation frequency (legend in Fig. 2b). c. Average coherence values. d. Seasonal amplitudes of the seasonal model fitted to the coherence time series. e. RMSE of the seasonal model fitted to coherence time series. f. NSI.</figcaption></figure>

forests. Shrubs have the highest seasonal amplitude in coherence values. The seasonal amplitude decreases as the inundation frequency increases, with a strong contrast between areas that are never flooded and occasionally flooded areas (Fig. 8).

RMSE values vary considerably across the Delta (Fig. 7). The northwestern part of the Delta has lower RMSE than surrounding dry areas, whereas its southwestern half has significantly higher RMSE. Consequently, the wetland class displays a wider range of RMSE values than other land cover classes (Fig. 8). This distribution of RMSE values appears to be correlated with inundation frequency: RSME values increase until inundation frequency reaches 70 % and then strongly decrease in more frequently flooded areas (Fig. 8). Permanent waters have the lowest RMSE, consistent with stable low coherence values over time.

NSI values exhibit a clear bimodal spatial distribution with negative values concentrated in wetlands and positive values associated with dry areas (Fig. 7). Permanent waters and wetlands are the only two classes where most pixels have negative NSI values (Fig. 8). Accordingly, NSI values decrease with flood frequency, except where flood frequencies are higher than 80 % (Fig. 8). In these frequently flooded areas, low seasonal amplitude and low RMSE lead to NSI values higher than in occasional wetlands. Overall, the spatial distribution of NSI values shows that, in the Okavango Delta wetlands, coherence time series deviate from a seasonal pattern, with RMSE exceeding seasonal amplitude values.

The wetland class is the one that encompasses the widest range of values for every modeled parameter, except for the seasonal amplitude (Fig. 8). Thus, the temporal pattern of coherence time series is less
<figure><img src="image_10.png" /><figcaption>**Fig. 8.** Boxplots of the seasonal parameters for each land cover class and flood frequency interval. The left column shows boxplots of each seasonal parameter for each land cover class. The right column shows boxplots of each seasonal parameter for each flood frequency interval (top graduations). In boxplots, the horizontal bar represents the median and the cross represents the average, with the box representing the inter-quantile range. Average, seasonal amplitude, RMSE, and NSI values are extracted from maps in Fig. 7. Grey bars indicate the pixel count for each class.</figcaption></figure>

homogenous in this class than in others. This is because the CGLS-LC100m land cover categorizes the entire Delta under a single “herbaceous wetland” class, without distinguishing between different wetland types. This indicates that the different wetland types and the variability in inundation must be considered to fully understand the coherence patterns in the wetland class.

The RGB composite presented in Fig. 9 offers a synthetic view of the seasonal model coefficients fitted to coherence time series at each pixel and illustrates the differences in temporal patterns of coherence time series at selected locations validated from fieldwork. Areas outside the Okavango Delta, dominated by shrubs, herbaceous vegetation, and

forests, tend to appear in light green to light blue, reflecting high average coherence, high seasonal amplitude and low RMSE. These pixels reflect a clear seasonal signal, with high coherence values during the dry season and low coherence values during the rainy season. In contrast, the Okavango Delta is characterized by magenta to red pixels in its lower part, representing occasional wetlands, and black pixels in its upper part, mostly corresponding to permanent wetlands. Magenta to red pixels indicate high RMSE, showing that coherence time series deviate from a seasonal pattern in these areas. Coherence time series in occasional wetlands (locations 4 in Fig. 9) show scattered values, with low coherence observed throughout the year. Lake Ngami (location 5 in
<figure><img src="image_11.png" /><figcaption>**Fig. 9.** RGB colour composite of statistics and seasonal parameters derived from coherence time series (Red: RMSE; Green: seasonal amplitude; Blue: average coherence). The prism underneath the map provides a 3-D schematic representation of the colour composite legend. The right column shows the coherence ($\gamma$) time series of the pixel indicated on the map and legend. In the plots, grey dots represent coherence values, the horizontal dotted red line indicates the average coherence, and the red curve shows the fitted seasonal model. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

Fig. 9) maintains coherence close to zero throughout the study period, indicating presence of open water, except in 2019 when higher values reflect a dry episode related to low water inflow in the Okavango Delta. Black pixels in the Panhandle and upper part of the Delta (location 6 in Fig. 9) reflect low seasonal amplitude, low average, and low RMSE, indicating persistently low coherence throughout the year. Coherence time series in this area are not affected by the 2019 drought, as these locations are part of the permanently flooded upstream section of the Delta.

### 4.1.3. Clustering in classes of seasonality

Clustering of pixels based on the parameters extracted from the seasonal model (seasonal amplitude, RMSE, NSI, temporal average) is presented in Fig. 10. Fig. 11 shows the proportions of pixels matching between the classes resulting from k-means clustering, the land cover, and flood frequencies.

Consistent with the colour composite presented in Fig. 9, coherence time series show a clear contrast between areas inside and outside the Okavango Delta. Classes 1–3 are dominated by wetlands, which comprise 66–93 % of the pixels, while classes 4–6 mostly correspond to

non-wetland classes, with only 3–14 % of their pixels classified as wetlands (Fig. 11a and b). This pattern is also evident when comparing the clustering classes to inundation maps: 86–97 % of pixels in classes 1–3 were flooded at least once while this proportion drops to 5–19 % for classes 4–6 (Fig. 11c and d).

The main distinction in coherence pattern between these two groups of classes is the seasonality of the time series, reflected by the negative NSI values in classes 1–3 and the positive values in classes 4–6 (Fig. 10). Coherence time series associated with wetlands have lower seasonality compared to non-wetland areas, and the boxplot of NSI values in Fig. 10 shows that a simple threshold of NSI values around 0.2 would accurately distinguish classes 1–3 from classes 4–6.

Additionally, distinct coherence patterns are observable among the classes associated with wetlands (classes 1–3). Class 1 represents pixels where coherence values remain low throughout the time series, resulting in the lowest average coherence, seasonal amplitude, and RMSE among all classes (Fig. 10). This class is strongly associated with permanently and frequently flooded areas: 60 % of the pixels were permanently flooded and 86 % were inundated for more than half of the time (Fig. 11a and b).
<figure><img src="image_12.png" /><figcaption>**Fig. 10.** Result of the k-means clustering of seasonal model parameters. The left panel shows the map of classes resulting from the unsupervised clustering, and the right panel displays the average coherence (γ) time series of each class. Classes are ordered by seasonal amplitude. Grey dots and grey bars indicate the mean and standard deviation of coherence values across all the pixels in each class, and the red curve represents the seasonal model fitted to the class average coherence. Boxplots at the bottom summarize the statistics for each class and for each parameter used as input in the clustering. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

Class 2 is characterized by high RMSE and no clear seasonal pattern (Fig. 10), and is primarily associated with temporary wetlands. While few pixels are permanently flooded, most are inundated for part of the year, with over 62 % flooded more than half of the time and 90 % flooded at least once (Fig. 11c and d).

Finally, Class 3 exhibits a stronger seasonality than classes 1 and 2, although RMSE remains high. It is mostly composed of wetlands but also includes open forests and herbaceous vegetation (Fig. 10). Similarly to class 2, pixels of class 3 are strongly associated with areas subject to flood pulses, with 86 % flooded at least once during the study period (Fig. 11c and d). However, these areas are less frequently inundated than those in class 2, representing more occasional wetlands.

A substantial proportion of non-wetland pixels in classes 1–3 correspond to areas adjacent to wetlands, particularly herbaceous vegetation and closed forests, extensively found around wetland margins (Fig. S4). These pixels may be affected by flood pulses despite not being classified as wetlands in the reference land cover dataset. Additionally, the 117 m resolution of the data likely produces pixels of mixed land cover containing both wetlands and other land cover classes. Similar boundary

and resolution effects may also explain why some wetland pixels are assigned to classes 4–6.

## 4.2. Inter-annual seasonality

To better relate the coherence temporal patterns to flood pulses, the seasonal model was fitted to coherence time series on a per-year basis. The resulting NSI maps for each year (Fig. 12a) were compared to the corresponding flood pulse extents derived from the reference inundation maps (Fig. 12b, c, and d).

Our results show a clear correlation between the flood pulse extent and the seasonality of coherence time series. The lowest NSI values are located in regions corresponding to the flood extents derived from the reference inundation maps (Fig. 12a and b). Histograms of NSI values show a pronounced asymmetry, with lower NSI values correlated with flooded areas and higher NSI values correlated with dry areas (Fig. 12c).

To illustrate this relationship, we selected an NSI threshold value that corresponds to the 5 % cutoff value of the dry pixels (Fig. 12d). Depending on the year, the corresponding NSI threshold values range
<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /></figure>

<figure><img src="image_16.png" /><figcaption>Fig. 11. Representation of the contingency tables between k-means clustering classes, land cover classes, and flood frequency. a. Distribution of land cover classes per k-means clustering class. Maps of k-means classes per land cover occupations are shown in Fig. S4. b. Distribution of k-means clustering classes per land cover class. c. Distribution of inundation frequency per k-means clustering class. d. Distribution of k-means clustering class per 10 % intervals of inundation frequency.</figcaption></figure>

between 0.06 and 0.11. These NSI threshold values are consistent with the NSI values separating classes 1–3 and classes 4–7 in the multi-annual analysis (see NSI boxplot in Fig. 10). Between 78 % and 82 % of the flooded pixels fall below these thresholds.

Binary maps generated using these thresholds (Fig. 12e) delineate areas consistent with the reference inundation maps (Fig. 12b), as confirmed by the user and producer accuracies of 70–87 % for flooded pixels, and over 90 % for dry pixels (Table S2). These results highlight the strong influence of flooding on the seasonality of coherence time series and demonstrate that the NSI is a reliable indicator for delineating the flood pulse extent on an annual basis. Histograms show that 18–22 % of flooded pixels have NSI values above the threshold, making them indistinguishable from non-flooded pixels (Fig. 12c and d). Additionally, the low producer accuracies of the flood class in coherence maps’ resolution indicate that many pixels classified as not-flooded correspond to flooded areas in the reference dataset (Table S2). Part of this apparent mismatch arises due to the larger resolution of the reference inundation maps compared to the coherence dataset. Indeed, pixels of the reference flood maps often cover a mosaic of intricate flood plains and dry islands, mixing low and high-NSI pixels from the coherence dataset.

### 4.3. Flood pulse dynamics from change detection

As demonstrated above, the seasonality of coherence time series is strongly influenced by land cover and flood pulse dynamics. Pixels that are never flooded exhibit a seasonal pattern, with seasonal amplitude varying with land cover. In contrast, flooding disrupts this seasonality. The following section presents the results of applying the change detection approach to coherence time series for detecting flood arrival.

#### 4.3.1. Detection of flood arrival

Fig. 13a shows the 2020 flood pulse propagation map derived from coherence time series at 12-day temporal resolution. The change detection approach assigns a date of change to each pixel, including in non-flooded areas. To distinguish flooded from non-flooded pixels, we used a visual representation inspired by the REACTIV method (Koeniguer et al., 2018; Colin Koeniguer and Nicolas, 2020). The output is displayed in Hue-Saturation-Value (HSV) colour space, where hue (H) represents the day of change, saturation (S) decreases with increasing average coherence, and the value (V) decreases with increasing NSI (Fig. 13a and b). As shown in previous sections, non-flooded pixels tend to have higher coherences and higher NSI values than flooded ones. Consequently, in the HSV representation, non-flooded pixels appear in grey to black, whereas flooded pixels display pronounced colors related to the date of flood arrival.

In 2020, the flood pulse was first detected in January in the upper part of the Panhandle and reached the upper part of the alluvial fan by early March. It then propagated through the Delta over two months (yellow to cyan gradient from northwest to southeast on Fig. 13a), reaching the Thamalakane River by mid-May and the upper Boteti River in early June. The southeasternmost section of the Boteti River shown on the map was inundated by the end of June. To the south, the flood progressed along the Thamalakane and Kunyere rivers in July, reaching their outlet at Lake Ngami during the first two weeks of that month. Subsequently, Lake Ngami filled up from northeast to southwest from July to early September (Fig. 13c). To the north, the flood propagated through the Selinda Spillway from early May to early July but did not reach the Linyanti Swamps.
<figure><img src="image_17.png" /><figcaption>**Fig. 12.** Comparison of NSI values of yearly coherence time series and flood pulse extent. a. NSI maps calculated from coherence time series for each year. b. Yearly flood extent calculated from the reference inundation maps (Wolski et al., 2017). c. Frequency histograms of NSI values in flooded and unflooded areas. d. Cumulative density histograms of NSI values in flooded and unflooded areas. Red lines indicate the value of each histogram at the 5 % cutoff value of dry pixels. e. Binary maps resulting from thresholding applied to NSI data. The threshold value is the one determined in d with the 5 % cutoff value of dry pixels. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
<figure><img src="image_18.png" /><figcaption>Fig. 13. Date of arrival of the 2020 flood pulse. a. Map in Hue-Saturation-Value (HSV) colour showing the date of flood pulse arrival derived from change detection in coherence time series. The corresponding colour scale is provided in b. b. Schematic 3-D block of the HSV symbology of the map in a. Hue (H) corresponds to the day of flood arrival in 2020; Saturation (S) decreases as the average coherence increases; Value (V) decreases when the NSI increases. c. Lake Ngami section of map a. d. Differences between detected dates of flood arrival derived from coherence time series and from the reference inundation maps (Wolski et al., 2017).</figcaption></figure>

### 4.3.2. Accuracy of the flood pulse detection

The comparison of flood arrival dates with the reference dataset shows good accuracy, with 74 % of pixels showing matching flood arrival dates (Fig. 13d). This agreement reaches 83 % when allowing difference of up to 12 days (corresponding to the temporal resolution of the coherence data).

The flood map is noisy in several areas, such as in the upper section of the Panhandle and in some regions north of Chief's Island where detected dates fluctuate strongly from one pixel to another, complicating interpretation of flood arrival. Areas dominated by permanent open water, such as north of Chief's Island, maintain low coherence throughout the year, so the flood arrival date does not induce a clear decrease in coherence values. In these areas, the detected change is likely unrelated to flooding, leading to high errors in estimated flood arrival dates (often over 80 days). Additionally, regions affected by the flood pulse earlier in the year, closer to the rainy season, such as the Panhandle, have coherence values that diverge from a seasonal pattern when the overall coherence of the track is the lowest. Thus, the deviation of the coherence from the seasonal model is harder to detect during this period.

## 5. Discussion

### 5.1. The spatiotemporal patterns of coherence time series in wetlands

Previous studies that applied parametric models to coherence time series in wetlands used exponential functions fitted to time series constructed with increasing temporal baselines from a fixed reference date to analyze temporal decorrelation (Sica et al. (2019) and references therein). These approaches enabled classifications based on the varying

rates of temporal decorrelation associated with different landscapes. However, such time series have limitations over long periods, as coherence converges toward zero with increasing temporal baselines (Sica et al., 2019; Hrysiewicz et al., 2023). In contrast, the isotemporal baseline coherence time series used in this work maintain a constant temporal decorrelation, making them more suitable for long-term monitoring and capturing outlier events relative to the general trend of time series (Jacob et al., 2020).

Consistent with previous studies, the isotemporal baseline coherence time series analyzed in this work exhibit a clear seasonal pattern (Brisco et al., 2017; Liao et al., 2020; Amani et al., 2021; Hrysiewicz et al., 2023) (Fig. 6). While earlier studies provided only a qualitative description of the seasonality based on raw time series and basic spatio-temporal statistics (Table S1), here we produce indicators (seasonal amplitude, $t_{max}$, RMSE, and NSI) that quantify the seasonal shape of coherence time series. These indicators offer a simple way to visualize spatiotemporal patterns of time series over large areas and enable quantitative comparisons with other variables, such as land cover or flood extent.

The seasonality of coherence time series in the Okavango Region is closely linked to the precipitation cycle. Rainfall induces changes in the soil moisture, fills temporary ponds, and triggers phenological changes in the shrub vegetation of the Kalahari area (Jolly and Running, 2004). These processes modify surface conditions, explaining the observed decrease in coherence during the rainy season (e.g., Zwieback et al., 2015; Amani et al., 2021; Villarroya-Carpio et al., 2022; Hrysiewicz et al., 2023).

However, our results show that flood pulses disrupt this seasonal pattern in wetlands. In areas that are never flooded, coherence time series exhibit a clear seasonal pattern with high coherence during the dry season. In temporary wetlands, the delayed arrival of the flood pulse
compared to rainfall induces a decrease in coherence during the dry season. In permanently flooded areas, primarily located in the upstream part of the Okavango Delta, the coherence remains low throughout the year. These low coherence values in flooded areas are likely due to the relatively large pixel size (117 m), combined with the heterogeneous wetland landscape in the Delta (intricate mosaic of water channels, herbaceous floodplains, and isolated islands) which results in multiple coexisting backscattering mechanisms within each pixel (specular, double-bounce, and volume scattering), leading to noise and phase decorrelation (Hall-Atkinson and Smith, 2001; Lu and Kwoun, 2008; Zhang et al., 2015; Siles et al., 2020).

## 5.2. Land cover classifications based on the seasonality of coherence time series

Due to the strong contrasts in the seasonal pattern of coherence data, the unsupervised clustering approach, using only four descriptive parameters of the coherence time series (average, seasonal amplitude, RMSE, and NSI), produces maps that clearly delineate wetland areas from dry areas (Figs. 10 and 11). The wetland-associated classes form three groups: one characterized by consistently low coherence values, mostly corresponding to permanent wetlands, and two others displaying higher coherence without a clear seasonal pattern, strongly associated with seasonal and occasional wetlands.

Among the different descriptive parameters of seasonality, the NSI best separates wetland-associated classes from other land cover types, enabling rapid, low computational-cost classification through simple thresholding. Applying such thresholds to the NSI generates binary maps of the yearly maximum extent of flood pulses (Fig. 12), with producer and user accuracies between 70 % and 87 %, and overall accuracies over 90 % (Table S2).

The observed links between coherence seasonality and land cover highlight the potential of using seasonal indicators as inputs for classification. Previous studies applying classifications using coherence data have either relied on raw time series values (Mohammadimanesh et al., 2018; Mestre-Quereda et al., 2020; Millard et al., 2020; Nikaein et al., 2021), which require significant computational resources for long time series, or on simple statistical indicators (Canisius et al., 2019; Jacob et al., 2020; Siles et al., 2020), which do not account for the temporal trends. In contrast, using seasonal indicators offers a promising alternative by incorporating temporal information while reducing the number of input variables.

However, this approach would require further enhancement to mitigate key uncertainties:

(1) Better accounting for the presence of pixels with composite landcover, especially at the transition between wetlands and permanently dry areas, where the resulting measured seasonality combines very different coherence behaviors (Fig. S4).

(2) Better accounting for the tendency of some dry vegetation types to present weak seasonal pattern in coherence time series, likely due to factors other than flood, rainfall, or phenology, such as the effect of wind or fires (Hagberg et al., 1995; Hrysiewicz et al., 2023), which can be mistaken as flooding.

(3) A better assessment of the impact of the “double-bounce effect”, which can increase coherence in flooded vegetation under certain conditions (e.g., Chul Jung and Alsdorf, 2010). If persistent, this effect would produce high average coherence and low seasonality in areas that are permanently flooded, and high seasonality where vegetation is seasonally inundated (Zhang et al., 2016). In our dataset, we observe the opposite pattern (Fig. 7), indicating that the double-bounce effect is not predominant and/or persistent. This can be explained both by the characteristics of the used radar acquisitions and the vegetation type of the Okavango Delta. Specifically, C-band and VV polarizations are less sensitive to double-bounce effects compared to longer-wavelength and

horizontal polarizations (Zhang et al., 2016; Wilusz et al., 2017; Adeli et al., 2020). Additionally, as mentioned in Section 5.1, the relatively large pixel size likely leads to mixed scattering mechanisms, reducing the likelihood of detecting consistent double-bounce signals. Finally, the vegetation type in the Delta mostly consists in dense herbaceous, reed and papyrus flood plains (McCarthy et al., 2005; Milzow et al., 2009; McCarthy, 2013), which favor volumetric scattering over double-bounce backscattering beyond a certain vegetation density (Lu and Kwoun, 2008; Wilusz et al., 2017; Zhang et al., 2016). Nevertheless, if working with data and in areas favoring the double-bounce effect, this phenomenon must be carefully considered, as it can result in very contrasted temporal patterns in coherence time series (e.g. an increase of coherence instead of a decrease during flood periods) even for similar wetland types.

## 5.3. Detecting flood-pulses with InSAR coherence time series

Coherence-based flood detection methods commonly rely on low coherence values as a proxy for change in surface conditions between two SAR acquisitions (Nico et al., 2000; Ramsey et al., 2006; Refice et al., 2014). This approach works well when the flood event generates a distinct outlier of low coherence within a set of otherwise high coherence acquisitions. However, it becomes less reliable when other processes, such a rainfall, also cause temporary drop in coherence. To address this limitation in the Okavango Delta, we mapped flooding by identifying periods when yearly coherence time series diverged from the expected seasonal cycle. This approach produced a flood arrival map with an overall accuracy of 74–83 % over the entire Okavango Delta.

A key element in our approach is the model used to define the expected pattern of coherence time series in absence of flooding. For this, we fitted a seasonal model to the average coherence time series computed over the entire scene and used it as a reference. This reference model was then applied to weight coherence values of individual pixels when estimating their expected seasonal pattern in absence of flooding. Given that more than 75 % of the pixels in the scene correspond to permanently dry areas, this average model can reasonably be assumed to represent the typical seasonal coherence behavior without flooding. However, this approach implicitly assumes that without flooding, the seasonal decrease and increase in coherence occur at the same time across all pixels, with only the amplitude of these fluctuations varying according to local surface conditions. Two main factors influence coherence in absence of flood: (1) soil moisture, closely influenced by rainfalls in the Kalahari environments (Milzow et al., 2009; Zwieback et al., 2015; Hrysiewicz et al., 2023), and (2) phenological changes (Liao et al., 2020; Brisco et al., 2017; Amani et al., 2021). These are considered below.

(1) Since the climatic gradient across the study area is negligible (Fig. 1c), we can assume that the coherence decreases related to rainfall-induced moisture changes occur synchronously across the Okavango Delta. However, if this method were applied to a region with stronger climatic gradients, local variations should be accounted for, for example by stratifying the scene into subregions with more homogeneous climatic conditions.

(2) The impact of phenology on coherence time series is less clear. Amani et al. (2021) reported similar timing of coherence decrease across different wetland types in temperate regions. In contrast, Tampuu et al. (2020) showed that coherence trends in Northern European peatlands vary significantly by vegetation type, largely due to water table fluctuations and their impact on vegetation. In the Okavango Delta, phenological changes may respond either to rainfall or to flood-pulses (Jolly and Running, 2004; McCarthy, 2013), which could lead to non-uniform coherence behavior across different vegetation types. Therefore, using separate reference seasonal models for different wetland types could
improve detection accuracy. Since wetlands of the Okavango Delta consists mainly of herbaceous, reed, and papyrus flood-plains (McCarthy, 2013), the relative vegetation homogeneity may explain the strong performance of the flood detection based on a single average coherence model. Nevertheless, this simplification may explain part of the flood detection errors shown in Fig. 13d.

Another key factor influencing the performance of our flood detection approach is the time lag between rainfall and flood arrival. In the Okavango Delta, the flood pulse occurs after the rainy season, so the coherence decrease induced by the flooding contrasts with the high coherence of the dry season, which enhances detection. However, in wetlands where flooding and rainfall are more synchronous, the low coherence induced by floods would be more challenging to distinguish from that caused by rainfall. Despite this limitation, coherence remains advantageous over optical data, which suffer from acquisition gaps not only during the rainy season but also whenever cloud cover is present. Finally, our approach remains applicable and should be further tested on various other flood-pulsed wetlands where flooding is not synchronous with rainfall, such as the Inner Niger Delta (Mali), the Amazon flood plains or the Pantanal (Brazil).

## 6. Conclusion

In this study, we demonstrated the potential of isotemporal baseline InSAR coherence time series to characterize flood dynamics in flood-pulsed wetlands, using the Okavango Delta as a study case. By fitting a first order harmonic model to 12-day temporal baseline Sentinel-1 coherence time series, we extracted descriptive indicators of coherence seasonality related to rainfall, flood pulses and different land cover types. We show that coherence in dry areas follows a strong seasonal pattern driven by alternating rainy and dry seasons, while this seasonality is disrupted in wetland areas due to the flood pulse propagation. RGB composites and k-means clustering applied to the indicators computed from the seasonal model successfully distinguish dry areas, permanent, and temporary wetlands. From these indicators, we also defined a Normalized Seasonal Index (NSI) that effectively delineates flood extents through simple thresholding. Furthermore, a change detection approach based on the deviation of coherence values from an average seasonal model enabled mapping of flood arrival dates with 74–83 % accuracy. These results highlight the potential of harmonic analyses of InSAR coherence data to simplify the analysis of dense time series, support land cover classification, and enhance flood monitoring in wetlands.

### CRediT authorship contribution statement

**Louis Gaudaré:** Writing – original draft, Visualization, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization.
**Samuel Corgne:** Writing – review & editing, Validation, Methodology, Conceptualization.
**Marc Jolivet:** Writing – review & editing, Supervision, Resources, Project administration, Funding acquisition, Conceptualization.
**Olivier Dauteuil:** Writing – review & editing, Supervision, Resources, Project administration, Funding acquisition, Conceptualization.
**Cécile Doubre:** Writing – review & editing, Validation, Supervision, Data curation, Conceptualization.
**Piotr Wolski:** Validation, Data curation.
**Raphaël Grandin:** Validation, Data curation.
**Marie-Pierre Doin:** Validation, Data curation.
**Philippe Durand:** Validation, Data curation.

### Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgements

This study is based on products of the FLATSIM service (https://www.doi.org/10.24400/253171/FLATSIM2020), developed as part of the FormaTerre Solid Earth data and services center and supported and operated by the CNES. This work was supported by the CNRS (INSU-TELLUS program), the University of Rennes, and the CNRS - GDR Rift. Work in Botswana was conducted under the research permit ENT8/36/4L(83) kindly provided by the Ministry of Environment, Natural Resources Conservation and Tourism of Botswana. We would like to thank the staff of the Okavango Research Institute, especially Kaelo Makati, Nashaat Mazrui, and Mike Murray-Hudson, for their valuable insights on the dynamics of the Okavango Delta.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.rse.2025.115173.

### Data availability

Links and DOIs are given in the reference section. FLATSIM dataset: ForM@Ter (2020); Reference Land Cover dataset: Buchhorn et al. (2020); Reference inundation maps: ORI Monitoring (2024).

## References

Adams, B., Iverson, L., Matthews, S., Peters, M., Prasad, A., Hix, D.M., 2020. Mapping forest composition with Landsat time series: an evaluation of seasonal composites and harmonic regression. Remote Sens. 12, 610. https://doi.org/10.3390/rs12040610.

Adeli, S., Salehi, B., Mahdianpari, M., Quackenbush, L.J., Brisco, B., Tamiminia, H., Shaw, S., 2020. Wetland monitoring using SAR data: a meta-analysis and comprehensive review. Remote Sens. 12, 2190. https://doi.org/10.3390/rs12142190.

Alsdorf, D.E., Melack, J.M., Dunne, T., Mertes, L.A.K., Hess, L.L., Smith, L.C., 2000. Interferometric radar measurements of water level changes on the Amazon flood plain. Nature 404, 174–177. https://doi.org/10.1038/35004560.

Alsdorf, D.E., Smith, L.C., Melack, J.M., 2001. Amazon floodplain water level changes measured with interferometric SIR-C radar. IEEE Trans. Geosci. Remote Sens. 39, 423–431. https://doi.org/10.1109/36.905250.

Alshammari, L., Boyd, D.S., Sowter, A., Marshall, C., Andersen, R., Gilbert, P., Marsh, S., Large, D.J., 2020. Use of surface motion characteristics determined by InSAR to assess peatland condition. J. Geophys. Res. Biogeosci. 125, e2018JG004953. https://doi.org/10.1029/2018JG004953.

Amani, M., Poncos, V., Brisco, B., Foroughnia, F., DeLancey, E.R., Ranjbar, S., 2021. InSAR coherence analysis for wetlands in Alberta, Canada using time-series Sentinel-1 data. Remote Sens. 13, 3315. https://doi.org/10.3390/rs13163315.

Andres, L., Salas, W.A., Skole, D., 1994. Fourier analysis of multi-temporal AVHRR data applied to a land cover classification. Int. J. Remote Sens. 15, 1115–1121. https://doi.org/10.1080/01431169408954145.

Ashok, A., Rani, H.P., Jayakumar, K.V., 2021. Monitoring of dynamic wetland changes using NDVI and NDWI based Landsat imagery. Remote Sens. Appl. Soc. Environ. 23, 100547. https://doi.org/10.1016/j.rsase.2021.100547.

Azzali, S., Menenti, M., 2000. Mapping vegetation-soil-climate complexes in southern Africa using temporal Fourier analysis of NOAA-AVHRR NDVI data. Int. J. Remote Sens. 21, 973–996. https://doi.org/10.1080/014311600210380.

Bouaraba, A., Younsi, A., Belhadj Aissa, A., Acheroy, M., Milisavljevic, N., Closson, D., 2012. Robust techniques for coherent change detection using COSMO-SkyMed SAR images. Prog. Electromagn. Res. M 22, 219–232. https://doi.org/10.2528/PIERM11110707.

Brisco, B., Ahern, F., Murnaghan, K., White, L., Canisus, F., Lancaster, P., 2017. Seasonal change in wetland coherence as an aid to wetland monitoring. Remote Sens. 9, 158. https://doi.org/10.3390/rs9020158.

Brooks, E.B., Thomas, V.A., Wynne, R.H., Coulston, J.W., 2012. Fitting the multitemporal curve: a Fourier series approach to the missing data problem in remote sensing analysis. IEEE Trans. Geosci. Remote Sens. 50, 3340–3353. https://doi.org/10.1109/TGRS.2012.2183137.

Buchhorn, M., Bertels, L., Smets, B., Roo, B.D., Lesiv, M., Tsendbazar, N.-E., Masiliunas, D., Li, L., 2020. Copernicus Global Land Service: Land Cover 100m: Version 3 Globe 2015-2019: Algorithm Theoretical Basis Document. Zenodo. https://doi.org/10.5281/zenodo.3938968.

Buckley, S., Rosen, P., Persaud, P., 2000. ROI_PAC Documentation-Repeat Orbit Interferometry Package. UT Austin.

Canisius, F., Brisco, B., Murnaghan, K., Van Der Kooij, M., Keizer, E., 2019. SAR backscatter and InSAR coherence for monitoring wetland extent, flood pulse and vegetation: a study of the Amazon lowland. Remote Sens. 11, 720. https://doi.org/10.3390/rs11060720.
Chul Jung, H., Alsdorf, D., 2010. Repeat-pass multi-temporal interferometric SAR coherence variations with Amazon floodplain and lake habitats. Int. J. Remote Sens. 31, 881–901. https://doi.org/10.1080/01431160902902609.

Colin Koeniguer, E., Nicolas, J.-M., 2020. Change detection based on the coefficient of variation in SAR time-series of urban areas. Remote Sens. 12, 2089. https://doi.org/10.3390/rs12132089.

Coppin, P., Jonckheere, I., Nackaerts, K., Muys, B., Lambin, E., 2004. Digital change detection methods in ecosystem monitoring: a review. Int. J. Remote Sens. 25, 1565–1596. https://doi.org/10.1080/0143116031000101675.

Dauteuil, O., Jolivet, M., Gaudaré, L., Pastier, A.-M., 2023. Rainfall-induced ground deformation in southern Africa. Terra Nova 1–7. https://doi.org/10.1111/ter.12650 n/a.

Davidson, T.A., Mackay, A.W., Wolski, P., Mazebedi, R., Murray-Hudson, M., Todd, M., 2012. Seasonal and spatial hydrological variability drives aquatic biodiversity in a flood-pulsed, sub-tropical wetland. Freshw. Biol. 57, 1253–1265. https://doi.org/10.1111/j.1365-2427.2012.02795.x.

De Zan, F., Gomba, G., 2018. Vegetation and soil moisture inversion from SAR closure phases: first experiments and results. Remote Sens. Environ. 217, 562–572. https://doi.org/10.1016/j.rse.2018.08.034.

Dean, W.E., Gorham, E., 1998. Magnitude and significance of carbon burial in lakes, reservoirs, and peatlands. Geology 26, 535–538. https://doi.org/10.1130/0091-7613(1998)026<0535:MASOC>2.3.CO;2.

Doin, M.-P., Lodge, F., Guillaso, S., Jolivet, R., Lasserre, C., Ducret, G., Grandin, R., Pathier, E., Pinel, V., 2011. Presentation of the Small Baseline NSBAS Processing Chain on a Case Example: The Etna Deformation Monitoring from 2003 to 2010 Using ENVISAT Data.

Eastman, R.J., Sangermano, F., Ghimire, B., Zhu, H., Chen, H., Neeti, N., Cai, Y., Machado, E.A., Crema, S.C., 2009. Seasonal trend analysis of image time series. Int. J. Remote Sens. 30, 2721–2726. https://doi.org/10.1080/014311609027553338.

Erwin, K.L., 2009. Wetlands and global climate change: the role of wetland restoration in a changing world. Wetl. Ecol. Manag. 17, 71–84. https://doi.org/10.1007/s11273-008-9119-1.

ESA, 2019. Copernicus DEM - Global and European Digital Elevation Model (COP-DEM). https://doi.org/10.5270/ESA-c5d3d65.

Evans, J.P., Geerken, R., 2006. Classifying rangeland vegetation type and coverage using a Fourier component based similarity measure. Remote Sens. Environ. 105, 1–8. https://doi.org/10.1016/j.rse.2006.05.017.

ForM@Ter, 2020. FLATSIM Data Products. CNES. 10.24400/253171/FLATSIM2020.

Grandin, R., 2015. Interferometric processing of SLC Sentinel-1 TOPS data. In: FRINGE'15: Advances in the Science and Applications of SAR Interferometry and Sentinel-1 InSAR Workshop, Frascati, Italy, 23-27 March 2015. https://doi.org/10.5270/Fringe2015.pp116. Frascati, Italy.

Gumbricht, T., Wolski, P., Frost, P., McCarthy, T.S., 2004. Forecasting the spatial extent of the annual flood in the Okavango delta, Botswana. J. Hydrol. 290, 178–191.

Hagberg, J.O., Ulander, L.M.H., Askne, J., 1995. Repeat-pass SAR interferometry over forested terrain. IEEE Trans. Geosci. Remote Sens. 33, 331–340. https://doi.org/10.1109/TGRS.1995.746041.

Hall-Atkinson, C., Smith, L.C., 2001. Delineation of delta ecozones using interferometric SAR phase coherence Mackenzie River Delta, N.W.T., Canada. Remote Sens. Environ. 78, 229–238. https://doi.org/10.1016/S0034-4257(01)00221-8.

Hong, S.-H., Wdowinski, S., Kim, S.-W., Won, J.-S., 2010. Multi-temporal monitoring of wetland water levels in the Florida Everglades using interferometric synthetic aperture radar (InSAR). Remote Sens. Environ. 114, 2436–2447. https://doi.org/10.1016/j.rse.2010.05.019.

Hoyt, A.M., Chaussard, E., Seppalainen, S.S., Harvey, C.F., 2020. Widespread subsidence and carbon emissions across Southeast Asian peatlands. Nat. Geosci. 13, 435–440. https://doi.org/10.1038/s41561-020-0575-4.

Hrysiewicz, A., Holohan, E.P., Donohue, S., Cushman, H., 2023. SAR and InSAR data linked to soil moisture changes on a temperature raised peatland subjected to a wildfire. Remote Sens. Environ. 291, 113516. https://doi.org/10.1016/j.rse.2023.113516.

Huffman, G.J., Stocker, E.F., Bolvin, D.T., Nelkin, E.J., Tan, J., 2019. In: Savtchenko, Andrey (Ed.), GPM IMERG Final Precipitation L3 1 Day 0.1 Degree x 0.1 Degree V06. Goddard Earth Sciences Data and Information Services Center (GES DISC), Greenbelt, MD. https://doi.org/10.5067/GPM/IMERGDF/DAY/06.

Jacob, A.W., Vicente-Guijalba, F., Lopez-Martinez, C., Lopez-Sanchez, J.M., Litzinger, M., Kristen, H., Mestre-Quereda, A., Ziółkowski, D., Lavalle, M., Notarnicola, C., Suresh, G., Antropov, O., Ge, S., Praks, J., Ban, Y., Pottier, E., Mallorqu Franquet, J., Duro, J., Engdahl, M.E., 2020. Sentinel-1 InSAR coherence for land cover mapping: a comparison of multiple feature-based classifiers. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 13, 535–552. https://doi.org/10.1109/JSTARS.2019.2958847.

Jakowitz, C.V., Wahl, D.E., Eichel, P.H., Ghiglia, D.C., Thompson, P.A., 1996. Interferometric processing of SAR data. In: Jakowitz, C.V., Wahl, D.E., Eichel, P.H., Ghiglia, D.C., Thompson, P.A. (Eds.), Spotlight-Mode Synthetic Aperture Radar: A Signal Processing Approach. Springer US, Boston, MA, pp. 273–351. https://doi.org/10.1007/978-1-4613-1333-5_5.

Jolivet, M., 2025. Avulsion of the Nyabarongo River into Lake Rweru (Rwanda) under anthropic pressure: morpho-sedimentary and ecological implications. Wetlands 45 (6), 75. https://doi.org/10.1007/s13157-025-01966-0.

Jolly, W.M., Running, S.W., 2004. Effects of precipitation and soil water availability on drought deciduous phenology in the Kalahari. Glob. Chang. Biol. 10, 303–308. https://doi.org/10.1046/j.1365-2486.2003.00701.x.

Junk, W.J., Bayley, P.B., Sparcks, R.E., 1989. The flood pulse concept in river-floodplain systems. Can. J. Fish. Aquat. Sci. 110–127.

Kim, S.-W., Wdowinski, S., Amelung, F., Dixon, T.H., Won, J.-S., 2013. Interferometric coherence analysis of the Everglades wetlands, South Florida. IEEE Trans. Geosci. Remote Sens. 51, 5210–5224. https://doi.org/10.1109/TGRS.2012.2231418.

Kim, J.-W., Lu, Z., Gutenberg, L., Zhu, Z., 2017. Characterizing hydrologic changes of the great dismal swamp using SAR/InSAR. Remote Sens. Environ. 198, 187–202. https://doi.org/10.1016/j.rse.2017.06.009.

Koch, M., Schmidt, T., Reyes, M., Gumuzzio, J., 2012. Evaluating full polarimetric C- and L-band data for mapping wetland conditions in a semi-arid environment in Central Spain. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 5, 1033–1044. https://doi.org/10.1109/JSTARS.2012.2202091.

Koeniguer, E., Nicolas, J.-M., Pinel-Puyssegur, B., Lagrange, J.-M., Janez, F., 2018. Visualisation des changements sur séries temporelles radar: méthode REACTIV évaluée à l'échelle mondiale sous Google Earth Engine. Rev. Fr. Photogramm. Télédetect. 99–108. https://doi.org/10.52638/rftpt.2018.409.

Kurugundla, C., Parida, B., Buru, J., 2018. Revisiting hydrology of Lake Ngami in Botswana. Hydrol. Curr. Res. 09. https://doi.org/10.4172/2157-7587.1000301.

Lal, R., 2008. Carbon sequestration. Philos. Trans. R. Soc. B Biol. Sci. 363, 815–830. https://doi.org/10.1098/rstb.2007.2185.

Lemrabet, L., Doin, M.-P., Lasserre, C., Durand, P., 2023. Referencing of continental-scale InSAR-derived velocity fields: case study of the eastern Tibetan plateau. J. Geophys. Res. Solid Earth 128 (7), e2022JB026251. https://doi.org/10.1029/2022JB026251.

Liao, T.-H., Simard, M., Denbina, S., Lamb, M.P., 2020. Monitoring water level change and seasonal vegetation change in the coastal wetlands of Louisiana using L-band time-series. Remote Sens. 12, 2351. https://doi.org/10.3390/rs12152351.

Lu, Z., Kwoun, O., 2008. Radarsat-1 and ERS InSAR analysis over southeastern coastal Louisiana: implications for mapping water-level changes beneath swamp forests. IEEE Trans. Geosci. Remote Sens. 46, 2167–2184. https://doi.org/10.1109/TGRS.2008.917271.

Mackay, A.W., Davidson, T., Wolski, P., Mazebedi, R., Masamba, W.R.L., Huntsman-Mapila, P., Todd, M., 2011. Spatial and seasonal variability in surface water chemistry in the Okavango Delta, Botswana: a multivariate approach. Wetlands 31, 815–829. https://doi.org/10.1007/s13157-011-0196-1.

MacQueen, J., 1967. Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Statistics. University of California Press, pp. 281–298.

Manzoni, M., Monti-Guarnieri, A., Molinari, M.E., 2021. Joint exploitation of spaceborne SAR images and GIS techniques for urban coherent change detection. Remote Sens. Environ. 253, 112152. https://doi.org/10.1016/j.rse.2020.112152.

McCarthy, T.S., 2006. Groundwater in the wetlands of the Okavango Delta, Botswana, and its contribution to the structure and function of the ecosystem. J. Hydrol Groundw. 320, 264–282. https://doi.org/10.1016/j.jhydrol.2005.07.045.

McCarthy, T.S., 2013. The Okavango Delta and its place in the geomorphological evolution of Southern Africa. S. Afr. J. Geol. 116, 1–54. https://doi.org/10.2113/gssajg.116.1.1.

McCarthy, T.S., Green, R.W., Franey, N.J., 1993. The influence of neo-tectonics on water dispersal in the northeastern regions of the Okavango swamps, Botswana. J. Afr. Earth Sci. Middle East 17, 23–32. https://doi.org/10.1016/0899-5362(93)90019-M.

McCarthy, T.S., Ellery, W.N., Dangerfield, J.M., 1998. The role of biota in the initiation and growth of islands on the floodplain of the Okavango alluvial fan, Botswana. Earth Surf. Process. Landf. 23, 291–316. https://doi.org/10.1002/(SICI)1096-9837(199804)23:4<291::AID-ESP844>3.0.CO;2-A.

McCarthy, J.M., Gumbricht, T., McCarthy, T., Frost, P., Wessels, C., Seidel, F., 2003. Flooding patterns of the Okavango wetland in Botswana between 1972 and 2000. AMBIO J. Hum. Environ. 32, 453–457. https://doi.org/10.1579/0044-7447-32.7.453.

McCarthy, J.M., Gumbricht, T., McCarthy, T.S., 2005. Ecoregion classification in the Okavango Delta, Botswana from multitemporal remote sensing. Int. J. Remote Sens. 26 (19), 4339–4357. https://doi.org/10.1080/01431160500113583.

Mestre-Quereda, A., Lopez-Sanchez, J.M., Vicente-Guijalba, F., Jacob, A.W., Engdahl, M. E., 2020. Time-series of Sentinel-1 interferometric coherence and backscatter for crop-type mapping. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 13, 4070–4084. https://doi.org/10.1109/JSTARS.2020.3008096.

Millard, K., Kirby, P., Nandlall, S., Behnamian, A., Banks, S., Pacini, F., 2020. Using growing-season time series coherence for improved peatland mapping: comparing the contributions of Sentinel-1 and RADARSAT-2 coherence in full and partial time series. Remote Sens. 12, 2465. https://doi.org/10.3390/rs12152465.

Millennium Ecosystem Assessment Board (Ed.), 2005. Ecosystems and Human Well-Being: Wetlands and Water Synthesis: A Report of the Millennium Ecosystem Assessment. World Resources Institute, Washington, DC.

Milzow, C., Kgotlhang, L., Bauer-Gottwein, P., Meier, P., Kinzelbach, W., 2009. Regional review: the hydrology of the Okavango Delta, Botswana—processes, data and modelling. Hydrogeol. J. 17, 1297–1328. https://doi.org/10.1007/s10040-009-0436-0.

Mitsch, W.J., Gosselink, J.G., 2000. The value of wetlands: importance of scale and landscape setting. Ecol. Econ. 35, 25–33. https://doi.org/10.1016/S0921-8009(00)00165-8.

Mitsch, W.J., Gosselink, J.G., Zhang, L., Anderson, C.J., 2009. Wetland Ecosystems. John Wiley & Sons.

Mohammadimanesh, F., Salehi, B., Mahdianpari, M., Brisco, B., Motagh, M., 2018. Multi-temporal, multi-frequency, and multi-polarization coherence and SAR backscatter analysis of wetlands. ISPRS J. Photogramm. Remote Sens. 142, 78–93. https://doi.org/10.1016/j.isprsjprs.2018.05.009.

Mokgedi, L., Nobert, J., Munishi, S., 2019. Assessment of lake surface dynamics using satellite imagery and in-situ data; case of Lake Ngami in North-West Botswana. Phys. Chem. Earth Parts ABC. In: 18th WaterNet/WARFSA/GWPSA Symposium on
Integrated Water Resources Development and Management: Innovative Technological Advances for Water Security in Eastern and Southern Africa - Part B, 112, pp. 175-186. https://doi.org/10.1016/j.pce.2018.12.008.

Moody, A., Johnson, D.M., 2001. Land-surface phenologies from AVHRR using the discrete Fourier transform. Remote Sens. Environ. 75, 305-323. https://doi.org/10.1016/S0034-4257(00)00175-9.

Neyrinck, E., Rousset, B., Doubre, C., Rivera, L., Lasserre, C., Doin, M.-P., Durand, P., Bilham, R., Çakir, Z., 2024. The slow slip event cycle along the Izmit segment of the North Anatolian Fault. Earth Planet. Sci. Lett. 648, 119104. https://doi.org/10.1016/j.epsl.2024.119104.

Nico, G., Pappalepore, M., Pasquariello, G., Refice, A., Samarelli, S., 2000. Comparison of SAR amplitude vs. coherence flood detection methods - a GIS application. Int. J. Remote Sens. 21, 1619-1631. https://doi.org/10.1080/014311600209931.

Nikaein, T., Iannini, L., Molijn, R.A., Lopez-Dekker, P., 2021. On the value of Sentinel-1 InSAR coherence time-series for vegetation classification. Remote Sens. 13, 3300. https://doi.org/10.3390/rs13163300.

Okavango Data, 2024. Okavango Delta Biodiversity and Environmental Data. Okavango Research Institute, University of Botswana. Online. https://monitoringdata.ub.bw/ (Accessed 29 May 2024).

OpenStreetMap contributors, 2015. Planet Dump [Data File from 5 November 2021] [dataset] Retrieved from. https://planet.openstreetmap.org.

ORI Monitoring, 2024. Okavango Delta Monitoring & Forecasting. Okavango Research Institute, University of Botswana. Online. http://www.okavangodata.ub.bw/ori/ (Accessed 29 May 2024).

Prigent, C., Matthews, E., Aires, F., Rossow, W.B., 2001. Remote sensing of global wetland dynamics with multiple satellite data sets. Geophys. Res. Lett. 28, 4631-4634. https://doi.org/10.1029/2001GL013263.

Ramsey, I., Elijah, Lu, Z., Rangoonwala, A., Rykhus, R., 2006. Multiple baseline radar interferometry applied to coastal land cover classification and change analyses. GISci. Remote Sens. 43, 283-309. https://doi.org/10.2747/1548-1603.43.4.283.

Refice, A., Capolongo, D., Pasquariello, G., D'Addabbo, A., Bovenga, F., Nutricato, R., Lovinega, F.P., Pietranera, L., 2014. SAR and InSAR for flood monitoring: examples with COSMO-SkyMed data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 7, 2711-2722. https://doi.org/10.1109/JSTARS.2014.2305165.

Rignot, E.J.M., van Zyl, J.J., 1993. Change detection techniques for ERS-1 SAR data. IEEE Trans. Geosci. Remote Sens. 31, 896-906. https://doi.org/10.1109/36.239913.

Rocca, F., 2007. Modeling interferogram stacks. IEEE Trans. Geosci. Remote Sens. 45, 3289-3299. https://doi.org/10.1109/TGRS.2007.902286.

Rosen, P.A., Hensley, S., Peltzer, G., Simons, M., 2004. Updated repeat orbit interferometry package released. EOS Trans. Am. Geophys. Union 85, 47. https:// doi.org/10.1029/2004EO050004.

Rousseuw, P.J., 1987. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53-65. https://doi.org/10.1016/0377- 0427(87)90125-7.

Roy, D.P., Yan, L., 2020. Robust Landsat-based crop time series modelling. Remote Sens. Environ. 238, 110810. https://doi.org/10.1016/j.rse.2018.06.038.

Schlaffer, S., Matgen, P., Hollaus, M., Wagner, W., 2015. Flood detection from multi-temporal SAR data using harmonic analysis and change detection. Int. J. Appl. Earth Obs. Geoinf. 38, 15-24. https://doi.org/10.1016/j.jag.2014.12.001.

Schlaffer, S., Chini, M., Dettmering, D., Wagner, W., 2016. Mapping wetlands in Zambia using seasonal backscatter signatures derived from ENVISAT ASAR time series. Remote Sens. 8, 402. https://doi.org/10.3390/rs8050402.

Schlaffer, S., Chini, M., Giustarini, L., Matgen, P., 2017. Probabilistic mapping of flood-induced backscatter changes in SAR time series. Int. J. Appl. Earth Obs. Geoinf. 56, 77-87. https://doi.org/10.1016/j.jag.2016.12.003.

Shaw, P., 1983. Fluctuations in the level of Lake Ngami: the historical evidence. Botsw. Notes Rec. 15, 79-84.

Sica, F., Pulella, A., Nannini, M., Pinheiro, M., Rizzoli, P., 2019. Repeat-pass SAR interferometry for land cover classification: a methodology using Sentinel-1 short-time-series. Remote Sens. Environ. 232, 111277. https://doi.org/10.1016/j. rse.2019.111277.

Siles, G., Trudel, M., Peters, D.L., Leconte, R., 2020. Hydrological monitoring of high-latitude shallow water bodies from high-resolution space-borne D-InSAR. Remote Sens. Environ. 236, 111444. https://doi.org/10.1016/j.rse.2019.111444.

Tampuu, T., Praks, J., Uiboupin, R., Kull, A., 2020. Long term interferometric temporal coherence and DInSAR phase in northern peatlands. Remote Sens. 12, 1566. https:// doi.org/10.3390/rs12101566.

Thito, K., Wolski, P., Murray-Hudson, M., 2016. Mapping inundation extent, frequency and duration in the Okavango Delta from 2001 to 2012. Afr. J. Aquat. Sci. 41, 267-277. https://doi.org/10.2989/16085914.2016.1173009.

Thollard, F., Clesse, D., Doin, M.-P., Donadieu, J., Durand, P., Grandin, R., Lasserre, C., Laurent, C., Deschamps-Ostanciaux, E., Pathier, E., Pointal, E., Proy, C., Specht, B., 2021. FLATSIM: the ForM@Ter LArge-scale multi-temporal Sentinel-1 InterferoMetry service. Remote Sens. 13, 3734. https://doi.org/10.3390/ rs13183734.

Tsendbazar, N., Herold, M., Li, L., Tarko, A., de Bruin, S., Masiliunas, D., Lesiv, M., Fritz, S., Buchhorn, M., Smets, B., Van De Kerchove, R., Duerauer, M., 2021. Towards operational validation of annual global land cover maps. Remote Sens. Environ. 266, 112686. https://doi.org/10.1016/j.rse.2021.112686.

Tsyganskaya, V., Martinis, S., Marzahn, P., Ludwig, R., 2018. SAR-based detection of flooded vegetation - a review of characteristics and approaches. Int. J. Remote Sens. 39, 2255-2293. https://doi.org/10.1080/01431161.2017.1420938.

van Asselen, S., Verburg, P.H., Vermaat, J.E., Janse, J.H., 2013. Drivers of wetland conversion: a global meta-analysis. PLoS One 8, e81292. https://doi.org/10.1371/ journal.pone.0081292.

Verbesselt, J., Hyndman, R., Newnham, G., Culvenor, D., 2010a. Detecting trend and seasonal changes in satellite image time series. Remote Sens. Environ. 114, 106-115. https://doi.org/10.1016/j.rse.2009.08.014.

Verbesselt, J., Hyndman, R., Zeileis, A., Culvenor, D., 2010b. Phenological change detection while accounting for abrupt and gradual trends in satellite image time series. Remote Sens. Environ. 114, 2970-2980. https://doi.org/10.1016/j. rse.2010.08.003.

Verbesselt, J., Zeileis, A., Herold, M., 2012. Near real-time disturbance detection using satellite image time series. Remote Sens. Environ. 123, 98-108. https://doi.org/ 10.1016/j.rse.2012.02.022.

Villarroya-Carpio, A., Lopez-Sanchez, J.M., Engdahl, M.E., 2022. Sentinel-1 interferometric coherence as a vegetation index for agriculture. Remote Sens. Environ. 280, 113208. https://doi.org/10.1016/j.rse.2022.113208.

Wahl, D.E., Yocky, D.A., Jakowatz, C.V., Simonson, K.M., 2016. A new maximum- likelihood change estimator for two-pass SAR coherent change detection. IEEE Trans. Geosci. Remote Sens. 54, 2460-2469. https://doi.org/10.1109/ TGRS.2015.2502219.

Wang, S., Azzari, G., Lobell, D.B., 2019. Crop type mapping without field-level labels: random forest transfer and unsupervised clustering techniques. Remote Sens. Environ. 222, 303-317. https://doi.org/10.1016/j.rse.2018.12.026.

Wdowinski, S., Kim, S.-W., Amelung, F., Dixon, T.H., Miralles-Wilhelm, F., Sonenshein, R., 2008. Space-based detection of wetlands' surface water level changes from L-band SAR interferometry. Remote Sens. Environ. 112, 681-696. https://doi.org/10.1016/j.rse.2007.06.008.

Wegmuller, U., Werner, C.L., 1995. SAR interferometric signatures of forest. IEEE Trans. Geosci. Remote Sens. 33, 1153-1161. https://doi.org/10.1109/36.469479.

Wilusz, D.C., Zaitchik, B.F., Anderson, M.C., Hain, C.R., Yilmaz, M.T., Mladenova, I.E., 2017. Monthly flooded area classification using low resolution SAR imagery in the Sudd wetland from 2007 to 2011. Remote Sens. Environ. 194, 205-218. https://doi. org/10.1016/j.rse.2017.03.005.

Wolski, P., Murray-Hudson, M., 2006. Flooding dynamics in a large low-gradient alluvial fan, the Okavango Delta, Botswana, from analysis and interpretation of a 30-year hydrometric record. Hydrol. Earth Syst. Sci. 10, 127-137. https://doi.org/10.5194/ hess-10-127-2006.

Wolski, P., Savenije, H.H.G., Murray-Hudson, M., Gumbricht, T., 2006. Modelling of the flooding in the Okavango Delta, Botswana, using a hybrid reservoir-GIS model. J. Hydrol. 331, 58-72. https://doi.org/10.1016/j.jhydrol.2006.04.040.

Wolski, P., Todd, M.C., Murray-Hudson, M.A., Tadross, M., 2010. Multi-decadal oscillations in the hydro-climate of the Okavango River system during the past and under a changing climate. J. Hydrol. 475, 294-305. https://doi.org/10.1016/j. jhydrol.2012.10.018.

Wolski, P., Murray-Hudson, M., Thito, K., Cassidy, L., 2017. Keeping it simple: monitoring flood extent in large data-poor wetlands using MODIS SWIR data. Int. J. Appl. Earth Obs. Geoinf. 57, 224-234. https://doi.org/10.1016/j.jag.2017.01.005.

Yang, X., Zhu, Zhe, Qiu, S., Kroeger, K.D., Zhu, Zhiliang, Covington, S., 2022. Detection and characterization of coastal tidal wetland change in the northeastern US using Landsat time series. Remote Sens. Environ. 276, 113047. https://doi.org/10.1016/j. rse.2022.113047.

Yun, S.-H., Hudnut, K., Owen, S., Webb, F., Simons, M., Sacco, P., Gurrola, E., Manipon, G., Liang, C., Fielding, E., Milillo, P., Hua, H., Coletta, A., 2015. Rapid damage mapping for the 2015 mw 7.8 Gorkha earthquake using synthetic aperture radar data from COSMO-SkyMed and ALOS-2 satellites. Seismol. Res. Lett. 86, 1549-1556. https://doi.org/10.1785/0220150152.

Zebker, H.A., Villasenor, J., 1992. Decorrelation in interferometric radar echoes. IEEE Trans. Geosci. Remote Sens. 30, 950-959. https://doi.org/10.1109/36.175330.

Zhang, M., Li, Z., Tian, B., Zhou, J., Zeng, J., 2015. A method for monitoring hydrological conditions beneath herbaceous wetlands using multi-temporal ALOS PALSAR coherence data. Remote Sens. Lett. 6, 618-627. https://doi.org/10.1080/ 2150704X.2015.1066520.

Zhang, M., Li, Z., Tian, B., Zhou, J., Tang, P., 2016. The backscattering characteristics of wetland vegetation and water-level changes detection using multi-mode SAR: a case study. Int. J. Appl. Earth Obs. Geoinf. 45, 1-13. https://doi.org/10.1016/j. jag.2015.10.001.

Zhou, Q., Zhu, Z., Xian, G., Li, C., 2022. A novel regression method for harmonic analysis of time series. ISPRS J. Photogramm. Remote Sens. 185, 48-61. https://doi.org/ 10.1016/j.isprsjprs.2022.01.006.

Zhu, Z., Woodcock, C.E., 2014. Continuous change detection and classification of land cover using all available Landsat data. Remote Sens. Environ. 144, 152-171. https:// doi.org/10.1016/j.rse.2014.01.011.

Zwieback, S., Hensley, S., Hajnsek, I., 2015. Assessment of soil moisture effects on L-band radar interferometry. Remote Sens. Environ. 164, 77-89. https://doi.org/10.1016/j. rse.2015.04.012.