Monthly estimation of the surface water extent in France at a 10-m resolution using Sentinel-2 data

Yang, Xiucheng; Qin, Qiming; Yésou, H.; Ledauphin, Thomas; Koehl, Mathieu; Grussenmeyer, Pierre; Zhu, Zhe

doi:10.1016/j.rse.2020.111803

Cited by 130 publications

(78 citation statements)

References 99 publications

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“…Since points are not a comprehensive evaluation of all pixels, it is likely that there are omission and commission errors that are not accounted for by the points. This is supported by the GSW omission errors reported in recent studies that compared satellite detected water from higher resolution data [52,62]. Wu et al 2019 [62] have shown considerable omission errors in the GSW product when compared to 1 m detected surface water.…”

Section: Stage 1-surface Water Detectionmentioning

confidence: 75%

“…This MNDWIp95 layer was then used within an inequality (6) to detect surface water. Additional pre-processing steps included the removal of sensor anomalies and steep slopes by removing those pixels with MNDWIp95 values greater than 0.9984 or with a slope greater than 10 degrees [52]. The 0.9984 threshold was determined by manually inspecting the minimum value of scene overlap artefacts.…”

Section: Stage 1: Surface Water Detectionmentioning

confidence: 99%

“…To enable the comparison, all water related class points were reclassified as water (class 12-21, n = 1316) or non-water (remaining classes, n = 5254). Lastly, Sentinel-2 derived surface water masks for France were used to evaluate the method's transferability to different platforms, and its ability to generalize to different countries [52].…”

Section: Stage 1: Surface Water Detectionmentioning

confidence: 99%

“…A visual comparison of the GSW, RBSPderived water layer and the water layer derived using the introduced method can be found here. To evaluate the introduced water detection methods' transferability to Sentinel-2 and a different country, our method was compared to a 10 m surface water dataset for France [52]. The 10 m dataset was derived by a rule-based superpixel (RBSP) method [52].…”

Section: Evaluation Of Surface Water Detectionmentioning

confidence: 99%

“…To evaluate the introduced water detection methods' transferability to Sentinel-2 and a different country, our method was compared to a 10 m surface water dataset for France [52]. The 10 m dataset was derived by a rule-based superpixel (RBSP) method [52]. A visual comparison of the GSW, RBSP-derived water layer and the water layer derived using the introduced method can be found here.…”

Section: Evaluation Of Surface Water Detectionmentioning

confidence: 99%

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A Remote Sensing Method to Monitor Water, Aquatic Vegetation, and Invasive Water Hyacinth at National Extents

et al. 2020

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Diverse freshwater biological communities are threatened by invasive aquatic alien plant (IAAP) invasions and consequently, cost countries millions to manage. The effective management of these IAAP invasions necessitates their frequent and reliable monitoring across a broad extent and over a long-term. Here, we introduce and apply a monitoring approach that meet these criteria and is based on a three-stage hierarchical classification to firstly detect water, then aquatic vegetation and finally water hyacinth (Pontederia crassipes, previously Eichhornia crassipes), the most damaging IAAP species within many regions of the world. Our approach circumvents many challenges that restricted previous satellite-based water hyacinth monitoring attempts to smaller study areas. The method is executable on Google Earth Engine (GEE) extemporaneously and utilizes free, medium resolution (10–30 m) multispectral Earth Observation (EO) data from either Landsat-8 or Sentinel-2. The automated workflow employs a novel simple thresholding approach to obtain reliable boundaries for open-water, which are then used to limit the area for aquatic vegetation detection. Subsequently, a random forest modelling approach is used to discriminate water hyacinth from other detected aquatic vegetation using the eight most important variables. This study represents the first national scale EO-derived water hyacinth distribution map. Based on our model, it is estimated that this pervasive IAAP covered 417.74 km2 across South Africa in 2013. Additionally, we show encouraging results for utilizing the automatically derived aquatic vegetation masks to fit and evaluate a convolutional neural network-based semantic segmentation model, removing the need for detection of surface water extents that may not always be available at the required spatio-temporal resolution or accuracy. The water hyacinth species discrimination has a 0.80, or greater, overall accuracy (0.93), F1-score (0.87) and Matthews correlation coefficient (0.80) based on 98 widely distributed field sites across South Africa. The results suggest that the introduced workflow is suitable for monitoring changes in the extent of open water, aquatic vegetation, and water hyacinth for individual waterbodies or across national extents. The GEE code can be accessed here.

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Section: Stage 1-surface Water Detectionmentioning

confidence: 75%

Section: Stage 1: Surface Water Detectionmentioning

confidence: 99%

Section: Stage 1: Surface Water Detectionmentioning

confidence: 99%

Section: Evaluation Of Surface Water Detectionmentioning

confidence: 99%

Section: Evaluation Of Surface Water Detectionmentioning

confidence: 99%

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A Remote Sensing Method to Monitor Water, Aquatic Vegetation, and Invasive Water Hyacinth at National Extents

et al. 2020

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Evaluating the driving forces of spectral inversion methods used for assessing water quality parameters in Poyang Lake, China

Wang,

Zhai,

Yang

et al. 2023

Ecohydrology

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Water quality parameters are key indicators of quality of water and can indicate the algal biomass and eutrophication in lakes. Therefore, this study spectrally inverted and evaluated the water quality of the Poyang Lake in China by analysing the differences between the measured water quality parameters and the observed image spectra of Sentinel‐2 remote sensing. This analysis was done using statistical regression models (SRMs) and various machine learning models (e.g., support vector machine [SVM] and random forest [RF]). The following major conclusions were drawn: (1) nutrients accumulated more in summer (June–August) than in winter (December–February). For example, in local areas of the main river, total nitrogen (TN) concentration reached 3.4 mg/L in summer of 2016, whereas total phosphorus (TP) concentrations were below 0.052 mg/L in winters of 2016 and 2017. (2) The three models, SRM, RF and SVM, achieved good results in the inversions of Secchi depth and permanganate index. However, differences were noted in the inversions of other parameters. For example, the goodness‐of‐fit (r2) between the inversion and measured values of TN concentration from RF was 0.81, while that from SR was 0.73. (3) The spatial distribution patterns of the water quality parameters showed differences. The ammoniacal nitrogen concentration was higher in the central region than that in the western region of the lake, whereas TP concentration was higher at the shoreline of the lake at 0.07 mg/L on 2 April 2017 but was relatively low in the main channel.

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Regional matters: On the usefulness of regional land‐cover datasets in times of global change

Tulbure

Hostert

Kuemmerle

et al. 2021

Remote Sens Ecol Conserv

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Unprecedented amounts of analysis‐ready Earth Observation (EO) data, combined with increasing computational power and new algorithms, offer novel opportunities for analysing ecosystem dynamics across large geographic extents, and to support conservation planning and action. Much research effort has gone into developing global EO‐based land‐cover and land‐use datasets, including tree cover, crop types, and surface water dynamics. Yet there are inherent trade‐offs between regional and global EO products pertaining to class legends, availability of training/validation data, and accuracy. Acknowledging and understanding these trade‐offs is paramount for both developing EO products and for answering science questions relevant for ecology or conservation studies based on these data. Here we provide context on the development of global EO‐based land‐cover and land‐use datasets, and outline advantages and disadvantages of both regional and global datasets. We argue that both types of EO‐derived land‐cover datasets can be preferable, with regional data providing the context‐specificity that is often required for policy making and implementation (e.g., land‐use and management, conservation planning, payment schemes for ecosystem services), making use of regional knowledge, particularly important when moving from land cover to actors. Ensuring that global and regional land‐cover and land‐use products derived based on EO data are compatible and nested, both in terms of class legends and accuracy assessment, should be a key consideration when developing such data. Open access high‐quality training and validation data derived as part of such efforts are of utmost importance. Likewise, global efforts to generate sets of essential variables for climate change, biodiversity, or eventually land use, which often require land‐cover maps as inputs, should consider regionalized, hierarchical approaches to not sacrifice regional context. Global change impacts manifest in regions, and so must the policy and planning responses to these challenges. EO data should embrace that regions matter, perhaps more than ever, in an age of global data availability and processing.

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Monthly estimation of the surface water extent in France at a 10-m resolution using Sentinel-2 data

Cited by 130 publications

References 99 publications

A Remote Sensing Method to Monitor Water, Aquatic Vegetation, and Invasive Water Hyacinth at National Extents

A Remote Sensing Method to Monitor Water, Aquatic Vegetation, and Invasive Water Hyacinth at National Extents

Evaluating the driving forces of spectral inversion methods used for assessing water quality parameters in Poyang Lake, China

Regional matters: On the usefulness of regional land‐cover datasets in times of global change

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