Monitoring Post-Flood Recovery of Croplands Using the Integrated Sentinel-1/2 Imagery in the Yangtze-Huai River Basin

Li, Miao; Zhang, Tao; Tu, Ying; Ren, Zhehao; Xu, Bing

doi:10.3390/rs14030690

Cited by 13 publications

(16 citation statements)

References 60 publications

(91 reference statements)

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“…Getting closer to water mapping, it is relevant to mention one of the most widespread applications of Remote Sensing for emergency management (especially regarding SAR imagery), which is flood and post-flood mapping, as detailed in (Carreño & De Mata, 2019) and (Li et al, 2022); nonetheless, optical imagery as well can be exploited for delineating flooded areas, as shown by (Ajmar et al, 2017), and it can also be fruitfully used in combination with different data sources, as proven by Valvassori et al (2022), who integrated it within a Volunteered Geographic Information framework. Most of the cited studies converge on the findings of an increment of mapping accuracy that comes when optical and radar platforms are used in combination.…”

Section: Remote Sensing and Sensor Integration For Supporting Land Mo...mentioning

confidence: 99%

Integrating Optical and Radar Imagery to Enhance River Drought Monitoring

Conversi,

Carrion,

Norcini

et al. 2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Drought events are growingly affecting European and Italian territories, hampering local environments and biodiversity, such as the ones relying on rivers for their subsistence. Monitoring of rivers is becoming an important issue to face drought crisis and may be exploited with different tools. Among the most commons, satellite imagery is exploited to map water coverage, basing on optical or radar sources. This work proposes a combination of the two sensors to overcome possible limitations of the single dataset exploitation, reaching a reliable result. The methodology is applied to a stretch of Po River in Lombardy region (Italy). Through Google Earth Engine platform, optical satellite Sentinel-2 and radar satellite Sentinel-1 data are processed. The combination of the radar data and of the optical spectral indices is carried out through a pixel-based supervised classification, with a Random Forest classifier. Maps of water coverage are obtained, numerical outcomes of water surface evaluation are recorded and validated by the mean of reference hydrometric data. A multitemporal analysis is then reported, aiming to prove the efficiency of the procedure. All iterations show reliable accuracies and correlation among water surface estimation and water table measurements in two sections of interest. In perspective, the proposed methodology will be implemented in tools for supporting drought monitoring to be integrated in environmental public administration policies.

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Section: Remote Sensing and Sensor Integration For Supporting Land Mo...mentioning

confidence: 99%

Integrating Optical and Radar Imagery to Enhance River Drought Monitoring

Conversi,

Carrion,

Norcini

et al. 2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…It should be noted that the algorithm needs an initial flood map for generating the next images; thus, we assumed that the initial flood map on March 12, 2017 (the driest month in the VMD [77]) is all non-flooded to activate the flood monitoring algorithm. However, An Giang province is a very complex area in terms of water dynamics and vegetation existence, such as rice cultivation [106], hydrological variations [18], a full-dike system for water management [86], and differences in agricultural calendar practices.…”

Section: Flood Mapping Algorithm Using Sar Sentinel-1 Time Series Datamentioning

confidence: 99%

“…Thus, supervised learning methods could be less effective for applications that demand swift action and in near real-time (NRT) (e.g., flood monitoring). Nevertheless, machine-learning-based or deep-learning-based algorithms could be applicable for assessing pre and post-flooding events accurately [77].…”

Section: Introductionmentioning

confidence: 99%

Surface Water Mapping and Flood Monitoring in the Mekong Delta Using Sentinel-1 SAR Time Series and Otsu Threshold

Tran

Menenti

2022

Remote Sensing

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The annual flood and the alteration in hydrological regimes are the most vital concerns in the Vietnamese Mekong Delta (VMD). Although synthetic aperture radar (SAR) Sentinel-1 imagery is widely used for water management, only a few studies have used Sentinel-1 data for mapping surface water and monitoring flood events in the VMD. This study developed an algorithm to implement (i) automatic Otsu threshold on a series of Sentinel-1 images to extract surface water and (ii) time series analyses on the derived surface water maps to detect flood water extent in near-real-time (NRT). Specifically, only cross-polarized VH was selected after an assessment of different Sentinel-1 polarizations. The dynamic Otsu thresholding algorithm was applied to identify an optimal threshold for each pre-processed Sentinel-1 VH image to separate water from non-water pixels for producing a time series of surface water maps. The derived Sentinel-1 surface water maps were visually compared with the Sentinel-2 Full Resolution Browse (FRB) and statistically examined with the Sentinel-2 Multispectral Instrument (MSI) surface water maps, which were generated by applying the Otsu threshold on the normalized difference water index (NDWI) and modified normalized difference water index (MNDWI) images. The visual comparison showed a strong correspondence between the Sentinel-1 surface water maps and Sentinel-2 FRB images in three periods, including rice’s sowing season, flood period, and rice’s maturation stage. A good statistical agreement suggested that the performance of the dynamic Otsu thresholding algorithm on Sentinel-1 image time series to map surface water is effective in river areas (R2 = 0.97 and RMSE = 1.18%), while it is somewhat lower in paddy field areas (R2 = 0.88 and RMSE = 3.88%). Afterward, a flood mapping algorithm in NRT was developed by applying the change-detection-based time series analyses on the derived Sentinel-1 surface water maps. Every single pixel at the time is respectively referred to its state in the water/non-water and flooded/non-flooded maps at the previous time to be classified into a flooded or non-flooded pixel. The flood mapping algorithm enables updates at each time step to generate temporal flood maps in NRT for monitoring flood water extent in large-scale areas. This study provides a tool to rapidly generate surface water and flood maps to support water management and risk reduction in the VMD. The future improvement of the current algorithm is discussed.

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“…Floods, account for 40% of annual global losses from natural disasters [2], are common in Asia because the growing season of main crops overlaps with the monsoon season [3]. Agricultural production is vulnerable to destructive flooding due to the monospecificity of the ecosystem and the large scale of agricultural production [4]. In the event of a flood, it is of great importance to extract the information about the water bodies quickly, dynamically, and accurately to determine the extent of the flood inundation and the degree of damage.…”

Section: Introductionmentioning

confidence: 99%

Extraction of Information on the Flooding Extent of Agricultural Land in Henan Province Based on Multi-Source Remote Sensing Images and Google Earth Engine

Cui

Guo

et al. 2023

Agronomy

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Sudden flood disasters cause serious damage to agricultural production. Rapidly extracting information such as the flooding extent of agricultural land and capturing the influence of flooding on crops provides important guidelines for estimating the flood-affected area, promoting post-disaster farmland restoration, and providing an auxiliary decision-making basis for flood prevention and disaster relief departments. Taking the flood event in Henan and Shanxi Provinces as example, based on the characteristics of the variations in radar data and optical data before and after the disaster, we propose an extent information extraction method for the flood inundation area and the flood-affected area of agricultural land. This method consists of change detection, threshold extraction, and superposition analysis, which weakens the negative impact of the radar data speckle noise and cloud contamination of the optical data on the extraction of the agricultural land flooding to a certain extent. The method was developed based on a flood event in Henan Province and validated in Shanxi Province. The results show that the production of this method have a clear boundary and accurate extent, and the overall precisions of the flood inundation area and flood-affected area extraction are 0.87 and 0.92, respectively. The proposed method combines the advantages of both radar and optical remote sensing data in extracting the specific extents of the flood inundation area and the flood-affected area in large spatial scale. Finally, the impact of time window size to the performance of the method is further analyzed. In the application of the proposed method, the Google Earth Engine (GEE) platform provides a low-cost, fast, and convenient way to extract flood information from remote sensing data. The proposed scheme provides a scientific data basis for restoring production of agricultural land after a flood disaster, as well as for national post-disaster damage assessment and disaster relief decision making.

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Monitoring Post-Flood Recovery of Croplands Using the Integrated Sentinel-1/2 Imagery in the Yangtze-Huai River Basin

Cited by 13 publications

References 60 publications

Integrating Optical and Radar Imagery to Enhance River Drought Monitoring

Integrating Optical and Radar Imagery to Enhance River Drought Monitoring

Surface Water Mapping and Flood Monitoring in the Mekong Delta Using Sentinel-1 SAR Time Series and Otsu Threshold

Extraction of Information on the Flooding Extent of Agricultural Land in Henan Province Based on Multi-Source Remote Sensing Images and Google Earth Engine

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