Lake water surface mapping in the Tibetan Plateau using the MODIS MOD09Q1 product

Lu, Shanlong; Li, Jia; Zhang, Lei; Wei, Yongping; Baig, Muhammad Hasan Ali; Zhai, Zhongsheng; Meng, Jun; Li, Xiaosong; Zhang, Guifang

doi:10.1080/2150704x.2016.1260178

Cited by 46 publications

(28 citation statements)

References 17 publications

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“…Besides, MODIS sensors have been continuously monitoring the Earth's surface for over a decade and have accumulated a long record that is perfect for spatio‐temporal analysis of surface water dynamics. Therefore, MODIS time series data have been applied to detect lake water variations (Cai et al, ; Feng et al, ; Lu et al, ; Peng et al, ) and floodplain inundation (Huang, Chen, & Wu, ; Islam et al, ; Ordoyne & Friedl, ; Yan et al, ) over many years.…”

Section: Detection Of Surface Water With Different Sensorsmentioning

confidence: 99%

Detecting, Extracting, and Monitoring Surface Water From Space Using Optical Sensors: A Review

Huang

Chen

Zhang

et al. 2018

Reviews of Geophysics

500

339

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Observation of surface water is a functional requirement for studying ecological and hydrological processes. Recent advances in satellite-based optical remote sensors have promoted the field of sensing surface water to a new era. This paper reviews the current status of detecting, extracting, and monitoring surface water using optical remote sensing, especially progress in the last decade. It also discusses the current status and challenges in this field, including spatio-temporal scale issues, integration with in situ hydrological data and elevation data, obscuration caused by clouds and vegetation, and the growing need to map surface water at a global scale. Historically, sensors have exhibited a contradiction in resolutions. Techniques including pixel unmixing and reconstruction, and spatio-temporal fusion have been developed to alleviate this contradiction. Spatio-temporal dynamics of surface water have been modeled by combining remote sensing data with in situ river flow. Recent studies have also demonstrated that the river discharge can be estimated using only optical remote sensing imagery, providing valuable information for hydrological studies in ungauged areas. Another historical issue for optical sensors has been obscuration by clouds and vegetation. An effective approach of reducing this limitation is to combine with synthetic aperture radar data. Digital elevation model data have also been employed to eliminate cloud/terrain shadows. The development of big data and cloud computation techniques makes the increasing demand of monitoring global water dynamics at high resolutions easier to achieve. An integrated use of multisource data is the future direction for improved global and regional water monitoring.Plain Language Summary Observing surface water is essential for ecological and hydrological studies. This paper reviews the current status of detecting, extracting, and monitoring surface water using optical remote sensing, especially progress in the last decade. It also discusses the current status and challenges in this field. For example, it was found that pixel unmixing and reconstruction, and spatio-temporal fusion are two common and low-cost approaches to enhance surface water monitoring. Remote sensing data have been integrated with in situ river flow to model spatio-temporal dynamics of surface water. Recent studies have also proved that the river discharge can be estimated using only optical remote sensing imagery. This will be a breakthrough for hydrological studies in ungauged areas. Optical sensors are also easily obscured by clouds and vegetation. This limitation can be reduced by integrating optical data with synthetic aperture radar data and digital elevation model data. There is increasing demand of monitoring global water dynamics at high resolutions. It is now easy to achieve with the development of big data and cloud computation techniques. Enhanced global or regional water monitoring in the future requires integrated use of multiple sources of remote sensing data.

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Section: Detection Of Surface Water With Different Sensorsmentioning

confidence: 99%

Detecting, Extracting, and Monitoring Surface Water From Space Using Optical Sensors: A Review

Huang

Chen

Zhang

et al. 2018

Reviews of Geophysics

500

339

View full text Add to dashboard Cite

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Section: Introductionmentioning

confidence: 99%

“…In China numerous regional case studies have been done and produced some surface water datasets but only in bits and pieces (Du et al, 2012;Lai et al, 2013;Luo et al, 2017). Their research mainly focused on lakes in the Tibetan Plateau (Lu et al, 2017). Several research groups are focusing on lake water changes of this region and have produced decadal lake surface water datasets since the 1960s (Song et al, 2014;G.…”

Section: Introductionmentioning

confidence: 99%

“…The most commonly used method of water extraction is based on water indices, such as the normalized difference water index (NDWI) (Gao, 1996;McFeeters, 1996;Rogers and Kearney, 2004), the modified normalized difference water index (MNDWI) (Xu, 2006), the automated water extraction index (AWEI) (Feyisa et al, 2014), and the enhanced water index (EWI) (Wang et al, 2015). Furthermore, the single band threshold segmentation method (Li et al, 2012;Lu et al, 2017) and the multiband transformation method (Pekel et al, 2014) are also in practice. The key step for using these methods in extracting the water boundary is to determine the threshold value for segmentation.…”

Section: Introductionmentioning

confidence: 99%

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Time series of the Inland Surface Water Dataset in China (ISWDC) for 2000–2016 derived from MODIS archives

et al. 2019

Earth Syst. Sci. Data

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Abstract. The moderate spatial resolution and high temporal resolution of MODIS imagery make it an ideal resource for time series surface water monitoring and mapping. We used MODIS MOD09Q1 surface reflectance archive images to create an Inland Surface Water Dataset in China (ISWDC), which maps water bodies larger than 0.0625 km2 within the land mass of China for the period 2000–2016, with 8 d temporal and 250 m spatial resolution. We assessed the accuracy of the ISWDC by comparing it with the national land cover derived surface water data and global surface water (GSW) data. The results show that the ISWDC is closely correlated with the national reference data with coefficient of determination (R2) greater than 0.99 in 2000, 2005, and 2010, while the ISWDC possesses very good consistency, very similar change dynamics, and similar spatial patterns in different regions with the GSW dataset. The ISWDC dataset can be used for studies on the inter-annual and seasonal variation of the surface water systems. It can also be used as reference data for verification of the other surface water dataset and as an input parameter for regional and global hydro-climatic models. The ISWDC data are available at: https://doi.org/10.5281/zenodo.2616035.

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“…Remote sensing provides repetitive mapping in time-and cost-saving modes. Various remote sensing data have been utilised in water surface mapping, including Synthetic Aperture Radar (SAR) satellites [1,2], LiDAR data [3], and various spatial resolution optical satellite images ranging from low-resolution [4,5] to very high-resolution imagery [6,7].…”

Section: Introductionmentioning

confidence: 99%

Mapping of Urban Surface Water Bodies from Sentinel-2 MSI Imagery at 10 m Resolution via NDWI-Based Image Sharpening

et al. 2017

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Abstract:This study conducts an exploratory evaluation of the performance of the newly available Sentinel-2A Multispectral Instrument (MSI) imagery for mapping water bodies using the image sharpening approach. Sentinel-2 MSI provides spectral bands with different resolutions, including RGB and Near-Infra-Red (NIR) bands in 10 m and Short-Wavelength InfraRed (SWIR) bands in 20 m, which are closely related to surface water information. It is necessary to define a pan-like band for the Sentinel-2 image sharpening process because of the replacement of the panchromatic band by four high-resolution multi-spectral bands (10 m). This study, which aimed at urban surface water extraction, utilised the Normalised Difference Water Index (NDWI) at 10 m resolution as a high-resolution image to sharpen the 20 m SWIR bands. Then, object-level Modified NDWI (MNDWI) mapping and minimum valley bottom adjustment threshold were applied to extract water maps. The proposed method was compared with the conventional most related band-(between the visible spectrum/NIR and SWIR bands) based and principal component analysis first component-based sharpening. Results show that the proposed NDWI-based MNDWI image exhibits higher separability and is more effective for both classification-level and boundary-level final water maps than traditional approaches.

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Lake water surface mapping in the Tibetan Plateau using the MODIS MOD09Q1 product

Cited by 46 publications

References 17 publications

Detecting, Extracting, and Monitoring Surface Water From Space Using Optical Sensors: A Review

Detecting, Extracting, and Monitoring Surface Water From Space Using Optical Sensors: A Review

Time series of the Inland Surface Water Dataset in China (ISWDC) for 2000–2016 derived from MODIS archives

Mapping of Urban Surface Water Bodies from Sentinel-2 MSI Imagery at 10 m Resolution via NDWI-Based Image Sharpening

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