Assessment of inundation changes of Poyang Lake using MODIS observations between 2000 and 2010

Feng, Lian; Hu, Chuanmin; Chen, Xiaoling; Cai, Xiaobin; Tian, Liqiao; Gan, Wenxia

doi:10.1016/j.rse.2012.01.014

Cited by 463 publications

(335 citation statements)

References 33 publications

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“…The HJ-1A/1B CCD data, which have a high spatial resolution (30 m), short revisiting period (2 days), and large swath (720 km), are designed for mapping the land surface and coastal water quality in environmental and disaster monitoring/forecasts [4]. These data can be obtained from the China Center for Resources Satellite Data and Application (http://www.cresda.com/EN/).…”

Section: Hj-1a/1b Imagesmentioning

confidence: 99%

“…In recent decades, significant changes have been observed in lake expansion and shrinkage around the world under the combined influence of global climate change and human activities. Thus, changes in lake areas have received the attention of many scientists [2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

“…In contrast, MODIS data are characterized by short revisit periods, low cost, large coverage areas, stable data sources, and availability of relatively long time series (since 2000). Despite their relatively low spatial resolution, MODIS data have widely been used in the dynamic monitoring of water bodies with large seasonal variations [4].…”

Section: Introductionmentioning

confidence: 99%

“…The NDWI and MNDWI have been widely used to extract water body information in a highly targeted manner [21][22][23][24]. In some studies, NDWI has been recommended for extracting water body information; however, NDWI may be unreliable for shallow-water areas with a varied boundary [4,25]. Li et al [24] found that MNDWI is more effective than NDWI at distinguishing water bodies, and this may be attributed to the ability of MNDWI to distinguish built-up land areas from water bodies effectively [18].…”

Section: Introductionmentioning

confidence: 99%

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Changes in the Lake Area of Tonle Sap: Possible Linkage to Runoff Alterations in the Lancang River?

Luo

et al. 2018

Remote Sensing

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Tonle Sap Lake is the largest freshwater lake in Southeast Asia. Water development infrastructures are increasingly being constructed in the Lancang-Mekong River Basin, which is a major concern considering its potential impact on Tonle Sap Lake. This study aimed to investigate variations in the area of the lake and discuss their possible linkage to runoff alterations in the Lancang River (Upper Mekong) by comparing runoff at the Yunjinghong hydrological station before and after significant changes in runoff trends that occurred in 2008. First, four commonly used water body extraction methods (MNDWI, NDWI, NDVI, and EVI) were compared and MNDWI was found to provide a better and more stable performance. Based on MOD09A1 data, MNDWI was used to extract the water area of the lake from 2000 to 2014, and characteristics of variations in the area before and after 2008 were analyzed. The water area of Tonle Sap Lake displayed an overall decreasing trend, and specifically decreased by 8.3% during the flood season and by 1.5% on average during the dry season after 2008. Seasonal variations in the water area of Tonle Sap Lake were dominantly influenced by runoff from the Mekong River. Compared with the period 2000-2007, runoff at Yunjinghong station were increased during the dry season (20.74%) and decreased during the flood season (34.25%) between 2008 and 2014. Changes in upstream runoff contributed to runoff at the Stung Treng station in the lower Mekong River by 6.17% (dry season) and −2.41% (flood season). Evidently, the operation of dams in the Lancang River does not primarily account for the area decrease of Tonle Sap Lake during the flood season. In contrast, runoff increase during the dry season mitigates the area decrease of Tonle Sap Lake to a certain extent.

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Section: Hj-1a/1b Imagesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Changes in the Lake Area of Tonle Sap: Possible Linkage to Runoff Alterations in the Lancang River?

Luo

et al. 2018

Remote Sensing

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“…However, the differences and standard deviations at low reflectance (0-0.1) of red and NIR bands are relatively larger than other bands. The surface type associates with low reflectance in red and NIR bands is likely to be water, as the water absorbs most of the solar radiances in these spectral ranges [27]. The radiance interactions with the air and water surface were not considered in this study, which may explain the large discrepancies in these two bands.…”

Section: Reflectancementioning

confidence: 97%

Radiometric cross Calibration of Gaofen-1 WFV Cameras Using Landsat-8 OLI Images: A Simple Image-Based Method

Feng

Pang

et al. 2016

Remote Sensing

Self Cite

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WFV (Wide Field of View) cameras on-board Gaofen-1 satellite (gaofen means high resolution) provide unparalleled global observations with both high spatial and high temporal resolutions. However, the accuracy of the radiometric calibration remains unknown. Using an improved cross calibration method, the WFV cameras were re-calibrated with well-calibrated Landsat-8 OLI (Operational Land Imager) data as reference. An objective method was proposed to guarantee the homogeneity and sufficient dynamic coverage for calibration sites and reference sensors. The USGS spectral library was used to match the most appropriate hyperspectral data, based on which the spectral band differences between WFV and OLI were adjusted. The TOA (top-of-atmosphere) reflectance of the cross-calibrated WFV agreed very well with that of OLI, with the mean differences between the two sensors less than 5% for most of the reflectance ranges of the four spectral bands, after accounting for the spectral band difference between the two sensors. Given the calibration error of 3% for Landsat-8 OLI TOA reflectance, the uncertainty of the newly-calibrated WFV should be within 8%. The newly generated calibration coefficients established confidence when using Gaofen-1 WFV observations for their further quantitative applications, and the proposed simple cross calibration method here could be easily extended to other operational or planned satellite missions.

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Atmospheric residence times from transpiration and evaporation to precipitation: An age‐weighted regional evaporation tagging approach

2016

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The atmospheric water residence time is a fundamental descriptor that provides information on the timescales of evaporation and precipitation. In this study, a regional climate model-based evaporation tagging algorithm is extended with an age tracer approach to calculate moisture residence times, defined as time between the original evaporation and the returning of water masses to the land surface as precipitation. Our case study addresses how long this time is for the transpired and for the direct evaporated moisture. Our study region is the Poyang Lake region in Southeast China, the largest freshwater lake in the country. We perform simulations covering the period from October 2004 to December 2005. In 2005, 11% of direct evaporated water (10% of transpired water) precipitates locally. Direct evaporated water accounts for 64% and transpired water for 36% of the total tagged moisture with a mean age of around 36 h for both. Considering precipitation, a large proportion (69%) originates from direct evaporated water with a mean atmospheric residence time of 6.6 h and a smaller amount from transpired water with a longer residence time of 10.7 h. Modulated by the East Asian monsoon, the variation of the meteorological conditions, the magnitude of the partitioned moisture, and the corresponding residence time patterns change seasonally and spatially and reveal the different fate of transpired and direct evaporated water in the atmospheric hydrological cycle. We conclude that our methodological approach has the potential to be used for addressing how timescales of the hydrological cycle changes regionally under global warming.

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Assessment of inundation changes of Poyang Lake using MODIS observations between 2000 and 2010

Cited by 463 publications

References 33 publications

Changes in the Lake Area of Tonle Sap: Possible Linkage to Runoff Alterations in the Lancang River?

Changes in the Lake Area of Tonle Sap: Possible Linkage to Runoff Alterations in the Lancang River?

Radiometric cross Calibration of Gaofen-1 WFV Cameras Using Landsat-8 OLI Images: A Simple Image-Based Method

Atmospheric residence times from transpiration and evaporation to precipitation: An age‐weighted regional evaporation tagging approach

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