Retrieving High-Resolution Surface Soil Moisture by Downscaling AMSR-E Brightness Temperature Using MODIS LST and NDVI Data

Song, Chengyun; Li, Jia; Menenti, Massimo

doi:10.1109/jstars.2013.2272053

Cited by 68 publications

(45 citation statements)

References 27 publications

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“…The SMAP Level 4 product will provide a global surface and root zone soil moisture data set at a spatial resolution of 9 km. Besides, some downscaling methods to downscale the spatial resolution of passive microwave soil moisture data have achieved some developments [84], and a soil moisture product with a spatial resolution of 1 km will be generated by merging microwave remote sensing data and MODIS VI products. This is very useful to develop and improve the performance of ETa models based on soil moisture data.…”

Section: Discussionmentioning

confidence: 99%

Monitoring of Evapotranspiration in a Semi-Arid Inland River Basin by Combining Microwave and Optical Remote Sensing Observations

2015

Remote Sensing

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Abstract:As a typical inland river basin, Heihe River basin has been experiencing severe water resource competition between different land cover types, especially in the middle stream and downstream areas. Terrestrial actual evapotranspiration (ETa), including evaporation from soil and water surfaces, evaporation of rainfall interception, transpiration of vegetation canopy and sublimation of snow and glaciers, is an important component of the water cycle in the Heihe River basin. We developed a hybrid remotely sensed ETa estimation model named ETMonitor to estimate the daily actual evapotranspiration of the Heihe River basin for the years 2009-2011 at a spatial resolution of 1 km. The model was forced by a variety of biophysical parameters derived from microwave and optical remote sensing observations. The estimated ETa was evaluated using eddy covariance (EC) flux observations at local scale and compared with the annual precipitation and the MODIS ETa product (MOD16) at regional scale. The spatial distribution and the seasonal variation of the estimated ETa were analyzed. The results indicate that the estimated ETa shows reasonable spatial and temporal patterns with respect to the diverse cold and arid landscapes in the upstream, middle stream and downstream regions, and is useful for various applications to improve the rational allocation of water resources in the Heihe River basin. OPEN ACCESSRemote Sens. 2015, 7 3057

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Section: Discussionmentioning

confidence: 99%

Monitoring of Evapotranspiration in a Semi-Arid Inland River Basin by Combining Microwave and Optical Remote Sensing Observations

2015

Remote Sensing

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115

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“…Such discontinuities might be problematic for applications like routing vehicles across the landscape [Flores et al, 2014]. Song et al [2014] downscaled in a way that uses information from neighboring coarse grid values and avoids such discontinuities. Only a few studies have directly discussed the treatment of multiple coarse grid cells [Kaheil et al, 2008, Kim and Barros, 2002, Sahoo et al, 2013.…”

Section: Perry and Niemannmentioning

confidence: 99%

“…Fang and Lakshmi [2014] disaggregated SMOS and AMSR-E data to a 1 km resolution and compared the results to in situ observations. Using similar data in an empirical algorithm, Song et al [2014] downscaled 25 km AMSR-E data to 1 km using optical/thermal data, and it was more effective for soil moisture values less than 0.3 m 3 /m 3 .…”

Section: Introductionmentioning

confidence: 99%

Downscaling soil moisture over regions that include multiple coarse-resolution grid cells

Hoehn

Niemann

Green

et al. 2017

Remote Sensing of Environment

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“…Choi and Hur [13] x x x x x x Das et al, [11] x x x Fang et al, [14] x x x x x Ines et al, [15] x x x Kim and Hogue [16] x x x x x x Merlin et al, [27] x x x x x x x Merlin et al, [18] x x x x x x x Parinussa et al, [19] x x x Piles et al, [20] x x x x x Sánchez-Ruiz et al, [21] x x x x Shin and Mohanty [22] x x x Song et al, [23] x x x Srivastava et al, [24] x x x x x Srivastava et al, [25] x x x x x Zhao and Li [26] x…”

Section: Reference Rmsd R B S Lr Space Time Spaceandtimementioning

confidence: 99%

“…Yet, to date, little work has focused on the strategy to assess soil moisture downscaling methods. Table 1 lists some recently published disaggregation methods [11,[13][14][15][16][17][18][19][20][21][22][23][24][25][26]. For each method, Table 1 reports the performance metrics that were used to assess the error statistics in downscaled data, whether such results were compared with those obtained at high resolution in the non-disaggregation case and the nature (spatial, temporal and/or spatio-temporal) of the comparison between disaggregated and reference (often in situ) measurements.…”

Section: Introductionmentioning

confidence: 99%

Performance Metrics for Soil Moisture Downscaling Methods: Application to DISPATCH Data in Central Morocco

et al. 2015

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Data disaggregation (or downscaling) is becoming a recognized modeling framework to improve the spatial resolution of available surface soil moisture satellite products. However, depending on the quality of the scale change modeling and on the uncertainty in its input data, disaggregation may improve or degrade soil moisture information at high resolution. Hence, defining a relevant metric for evaluating such methodologies is crucial before disaggregated data can be eventually used in fine-scale studies. In this paper, a new metric, named G DOWN , is proposed to assess the potential gain provided by disaggregation relative to the non-disaggregation case. The performance metric is tested during a four-year period by comparing 1-km resolution disaggregation based on physical and theoretical scale change (DISPATCH) data with the soil moisture measurements collected by six stations in central Morocco. DISPATCH data are obtained every 2-3 days from 40-km resolution SMOS (Soil Moisture Ocean Salinity) and 1-km resolution optical MODIS (Moderate Resolution Imaging Spectroradiometer) data. The correlation coefficient between G DOWN and the disaggregation gain in time series correlation, mean bias and bias in the slope of the linear fit ranges from 0.5 to 0.8. The new metric is found to be a good Remote Sens. 2015, 7 3784 indicator of the overall performance of DISPATCH. Especially, the sign of G DOWN (positive in the case of effective disaggregation and negative in the opposite case) is independent of the uncertainties in SMOS data and of the representativeness of localized in situ measurements at the downscaling (1 km) resolution. In contrast, the traditional root mean square difference between disaggregation output and in situ measurements is poorly correlated (correlation coefficient of about 0.0) with the disaggregation gain in terms of both time series correlation and bias in the slope of the linear fit. The G DOWN approach is generic and thus could help test a range of downscaling methods dedicated to soil moisture and to other geophysical variables.

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Retrieving High-Resolution Surface Soil Moisture by Downscaling AMSR-E Brightness Temperature Using MODIS LST and NDVI Data

Cited by 68 publications

References 27 publications

Monitoring of Evapotranspiration in a Semi-Arid Inland River Basin by Combining Microwave and Optical Remote Sensing Observations

Monitoring of Evapotranspiration in a Semi-Arid Inland River Basin by Combining Microwave and Optical Remote Sensing Observations

Downscaling soil moisture over regions that include multiple coarse-resolution grid cells

Performance Metrics for Soil Moisture Downscaling Methods: Application to DISPATCH Data in Central Morocco

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