A combination of DISPATCH downscaling algorithm with CLASS land surface scheme for soil moisture estimation at fine scale during cloudy days

Djamai, Najib; Magagi, Ramata; Goı̈ta, Kalifa; Merlin, Olivier; Kerr, Yann H.; Roy, Alexandre

doi:10.1016/j.rse.2016.06.010

Cited by 58 publications

(37 citation statements)

References 48 publications

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“…To address any potential bias between the AMSR-2 SM values and the in situ measurements, we applied bias correction to the downscaled SM, which consist of subtracting the bias (mean difference) between the AMSR-2 SM values and the in situ measurements from the downscaled SM. Some studies have applied bias correction to remotely sensed SM products before downscaling (see [48]); however, such a procedure could cause error propagation during the downscaling process. As a preliminary exploration and given the lack of sufficient ground observations, here, we ignored error accumulation and we applied bias correction to the downscaled results before comparison with the ground reference data.…”

Section: A Methodological Setupmentioning

confidence: 99%

Downscaling AMSR-2 Soil Moisture Data With Geographically Weighted Area-to-Area Regression Kriging

Yan

Wang

et al. 2018

IEEE Trans. Geosci. Remote Sensing

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Abstract-Soil moisture (SM) plays an important role in the land surface energy balance and water cycle. Microwave remote sensing has been applied widely to estimate SM. However, the application of such data is generally restricted because of their coarse spatial resolution. Downscaling methods have been applied to predict fine-resolution SM from original data with coarse spatial resolution. Commonly, SM is highly spatially variable and, consequently, such local spatial heterogeneity should be considered in a downscaling process. Here, a hybrid geostatistical approach, which integrates geographically weighted regression and area-to-area kriging, is proposed for downscaling microwave SM products. The proposed geographically weighted area-to-area regression kriging (GWATARK) method combines fine-spatial-resolution optical remote sensing data and coarse-spatial-resolution passive microwave remote sensing data, because the combination of both information sources has great potential for mapping fine-spatial-resolution near-surface SM. The GWATARK method was evaluated by producing downscaled SM at 1-km resolution from the 25-km-resolution daily AMSR-2 SM product. Comparison of the downscaled predictions from the GWATARK method and two benchmark methods on three sets of covariates with in situ observations showed that the GWATARK method is more accurate than the two benchmarks. On average, the root-mean-square error value decreased by 20%. The use of additional covariates further increased the accuracy of the downscaled predictions, particularly when using topographycorrected land surface temperature and vegetation-temperature condition index covariates.

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Section: A Methodological Setupmentioning

confidence: 99%

Downscaling AMSR-2 Soil Moisture Data With Geographically Weighted Area-to-Area Regression Kriging

Yan

Wang

et al. 2018

IEEE Trans. Geosci. Remote Sensing

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“…In addition, it has very coarse spatial resolution (i.e., 0.25-1.0 degrees). For local and regional applications of soil moisture data on agriculture and water resources, such coarse resolution data is not particularly useful since it does not provide details on local variations in soil moisture [26,27]. Both microwave satellite sensor-derived soil moisture and reanalysis data have a common problem in that they have low spatial resolution; thus research efforts have been made to improve the spatial resolution of soil moisture data [28][29][30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

“…Optical/thermal data has been used to downscale soil moisture since the concept of the 'universal triangle' was introduced [39,40]. This concept explains the relationship between soil moisture, surface temperature, and vegetation indices [27]. Many studies have conducted downscaling of soil moisture data using empirical regression models [37,38,[41][42][43][44].…”

Section: Introductionmentioning

confidence: 99%

Downscaling GLDAS Soil Moisture Data in East Asia through Fusion of Multi-Sensors by Optimizing Modified Regression Trees

Park

et al. 2017

Water

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Soil moisture is a key part of Earth's climate systems, including agricultural and hydrological cycles. Soil moisture data from satellite and numerical models is typically provided at a global scale with coarse spatial resolution, which is not enough for local and regional applications. In this study, a soil moisture downscaling model was developed using satellite-derived variables targeting Global Land Data Assimilation System (GLDAS) soil moisture as a reference dataset in East Asia based on the optimization of a modified regression tree. A total of six variables, Advanced Microwave Scanning Radiometer 2 (AMSR2) and Advanced SCATterometer (ASCAT) soil moisture products, Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), and MODerate resolution Imaging Spectroradiometer (MODIS) products, including Land Surface Temperature, Normalized Difference Vegetation Index, and land cover, were used as input variables. The optimization was conducted through a pruning approach for operational use, and finally 59 rules were extracted based on root mean square errors (RMSEs) and correlation coefficients (r). The developed downscaling model showed a good modeling performance (r = 0.79, RMSE = 0.056 m 3 ·m −3 , and slope = 0.74). The 1 km downscaled soil moisture showed similar time series patterns with both GLDAS and ground soil moisture and good correlation with ground soil moisture (average r = 0.47, average RMSD = 0.038 m 3 ·m −3 ) at 14 ground stations. The spatial distribution of 1 km downscaled soil moisture reflected seasonal and regional characteristics well, although the model did not result in good performance over a few areas such as Southern China due to very high cloud cover rates. The results of this study are expected to be helpful in operational use to monitor soil moisture throughout East Asia since the downscaling model produces daily high resolution (1 km) real time soil moisture with a low computational demand. This study yielded a promising result to operationally produce daily high resolution soil moisture data from multiple satellite sources, although there are yet several limitations. In future research, more variables including Global Precipitation Measurement (GPM) precipitation, Soil Moisture Active Passive (SMAP) soil moisture, and other vegetation indices will be integrated to improve the performance of the proposed soil moisture downscaling model.

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“…5cm, 10cm, and 25cm). SM plays an important role in the water cycle, hydrologic studies, agricultural activities, and environmental monitoring [1][2][3][4][5][6]. Hydrologic-modeling systems are very sensitive to changes in SM values for applications involving flood control and drought assessment [1].…”

Section: Introductionmentioning

confidence: 99%

“…Predictions and results for environmental monitoring applications like climate change and weather forecasting have a high dependency on the accuracy of the SM data [5,6,8]. The spatio-temporal availability of accurate SM measurements rely on the quality of the instruments, frequency of retrieval, and management of the data [2]. There are three options for acquiring SM content; ground-based measurements, modeling predictions, and remotely sensed estimates [1,3].…”

Section: Introductionmentioning

confidence: 99%

Comparison and Downscale of AMSR2 Soil Moisture Products with In-Situ Measurements from the SCAN-NRCS Network over Puerto Rico

Olivieri¹,

Muñoz-Barreto²,

Tirado-Corbalá³

et al. 2017

Preprint

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Abstract:A continuous spatio-temporal database of accurate soil moisture (SM) measurements is an important asset for agricultural activities, hydrologic studies, and environmental monitoring. The Advanced Microwave Scanning Radiometer 2 (AMSR2), launched in May 2012, has been providing SM data globally with a revisit period of two days. It is imperative to assess the quality of this data before performing any application. Since resources of accurate SM measurements are very limited in Puerto Rico, this research will assess the quality of the AMSR2 data by comparing with ground-based measurements and perform a downscaling technique to provide a better description of how the sensor perceives the surface soil moisture as it passes over the island. The comparison consisted of the evaluation of the mean error, root mean squared error, and the correlation coefficient. Two downscaling techniques were used and their performances were studied. The results revealed that AMSR2 products tend to underestimate. This is due to the extreme heterogeneous distributions of elevations, vegetation densities, soil types, and weather events on the island. This research provides a comprehensive study on the accuracy and potential of the AMSR2 products over Puerto Rico. Further studies are recommended to improve the AMSR2 products.

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A combination of DISPATCH downscaling algorithm with CLASS land surface scheme for soil moisture estimation at fine scale during cloudy days

Cited by 58 publications

References 48 publications

Downscaling AMSR-2 Soil Moisture Data With Geographically Weighted Area-to-Area Regression Kriging

Downscaling AMSR-2 Soil Moisture Data With Geographically Weighted Area-to-Area Regression Kriging

Downscaling GLDAS Soil Moisture Data in East Asia through Fusion of Multi-Sensors by Optimizing Modified Regression Trees

Comparison and Downscale of AMSR2 Soil Moisture Products with In-Situ Measurements from the SCAN-NRCS Network over Puerto Rico

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