Towards soil property retrieval from space: An application with disaggregated satellite observations

Bandara, Ranmalee Sajeewanie; Walker, Jeffrey P.; Rüdiger, Christoph; Merlin, Olivier

doi:10.1016/j.jhydrol.2015.01.018

Cited by 20 publications

(19 citation statements)

References 27 publications

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“…The 1 km resolution SM disaggregated from SMOS produts are currently used in a range of disciplines including root-zone soil moisture monitoring [31], detecting irrigated areas at the perimeter scale [32,33], retrieving soil properties from space [34], preventing the spread of desert locust swarms [35], evapotranspiration monitoring over rainfed areas [36], flood forecasting over large basins [37], estimating crop yield [38], and the methods to produce them are continuously evolving and maturing . Note that few studies have applied the DISPATCH method to SMAP SM using MODIS data [39].…”

Section: Introductionmentioning

confidence: 99%

Stepwise Disaggregation of SMAP Soil Moisture at 100 m Resolution Using Landsat-7/8 Data and a Varying Intermediate Resolution

et al. 2019

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Global soil moisture (SM) products are currently available from passive microwave sensors at typically 40 km spatial resolution. Although recent efforts have been made to produce 1 km resolution data from the disaggregation of coarse scale observations, the targeted resolution of available SM data is still far from the requirements of fine-scale hydrological and agricultural studies. To fill the gap, a new disaggregation scheme of Soil Moisture Active and Passive (SMAP) data is proposed at 100 m resolution by using the disaggregation based on physical and theoretical scale change (DISPATCH) algorithm. The main objectives of this paper is (i) to implement DISPATCH algorithm at 100 m resolution using SMAP SM and Landsat land surface temperature and vegetation index data and (ii) to investigate the usefulness of an intermediate spatial resolution (ISR) between the SMAP 36 km resolution and the targeted 100 m resolution. The sequential disaggregation approach from 36 km to ISR (ranging from 1 km to 30 km) and from ISR to 100 m resolution is evaluated over 22 irrigated field crops in central Morocco using in-situ SM measurements collected from January to May 2016. The lowest root mean square difference (RMSD) between the 100 m resolution disaggregated and in-situ SM is obtained when the ISR is around 10 km. Therefore, the two-step disaggregation is more efficient than the direct disaggregation from SMAP to 100 m resolution. Moreover, we propose a moving average window algorithm to increase the accuracy in the 100 m resolution SM as well as to reduce the low-resolution boxy artifacts on disaggregated images. The correlation coefficient between 100 m resolution disaggregated and in situ SM ranges between 0.5–0.9 for four out of the six extensive sampling dates. This methodology relies solely on remote sensing data and can be easily implemented to monitor SM at a high spatial resolution over irrigated regions.

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Section: Introductionmentioning

confidence: 99%

Stepwise Disaggregation of SMAP Soil Moisture at 100 m Resolution Using Landsat-7/8 Data and a Varying Intermediate Resolution

et al. 2019

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“…Note that the use of soil moisture data in hydrology generally requires observations deeper than the surface soil layer (the top few cm) sensed by microwave radiometers. Therefore, solving the mismatch in the vertical representation would imply the (temporal) assimilation of superficial data into land surface models, e.g., [49,54], and its combination with (spatial) downscaling [55,56].…”

Section: Discussionmentioning

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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“…In previous works on the conventional 1-D LSMs, many land data assimilation systems (LDASs) have been proposed to accurately estimate model's state and parameter variables, which cannot be directly observed, by assimilating satellite and in-situ observations. For example, the optimization of LSM's unknown parameters (e.g., hydraulic conductivity) has been implemented by assimilating remotely sensed microwave observations (e.g., Yang et al 2007;Yang et al 2009;Bandara et al 2014;Bandara et al 2015;Sawada and Koike 2014;Han et al 2014). Kumar et al (2009) analyzed the simulated correlation between surface soil moisture and root-zone soil moisture to improve the simulation of root-zone soil moisture by assimilating remotely sensed surface soil moisture observations.…”

Section: Introductionmentioning

confidence: 99%

Do surface lateral flows matter for data assimilation of soil moisture observations into hyperresolution land models?

Sawada¹

2018

Preprint

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Hyperresolution land modeling is expected to innovate the simulation of terrestrial water, energy, and carbon cycles. One of the major advantages of existing hyperresolution land models against conventional 1-demensional land surface models is that surface and subsurface lateral water flows can be explicitly simulated. Despite a lot of efforts on assimilating hydrological observations into the hyperresolution integrated surface-groundwater land models, how and in what case topography-driven surface water flows matter for data assimilation of soil moisture observations has yet to be clarified. In this study, I perform a minimalist synthetic numerical experiment, in which shallow soil moisture observations are assimilated into an integrated surface-groundwater land model by the ensemble Kalman filter. Propagation of a background error due to surface lateral water flows is crucially important to adjust the unobserved model state and parameter variables by horizontally propagating the information of soil moisture observations. However, the non-Gaussianity of the background error induced by the nonlinear dynamics of topography-driven surface flows harms the performance of an ensemble Kalman filter. It is difficult to efficiently constrain model states at the edge of the area where topography-driven surface flows reach by linear-Gaussian filters, which brings the new challenge in land data assimilation for hyperresolution land models. The new capability of data assimilation with the hyperresolution land models found in this study may improve the monitoring and prediction of flash floods caused by local severe rainfalls.

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Towards soil property retrieval from space: An application with disaggregated satellite observations

Cited by 20 publications

References 27 publications

Stepwise Disaggregation of SMAP Soil Moisture at 100 m Resolution Using Landsat-7/8 Data and a Varying Intermediate Resolution

Stepwise Disaggregation of SMAP Soil Moisture at 100 m Resolution Using Landsat-7/8 Data and a Varying Intermediate Resolution

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

Do surface lateral flows matter for data assimilation of soil moisture observations into hyperresolution land models?

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