"uuid","repository link","title","author","contributor","publication year","abstract","subject topic","language","publication type","publisher","isbn","issn","patent","patent status","bibliographic note","access restriction","embargo date","faculty","department","research group","programme","project","coordinates"
"uuid:74422f15-70a3-4441-a463-261f8675b856","http://resolver.tudelft.nl/uuid:74422f15-70a3-4441-a463-261f8675b856","Satellite Soil Moisture Retrieval: Validation and Analysis of Satellite Retrieved Soil Moisture in the Tropics, a case study in Myanmar","de Kat, Ileen (TU Delft Civil Engineering and Geosciences)","Bogaard, T.A. (mentor); Steele-Dunne, S.C. (graduation committee); Schleiss, M.A. (graduation committee); van der Horst, Teije (graduation committee); Delft University of Technology (degree granting institution)","2021","The validation of satellite retrieved soil moisture over a tropical region (Bago, Myanmar) has been conducted in this research. The downscaled soil moisture products of VanderSat using the Land Parameter Retrieval Model, were compared with an in-situ soil moisture network that operated between 2017 and 2018 and a newly placed network installed during a fieldwork trip early 2020. Soil moisture is a key variable in the hydro- logical cycle, but the understanding of soil moisture content in tropical areas like Myanmar is substandard. This study provides new insights, being one of the first of its kind in the tropics. The reliability of both in-situ soil moisture networks was analysed and indicated that the 2017 network could be used for the validation, and the 2020 network for a visual comparison, with a focus on the soil temperature as a source of error. The results of the validation showed high correlation with Pearson’s R ranging between 0.70 and 0.89. The highest correlations were found for the L-band, between 0.78 and 0.89 and for the location Hpayargyi. The root mean square difference (RMSD), unbiased RMSD and bias indicated that the accuracy and precision need improve- ment. For the ubRMSD values between 0.059 m3/m3 and 0.129 m3/m3 are found. The best result is found for the L-band at Hpayargyi, which is the only location/band combination that meets the target accuracy of 0.06 m3/m3 that is often set for SMAP satellite retrieval. The visual comparison of the 2020 network showed good agreement for the soil moisture measurement. When the in-situ soil temperature measurements were compared with the satellite retrieved land surface temperature measurement, however, this resulted in a large bias of approximately 15 degrees Celsius. To evaluate possible improvements for the validation result, several sources of errors were explored. Two sources concerning the in-situ network indicated the challenges of an in-situ network in a remote tropical area. The first showed that re-calibration of the sensor is necessary to improve representation of the ground truth. The second discussed the spatial mismatch between the point scale in-situ measurement and the downscaled satellite products, with a pixel size of 100x100m. The sparse in-situ network with four stations cannot represent the entire area and can therefore result in errors. The other sources of errors that were discussed originated from the retrieval algorithm of the Land Parameter Re- trieval Model. A parameterisation was performed to assess the sensitivity of the ubRMSD to the polarisation mixing factor (Q), the single scattering albedo (!) and the roughness parameters (h1 and h2). The parame- ters were optimised to achieve the lowest ubRMSD, the result indicated that an improvement of the ubRMSD up to 40% can be achieved. However the evidence that linked the optimised parameters to the tropics was not found and it was concluded that the optimising could be covering the large temperature bias, that is a direct input in the Land Parameter Retrieval Model. The large bias in the temperature was evaluated as a source of error. The bias can partially be explained by the masking of water bodies, where the dynamic ef- fect of the tropical monsoon on the water bodies is not taken into account. Another explanation for the bias could potentially be found in the emissivity that is determined by optimising the ¢T , which can cause im- perfections in the prediction of the temperature. It was concluded that the satellite retrieved soil moisture products show good correlation but need improvement in accuracy and precision. This can be accomplished by updating the in-situ soil moisture network, creating new water masks, solving the retrieval algorithm for the temperature bias and then optimising tropical specific location parameters.
The added value of using CoVadem technology, however, is vulnerable to the number of participating vessels. A combination of morphological changes and the typical spatial spread of CoVadem data limits the extent of the navigation channel that can be derived from the data. With only a small part of the navigation channel known it is unclear where passing of other vessels is possible, where speeds should be adjusted due to e.g., bottlenecks and where shorter routes are present.
The objective of this research is to provide more information about the navigable area around CoVadem data in order to assist captains with navigation. During this research, two physics-based models have been developed, able to carry out this task. The ‘soil-model’ combines CoVadem data with assumptions about the maximum slope in the bed.
The ‘axi-symmetric model’ utilises CoVadem data, the axi-symmetric solution and assumptions about sand dunes and river banks in a model to estimate a navigable area.
Two ‘reliability indicators’ have been developed that can indicate areas of the river where the output of the axi-symmetric model is reliable in its estimate. One indicator is related to the channel stability, it is calculated from many years of satellite imagery. The other indicator is related to channel curvature.
To measure the performance of the models and the reliability indicators, two dimensionless performance indicators have been developed: the safety score and the channel coverage score. The models and the reliability indicators were consecutively tested and evaluated for four study cases located along the Chindwin river.
The results are promising. It follows from this research that navigation channel estimates around scarce CoVadem ship track data can substantially benefit from application of a physics-based model. The soil-model is very robust, but has limited added value for navigation. The axi-symmetric model increases the navigable width estimate around a single CoVadem trackline significantly O(100 m). The performance indicators can improve the reliability of the axi-symmetric model significantly.
This research combines Big Data, physics-based models and remote sensing in a not early demonstrated way: with models tailored for navigable area estimates and with measured data as the starting point. The correlation between axi-symmetric model reliability and remote sensing/curvature is, moreover, something that has not been demonstrated before. Finally, the two developed performance indicators show great promise for the evaluation of navigable area estimates. As such, this research adds to current advancements in (open-access) cross-platform data accumulation and utilisation.