Indirect Validation of Ocean Remote Sensing Data via Numerical Model: An Example of Wave Heights from Altimeter

Haoyu Jiang

doi:10.3390/rs12162627

Abstract

Using numerical model outputs as a bridge, an indirect validation method for remote sensing data was developed to increase the number of effective collocations between remote sensing data to be validated and reference data. The underlying idea for this method is that the local spatial-temporal variability of specific parameters provided by numerical models can compensate for the representativeness error induced by differences of spatial-temporal locations of the collocated data pair. Using this method, the spatial-temporal window for collocation can be enlarged for a given error tolerance. To test the effectiveness of this indirect validation approach, significant wave height (SWH) data from Envisat were indirectly compared against buoy and Jason-2 SWHs, using the SWH gradient information from a numerical wave hindcast as a bridge. The results indicated that this simple indirect validation method is superior to “direct” validation.

Highlights

Since the launch of Seasat in 1978, satellites have become an important tool for observing the global ocean and its overlying atmosphere
For 2011, ~8 × 103 collocations were available with a radius of 50 km, but the number of collocations (Ncol) becomes ~3 × 104/~8 × 104 when the radius increases to 100/150 km
After removing the systematical bias between remote sensing data and reference data, their variance can be decomposed as: σ2 = σ2rs + σ2re f + σ2st where σrs and σref represent the random error (RMSE) of the two datasets, and σst represents the representativeness error induced by the difference of spatial-temporal locations of the two datasets

Summary

Introduction

Since the launch of Seasat in 1978, satellites have become an important tool for observing the global ocean and its overlying atmosphere. Different types of spaceborne remote sensing systems, such as radiometers, altimeters, scatterometers, and synthetic aperture radars, provide information on many ocean surface dynamic parameters, such as sea surface temperature, sea surface heights, sea surface wind fields, and significant wave heights (SWHs). After a satellite is launched, the calibration and validation of the sensors are necessary because (1) a quantitative evaluation of the errors is required before the data products can be utilized; (2) systematic errors of the retrievals can be corrected during calibration; (3) long-term drifts and degradations of sensors need to be monitored and their impacts need to be corrected. Data from reliable in situ instruments or from well-calibrated remote sensing systems are usually selected as a reference dataset

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