Ordered Test Site Method for Onboard Measurement Results Validation of Medium Resolution Spectroradiometers





optimization, cost function, validation, remote sensing, test sites


The reduction or elimination of uncertainties is one of the main tasks in the analysis of remote sensing data. For this purpose, the upscaling and downscaling of spectroradiometric data operations are widely used. The downscaling operation is particularly used to validate medium resolution spectroradiometer data. The onboard measurement validation data is a complex task and it includes the solution of such important subtasks as (a) selection of the test site type; (b) determining the site size; (c) determining the sampling order. At the same time, after carrying out selective measurements, the question of scaling up (upscaling or generalization) of the obtained ground data arises, its purpose is to carry out validation of satellite data with low spatial resolution. Solving the problem of remote sensing data validation, terrestrial test sites are often used, their heterogeneity must always be taken into account. This problem is usually solved by applying special weighting coefficients and performing temporary periodic measurements, then using the regularization procedure for the averaged results of iterative calculations. In the absence of temporary changes, the need for regularization is eliminated. In this case, as an alternative, the method of the ordered test section can be proposed, it allows to determine the weight coefficients of the ground validation measurement  results, providing a minimum of the newly proposed quadrature cost function. To solve the problem of achieving the proposed cost function minimum, the ordered subsections method as part of a single heterogeneous test section is proposed, the measurements are carried out by a sensor mounted on a low-flying carrier. The optimization problem is formulated to calculate the correction coefficients for the measurement results, where the sum of the squares of difference between the corrected data and the known representative estimate is minimized. The optimization problem is solved using a certain restrictive condition imposed on the sum of the correction factors.



Tabari H., Paz S. M., Buekenhout D. and Willems P. (2021). Comparison of statistical downscaling methods for climate change impact analysis on precipitation-driven drought. Hydrology and Earth System Sciences, Vol. 25, Iss. 6, pp. 3493–3517; doi: 10.5194/hess-25-3493-2021.

Loew, A., et al. (2017). Validation practices for satellite-based Earth observation data across communities. Reviews of Geophysics, Vol. 55, Iss. 3, pp. 779-817; doi:10.1002/2017RG000562.

Fernandez-Carrilo A., Franco-Nieto A., Pinto-Banuls E., Basarte-Mena M. and Revilla-Remore B. (2020). Designing a Validatoin Protocol for Remote Sensing Based Operational Forest Masks Applications. Comparison of Products Across Europe. Remote Sensing, Vol. 12, Iss. 19, 3159; doi:10.3390/rs12193159.

Zuo J., Xu J., Chen Y. and Wang Ch. (2019). Downscaling Precipitation in the Data-Scarce Inland River Basin of Northwest China Based on Earth System Data Products. Atmosphere, Vol. 10, Iss. 10, 613; doi:10.3390/atmos10100613.

Estes Jr. M. G., Insaf T., Al-Hamdan M. Z., Adeyeye T., Crosson W. (2022). Validatin of North American land data assimilation system Phase 2 (NLDAS-2) air temperature forcing and downscaled data with New York State station observations. Remote Sensing Applications: Society and Environment, Vol. 25; doi: 10.1016/j.rsase.2021.100670.

Soto-Berelov, M., Jones, S., Farmer, E., Woodgate, E. (2015). Review of validation standards of biophysical Earth Observation products. In A. Held, S. Phinn, M. Soto-Berelov, & S. Jones (Eds.), AusCover Good Practice Guidelines: A technical handbook supporting calibration and validation activities of remotely sensed data product (pp. 8-32). Version 1.1. TERN AusCover, ISBN 978-0-646-94137-0.

Shi Y., Wang J., Qin J. and Qu Y. (2015). An Upscaling Algorithm to Obtain the Representative Ground Truth of LAI Time Series in Heterogeneous Land Surface. Remote Sensing, Vol. 7, Iss. 9, 12887-12908; doi:10.3390/rs71012887.

Fang, H., Wei, Sh., Liang, Sh. (2012). Validation of MODIS and CYCLOPES LAI products using global field measurement data. Remote Sensing of Enviroment, Vol. 119, pp. 43-54; doi: 10.1016/j.rse.2011.12.006.

Morisette, J. T., et al. (2006). Validation of global moderate-resolution LAI products: A framework proposed within the CEOS land product validation subgroup. IEEE Transactions on Geoscience and Remote Sensing, Vol. 44, Iss. 7, pp. 1804-1814; doi: 10.1109/TGRS.2006.872529.

Baret, F., Weiss, M., Allard, D., Garrigue, S., Leroy, M., et al. (2015). VALERI: A Network of Sites and a Methodology for the Validation of Medium Spatial Resolution Land Satellite Products. Remote Sensing of Environment.

Di L., Moe K. and van Zyl T. L. (2010). Earth Observation Sensor Web: An Overview. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 3, No. 4, pp. 415-417; doi: 10.1109/JSTARS.2010.2089575.

Moghaddam M., et al. (2010). A Wireless Soil Moisture Smart Sensor Web Using Physics-Based Optimal Control: Concept and Initial Demonstrations. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 3, No. 4, pp. 522-535; doi: 10.1109/JSTARS.2010.2052918.

Bauer, J., Siegmann, B., Jarmer, T., Aschenbruck, N. (2014). On the potential of Wireless sensor Networks for the in-field assessment of bio-physical crop parameters. 2014 IEEE 39th Conference on the Local Computer Networks Workshops, pp. 523-530; doi: 10.1109/LCNW.2014.6927698.

Qu, Y., Wang, J., Dong, J., Jiang, F. (2012). Design and experiment of crop structural parameters automatic measurement system. Transactions of the Chinese Society of Agricultural Engineering, Vol. 28, No. 2, pp. 160-165. [n Chinese].

Qu Y., Zhu Y., Han W., Wang J. and Ma M. (2014). Crop Leaf Area Index Observations With a Wireless Sensor Network and Its Potential for Validating Remote Sensing Products. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 7, No. 2, pp. 431-444; doi: 10.1109/JSTARS.2013.2289931.

Elsgolts L. E. (1974). Differentsialnye uravneniya i variatsionnoe ischisleniye [Differential Equations and the Calculus of Variations]. M. Nauka, 432 p. [In Russians].




How to Cite

Асадов, Х. Г. . and Алиева , А. Д. (2022) “Ordered Test Site Method for Onboard Measurement Results Validation of Medium Resolution Spectroradiometers”, Visnyk NTUU KPI Seriia - Radiotekhnika Radioaparatobuduvannia, (88), pp. 35-41. doi: 10.20535/RADAP.2022.88.35-41.



Telecommunication, navigation, radar systems, radiooptics and electroacoustics