@inproceedings{75673c3108ca46c598b8f6e7373bb6c9,
title = "Dependence of Soil Moisture Retrieval Accuracy on Backscatter Resolution",
abstract = "The accuracy of high resolution soil moisture estimated from SAR backscatter data is limited due to speckle in the native resolution backscatter data. However, reducing this speckly by means of spatial aggregation also removes useful information from the data. We therefore hypothesised that using unfiltered backscatter data in a soil moisture inversion model can be valuable in high resolution soil moisture applications. A field study combined with a synthetic experiment showed that calculating soil moisture prior to spatial averaging to the final target resolution (CtA) has substantial advantages over the average-then-calculate (AtC) approach. Currently, the AtC strategy is most often applied in soil moisture studies, mainly due to its computational advantage compared to the CtA approach. However, especially at high resolutions, using a slightly higher source resolution backscatter data than the target soil moisture resolution, can already improve accuracy of the soil moisture estimates.",
keywords = "field data, high resolution, Soil moisture, synthetic experiment",
author = "{Van Hateren}, {Theresa C.} and Marco Chini and Patrick Matgen and Luca Pulvirenti and Nazzareno Pierdicca and Teuling, {Adriaan J.}",
year = "2022",
month = sep,
day = "28",
doi = "10.1109/IGARSS46834.2022.9883437",
language = "English",
isbn = "9781665427937",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "IEEE",
pages = "5497--5500",
booktitle = "IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium",
address = "United States",
note = "2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022 ; Conference date: 17-07-2022 Through 22-07-2022",
}