Preet Lal , Gurjeet Singh , Narendra N. Das , Dara Entekhabi , Rowena B. Lohman , Andreas Colliander
{"title":"Uncertainty estimates in the NISAR high-resolution soil moisture retrievals from multi-scale algorithm","authors":"Preet Lal , Gurjeet Singh , Narendra N. Das , Dara Entekhabi , Rowena B. Lohman , Andreas Colliander","doi":"10.1016/j.rse.2024.114288","DOIUrl":null,"url":null,"abstract":"<div><p>It is important to know the amount of systematic and random uncertainties in any state variable to improve its geophysical application potential. The expected high-resolution (200 [m]) soil moisture product from the NASA-ISRO Synthetic Aperture Radar (NISAR) mission is no exception. Thus, knowing the quality of the soil moisture retrievals through the estimation of various error sources is imperative. The estimation error sources in soil moisture retrievals can be obtained by various methods. In situ measurements provide a reliable estimate of the uncertainty of soil moisture retrievals. However, in situ measurements are available only for limited locations, as they are typically very tedious and expensive to obtain. Thus, an analytical approach has been developed to obtain an estimate of the uncertainty in the soil moisture retrievals that vary in space and time across grid-cells. This uncertainty estimation is specifically developed for the multi-scale algorithm of the upcoming NISAR mission, which will provide soil moisture retrievals at 200 [m] resolution. The multi-scale algorithm for the NISAR mission disaggregates the coarser resolution soil moisture (∼9 [km]) to high-resolution (∼200 [m]) using NISAR L-band SAR measurements. However, uncertainty in high-resolution soil moisture retrievals might be introduced due to errors in input datasets (e.g., coarse resolution soil moisture, instrument error of SAR, etc.) and multi-scale algorithm parameters. Therefore, this study carried out a detailed sensitivity analysis of input datasets and algorithm parameters using the proposed approach. The sensitivity analysis shows that error in the input coarse resolution soil moisture is one of the primary drivers of uncertainty in the high-resolution soil moisture retrievals. The other portion of the uncertainty comes from errors in the algorithm parameters, and noise in SAR co-pol and cross-pol backscatter observations. Furthermore, the approach was tested on the UAVSAR L-band data time-series that had been simulated to closely match the expected characteristics of NISAR (e.g., spatial resolution and noise). The uncertainty estimates in UAVSAR-based high-resolution retrievals were compared with the SMAPVEX-12 in situ measurements. The uncertainties estimated for different crops were found to be close to the ubRMSE metric, which is also lower than the NISAR mission accuracy goal (0.06 [m<sup>3</sup>/m<sup>3</sup>]).</p></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":null,"pages":null},"PeriodicalIF":11.1000,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Remote Sensing of Environment","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0034425724003067","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
It is important to know the amount of systematic and random uncertainties in any state variable to improve its geophysical application potential. The expected high-resolution (200 [m]) soil moisture product from the NASA-ISRO Synthetic Aperture Radar (NISAR) mission is no exception. Thus, knowing the quality of the soil moisture retrievals through the estimation of various error sources is imperative. The estimation error sources in soil moisture retrievals can be obtained by various methods. In situ measurements provide a reliable estimate of the uncertainty of soil moisture retrievals. However, in situ measurements are available only for limited locations, as they are typically very tedious and expensive to obtain. Thus, an analytical approach has been developed to obtain an estimate of the uncertainty in the soil moisture retrievals that vary in space and time across grid-cells. This uncertainty estimation is specifically developed for the multi-scale algorithm of the upcoming NISAR mission, which will provide soil moisture retrievals at 200 [m] resolution. The multi-scale algorithm for the NISAR mission disaggregates the coarser resolution soil moisture (∼9 [km]) to high-resolution (∼200 [m]) using NISAR L-band SAR measurements. However, uncertainty in high-resolution soil moisture retrievals might be introduced due to errors in input datasets (e.g., coarse resolution soil moisture, instrument error of SAR, etc.) and multi-scale algorithm parameters. Therefore, this study carried out a detailed sensitivity analysis of input datasets and algorithm parameters using the proposed approach. The sensitivity analysis shows that error in the input coarse resolution soil moisture is one of the primary drivers of uncertainty in the high-resolution soil moisture retrievals. The other portion of the uncertainty comes from errors in the algorithm parameters, and noise in SAR co-pol and cross-pol backscatter observations. Furthermore, the approach was tested on the UAVSAR L-band data time-series that had been simulated to closely match the expected characteristics of NISAR (e.g., spatial resolution and noise). The uncertainty estimates in UAVSAR-based high-resolution retrievals were compared with the SMAPVEX-12 in situ measurements. The uncertainties estimated for different crops were found to be close to the ubRMSE metric, which is also lower than the NISAR mission accuracy goal (0.06 [m3/m3]).
期刊介绍:
Remote Sensing of Environment (RSE) serves the Earth observation community by disseminating results on the theory, science, applications, and technology that contribute to advancing the field of remote sensing. With a thoroughly interdisciplinary approach, RSE encompasses terrestrial, oceanic, and atmospheric sensing.
The journal emphasizes biophysical and quantitative approaches to remote sensing at local to global scales, covering a diverse range of applications and techniques.
RSE serves as a vital platform for the exchange of knowledge and advancements in the dynamic field of remote sensing.