Can Measurement and Input Uncertainty Explain Discrepancies Between the Wheat Canopy Scattering Model and SMAPVEX12 Observations?
Realistic representation of microwave backscattering from vegetated surfaces is important for developing accurate soil moisture retrieval algorithms that use synthetic aperture radar (SAR) imagery. Many studies have reported considerable discrepancies between the simulated and observed backscatter....
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/1/164 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841549001494626304 |
|---|---|
| author | Lilangi Wijesinghe Andrew W. Western Jagannath Aryal Dongryeol Ryu |
| author_facet | Lilangi Wijesinghe Andrew W. Western Jagannath Aryal Dongryeol Ryu |
| author_sort | Lilangi Wijesinghe |
| collection | DOAJ |
| description | Realistic representation of microwave backscattering from vegetated surfaces is important for developing accurate soil moisture retrieval algorithms that use synthetic aperture radar (SAR) imagery. Many studies have reported considerable discrepancies between the simulated and observed backscatter. However, there has been limited effort to identify the sources of errors and contributions quantitatively using process-based backscatter simulation in comparison with extensive ground observations. This study examined the influence of input uncertainties on simulated backscatter from a first-order radiative transfer model, named the Wheat Canopy Scattering Model (WCSM), using ground-based and airborne data collected during the SMAPVEX12 campaign. Input uncertainties to WCSM were simulated using error statistics for two crop growth stages. The Sobol’ method was adopted to analyze the uncertainty in WCSM-simulated backscatters originating from different inputs before and after the wheat ear emergence. The results show that despite the presence of wheat ears, uncertainty in root mean square (RMS) height of 0.2 cm significantly influences simulated co-polarized backscatter uncertainty. After ear emergence, uncertainty in ears dominates simulated cross-polarized backscatter uncertainty. In contrast, uncertainty in RMS height before ear emergence dominates the accuracy of simulated cross-polarized backscatter. These findings suggest that considering wheat ears in the model structure and precise representation of surface roughness is essential to accurately simulate backscatter from a wheat field. Since the discrepancy between the simulated and observed backscatter coefficients is due to both model and observation uncertainty, the uncertainty of the UAVSAR data was estimated by analyzing the scatter between multiple backscatter coefficients obtained from the same targets near-simultaneously, assuming the scatter represents the observation uncertainty. Observation uncertainty of UAVSAR backscatter for HH, VV, and HV polarizations are 0.8 dB, 0.87 dB, and 0.86 dB, respectively. Discrepancies between WCSM-simulated backscatter and UAVSAR observations are discussed in terms of simulation and observation uncertainty. |
| format | Article |
| id | doaj-art-7fa79495061f4265801d9ae418403432 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-7fa79495061f4265801d9ae4184034322025-01-10T13:20:27ZengMDPI AGRemote Sensing2072-42922025-01-0117116410.3390/rs17010164Can Measurement and Input Uncertainty Explain Discrepancies Between the Wheat Canopy Scattering Model and SMAPVEX12 Observations?Lilangi Wijesinghe0Andrew W. Western1Jagannath Aryal2Dongryeol Ryu3Department of Infrastructure Engineering, The University of Melbourne, Parkville, VIC 3010, AustraliaDepartment of Infrastructure Engineering, The University of Melbourne, Parkville, VIC 3010, AustraliaDepartment of Infrastructure Engineering, The University of Melbourne, Parkville, VIC 3010, AustraliaDepartment of Infrastructure Engineering, The University of Melbourne, Parkville, VIC 3010, AustraliaRealistic representation of microwave backscattering from vegetated surfaces is important for developing accurate soil moisture retrieval algorithms that use synthetic aperture radar (SAR) imagery. Many studies have reported considerable discrepancies between the simulated and observed backscatter. However, there has been limited effort to identify the sources of errors and contributions quantitatively using process-based backscatter simulation in comparison with extensive ground observations. This study examined the influence of input uncertainties on simulated backscatter from a first-order radiative transfer model, named the Wheat Canopy Scattering Model (WCSM), using ground-based and airborne data collected during the SMAPVEX12 campaign. Input uncertainties to WCSM were simulated using error statistics for two crop growth stages. The Sobol’ method was adopted to analyze the uncertainty in WCSM-simulated backscatters originating from different inputs before and after the wheat ear emergence. The results show that despite the presence of wheat ears, uncertainty in root mean square (RMS) height of 0.2 cm significantly influences simulated co-polarized backscatter uncertainty. After ear emergence, uncertainty in ears dominates simulated cross-polarized backscatter uncertainty. In contrast, uncertainty in RMS height before ear emergence dominates the accuracy of simulated cross-polarized backscatter. These findings suggest that considering wheat ears in the model structure and precise representation of surface roughness is essential to accurately simulate backscatter from a wheat field. Since the discrepancy between the simulated and observed backscatter coefficients is due to both model and observation uncertainty, the uncertainty of the UAVSAR data was estimated by analyzing the scatter between multiple backscatter coefficients obtained from the same targets near-simultaneously, assuming the scatter represents the observation uncertainty. Observation uncertainty of UAVSAR backscatter for HH, VV, and HV polarizations are 0.8 dB, 0.87 dB, and 0.86 dB, respectively. Discrepancies between WCSM-simulated backscatter and UAVSAR observations are discussed in terms of simulation and observation uncertainty.https://www.mdpi.com/2072-4292/17/1/164Wheat Canopy Scattering Modelactive microwave remote sensingSMAPVEX12uncertainty analysisSobol’ method |
| spellingShingle | Lilangi Wijesinghe Andrew W. Western Jagannath Aryal Dongryeol Ryu Can Measurement and Input Uncertainty Explain Discrepancies Between the Wheat Canopy Scattering Model and SMAPVEX12 Observations? Remote Sensing Wheat Canopy Scattering Model active microwave remote sensing SMAPVEX12 uncertainty analysis Sobol’ method |
| title | Can Measurement and Input Uncertainty Explain Discrepancies Between the Wheat Canopy Scattering Model and SMAPVEX12 Observations? |
| title_full | Can Measurement and Input Uncertainty Explain Discrepancies Between the Wheat Canopy Scattering Model and SMAPVEX12 Observations? |
| title_fullStr | Can Measurement and Input Uncertainty Explain Discrepancies Between the Wheat Canopy Scattering Model and SMAPVEX12 Observations? |
| title_full_unstemmed | Can Measurement and Input Uncertainty Explain Discrepancies Between the Wheat Canopy Scattering Model and SMAPVEX12 Observations? |
| title_short | Can Measurement and Input Uncertainty Explain Discrepancies Between the Wheat Canopy Scattering Model and SMAPVEX12 Observations? |
| title_sort | can measurement and input uncertainty explain discrepancies between the wheat canopy scattering model and smapvex12 observations |
| topic | Wheat Canopy Scattering Model active microwave remote sensing SMAPVEX12 uncertainty analysis Sobol’ method |
| url | https://www.mdpi.com/2072-4292/17/1/164 |
| work_keys_str_mv | AT lilangiwijesinghe canmeasurementandinputuncertaintyexplaindiscrepanciesbetweenthewheatcanopyscatteringmodelandsmapvex12observations AT andrewwwestern canmeasurementandinputuncertaintyexplaindiscrepanciesbetweenthewheatcanopyscatteringmodelandsmapvex12observations AT jagannatharyal canmeasurementandinputuncertaintyexplaindiscrepanciesbetweenthewheatcanopyscatteringmodelandsmapvex12observations AT dongryeolryu canmeasurementandinputuncertaintyexplaindiscrepanciesbetweenthewheatcanopyscatteringmodelandsmapvex12observations |