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Validation of SMOS soil moisture products over the Maqu and Twente regions.

Dente L, Su Z, Wen J - Sensors (Basel) (2012)

Bottom Line: However, the validation analysis resulted in determination coefficients of 0.55 and 0.51 over the Maqu and Twente region, respectively, for the ascending pass data, and of 0.24 and 0.41, respectively, for the descending pass data.Moreover, a systematic dry bias of the SMOS soil moisture was found of approximately 0.13 m(3)/m(3) for the Maqu region and 0.17 m(3)/m(3) for the Twente region for ascending pass data.Several factors might have affected the retrieval accuracy, such as the presence of Radio Frequency Interference (RFI), the use of inaccurate land cover information and the presence of frozen soils not correctly detected in winter.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Hengelosestraat 99, 7514AE Enschede, The Netherlands. dente@itc.nl

ABSTRACT
The validation of Soil Moisture and Ocean Salinity (SMOS) soil moisture products is a crucial step in the investigation of their inaccuracies and limitations, before planning further refinements of the retrieval algorithm. Therefore, this study intended to contribute to the validation of the SMOS soil moisture products, by comparing them with the data collected in situ in the Maqu (China) and Twente (The Netherlands) regions in 2010. The seasonal behavior of the SMOS soil moisture products is generally in agreement with the in situ measurements for both regions. However, the validation analysis resulted in determination coefficients of 0.55 and 0.51 over the Maqu and Twente region, respectively, for the ascending pass data, and of 0.24 and 0.41, respectively, for the descending pass data. Moreover, a systematic dry bias of the SMOS soil moisture was found of approximately 0.13 m(3)/m(3) for the Maqu region and 0.17 m(3)/m(3) for the Twente region for ascending pass data. Several factors might have affected the retrieval accuracy, such as the presence of Radio Frequency Interference (RFI), the use of inaccurate land cover information and the presence of frozen soils not correctly detected in winter. Improving the RFI filtering method and the quality of the retrieval algorithm inputs, such as land surface temperature and land cover, would certainly improve the accuracy of the retrieved soil moisture.

No MeSH data available.


UMd map (top-left), IGBP-DIS map (top-right) and MICLCover map (bottom-right) over the Maqu region and surrounding areas. The black rectangle highlights the Maqu network location.
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f9-sensors-12-09965: UMd map (top-left), IGBP-DIS map (top-right) and MICLCover map (bottom-right) over the Maqu region and surrounding areas. The black rectangle highlights the Maqu network location.

Mentions: The second issue concerns the incorrectly classified land cover in the Maqu region. According to information collected in situ, homogenous grasslands cover the network area and its surrounding areas within tens of kilometers, and the valley near to the Yellow River is characterized by wetlands. However, the forest flag is raised for half of the nodes in this region and the wetland flag is never raised. For this reason, accuracy of the land cover information used by the SMOS L2 processor was investigated. The two cover maps on which the ECOCLIMAP is based, the UMd map and the IGBP-DIS map, were compared with an independent land cover map, the MICLCover map. Figure 9 shows the three maps for the Maqu region.


Validation of SMOS soil moisture products over the Maqu and Twente regions.

Dente L, Su Z, Wen J - Sensors (Basel) (2012)

UMd map (top-left), IGBP-DIS map (top-right) and MICLCover map (bottom-right) over the Maqu region and surrounding areas. The black rectangle highlights the Maqu network location.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3472810&req=5

f9-sensors-12-09965: UMd map (top-left), IGBP-DIS map (top-right) and MICLCover map (bottom-right) over the Maqu region and surrounding areas. The black rectangle highlights the Maqu network location.
Mentions: The second issue concerns the incorrectly classified land cover in the Maqu region. According to information collected in situ, homogenous grasslands cover the network area and its surrounding areas within tens of kilometers, and the valley near to the Yellow River is characterized by wetlands. However, the forest flag is raised for half of the nodes in this region and the wetland flag is never raised. For this reason, accuracy of the land cover information used by the SMOS L2 processor was investigated. The two cover maps on which the ECOCLIMAP is based, the UMd map and the IGBP-DIS map, were compared with an independent land cover map, the MICLCover map. Figure 9 shows the three maps for the Maqu region.

Bottom Line: However, the validation analysis resulted in determination coefficients of 0.55 and 0.51 over the Maqu and Twente region, respectively, for the ascending pass data, and of 0.24 and 0.41, respectively, for the descending pass data.Moreover, a systematic dry bias of the SMOS soil moisture was found of approximately 0.13 m(3)/m(3) for the Maqu region and 0.17 m(3)/m(3) for the Twente region for ascending pass data.Several factors might have affected the retrieval accuracy, such as the presence of Radio Frequency Interference (RFI), the use of inaccurate land cover information and the presence of frozen soils not correctly detected in winter.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Hengelosestraat 99, 7514AE Enschede, The Netherlands. dente@itc.nl

ABSTRACT
The validation of Soil Moisture and Ocean Salinity (SMOS) soil moisture products is a crucial step in the investigation of their inaccuracies and limitations, before planning further refinements of the retrieval algorithm. Therefore, this study intended to contribute to the validation of the SMOS soil moisture products, by comparing them with the data collected in situ in the Maqu (China) and Twente (The Netherlands) regions in 2010. The seasonal behavior of the SMOS soil moisture products is generally in agreement with the in situ measurements for both regions. However, the validation analysis resulted in determination coefficients of 0.55 and 0.51 over the Maqu and Twente region, respectively, for the ascending pass data, and of 0.24 and 0.41, respectively, for the descending pass data. Moreover, a systematic dry bias of the SMOS soil moisture was found of approximately 0.13 m(3)/m(3) for the Maqu region and 0.17 m(3)/m(3) for the Twente region for ascending pass data. Several factors might have affected the retrieval accuracy, such as the presence of Radio Frequency Interference (RFI), the use of inaccurate land cover information and the presence of frozen soils not correctly detected in winter. Improving the RFI filtering method and the quality of the retrieval algorithm inputs, such as land surface temperature and land cover, would certainly improve the accuracy of the retrieved soil moisture.

No MeSH data available.