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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.


Percentage of SMOS grid nodes in the Maqu region, where the flag for frozen soil (top-left) and for out-of-range values and high retrieval errors (top-right) were raised. Spatial average of soil temperature measured in the Maqu region at 5 cm depth (bottom-left). Percentage of disregarded SMOS brightness temperature observations because of suspected RFI, averaged over the Maqu region (bottom-right), when there was no retrieval. Legend refers to all figures.
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f5-sensors-12-09965: Percentage of SMOS grid nodes in the Maqu region, where the flag for frozen soil (top-left) and for out-of-range values and high retrieval errors (top-right) were raised. Spatial average of soil temperature measured in the Maqu region at 5 cm depth (bottom-left). Percentage of disregarded SMOS brightness temperature observations because of suspected RFI, averaged over the Maqu region (bottom-right), when there was no retrieval. Legend refers to all figures.

Mentions: As mentioned in Section 2.3, no retrieval is performed when the soil is frozen. Figure 5 (top-left) shows the percentage of nodes included in the Maqu region with a raised frost flag over the total number of nodes in the region lacking retrieval, for both the case of no retrieval over a part of the area and the case of no retrieval over the whole area. The same plot is presented for the Twente region in Figure 6. Figure 5 shows that the complete area of the Maqu network was characterized by the frost flag several times in winter, resulting in no data, whereas the Twente region was not considered to be covered by frozen soil except for a few days in January and December. When the soil moisture was only retrieved over part of the area, frozen soil was not the main cause of unsuccessful retrieval for the rest of the area.


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

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

Percentage of SMOS grid nodes in the Maqu region, where the flag for frozen soil (top-left) and for out-of-range values and high retrieval errors (top-right) were raised. Spatial average of soil temperature measured in the Maqu region at 5 cm depth (bottom-left). Percentage of disregarded SMOS brightness temperature observations because of suspected RFI, averaged over the Maqu region (bottom-right), when there was no retrieval. Legend refers to all figures.
© Copyright Policy
Related In: Results  -  Collection

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

f5-sensors-12-09965: Percentage of SMOS grid nodes in the Maqu region, where the flag for frozen soil (top-left) and for out-of-range values and high retrieval errors (top-right) were raised. Spatial average of soil temperature measured in the Maqu region at 5 cm depth (bottom-left). Percentage of disregarded SMOS brightness temperature observations because of suspected RFI, averaged over the Maqu region (bottom-right), when there was no retrieval. Legend refers to all figures.
Mentions: As mentioned in Section 2.3, no retrieval is performed when the soil is frozen. Figure 5 (top-left) shows the percentage of nodes included in the Maqu region with a raised frost flag over the total number of nodes in the region lacking retrieval, for both the case of no retrieval over a part of the area and the case of no retrieval over the whole area. The same plot is presented for the Twente region in Figure 6. Figure 5 shows that the complete area of the Maqu network was characterized by the frost flag several times in winter, resulting in no data, whereas the Twente region was not considered to be covered by frozen soil except for a few days in January and December. When the soil moisture was only retrieved over part of the area, frozen soil was not the main cause of unsuccessful retrieval for the rest of the area.

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.