# Evaluation of Satellite-Derived Soil Moisture in Qinghai Province Based on Triple Collocation

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## Abstract

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## 1. Introduction

## 2. Materials and Methods

#### 2.1. Triple Collocation (TC) Method

#### 2.2. Data Sources

#### 2.3. Data Preprocessing

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^{3}or %) and unit conversion was therefore unnecessary. Furthermore, although the active and passive satellite remote sensing soil moisture data used in this study have slightly different observation depths, the depths of both are on the order of centimeters beneath the soil surface, so we calculate it directly and less impact on the final TC result. In order to ensure the accuracy of the TC results, the regions with correlation coefficients less than 0.2 for the passive products, active products, and reanalysis data were masked and not used for the TC calculation. It is worth noting that the raster data calculated using the TC method show that the correlation coefficients among the three groups of data are all greater than 0.2 and pass the significance test for correlation coefficients (a = 0.05).

## 3. Results

#### 3.1. Correlation between Satellite Soil Moisture Products

#### 3.1.1. Correlation between Passive Satellite Soil Moisture Products

#### 3.1.2. Correlation Analysis between Different Types of Soil Moisture Products

#### 3.2. Analysis of Triple Collocation Results

#### 3.3. Correlation between TC Results and Vegetation Coverage

## 4. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**The enhanced vegetation index (EVI) in the study area of Qinghai Province, China, based on the MYD13A3 MODIS satellite data product (2017).

**Figure 2.**The picture of passive satellite products: (

**a**) The average daily soil moisture in the study area from various passive satellite products (leftmost column); (

**b**) Scatterplots showing the correlation between these products (two rightmost columns).

**Figure 3.**Correlation coefficients between soil moisture data. The five columns of sub-figures show, from top to bottom: (

**a**) [SMAP, ASCAT, GEOS5]; (

**b**) [SMOS, ASCAT, GEOS5]; (

**c**) [FY3B, ASCAT, GEOS5]; (

**d**) [FY3C, ASCAT, GEOS5]; (

**e**) [AMSR2, ASCAT, GEOS5]. The three rows of sub-figures show, from left to right, the correlation coefficients between passive and active, passive and reanalysis, and active and reanalysis data.

**Figure 4.**Spatial characteristics of the triple collocation (TC) error variance (σ2) in the study area. The five columns of sub-figures show, from top to bottom: (

**a**) [SMAP, ASCAT, GEOS5]; (

**b**) [SMOS, ASCAT, GEOS5]; (

**c**) [FY3B, ASCAT, GEOS5]; (

**d**) [FY3C, ASCAT, GEOS5]; (

**e**) [AMSR2, ASCAT, GEOS5]. The three rows of sub-figures show, from left to right, the error variance between the passive and true soil moisture, the error variance between the active and true soil moisture, and the error variance between the reanalysis and true soil moisture.

**Figure 6.**Spatial characteristics of the fractional mean-squared-error (fMSE) in the study area. The five columns of sub-figures show, from top to bottom: (

**a**) [SMAP, ASCAT, GEOS5]; (

**b**) [SMOS, ASCAT, GEOS5]; (

**c**) [FY3B, ASCAT, GEOS5]; (

**d**) [FY3C, ASCAT, GEOS5]; (

**e**) [AMSR2, ASCAT, GEOS5]. The three rows of sub-figures show, from left to right, the fractional mean-squared-error between the passive and true soil moisture, the fractional mean-squared-error between the active and true soil moisture, and the fractional mean-squared-error between the reanalysis and true soil moisture.

**Figure 8.**Spatial characteristics of the correlation coefficient (R) in the study area. The five columns of sub-figures show, from top to bottom: (

**a**) [SMAP, ASCAT, GEOS5]; (

**b**) [SMOS, ASCAT, GEOS5]; (

**c**) [FY3B, ASCAT, GEOS5]; (

**d**) [FY3C, ASCAT, GEOS5]; (

**e**) [AMSR2, ASCAT, GEOS5]. The three rows of sub-figures show, from left to right, the correlation coefficients between the passive and true soil moisture, the correlation coefficients between the active and true soil moisture, and the correlation coefficients between the reanalysis and true soil moisture.

**Figure 10.**Scatterplots of the TC σ

^{2}for the SMAP, SMOS, FY3B, FY3C, and AMSR2 passive satellite data and EVI.

**Figure 11.**Scatterplots of the TC correlation between the EVI, and soil moisture obtained from SMAP, SMOS, FY3B, FY3C, and AMSR2 passive satellite data.

**Table 1.**Information of the satellites which were used to estimate global soil moisture in this study.

Dataset Name | Sensor | Retrieval Band | Time of Ascending Pass/Descending Pass | Unit | Spatial Resolution | Time Period | Coverage |
---|---|---|---|---|---|---|---|

L3_SM_P | Passive | 1.4 GHz | 06:00/18:00 | m^{3}/m^{3} | 36 km | 2015–present | Global |

SMOS-CATDS | Passive | 1.4 GHz | 06:00/18:00 | m^{3}/m^{3} | 25 km | 2010–present | Global |

FY3B-MWRI | Passive | 10.7/36.5 GHz | 13:30/01:30 | m^{3}/m^{3} | 25 km | 2011–present | Global |

FY3C-MWRI | Passive | 10.7/36.5 GHz | 13:30/01:30 | m^{3}/m^{3} | 25 km | 2014–present | Global |

AMSR2_L3 | Passive | 10.7/36.5 GHz | 13:30/01:30 | Saturation (%) | 0.25° | 2012–present | Global |

ASCAT_L2 | Active | 5.3 GHz | 21:30/09:30 | Saturation (%) | 12.5 km | 2012–present | Global |

GEOS5 | Fusion | m^{3}/m^{3} | 0.25° *0.3125° | 2014–present | Global |

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**MDPI and ACS Style**

Zhu, H.; Zhang, Z.; Lv, A.
Evaluation of Satellite-Derived Soil Moisture in Qinghai Province Based on Triple Collocation. *Water* **2020**, *12*, 1292.
https://doi.org/10.3390/w12051292

**AMA Style**

Zhu H, Zhang Z, Lv A.
Evaluation of Satellite-Derived Soil Moisture in Qinghai Province Based on Triple Collocation. *Water*. 2020; 12(5):1292.
https://doi.org/10.3390/w12051292

**Chicago/Turabian Style**

Zhu, Hongchun, Zhilin Zhang, and Aifeng Lv.
2020. "Evaluation of Satellite-Derived Soil Moisture in Qinghai Province Based on Triple Collocation" *Water* 12, no. 5: 1292.
https://doi.org/10.3390/w12051292