Soil Moisture Mapping from Satellites: An Intercomparison of SMAP, SMOS, FY3B, AMSR2, and ESA CCI over Two Dense Network Regions at Different Spatial Scales
Abstract
:1. Introduction
2. Data
2.1. Study Area and In-Situ Soil Moisture Data
- (1)
- LWW Network: The LWW network was built between Chickasha and Lawton, and is situated within the Great Plains region of the United States, with an area of 611 km2. Currently, it consists of 20 stations, which measure soil moisture at 5, 25, and 45 cm below the surface and soil temperature at 5, 10, 15, 25, 30, and 45 cm in 5-min intervals. The climate of this region is sub-humid, with a stable annual precipitation of 760 mm. The LWW area is moderately rolling, with elevations ranging from 300 m to 500 m, and is mainly covered by grassland, with a wide range of soil textures from fine sand to silty loam. The LWW network has been widely used for monitoring hydrological and meteorological measurements since 1961. It has been extensively used for assessing satellite soil moisture products, due to the large dynamic range of soil moisture and flat terrain in this region [20,21,23,31].
- (2)
- REMEDHUS Network: The REMEDHUS network is a dense network in Spain, which is located in the central semiarid sector of the Duero Basin, with an area of 1300 km2. It is composed of 20 stations that measure soil moisture and surface temperature at 5 cm in 60-min intervals. These data are collected and updated continuously via the International Soil Moisture Network (ISMN) [32]. Croplands and shrublands dominate the land cover of this region. The land usage of the REMEDHUS network region is covered by cereals (78%), forest and pasture (13%), irrigated crops (5%), and vineyards (3%) [30]. The elevation of this region ranges from 700 to 900 m above sea level, with a continental semiarid Mediterranean climate, which brings dry and warm summers and cool and wet winters to this region [33,34]. The mean annual rainfall and average temperature of this region are 385 mm and 12 °C, respectively. The REMEDHUS network has a long history of hydrological-related applications, including the validation of satellite soil moisture products [30,35], the evaluation of soil moisture downscaling algorithm [36,37], and parameterization of water balance models [38].
2.2. Satellite Soil Moisture Products
2.2.1. SMAP Passive and Enhanced Passive Soil Moisture Products
2.2.2. FY3B Soil Moisture Product
2.2.3. SMOS Soil Moisture Product
2.2.4. AMSR2 Soil Moisture Products
2.2.5. ESA CCI Soil Moisture Product
3. Methods
4. Results
5. Discussion
5.1. Assessment of Satellite Surface Temperature Data
5.2. Temporal Behavior of Satellite Vegetation Optical Depth
6. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Resolution | Products | ubRMSE (m3 m−3) | RMSE (m3 m−3) | Bias (m3 m−3) | R | N |
---|---|---|---|---|---|---|
0.25° | SMAP passive | 0.027 | 0.032 | −0.018 | 0.89 | 311 |
SMOS | 0.057 | 0.057 | −0.004 | 0.71 | 328 | |
FY3B | 0.041 | 0.078 | 0.067 | 0.71 | 311 | |
JAXA | 0.050 | 0.125 | −0.114 | 0.48 | 478 | |
LPRM | 0.108 | 0.141 | 0.091 | 0.54 | 464 | |
ESA CCI | 0.044 | 0.045 | 0.009 | 0.66 | 585 | |
0.1° | SMAP enhanced passive | 0.040 | 0.064 | −0.050 | 0.88 | 312 |
JAXA | 0.073 | 0.168 | −0.151 | 0.41 | 468 |
Resolution | Products | ubRMSE (m3 m−3) | RMSE (m3 m−3) | Bias (m3 m−3) | R | N |
---|---|---|---|---|---|---|
0.25° | SMAP passive | 0.044 | 0.047 | 0.016 | 0.83 | 348 |
SMOS | 0.038 | 0.040 | −0.012 | 0.83 | 236 | |
FY3B | 0.025 | 0.027 | 0.009 | 0.75 | 325 | |
JAXA | 0.035 | 0.065 | −0.055 | 0.60 | 538 | |
LPRM | 0.121 | 0.161 | 0.106 | 0.82 | 518 | |
ESA CCI | 0.036 | 0.104 | 0.097 | 0.83 | 464 | |
0.1° | SMAP enhanced passive | 0.039 | 0.042 | −0.015 | 0.83 | 332 |
JAXA | 0.046 | 0.091 | −0.079 | 0.61 | 517 |
Products | ubRMSE (K) | RMSE (K) | Bias (K) | R | N |
---|---|---|---|---|---|
SMAP passive | 1.062 | 1.153 | −0.449 | 0.99 | 315 |
SMAP enhanced passive | 1.030 | 1.031 | −0.047 | 0.99 | 314 |
SMOS * | 2.035 | 2.413 | −1.297 | 0.97 | 333 |
LPRM | 3.337 | 3.338 | −0.088 | 0.91 | 464 |
Products | ubRMSE (K) | RMSE (K) | Bias (K) | R | N |
---|---|---|---|---|---|
SMAP passive | 1.029 | 2.194 | −1.937 | 0.99 | 348 |
SMAP enhanced passive | 1.110 | 2.315 | −2.032 | 0.99 | 339 |
SMOS * | 1.440 | 3.903 | −3.628 | 0.98 | 347 |
LPRM | 2.653 | 3.833 | −2.767 | 0.94 | 516 |
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Cui, C.; Xu, J.; Zeng, J.; Chen, K.-S.; Bai, X.; Lu, H.; Chen, Q.; Zhao, T. Soil Moisture Mapping from Satellites: An Intercomparison of SMAP, SMOS, FY3B, AMSR2, and ESA CCI over Two Dense Network Regions at Different Spatial Scales. Remote Sens. 2018, 10, 33. https://doi.org/10.3390/rs10010033
Cui C, Xu J, Zeng J, Chen K-S, Bai X, Lu H, Chen Q, Zhao T. Soil Moisture Mapping from Satellites: An Intercomparison of SMAP, SMOS, FY3B, AMSR2, and ESA CCI over Two Dense Network Regions at Different Spatial Scales. Remote Sensing. 2018; 10(1):33. https://doi.org/10.3390/rs10010033
Chicago/Turabian StyleCui, Chenyang, Jia Xu, Jiangyuan Zeng, Kun-Shan Chen, Xiaojing Bai, Hui Lu, Quan Chen, and Tianjie Zhao. 2018. "Soil Moisture Mapping from Satellites: An Intercomparison of SMAP, SMOS, FY3B, AMSR2, and ESA CCI over Two Dense Network Regions at Different Spatial Scales" Remote Sensing 10, no. 1: 33. https://doi.org/10.3390/rs10010033