NIR-Red Spectra-Based Disaggregation of SMAP Soil Moisture to 250 m Resolution Based on OzNet in Southeastern Australia
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area and Data Collection
2.2. Assumptions and Applicability Domains of the NRSD Method
- Cloud-free conditions: In contrast with microwave, NIR and red spectra have relatively lower penetration through cloud. A cloud-free condition is recommended in this study, whereas the cloud-cover pixels can also be inputs to the NRSD method with poorer data quality.
- Precipitation-free conditions from 6:00 a.m. to 10:30 a.m.: The performance of the NRSD method is influenced by the mismatch of SMAP and MODIS overpass times. In this study, 6:00 a.m. SMAP soil moisture and 10:30 a.m. MODIS data were collected; thus, a precipitation-free condition within study area is preferred during the period. It should be noted that although precipitation can influence the performance of NRSD theoretically, it was not considered in this study because ancillary meteorological data were lacking.
- Relative heterogeneity of soil moisture within SMAP pixels: Low spatial variance of soil moisture results in less effective disaggregation methods because SMAP soil moisture itself can theoretically represent the soil moisture well at 36 km resolution homogeneous scales; thus, disaggregation methods may introduce redundant errors in such a situation.
- Mismatch of soil moisture sensing depth: The SMAP radiometer has a sensing depth of ~5 cm from the surface due to its strong penetration, whereas the optical sensor measures the surface skin. The NRSD method contains an assumption that soil moisture at the soil skin and at an ~5 cm depth from the top are correlated [27].
2.3. Flowchart of NRSD
2.4. NSMI: Normalized Soil Moisture Index
2.5. Disaggregation Method
2.6. Validation, Comparison and Evaluation
3. Results
3.1. Extraction of the Variance Conversion Factor
3.2. Disaggregated Soil Moisture
3.3. Validation and Comparison with DISPATCH
3.3.1. Overall Accuracy and Performance
3.3.2. Capacity Test for Spatial Representation
3.3.3. Assessments of the Algorithm Applicable Scope
4. Discussion
4.1. Performance of NSMI
4.2. Error in SMAP Data
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Overpass Day 1 | DOY 2 | Cloud Condition 3 | MODIS Data Input 4 | Overpass Day | DOY | Cloud Condition | MODIS Data Input |
---|---|---|---|---|---|---|---|
2 May | 121 | √ | √ | 8 September | 250 | X | X |
122 | X | X | 251 | X | X | ||
123 | √ | √ | 252 | √ | √ | ||
5 May | 124 | √ | √ | 10 September | 252 | √ | √ |
125 | X | X | 253 | X | X | ||
126 | √ | √ | 254 | √ | √ | ||
8 May | 128 | √ | √ | 13 September | 256 | √ | √ |
16 May | 135 | √ | √ | 16 September | 259 | √ | √ |
136 | X | X | 18 September | 261 | √ | √ | |
137 | √ | √ | 21 September | 263 | √ | √ | |
18 May | 137 | √ | √ | 264 | X | X | |
138 | √ | √ | 265 | √ | √ | ||
139 | X | X | 23 September | 266 | √ | √ | |
21 May | 140 | X | X | 26 September | 268 | √ | √ |
141 | X | X | 269 | X | X | ||
142 | √ | √ | 270 | √ | √ |
Overpass Day | NOIS 1 | NRSD | DISPATCH | SMAP | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
RMSE | Bias | bVariance | R | p | RMSE | Bias | bVariance | R | p | RMSE | Bias | ||
2 May | 11 | 0.04 | 0.01 | 0.33 | –0.6 | 0.08 | 0.07 | 0.05 | 3.07 | 0.33 | 0.33 | 0.02 | –0.01 |
5 May | 15 | 0.03 | –0.01 | 1.87 | –0.3 | 0.22 | 0.06 | 0.01 | 4.43 | –0.3 | 0.35 | 0.04 | –0.03 |
8 May | 18 | 0.04 | 0.01 | 1.39 | –0.4 | 0.15 | 0.06 | 0.04 | 3.13 | 0.25 | 0.31 | 0.01 | –0.01 |
16 May | 18 | 0.04 | 0.03 | 1.41 | –0.3 | 0.22 | 0.06 | 0.03 | 3.24 | 0.05 | 0.83 | 0.06 | 0.05 |
18 May | 14 | 0.04 | 0.02 | 2.01 | –0.3 | 0.23 | 0.08 | 0.05 | 4.7 | –0.1 | 0.95 | 0.05 | 0.05 |
21 May | 18 | 0.06 | 0.05 | 2.64 | 0.33 | 0.18 | 0.11 | 0.11 | 2.55 | 0.72 | 0.01 | 0.07 | 0.07 |
8 September | 21 | 0.24 | 0.24 | 2.49 | –0.1 | 0.62 | 0.27 | 0.27 | - | 0 | 1 | 0.27 | 0.26 |
10 September | 22 | 0.21 | 0.20 | 2.19 | 0.10 | 0.98 | 0.23 | 0.23 | 0.03 | 0.11 | 0.61 | 0.23 | 0.22 |
13 September | 15 | 0.13 | 0.12 | 4.37 | 0.02 | 0.95 | 0.15 | 0.14 | 2.92 | –0.3 | 0.30 | 0.14 | 0.14 |
16 September | 0 | - | - | - | - | - | - | - | - | - | - | - | - |
18 September | 15 | 0.09 | 0.09 | 2.64 | 0.34 | 0.21 | 0.14 | 0.12 | 3.95 | –0.1 | 0.72 | 0.13 | 0.13 |
21 September | 19 | 0.05 | 0.04 | 0.78 | –0.5 | 0.01 | 0.07 | 0.06 | 2.08 | –0.3 | 0.22 | 0.06 | 0.05 |
23 September | 10 | 0.09 | 0.08 | 2.68 | 0.15 | 0.67 | 0.19 | 0.17 | 5.39 | –0.1 | 0.76 | 0.11 | 0.10 |
26 September | 18 | 0.05 | 0.03 | 2.09 | –0.2 | 0.55 | 0.12 | 0.11 | 1.52 | –0.3 | 0.28 | 0.05 | 0.05 |
Mean | May | 0.04 | 0.02 | 1.61 | - | - | 0.07 | 0.05 | 3.52 | - | - | 0.04 | 0.02 |
September | 0.12 | 0.11 | 2.46 | - | - | 0.17 | 0.16 | 2.65 | - | - | 0.14 | 0.14 |
DOY | SMAP Soil Moisture | NSMI36km | Number of In Situ Sites | R-Value | Slope | p-Value |
---|---|---|---|---|---|---|
121 | 0.10 | 0.65 | 8 | 0.11 | 0.03 | 0.79 |
123 | 0.10 | 0.60 | 9 | 0.55 | 0.19 | 0.12 |
124 | 0.09 | 0.68 | 4 | 0.40 | 0.13 | 0.60 |
126 | 0.09 | 0.69 | 14 | 0.01 | 0.01 | 0.97 |
128 | 0.07 | 0.64 | 17 | 0.31 | 0.13 | 0.21 |
135 | 0.11 | 0.67 | 11 | 0.67 | 0.09 | 0.02 |
137 | 0.11 | 0.63 | 11 | 0.70 | 0.12 | 0.01 |
138 | 0.12 | 0.57 | 1 | - | - | - |
142 | 0.16 | 0.68 | 18 | 0.40 | 0.06 | 0.10 |
252 | 0.32 | 0.79 | 17 | 0.31 | 0.05 | 0.25 |
254 | 0.28 | 0.81 | 16 | 0.15 | 0.07 | 0.59 |
256 | 0.23 | 0.80 | 15 | 0.04 | 0.03 | 0.89 |
259 | 0.19 | 0.81 | 0 | - | - | - |
261 | 0.16 | 0.79 | 17 | 0.25 | 0.07 | 0.35 |
263 | 0.16 | 0.86 | 19 | −0.1 | −0.04 | 0.75 |
265 | 0.16 | 0.86 | 10 | 0.30 | 0.11 | 0.43 |
266 | 0.15 | 0.77 | 12 | −0.1 | −0.03 | 0.81 |
268 | 0.12 | 0.73 | 15 | −0.5 | −0.14 | 0.05 |
270 | 0.12 | 0.75 | 18 | 0.31 | 0.10 | 0.20 |
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Chen, N.; He, Y.; Zhang, X. NIR-Red Spectra-Based Disaggregation of SMAP Soil Moisture to 250 m Resolution Based on OzNet in Southeastern Australia. Remote Sens. 2017, 9, 51. https://doi.org/10.3390/rs9010051
Chen N, He Y, Zhang X. NIR-Red Spectra-Based Disaggregation of SMAP Soil Moisture to 250 m Resolution Based on OzNet in Southeastern Australia. Remote Sensing. 2017; 9(1):51. https://doi.org/10.3390/rs9010051
Chicago/Turabian StyleChen, Nengcheng, Yuqi He, and Xiang Zhang. 2017. "NIR-Red Spectra-Based Disaggregation of SMAP Soil Moisture to 250 m Resolution Based on OzNet in Southeastern Australia" Remote Sensing 9, no. 1: 51. https://doi.org/10.3390/rs9010051
APA StyleChen, N., He, Y., & Zhang, X. (2017). NIR-Red Spectra-Based Disaggregation of SMAP Soil Moisture to 250 m Resolution Based on OzNet in Southeastern Australia. Remote Sensing, 9(1), 51. https://doi.org/10.3390/rs9010051