Soil Moisture Content Retrieval from Remote Sensing Data by Artificial Neural Network Based on Sample Optimization
Abstract
:1. Introduction
2. Study Area and Dataset
2.1. Study Area and Ground Truth Data
2.2. Remote Sensing Data
2.3. Auxiliary Data
2.4. Sample Pool
3. Methodology
3.1. Sample Quantity Optimization: Sparse Sample Exploitation
- 1.
- For dates when the sky above the study area is clear and no in situ observation is absent, all the samples are recorded in the sample pool.
- 2.
- For dates when the study area is partially blocked by clouds or in situ observations are absent, the “sparse samples” with available optical, microwave data, and ground truth observations are recorded similarly in the sample pool.
3.2. Sample Quality Optimization: Input Parameter Selection
- 1.
- Data acquisition time: the data acquisition time was strongly correlated to the surface soil hydraulic conductivities [49]. Meanwhile, the phenological traits of vegetation follow a circulation of alteration on an annual basis [50,51], which plays an essential role in vegetation effect elimination during the process of SMC retrieval in vegetation-covered areas.
- 2.
- 3.
- Elevation and slope: soil moisture was closely related to the local topographical heterogeneity. The landscape shapes physically controlled the hydrological processes and SMC time stability [57,58], with upland water moving to the groundwater and lowland water coming from the groundwater, and water content increasing from the top to the bottom of a slope in a non-linear pattern [59,60].
- 4.
- Land cover type: the land use was analyzed as a factor influencing soil hydraulic attributes and SMC distribution. For example, human activities such as grazing, plowing, and urban development impact the macropores and the continuity of the macropore network of soil, thus altering the mode of local soil water supply and SMC distribution [49,61].
3.3. ANN and SMC Retrieval
3.4. Statistical Metrics
4. Results and Discussion
4.1. Evaluation of Overall Accuracy
4.2. Evaluation of SSE Method
4.3. Sensitivity Analysis of Input Parameters
4.3.1. Data Acquisition Time
4.3.2. LST
4.3.3. Elevation and Slope
4.3.4. Land Cover Type
4.4. SMC Mapping
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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# | Lat. and Long. | Network | Landcover | # | Lat. and Long. | Network | Landcover |
---|---|---|---|---|---|---|---|
1 | 46.91691° N 15.78112° E | WEGENERNET | farmland | 12 | 48.15202° N 15.15303° E | GROW | farmland |
2 | 46.97232° N 15.81499° E | WEGENERNET | farmland | 13 | 48.15257° N 15.15104° E | GROW | farmland |
3 | 46.99726° N 15.85507° E | WEGENERNET | farmland | 14 | 48.15356° N 15.14857° E | GROW | farmland |
4 | 46.98299° N 15.87115° E | WEGENERNET | farmland | 15 | 48.15403° N 15.15299° E | GROW | farmland |
5 | 46.93296° N 15.90710° E | WEGENERNET | farmland | 16 | 48.15474° N 15.14844° E | GROW | farmland |
6 | 46.93291° N 15.92462° E | WEGENERNET | grassland | 17 | 48.15562° N 15.14804° E | GROW | farmland |
7 | 46.97970° N 15.94122° E | WEGENERNET | grassland | 18 | 48.15645° N 15.14799° E | GROW | farmland |
8 | 46.92135° N 16.03337° E | WEGENERNET | farmland | 19 | 48.15709° N 15.13658° E | GROW | farmland |
9 | 46.93427° N 16.04056° E | WEGENERNET | farmland | 20 | 48.15725° N 15.15149° E | GROW | farmland |
10 | 48.15117° N 15.15417° E | GROW | farmland | 21 | 48.15804° N 15.14731° E | GROW | farmland |
11 | 48.15179° N 15.15424° E | GROW | farmland | 22 | 48.18776° N 15.98071° E | GROW | grassland |
# | Dates of Radar Images | Dates of Optical Images | # | Dates of Radar Images | Dates of Optical Images | # | Dates of Radar Images | Dates of Optical Images |
---|---|---|---|---|---|---|---|---|
1 | 18 January 2016 | 18 January 2016 | 24 | 24 June 2017 | 22 June 2017 | 47 | 3 February 2019 | 4 February 2019 |
2 | 26 January 2016 | 27 January 2016 | 25 | 31 July 2017 | 31 July 2017 | 48 | 27 February 2019 | 27 February 2019 |
3 | 30 March 2016 | 31 March 2016 | 26 | 11 August 2017 | 9 August 2017 | 49 | 23 March 2019 | 24 March 2019 |
4 | 18 April 2016 | 16 April 2016 | 27 | 4 November 2017 | 4 November 2017 | 50 | 30 March 2019 | 31 March 2019 |
5 | 23 April 2016 | 23 April 2016 | 28 | 20 November 2017 | 20 November 2017 | 51 | 16 April 2019 | 16 April 2019 |
6 | 4 July 2016 | 5 July 2016 | 29 | 5 December 2017 | 6 December 2017 | 52 | 27 April 2019 | 25 April 2019 |
7 | 12 July 2016 | 12 July 2016 | 30 | 24 February 2018 | 24 February 2018 | 53 | 2 May 2019 | 2 May 2019 |
8 | 23 July 2016 | 21 July 2016 | 31 | 21 April 2018 | 22 April 2018 | 54 | 18 May 2019 | 18 May 2019 |
9 | 29 August 2016 | 29 August 2016 | 32 | 28 April 2018 | 29 April 2018 | 55 | 3 June 2019 | 3 June 2019 |
10 | 22 September 2016 | 23 September 2016 | 33 | 31 May 2018 | 31 May 2018 | 56 | 14 June 2019 | 12 June 2019 |
11 | 29 September 2016 | 30 September 2016 | 34 | 2 July 2018 | 2 July 2018 | 57 | 19 June 2019 | 19 June 2019 |
12 | 16 October 2016 | 16 October 2016 | 35 | 18 July 2018 | 18 July 2018 | 58 | 27 June 2019 | 28 June 2019 |
13 | 1 November 2016 | 1 November 2016 | 36 | 26 July 2018 | 27 July 2018 | 59 | 4 July 2019 | 5 July 2019 |
14 | 9 November 2016 | 10 November 2016 | 37 | 2 August 2018 | 3 August 2018 | 60 | 14 August 2019 | 15 August 2019 |
15 | 3 December 2016 | 3 December 2016 | 38 | 19 August 2018 | 19 August 2018 | 61 | 2 September 2019 | 31 August 2019 |
16 | 14 December 2016 | 12 December 2016 | 39 | 30 August 2018 | 28 August 2018 | 62 | 8 October 2019 | 9 October 2019 |
17 | 20 January 2017 | 20 January 2017 | 40 | 19 September 2018 | 20 September 2018 | 63 | 20 October 2019 | 18 October 2019 |
18 | 5 February 2017 | 5 February 2017 | 41 | 28 September 2018 | 29 September 2018 | 64 | 1 November 2019 | 25 October 2019 |
19 | 9 March 2017 | 9 March 2017 | 42 | 6 October 2018 | 6 October 2018 | 65 | 5 January 2020 | 6 January 2020 |
20 | 2 April 2017 | 3 April 2017 | 43 | 22 October 2018 | 22 October 2018 | 66 | 9 March 2020 | 10 March 2020 |
21 | 9 April 2017 | 10 April 2017 | 44 | 30 October 2018 | 31 October 2018 | 67 | 2 April 2020 | 2 April 2020 |
22 | 27 May 2017 | 28 May 2017 | 45 | 11 November 2018 | 7 November 2018 | 68 | 10 April 2020 | 11 April 2020 |
23 | 13 June 2017 | 13 June 2017 | 46 | 15 November 2018 | 16 November 2018 | 69 | 26 April 2020 | 27 April 2020 |
Date | d1 | d2 | d3 | d4 | d5 | d6 |
---|---|---|---|---|---|---|
traditional method | ABCD | - | - | - | - | - |
SSE method | ABCD | C | AB | ACD | D | - |
Scenario | Input Parameters |
---|---|
0 | θ, σVH, σVV, NDVI, month, LST, elevation, slope, land cover |
1 | θ, σVH, σVV, NDVI |
2 | θ, σVH, σVV, NDVI, month |
3 | θ, σVH, σVV, NDVI, LST |
4 | θ, σVH, σVV, NDVI, elevation |
5 | θ, σVH, σVV, NDVI, slope |
6 | θ, σVH, σVV, NDVI, land cover |
Dataset | Training | Validation | Testing |
---|---|---|---|
RMSE (m3m−3) | 0.048 | 0.054 | 0.052 |
Without SSE | With SSE | |
---|---|---|
RMSE (m3m−3) | 0.090 | 0.068 |
r | 0.635 | 0.736 |
Scenarios | Input Parameters | Statistical Metrics | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Month | σVH | σVV | NDVI | LST | Elevation | Slope | Land Cover | θ | RMSE (m3m−3) | r | |
1 | √ | √ | √ | √ | 0.089 | 0.588 | |||||
2 | √ | √ | √ | √ | √ | 0.078 | 0.637 | ||||
3 | √ | √ | √ | √ | √ | 0.084 | 0.616 | ||||
4 | √ | √ | √ | √ | √ | 0.070 | 0.689 | ||||
5 | √ | √ | √ | √ | √ | 0.083 | 0.639 | ||||
6 | √ | √ | √ | √ | √ | 0.091 | 0.599 |
Of All Samples | Of Cropland Samples | Percentage of Cropland Samples | |
---|---|---|---|
372 | 287 | 77.2% | |
6.64% | 5.66% | 85.2% |
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Liu, Q.; Gu, X.; Chen, X.; Mumtaz, F.; Liu, Y.; Wang, C.; Yu, T.; Zhang, Y.; Wang, D.; Zhan, Y. Soil Moisture Content Retrieval from Remote Sensing Data by Artificial Neural Network Based on Sample Optimization. Sensors 2022, 22, 1611. https://doi.org/10.3390/s22041611
Liu Q, Gu X, Chen X, Mumtaz F, Liu Y, Wang C, Yu T, Zhang Y, Wang D, Zhan Y. Soil Moisture Content Retrieval from Remote Sensing Data by Artificial Neural Network Based on Sample Optimization. Sensors. 2022; 22(4):1611. https://doi.org/10.3390/s22041611
Chicago/Turabian StyleLiu, Qixin, Xingfa Gu, Xinran Chen, Faisal Mumtaz, Yan Liu, Chunmei Wang, Tao Yu, Yin Zhang, Dakang Wang, and Yulin Zhan. 2022. "Soil Moisture Content Retrieval from Remote Sensing Data by Artificial Neural Network Based on Sample Optimization" Sensors 22, no. 4: 1611. https://doi.org/10.3390/s22041611