Satellite Soil Moisture Data Reconstruction in the Temporal and Spatial Domains: Latent Error Assessments and Performances for Tracing Rainstorms and Droughts
"> Figure 1
<p>(<b>a</b>) Maximum, mean, and minimum coverage of available ESA CCI SM data in China and the distribution (i.e., the Days of Year (DOYs)) of data coverage over multiple years. (<b>b</b>) Fractional coverage of available data for each grid in China during 1979−2019.</p> "> Figure 2
<p>Spatial distribution of the national meteorological stations and Chinese agrometeorological stations and climate zones.</p> "> Figure 3
<p>A sketch map of reconstructing soil moisture in the temporal and spatial domains. (<b>a</b>) Soil moisture in the temporal domain. (<b>b</b>) Accumulated precipitation in the temporal domain. (<b>c</b>) Moving average temperature in the temporal domain. (<b>d</b>) NDVI and LAI in the temporal domain.</p> "> Figure 4
<p>Comparison of reconstructed soil moisture series in different scenarios against original ESA CCI SM series. (<b>a</b>,<b>b</b>) are the CC values for the spatial and temporal domains, respectively, and (<b>c</b>,<b>d</b>) are corresponding RMSE values.</p> "> Figure 5
<p>Comparison of reconstructed soil moisture series in the spatial and temporal domains by using the ANN and RF models over China and in four climate zones. (<b>a</b>,<b>d</b>,<b>g</b>,<b>j</b>,<b>m</b>) The comparison of average values of reconstructed soil moisture series obtained in the spatial and temporal domains by using the ANN model. (<b>b</b>,<b>e</b>,<b>h</b>,<b>k</b>,<b>n</b>) The comparison of average values of reconstructed soil moisture series obtained in the spatial and temporal domains by using the RF model. (<b>c</b>,<b>f</b>,<b>i</b>,<b>l</b>,<b>o</b>) Scatter plot of all spatial values and temporal values in the area.</p> "> Figure 6
<p>Distribution of reconstructed daily soil moisture in the temporal and spatial domains with the ANN (<b>a</b>–<b>d</b>) and RF (<b>e</b>–<b>h</b>) models in four climate zones during 1979−2019. The blue and red solid lines are the mean values in each day. The blue and red shades represent the 5% and 95% percentiles of soil moisture in each climate zone.</p> "> Figure 7
<p>Affected areas and migration routes of the five major typhoons in 2006.</p> "> Figure 8
<p>Spatial variations in anomalies of reconstructed soil moisture series in the temporal and spatial domains with the ANN and RF models (denoted as SM_ANNt, SM_ANNs, SM_RFt, and SM_RFs, respectively) during the fourth typhoon Bilis (from 13 July 2006 to 18 July 2006). The black solid circles show the regions where SM_ANNt accurately capture the increments in soil moisture, and the red solid rectangles show the poor performances of SM_ANNs.</p> "> Figure 9
<p>This is the same as <a href="#remotesensing-14-04841-f008" class="html-fig">Figure 8</a>, but the fifth typhoon Kaemi (from 25 July to 28 July) and sixth typhoon Prapiroon (from 2 August 2006 to 6 August 2006) are displayed. The red solid rectangle shows the poor performances of SM_ANNs.</p> "> Figure 10
<p>(<b>a</b>) Annual drought area in China during 2000–2019. (<b>b</b>–<b>d</b>) The percentage of affected area in each province to total drought affected area in China in 2007, 2009, and 2011, respectively. The data were collected from the Bulletin of flood and drought disasters released by the Ministry of Water Resources of the People’s Republic of China. Drought affected areas for each province were not available before 2006 and are marked in light-colored shades in <a href="#remotesensing-14-04841-f010" class="html-fig">Figure 10</a>a.</p> "> Figure 11
<p>Spatial distribution of average anomalies of reconstructed soil moisture in the temporal and domains with the ANN and RF models (denoted as SM_ANNt, SM_ANNs, SM_RFt, and SM_RFs) in 2007, 2009, and 2011, respectively.</p> "> Figure 12
<p>(<b>a</b>) The correlation coefficients and (<b>b</b>) root mean square errors for the RF gap-filling procedure in the temporal domain under different percentages of data coverage for model training. The dashed lines represent the average results of all grids over China.</p> "> Figure 13
<p>(<b>a</b>) The correlation coefficients and (<b>b</b>) root mean square errors for the RF gap-filling procedure in the spatial domain under different percentages of data coverage by uniform and non-uniform sampling schemes for model training over China.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Soil Moisture Products
2.1.1. ESA CCI SM
2.1.2. ERA-Interim Reanalysis
2.1.3. In Situ Soil Moisture Measurements
2.2. Ancillary Data
2.3. Machine Learning
2.3.1. Random Forest
2.3.2. Artificial Neural Network
2.4. Reconstruction of Soil Moisture in the Temporal and Spatial Domains
3. Results
3.1. Performances Evaluation of the Machine Learning Approaches
3.2. Comparison of Spatial and Temporal Reconstructed Series
3.3. Performances for Tracing Typhoon Rainstorm and Drought Extreme Events
3.3.1. Performances for Tracing Typhoon Rainstorm Events
3.3.2. Performances for Tracing Drought Events
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Climate Zone | Statistics | In Situ | ERA | Original | ANNt | ANNs | RFt | RFs |
---|---|---|---|---|---|---|---|---|
(m3∙m−3) | ||||||||
Arid | Mean | 0.23 | 0.26 | 0.26 | 0.25 | 0.23 | 0.25 | 0.24 |
Min | 0.13 | 0.18 | 0.14 | 0.14 | 0.15 | 0.17 | 0.16 | |
Max | 0.37 | 0.38 | 0.41 | 0.4 | 0.44 | 0.35 | 0.4 | |
s.d. | 0.05 | 0.03 | 0.03 | 0.04 | 0.05 | 0.03 | 0.04 | |
Semi-arid | Mean | 0.25 | 0.26 | 0.25 | 0.25 | 0.24 | 0.25 | 0.24 |
Min | 0.08 | 0.17 | 0.12 | 0.13 | 0.02 | 0.16 | 0.11 | |
Max | 0.32 | 0.39 | 0.42 | 0.4 | 0.47 | 0.35 | 0.4 | |
s.d. | 0.03 | 0.04 | 0.03 | 0.04 | 0.05 | 0.03 | 0.04 | |
Semi-humid | Mean | 0.25 | 0.31 | 0.25 | 0.24 | 0.24 | 0.24 | 0.24 |
Min | 0.12 | 0.19 | 0.12 | 0.07 | 0.02 | 0.12 | 0.1 | |
Max | 0.39 | 0.38 | 0.42 | 0.38 | 0.47 | 0.35 | 0.38 | |
s.d. | 0.05 | 0.03 | 0.05 | 0.05 | 0.06 | 0.05 | 0.05 | |
Humid | Mean | 0.28 | 0.31 | 0.25 | 0.24 | 0.24 | 0.24 | 0.24 |
Min | 0.16 | 0.19 | 0.12 | 0.07 | 0.02 | 0.11 | 0.09 | |
Max | 0.39 | 0.38 | 0.42 | 0.38 | 0.47 | 0.35 | 0.38 | |
s.d. | 0.05 | 0.03 | 0.04 | 0.06 | 0.06 | 0.05 | 0.05 |
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Liu, Y.; Chen, R.; Yuan, S.; Ren, L.; Zhang, X.; Liu, C.; Ma, Q. Satellite Soil Moisture Data Reconstruction in the Temporal and Spatial Domains: Latent Error Assessments and Performances for Tracing Rainstorms and Droughts. Remote Sens. 2022, 14, 4841. https://doi.org/10.3390/rs14194841
Liu Y, Chen R, Yuan S, Ren L, Zhang X, Liu C, Ma Q. Satellite Soil Moisture Data Reconstruction in the Temporal and Spatial Domains: Latent Error Assessments and Performances for Tracing Rainstorms and Droughts. Remote Sensing. 2022; 14(19):4841. https://doi.org/10.3390/rs14194841
Chicago/Turabian StyleLiu, Yi, Ruiqi Chen, Shanshui Yuan, Liliang Ren, Xiaoxiang Zhang, Changjun Liu, and Qiang Ma. 2022. "Satellite Soil Moisture Data Reconstruction in the Temporal and Spatial Domains: Latent Error Assessments and Performances for Tracing Rainstorms and Droughts" Remote Sensing 14, no. 19: 4841. https://doi.org/10.3390/rs14194841