This thesis seeks to 1) better understand the characteristics of drought factors in different climate regions (arid and humid regions), 2) monitor drought effectively using improved soil moisture for high resolution data (~1 km) by developing drought indices, 3) predict drought by identify the link between drought and climate variability. In Chapter 2, characteristics of drought factors in different climate regions were understood from relative variable importance given from machine learning approaches. Three machine learning methods (Random forest, Boosted regression trees, and Cubist) were used to target standardized precipitation index (SPI) and crop yield, and Random forest outperformed the other approaches.
In Chapter 3, coarse spatial resolution soil moisture (25 km) was downscaled to high resolution (1 km), and a drought index (High Resolution Soil Moisture Drought Index; HSMDI) was developed using high resolution soil moisture. In Chapter 4, short-term (one-fifty) drought prediction in East Asia by integrating remote sensing data and climate variability.
Scatter plots between annual crop yield (sesame, highland radish and highland napa cabbage) and HSMDI (0.05 significant level) in August. Correlation coefficients between SPIs and upland crop yields (radish and napa cabbage) at two administrative districts (p < 0.05).
List of Tables
Drought monitoring and forecasting through multi -sensor satellite data fusion using machine learning approaches
Introduction
- Background
- Overview of papers
- Research highlight Chapter 2
Satellite-based drought monitoring is very useful to monitor drought at local and regional scales because satellite-based remote sensing data provide continuous spatio-temporal information with high resolution (~1 km), which it is economical and effective. Therefore, remote sensing can be useful to monitor and predict drought in developing countries, including Africa and Southeast Asia. Remote sensing data has been used in various fields including land, atmosphere and ocean studies.
In this study, sixteen remotely sensed drought factors from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Tropical Rainfall Measuring Mission (TRMM) satellite sensors were used to monitor meteorological and agricultural drought in the 2000–2012 growing seasons for different climate regions of the United States. Very short-term prediction of drought using remote sensing data and MJO index through Random forest over East Asia, Remote sensing of Environment, to be submitted.
Drought assessment and monitoring through blending of multi-sensor indices using machine learning approaches for different climate regions
- Background
- Study area and data .1 Study area
- Methods
- Results and discussion
- Conclusion
Notably, the irrigation rate in the North and South Carolina area (ie, the wet region in this study) is below 10%. The relative importance of sixteen drought factors for meteorological drought and 12-month SPIs) was identified using three machine learning approaches in dry, wet and combined regions. The relative importance of six selected meteorological (i.e., use SPIs as reference) and agricultural (i.e., use crop yield as reference) drought factors was further identified using three machine learning approaches across regions dry, wet and combined.
Model performance of machine learning approaches varied across arid, humid and combined regions (Figure 2.3). All three machine learning approaches performed better for the arid region than for the humid and combined regions to estimate drought conditions (i.e., SPI), as the drought factors representing the environmental characteristics of the regions were more distinct in the arid region relative to the SPI. The vegetation index-based variables showed high relative importance for long-term meteorological drought (9- and 12-month SPI) in the dry zone, but otherwise appeared in the humid zone.
Relative importance (%) of the most important five drought factors among sixteen variables for meteorological drought (SPI) using random forests (RF) and augmented regression trees (BRT) in arid, humid and combined regions for and 12-month SPI. a) Arid zone. The relative importance of drought factors for corn and soybean yields can be very useful for agricultural drought monitoring. The rRMSE divides the RMSE by the average of the yield in dry (irrigated) and wet (non-irrigated) regions.
Based on R2 and rRMSE, the modeling result for corn yield in the dry area was better compared to the other cases (Table 2.6). The scaled relative importance values of the six variables for corn and soybeans in the dry and humid areas (Table 2.6) were used as weights, and the weighted combinations of the six variables were calculated and used as drought indicators to monitor agricultural drought. In the humid region, two types of remote sensing-based maps well captured the most extreme drought in 2002 and 2007 (Fig. 2.8).
The modeling results based on the relative importance of the six variables in terms of 3-month SPI and crop yield were shown in the maps to compare and validate spatial distributions of drought with USDM maps. In future study, agricultural drought modeling will be tested in regions with more dominant corn and soybean agriculture (i.e. the Midwest) and with different dominant crops (e.g. wheat) to more generalize parameters in the proposed approach.
Drought monitoring using high resolution soil moisture through multi-sensor satellite data fusion over the Korean peninsula
- Background
- Study area and data .1 Study area
- Methods
- Results and Discussion
- Monitoring meteorological drought
- Monitoring agricultural drought
- Monitoring hydrological drought
- Novelty, opportunities, and limitations
- Conclusion
Soil moisture data were obtained from two Korean regional flux monitoring networks (KoFlux), Seolmacheon (SMC) and Cheongmicheon (CMC), for the period from March to November in 2010 (drought year). KoFlux data at 1:00 PM were used to validate the reduced soil moisture and AMSR-E soil moisture. Soil moisture at KoFlux is measured using Time Domain Reflectometry (TDR), which is used worldwide to measure soil moisture through travel time analysis (Lin, 2003).
In this study, the soil moisture reduction approach proposed by Im et al. 2016) reduced 25 km AMSR-E soil moisture to 1 km soil moisture using 1 km MODIS variables through machine learning approaches. Although the proposed HSMDI is similar to soil moisture-based drought indices introduced by Zhang et al. High surface temperature and albedo result in a decrease in soil moisture (Im et al., 2016; Sugathan et al., 2014).
Since AMSR-E, downscaled, and in situ soil moisture data have different spatial representation, it is difficult to directly compare them. Downscaled soil moisture also shows a strong correlation with AMSR-E soil moisture, with an average R2 of 0.612. Validation of downscaled soil moisture with AMSR-E soil moisture and in situ soil moisture from March to November 2010.
Scatter plots between in situ and satellite-derived soil moisture (p < 0.05) at two flux locations (CMK and SMK). This is because the top soil moisture (< 10 cm) is more sensitive to the cumulative amount of precipitation over a shorter period (e.g. 1 and 3 months) (Zhang et al., 2013). This study developed a drought index, HSMDI, using high-resolution soil moisture by downscaling AMSR-E soil moisture based on a machine learning approach.
In this study, seven MODIS and TRMM surface factors were used to downscale AMSR-E soil moisture using random forest. Downscaled soil moisture resulted in a lower RMSE compared to in situ soil moisture than AMSR-E soil moisture.
Very short-term Prediction of drought using remote sensing data and MJO index through Random forest over East Asia
- Background
- Study area and data .1 Study area
- Methodology
- Results and discussion
- Conclusion
The East Asian monsoon is also influenced by variations in sea surface temperature (SST) in the Indian Ocean and the tropical Pacific Ocean (Yang et al. 2007). Wu et al. 2009) developed a statistical model for predicting the East Asian summer monsoon using ENSO and the North Atlantic Oscillation (NAO). The historical pattern of the short-term drought forecast is less valid than the long-term drought forecast because the drought factors respond slowly to sudden changes in drought conditions and have a delayed signal (Otkin et al, 2015; Otkin et al., 2013) .
Therefore, early warning of short-term drought to the agricultural community is necessary to mitigate the associated losses (Mo et al., 2015). A representative variability is the Madden–Julian Oscillation (MJO: Madden and Julian 1971), which leads to a changing teleconnection pattern affecting the extratropical circulation over East Asia during the boreal winter season (Jeong et al., 2005). The proposed drought prediction model considered the real-time multivariate (RMM) indices of the MJO because the MJO, which is a short-term climate variability, has significant implications for drought in East Asia (Chen et al., 2017; Lu et al., 2012; Jeong et al., 2008).
In this study, three satellite-based drought indices were used because satellite images have shown excellent performance in drought monitoring (Lorenz et al. 2017; SDCI proposed by Rhee et al. 2010) was calculated using the Precipitation Condition (PCI), Temperature Conditions Index (TCI) and Vegetation Condition Index (VCI) (equation 4.1). Near-surface atmospheric forcing provided by the Terrestrial Hydrology Research Group ( Sheffield et al. 2006 ) was used to drive the land surface model.
The result does not agree with the analysis in Jeong et al. 2005) during the boreal winter season, when MJO intensity is strong, and thus there is a significant cooling in phases 2–4 while there is a warming in phases 6–8 over the East Asia region. The degree of explanation of soil moisture variability over the range of 30–90 days compared to its total variance is practically high, about 40% over Central and Northeast China where soil moisture memory is relatively large in East Asia (Seneviratne et al., 2006). Although it is difficult to predict the sudden change of drought conditions (wet to dry or dry to wet) considering only past patterns of droughts (Hao et al. 2016; AghaKouchak 2015;.
Conclusion
Outlook and future works
Microwave optical/infrared decomposition to improve the spatial representation of soil moisture using AMSR-E and MODIS products. A review of available methods for estimating soil moisture and its implications for water resources management. ESA CCI Soil Moisture for better understanding of the Earth system: State of the art and future directions.
A simple soil moisture index representing multi-year drought impacts on aspen productivity in the western Canadian interior. Development of an Advanced Microwave Scanning Radiometer (AMSR-E) algorithm of soil moisture and vegetation water content. Satellite soil moisture for agricultural drought monitoring: Assessment of the SMOS-derived Soil Water Deficit Index.
Development and evaluation of soil moisture deficiency index (SMDI) and evapotranspiration deficiency index (ETDI) for agricultural drought monitoring. Assessing the evolution of soil moisture and vegetation conditions during the severe drought in the United States in 2012. Intercomparison of remotely sensed soil moisture products at different spatial scales in the Iberian Peninsula.
A new Soil Moisture Index for Soil Drought (SMADI) integrating MODIS and SMOS products: An example study over the Iberian Peninsula. Soil moisture memory in AGCM simulations: analysis of Global Land–Atmosphere Coupling Experiment (GLACE) data. Influence of soil moisture content on surface albedo and soil thermal parameters at a tropical station.
