Downscaling GLDAS Soil Moisture Data in East Asia through Fusion of Multi-Sensors by Optimizing Modified Regression Trees

Soilmoisture is a key part of Earth's climate systems, including agricultural and hydrological cycles. Soil moisture data from satellite and numerical models is typically provided at a global scale with coarse spatial resolution, which is not enough for local and regional applications. In this study, a soil moisture downscaling model was developed using satellite-derived variables targeting Global Land Data Assimilation System (GLDAS) soil moisture as a reference dataset in East Asia based on the optimization of a modified regression tree. A total of six variables, Advanced Microwave Scanning Radiometer 2 (AMSR2) and Advanced SCATterometer (ASCAT) soil moisture products, Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), and MODerate resolution Imaging Spectroradiometer (MODIS) products, including Land Surface Temperature, Normalized Difference Vegetation Index, and land cover, were used as input variables. The optimization was conducted through a pruning approach for operational use, and finally 59 rules were extracted based on root mean square errors (RMSEs) and correlation coefficients (r). The developed downscaling model showed a good modeling performance (r = 0.79, RMSE = 0.056 m(3)center dot m(3), and slope = 0.74). The 1 km downscaled soil moisture showed similar time series patterns with both GLDAS and ground soil moisture and good correlation with ground soil moisture (average r = 0.47, average RMSD = 0.038 m(3)center dot m(3)) at 14 ground stations. The spatial distribution of 1 km downscaled soil moisture reflected seasonal and regional characteristics well, although the model did not result in good performance over a few areas such as Southern China due to very high cloud cover rates. The results of this study are expected to be helpful in operational use to monitor soil moisture throughout East Asia since the downscaling model produces daily high resolution (1 km) real time soil moisture with a low computational demand. This study yielded a promising result to operationally produce daily high resolution soil moisture data from multiple satellite sources, although there are yet several limitations. In future research, more variables including Global Precipitation Measurement (GPM) precipitation, Soil Moisture Active Passive (SMAP) soil moisture, and other vegetation indices will be integrated to improve the performance of the proposed soil moisture downscaling model.ope

A Machine Learning Approach for Improving Near-Real-Time Satellite-Based Rainfall Estimates by Integrating Soil Moisture

Near-real-time (NRT) satellite-based rainfall estimates (SREs) are a viable option for flood/drought monitoring. However, SREs have often been associated with complex and nonlinear errors. One way to enhance the quality of SREs is to use soil moisture information. Few studies have indicated that soil moisture information can be used to improve the quality of SREs. Nowadays, satellite-based soil moisture products are becoming available at desired spatial and temporal resolutions on an NRT basis. Hence, this study proposes an integrated approach to improve NRT SRE accuracy by combining it with NRT soil moisture through a nonlinear support vector machine-based regression (SVR) model. To test this novel approach, Ashti catchment, a sub-basin of Godavari river basin, India, is chosen. Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA)-based NRT SRE 3B42RT and Advanced Scatterometer-derived NRT soil moisture are considered in the present study. The performance of the 3B42RT and the corrected product are assessed using different statistical measures such as correlation coeffcient (CC), bias, and root mean square error (RMSE), for the monsoon seasons of 2012–2015. A detailed spatial analysis of these measures and their variability across different rainfall intensity classes are also presented. Overall, the results revealed significant improvement in the corrected product compared to 3B42RT (except CC) across the catchment. Particularly, for light and moderate rainfall classes, the corrected product showed the highest improvement (except CC). On the other hand, the corrected product showed limited performance for the heavy rainfall class. These results demonstrate that the proposed approach has potential to enhance the quality of NRT SRE through the use of NRT satellite-based soil moisture estimates

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Department of Urban and Environmental Engineering (Environmental Science and Engineering)This thesis seek 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 to high resolution data (~1 km) by developing drought index, 3) predict drought by identifying the relationship between drought and climate variability. In this dissertation, there are five chapters. Chapter 1 summarizes the background of research and overviews of the thesis research. In Chapter 2, the characteristics of drought factors in different climate regions were understood based on relative variable importance which is provided from machine learning approaches. Three machine learning approaches (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. The variable importance targeting SPI and crop yield was applied as weight factors to produce drought maps. Those drought maps well monitored drought comparing U.S. Drought Monitor (USDM). In Chapter 3, Soil moisture with coarse spatial resolution (25 km) was downscaled to high resolution (1 km), and drought index (High resolution Soil Moisture Drought Index; HSMDI) was developed using high resolution soil moisture. HSMDI was evaluated for three types of droughts (meteorological drought, agricultural drought, and hydrological drought). The index well monitored meteorological and agricultural drought, but there is limitation in monitoring hydrological drought. In Chapter 4, short-term (one pentad) prediction of drought conducted over East Asia by integrating remote sensing data and climate variability. The real-time multivariate (RMM) Madden???Julian oscillation (MJO) indices were used because the MJO is intraseasonal climate variability. MJO improved the performance of prediction model. Chapter 5 provides a brief summary of this study. Chapter 6 discusses future work of this study.clos

Amélioration et désagrégation des données GRACE et GRACE-FO pour l’estimation des variations de stock d’eau terrestre et d’eau souterraine à fine échelle

Abstract : Groundwater is an essential natural resource for domestic, industrial and agricultural uses worldwide. Unfortunately, climate change, excess withdrawal, population growth and other human impacts can affect its dynamics and availability. These excessive demands can lead to lower groundwater levels and depletion of aquifers, and potentially to increased water scarcity. Despite the abundance of lakes and rivers in many parts of Canada, the potential depletion of groundwater remains a major concern, particularly in the southern Prairie. Groundwater is traditionally monitored through in-situ piezometric wells, which are scarcely distributed in Canada and many parts of the world. Consequently, its quantities, distribution and availability are not well known, both spatially and temporally. Fortunately, the launch of the twin satellite systems of Gravity Recovery And Climate Experiment (GRACE) in 2002 and its successor, GRACE Follow-On in 2018 (GRACE-FO) opened up new ways to study groundwater changes. These platforms measure the variations of the Earth's gravity field, which in turn can be related to terrestrial water storage (TWS). The main objective of this thesis is to improve the estimation and spatial resolution of TWS and related groundwater storage changes (GWS), using GRACE and GRACE-FO data. This challenge was addressed through four specific objectives, where original approaches were developed in each case. The first objective was to understand and better take into account the uncertainties associated with the hydrological models (the Global Land Data Assimilation System (GLDAS), and the Water Global Assessment Prognosis hydrological model (WGHM)), generally used in the processing of GRACE or GRACE-FO data. The thesis proposes a new approach based on the Gauss-Markov model to estimate the optimal hydrological parameters from GLDAS, considering six different surface schemes. The Förstner estimator and the best quadratic unbiased estimator of the variance components were used with a least-squares method to estimate the optimal hydrological parameters and their errors. The comparison of the optimal TWS derived from GLDAS to the TWS derived from WGHM showed a very significant correlation of r = 0.91. The correlation obtained with GRACE was r = 0.71, which increased to r = 0.81 when the groundwater component was removed from GRACE. Compared to WGHM and GRACE, the optimal TWS calculated from GLDAS had much smaller errors (RMSE = 7 to 8.5 mm) than those obtained when individual surface schemes are considered (RMSE = 10 to 21 mm); demonstrating the performance of the proposed approach. The second specific objective was to understand regional variations in TWS and their uncertainties. The approach was applied over the Canadian landmass. To achieve the goal, the thesis proposes a new modeling of glacial isostatic adjustment uplift (GIA) in Canada. The comparison of the results of the proposed model and three other existing models with data from 149 very high precision GPS stations demonstrated its superiority in the region considered. The regional approach proposed was then used to extract TWS by correcting the effects of the GIA and leakage. The analyzes showed patterns of significant seasonal variations in TWS, with values ranging between -160 mm and 80 mm. Overall TWS showed a positive slope of temporal variations over the Canadian landmass (+ 6.6 mm/year) with GRACE and GRACE-FO combined. The slope reached up to 45 mm/year in the Hudson Bay region. The third objective was to extract GWS component using a comprehensive rigorous approach to reconstruct, refine and map the variations of GWS and its associated uncertainties. The approach used the methods proposed in the two previous objectives. Moreover, a new filtering approach called Gaussian-Han-Fan (GHF) was developed and integrated into the process in order to have a more robust procedure for extracting information from GRACE and GRACE-FO data. The performance and merits of the proposed filter compared to previous filters were analyzed. Then, the groundwater signal was reconstructed by taking into account all the other components, including surface water variations (estimated using satellite altimetry data). The results showed that the average variations of GWS are between -200 mm and +230 mm in the Canadian Prairies. The maximum and minimum GWS trends were found around the Hudson Bay region (approximately 55 mm/year) and southern Prairies (approximately -20 mm/year), respectively. The error on GWS was around 10% (about 19 mm). The estimated GWS changes were validated using the data from 116 in-situ wells. This validation showed a significant level of correlation (r > |0.70|, P |0.90|, P |0,70|, P |0,90|, P < 10-4, RMSE < 30 mm). Enfin, le dernier objectif consistait à améliorer la résolution spatiale des résultats extraits des données GRACE de 1° à 0.25°. Ainsi, une nouvelle approche basée sur l'ajustement des conditions a d’abord été proposée pour estimer les paramètres hydrologiques optimaux et leurs erreurs. Elle est légèrement différente de la méthode proposée dans le premier objectif. Ensuite, les corrections requises pour extraire les anomalies de TWS et ses incertitudes de manière rigoureuse ont été effectuées suivant la méthodologie présentée à l’objectif 3. Par la suite une nouvelle méthode basée sur la combinaison spectrale-spatiale a été développée pour dériver les anomalies de TWS à échelle réduite (0.25°), en combinant de manière optimale les modèles GRACE et les paramètres hydrologiques. Enfin, les anomalies d’eau souterraines ont été dérivées en utilisant les anomalies de TWS estimées. Les validations ont été faites à partir des données de 75 puits en aquifère non confiné en Alberta. Elles démontrent le potentiel de l’approche proposée avec une corrélation très significative de = 0.80 et un RMSE de 11 mm. Ainsi, la recherche proposée dans la thèse a permis de faire des avancées importantes dans l’extraction d’information sur le stockage total d’eau et les eaux souterraines à partir des données des satellites gravimétriques GRACE et GRACE-FO. Elle propose et valide plusieurs nouvelles approches originales en s’appuyant sur des données in-situ. Elle ouvre également plusieurs nouvelles avenues de recherche, qui permettront de faciliter une utilisation plus opérationnelle de ces types de données à l’échelle régionale, voire locale

Mapping Soil Moisture from Remotely Sensed and In-situ Data with Statistical Methods

Soil moisture is an important factor for accurate prediction of agricultural productivity and rainfall runoff with hydrological models. Remote sensing satellites such as Soil Moisture Active Passive (SMAP) offer synoptic views of soil moisture distribution at a regional-to-global scale. To use the soil moisture product from these satellites, however, requires a downscaling of the data from an usually large instantaneous field of view (i.e. 36 km) to the watershed analysis scales ranging from 30 m to 1 km. In addition, validation of the soil moisture products using the ground station observations without an upscaling treatment would lead to cross-level fallacy. In the literature of geographical analysis, scale is one of the top research concens because of the needs for multi-source geospatial data fusion. This dissertation research introduced a multi-level soil moisture data assimilation and processing methodology framework based on spatial information theories. The research contains three sections: downscaling using machine learning and geographically weighted regression, upscaling ground network observation to calibrate satellite data, and spatial and temporal multi-scale data assimilation using spatio-temporal interpolation. (1) Soil moisture downscaling In the first section, a downscaling method is designed using 1-km geospatial data to obtain subpixel soil moisture from the 9-km soil moisture product of the SMAP satellite. The geospatial data includes normalized difference vegetation index (NDVI), land surface temperature (LST), gross primary productivity (GPP), topographical moisture index (TMI), with all resampled to 1-km resolution. The machine learning algorithm – random forest was used to create a prediction model of the soil moisture at a 1-km resolution. The 1-km soil moisture product was compared with the ground samples from the West Texas Mesonet (WTM) station data. The residual was then interpolated to compensate the unpredicted variability of the model. The entire process was based on the concept of regression kriging- where the regression was done by the random forest model. Results show that the downscaling approach was able to achieve better accuracy than the current statistical downscaling methods. (2) Station network data upscaling The Texas Soil Observation Network (TxSON) network was designed to test the feasibility of upscaling the in-situ data to match the scale of the SMAP data. I advanced the upscaling method by using the Voronoi polygons and block kriging with a Gaussian kernel aggregation. The upscaling algorithm was calibrated using different spatial aggregation parameters, such as the fishnet cell size and Gaussian kernel standard deviation. The use of the kriging can significantly reduce the spatial autocorrelation among the TxSON stations because of its declustering ability. The result proved the new upscaling method was better than the traditional ones. (3) Multi-scale data fusion in a spatio-temporal framework None of the current works for soil moisture statistical downscaling honors time and space equally. It is important, however, that the soil moisture products are consistent in both domains. In this section, the space-time kriging model for soil moisture downscaling and upscaling computation framework designed in the last two sections is implemented to create a spatio-temporal integrated solution to soil moisture multi-scale mapping. The present work has its novelty in using spatial statistics to reconcile the scale difference from satellite data and ground observations, and therefore proposes new theories and solutions for dealing with the modifiable areal unit problem (MAUP) incurred in soil moisture mapping from satellite and ground stations

Perspective Chapter: Downscaling of Satellite Soil Moisture Estimates

Soil moisture is a key parameter in the hydrological cycle and plays a critical role in global climate. The capacity to forecast drought and floods, manage water resources, and make field-scale decisions depends on accurate and thorough information on soil moisture. In addition to the instrument-based field observation approaches, dynamic mapping of soil moisture has been made possible by satellite remote sensing technologies. Estimates of soil moisture at a global and regional scale from optical and thermal remote sensing have been explored, and considerable advancements have been made. However, these global soil moisture products have coarse spatial resolutions and are typically unsuitable for field-level hydrological and agricultural applications. In this regard, this chapter presents a comprehensive review of the latest downscaling methods to improve the coarse-spatial and temporal resolution of soil moisture products. The main approaches discussed in the chapter include active passive fusion, optical/thermal based, topography based, and data assimilation methods. The physical background, current status, advantages and limitations associated with each downscaling approach has been thoroughly examined. Each of these optical/thermal, microwave-based methods for soil moisture estimation involves intricate derivation at different spatiotemporal scales, which can be combined using recent advances in machine learning