543 research outputs found

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

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    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

    Analysis of Large Scale Spatial Variability of Soil Moisture Using a Geostatistical Method

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    Spatial and temporal soil moisture dynamics are critically needed to improve the parameterization for hydrological and meteorological modeling processes. This study evaluates the statistical spatial structure of large-scale observed and simulated estimates of soil moisture under pre- and post-precipitation event conditions. This large scale variability is a crucial in calibration and validation of large-scale satellite based data assimilation systems. Spatial analysis using geostatistical approaches was used to validate modeled soil moisture by the Agriculture Meteorological (AGRMET) model using in situ measurements of soil moisture from a state-wide environmental monitoring network (Oklahoma Mesonet). The results show that AGRMET data produces larger spatial decorrelation compared to in situ based soil moisture data. The precipitation storms drive the soil moisture spatial structures at large scale, found smaller decorrelation length after precipitation. This study also evaluates the geostatistical approach for mitigation for quality control issues within in situ soil moisture network to estimates at soil moisture at unsampled stations

    Evaluation of a global soil moisture product from finer spatial resolution sar data and ground measurements at Irish sites

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    In the framework of the European Space Agency Climate Change Initiative, a global, almost daily, soil moisture (SM) product is being developed from passive and active satellite microwave sensors, at a coarse spatial resolution. This study contributes to its validation by using finer spatial resolution ASAR Wide Swath and in situ soil moisture data taken over three sites in Ireland, from 2007 to 2009. This is the first time a comparison has been carried out between three sets of independent observations from different sensors at very different spatial resolutions for such a long time series. Furthermore, the SM spatial distribution has been investigated at the ASAR scale within each Essential Climate Variable (ECV) pixel, without adopting any particular model or using a densely distributed network of in situ stations. This approach facilitated an understanding of the extent to which geophysical factors, such as soil texture, terrain composition and altitude, affect the retrieved ECV SM product values in temperate grasslands. Temporal and spatial variability analysis provided high levels of correlation (p < 0.025) and low errors between the three datasets, leading to confidence in the new ECV SM global product, despite limitations in its ability to track the driest and wettest conditions

    Geospatial Information as a Tool for Soil Resource Information, Management and Decision Support in Nigeria

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    Understanding and addressing the complexity of soil resources management and factors involved requires collection and interpretation of relevant data that will serve as decision support tools. Geospatial information is a veritable tool for soil resource information and decision support for soil management, which is yet to be well embraced in Nigeria. This paper emphasized the importance of geospatial information as a decision support tool to make better and informed decision in the management of soil resources. It also reviewed and discussed status of soil information systems and need to promote strategies for sustainable soil resource development in the country.Keywords: Soil information system, Decision support system, remote sensing, digital soil mappin

    Evaluating New Approaches to Measure and Map Soil Moisture Spatial Variability

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    Knowledge of soil moisture spatial patterns provides basic but important information in studies of hydrological processes. At the field to subwatershed scale, soil moisture spatial variability is critical to aid in hydrologic modeling, but has not been adequately studied. Two new approaches were taken to contribute to the study of soil moisture spatial variability at this scale. The Bayesian Maximum Entropy (BME) framework is a more general method than classical geostatistics and has not yet been applied to soil moisture spatial estimation. The recently developed mobile Cosmic-ray Soil Moisture Observing System (COSMOS), i.e. COSMOS rover, has a ~660 m diameter footprint which can potentially be used in field to subwatershed scale soil moisture mapping. The objectives of this research are to compare the effectiveness of BME versus ordinary kriging (OK) for spatial prediction of soil moisture at the field scale, and to calibrate and validate a COSMOS rover for mapping 0-5 cm soil moisture at spatial scales suitable for evaluating satellite-based soil moisture estimates. High resolution aerial photography was incorporated into the soil moisture spatial prediction using the BME method. Soil moisture maps based on the BME and the OK frameworks were cross-validated and compared. The BME method showed only slight improvement in the soil moisture mapping accuracy compared to the OK method. The COSMOS rover was calibrated to field average soil moisture measured with impedance probes which were themselves calibrated to 0-5 cm soil moisture measured by soil sampling. The resulting rover calibration was then applied to map soil moisture around the Marena, Oklahoma In Situ Sensor Testbed (MOISST) in north central Oklahoma, USA and in the Little Washita River watershed in southwest, Oklahoma. The maps showed reasonable soil moisture patterns and a clear response to soil wetting by an intervening rainfall. The rover measured field averaged soil moisture with an RMSD of 0.039 cm^3 cm^-3 relative to the impedance probes which themselves had an RMSE of 0.031 cm^3 cm^-3 relative to soil moisture measured by soil moisture sampling. The results provide evidence that a COSMOS rover can be used effectively for near surface soil moisture mapping with acceptable accuracy.Plant & Soil Scienc

    Hydrological Risk Assessment at Praia, Cape Verde

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    Hydrology modeling became a relevant topic for the Cidade da Praia, Cabo Verde, Africa, due to negative impact risk to local population and its assets. The modeling via Geographical Information Systems (GIS) can help the decision-making process of space occupation and characterization for this type of risk. Under the municipalities of Praia, the phenomenon of flash flood is common, causing soil erosion and landslide. This constitutes a risk for the local habitat, particularly in districts with a lack of strong human infrastructures. To simulate, analyze and generate risk maps using GIS to help this county governance authorities for decision-making, thus, becomes the main aim of this article

    Doctor of Philosophy

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    dissertationThis dissertation examines three separate data analysis studies which sought to estimate the spatial and temporal characteristics of seasonal snowpack variables in the mountainous areas of the western United States. Research began with the analysis of historical daily snow data from Snowpack Telemetry (SNOTEL) sites, located in the mountainous areas of the western United States. Three snowpack characteristics were analyzed from a climatological perspective: snow water equivalent (SWE); snow depth (SD); and snow density, all three being interrelated. Analysis of 7 years of data showed that at a given location, during the winter season, interannual snowpack density variability was smaller than the corresponding SD and SWE changes. Hence, reliable climatological estimates of snow density could be obtained from a relatively short record period. Additionally, the spatial pattern of snowpack densi fication was qualitatively characterized using cluster analysis. The second part of research developed a regional regression-based approach to creating monthly climatological SWE grids over the western United States. The western United States was partitioned into smaller, homogenous regions in consideration of seasonal snowpack accumulation and ablation processes. Using stepwise regression, various geographic and meteorological variables were investigated as potential predictors of change in climatological SWE within each subregion. Results indicate that a simple regional regression approach, coupled with readily available geographic and meteorological parameters as predictors, is reliable for mapping SWE climatology from October to March. For the period of April, however, the regional equations produced increased error, especially in the North Pacifi c and Southwest regions. Lastly, performance of space-borne passive microwave SWE retrieval algorithms for the Colorado River Basin was examined by comparing daily SWE estimates from selected algorithms with SNOTEL SWE measurements for each winter month

    Distributed hydrological modelling and application of remote sensing data

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    New Downscaling Approach Using ESA CCI SM Products for Obtaining High Resolution Surface Soil Moisture

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    ESA CCI SM products have provided remotely-sensed surface soil moisture (SSM) content with the best spatial and temporal coverage thus far, although its output spatial resolution of 25 km is too coarse for many regional and local applications. The downscaling methodology presented in this paper improves ESA CCI SM spatial resolution to 1 km using two-step approach. The first step is used as a data engineering tool and its output is used as an input for the Random forest model in the second step. In addition to improvements in terms of spatial resolution, the approach also considers the problem of data gaps. The filling of these gaps is the initial step of the procedure, which in the end produces a continuous product in both temporal and spatial domains. The methodology uses combined active and passive ESA CCI SM products in addition to in situ soil moisture observations and the set of auxiliary downscaling predictors. The research tested several variants of Random forest models to determine the best combination of ESA CCI SM products. The conclusion is that synergic use of all ESA CCI SM products together with the auxiliary datasets in the downscaling procedure provides better results than using just one type of ESA CCI SM product alone. The methodology was applied for obtaining SSM maps for the area of California, USA during 2016. The accuracy of tested models was validated using five-fold cross-validation against in situ data and the best variation of model achieved RMSE, R2 and MAE of 0.0518 m3/m3, 0.7312 and 0.0374 m3/m3, respectively. The methodology proved to be useful for generating high-resolution SSM products, although additional improvements are necessary
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