A Typical Review of Current and Prospective Microwave and Optical Remote Sensing Datasets for Soil Moisture Retrieval

Soil Moisture content is a vital indicator of both the weather and the water cycle. It has been a long-standing difficulty for the field of remote sensing to make sense of soil moisture's spatial and temporal distribution. For over five decades, researchers across the world have exclusively investigated the optical and microwave datasets for estimating soil moisture by developing various models, and algorithms. Nevertheless, challenges are faced in the consistent retrieval of SM at local, and global scales with higher accuracy in space and time resolution. The review was conducted in-depth, looking at the methods using optical and microwave data to determine soil moisture, and outlining the benefits and drawbacks considering the current needs.  With this research, a new age of widespread use of space technology for remote sensing of soil moisture has been ushered in. The study also acknowledges the scientific challenges of utilizing remote sensing datasets for soil moisture measurement

Spatial water table level modelling with multi-sensor unmanned aerial vehicle data in boreal aapa mires

Peatlands have been degrading globally, which is increasing pressure on restoration measures and monitoring. New monitoring methods are needed because traditional methods are time-consuming, typically lack a spatial aspect, and are sometimes even impossible to execute in practice. Remote sensing has been implemented to monitor hydrological patterns and restoration impacts, but there is a lack of studies that combine multi-sensor ultra-high-resolution data to assess the spatial patterns of hydrology in peatlands. We combine optical, thermal, and topographic unmanned aerial vehicle data to spatially model the water table level (WTL) in unditched open peatlands in northern Finland suffering from adjacent drainage. We predict the WTL with a linear regression model with a moderate fit and accuracy (R2 = 0.69, RMSE = 3.85 cm) and construct maps to assess the spatial success of restoration. We demonstrate that thermal-optical trapezoid-based wetness models and optical bands are strongly correlated with the WTL, but topography-based wetness indices do not. We suggest that the developed method could be used for quantitative restoration assessment, but before-after restoration imagery is required to verify our findings

Surface Soil Moisture Retrievals from Remote Sensing:Current Status, Products & Future Trends

Advances in Earth Observation (EO) technology, particularly over the last two decades, have shown that soil moisture content (SMC) can be measured to some degree or other by all regions of the electromagnetic spectrum, and a variety of techniques have been proposed to facilitate this purpose. In this review we provide a synthesis of the efforts made during the last 20 years or so towards the estimation of surface SMC exploiting EO imagery, with a particular emphasis on retrievals from microwave sensors. Rather than replicating previous overview works, we provide a comprehensive and critical exploration of all the major approaches employed for retrieving SMC in a range of different global ecosystems. In this framework, we consider the newest techniques developed within optical and thermal infrared remote sensing, active and passive microwave domains, as well as assimilation or synergistic approaches. Future trends and prospects of EO for the accurate determination of SMC from space are subject to key challenges, some of which are identified and discussed within. It is evident from this review that there is potential for more accurate estimation of SMC exploiting EO technology, particularly so, by exploring the use of synergistic approaches between a variety of EO instruments. Given the importance of SMC in Earth’s land surface interactions and to a large range of applications, one can appreciate that its accurate estimation is critical in addressing key scientific and practical challenges in today’s world such as food security, sustainable planning and management of water resources. The launch of new, more sophisticated satellites strengthens the development of innovative research approaches and scientific inventions that will result in a range of pioneering and ground-breaking advancements in the retrievals of soil moisture from space

Terrain characterization for site selection and preparation

Terrain characterization is a key component in autonomous base camp site selection and preparation. Aerial terrain characterization allows for large areas of interest to be characterized in a safe and efficient manner. In this work three terrain characteristics, terrain elevation/slope, land cover/land use classes, and soil moisture content were determined using UAV-mounted sensors to inform base camp site selection and preparation decisions. To determine accurate and real-time elevation/slope values, a stale a priori digital elevation model (DEM) was merged with a high-resolution, updated LIDAR DEM using the mblend method. The mblend method achieved better results than the traditional cover method by ensuring fewer height discontinuities along the edge of the two DEMs. To perform land cover/land use mapping, three semantic segmentation models (PSPNet, U-Net, and Segnet) and three base models (VGG, ResNet, and MobileNet) were modified to include multispectral imagery and compared. Seven land cover classes were determined with an accuracy of 82.71% by model ResNet/SegNet. To determine soil moisture content (SMC), ten models were developed to predict soil moisture – two machine learning models, support vector machine (SVM) and extremely randomized trees (ET), were paired with 5 input variables. The results indicated that SMC could be predicted with greater accuracy by reducing the dimensionality of a hyperspectral dataset to resemble a standard multispectral dataset. The ET model produced better estimations of SMC when trained with the reduced dimensionality (RD) input set and concatenated multispectral (CM) set – obtaining an increase of 1.3% (RD) and 5.4% (CM) in R-squared values and a decrease of .13 and .22 in mean absolute error (MAE) when compared to the baseline set. Finally, a process overview and use case is presented to illustrate the terrain characterization process as a whole

衛星搭載型多偏波SARを用いた土壌水分分布評価手法の開発とALOS/PALSARへの適用

学位の種別: 論文博士審査委員会委員 : （主査）東京大学教授 小池 俊雄, 東京大学教授 田島 芳満, 東京大学教授 西村 拓, 東京大学准教授 平林 由希子, 東京大学准教授 沖 一雄, 東京大学准教授 竹内 渉University of Tokyo(東京大学