Soil moisture is an important variable in many environmental researches and application areas as it affects the interactions between atmosphere and land surface by controlling the energy and water exchange. The current measurement techniques are insufficient to acquire accurate large-scale root zone soil moisture (RZSM) data at the spatial resolution of interest. Though assorted models have been successfully applied in relatively small areas to estimate RZSM, the large-scale estimation is still facing challenges as it requires the flexibility and practicality of the models for the applications under various conditions. Though physically based soil moisture models are widely used, the errors in model physics affect the flexibility of these models meanwhile their large demand of data and computational resources reduces the practicality. On the contrary, the statistical and data-driven methods have high potential but their applications for large-scale RZSM estimation have not been fully explored. To develop feasible models for large-scale RZSM estimation using the surface observations, artificial neural networks, specifically multilayer perceptrons (MLPs), were applied in this study to estimate RZSM at the depths of 20cm and 50cm, using the data of 557 stations in the United States. Two experiments including four models were developed and the input variables of the models were carefully selected. The sensitivity analysis found that surface soil moisture and the cumulative rainfall, snowfall, air temperature and surface soil temperature were important inputs. If given soil texture data as inputs, the models achieved better performance and were extremely sensitive to them. The results showed that the MLPs were effective and flexible for the estimation of soil moisture at 20cm under various climate types and were insensitive to the potential errors in soil moisture datasets. However, the results of the estimation at 50cm are not as good as that of the 20cm. / Thesis / Master of Science (MSc)

<p> Measurements of soil temperature and soil moisture values beneath and in close proximity to circular mulches of hay and straw were made. The experiments were conducted to determine whether sub-surface effects vary with mulch diameter, and to acquire information concerning the seasonal changes in such effects produced by a mulch of most favourable diameter. The modification of soil climate increased with a greater mulch size. A circular mulch with a diameter of 60 cms or less was considered to be of no practical value for winter protection of roots. The mulch with a 240 cms. diameter, provided some winter protection, preventing freezing of the soil, and, in summer caused considerable modification of the sub-surface climate. In the summer, soil temperatures were lowered by values of up to 5°C and 2.5°C at 5 and 100 cms. depth respectively. After a two-month period of dry weather, moisture values at 0-10 cms. depth beneath the mulch were 20% by volume, whereas, outside the mulch they were 5%. These differences decreased with increasing depth but were over 10% at 100 ems. </p> / Thesis / Master of Science (MSc)

Forests buffer temperature extremes through processes such as canopy shading, wind speed reduction, and evapotranspiration. As a result, microclimates are formed within forests whose climatic conditions are distinctly different from the surrounding macroclimate. This allows many species to thrive in the understory due to its reduced variation in temperature. However, the buffering capacity of these microclimates may be under threat as global temperatures keep rising and extreme events such as droughts are becoming more widespread. Prolonged exposure to droughts can lower the soil moisture, which, in turn, weakens the buffering by reducing the water available for evaporative cooling. Earlier research has shown that forest buffering is mainly dependent on the canopy cover, the local water balance, and the geographical location. At higher latitudes, the general consensus is that the temperature buffering depends mainly on the canopy cover as the solar radiation is not strong enough to initiate sufficient evaporative cooling. Yet, with ongoing climate change and the increasing frequency of heat waves, this might have changed. To that end, we locally investigate the influence of soil moisture and canopy openness on the temperature buffering at various forest stands within a Swedish coniferous forest during the summer months of 2021 and 2022. Our results showed that, in both years, the soil moisture had no significant impact on the forest temperature, while the canopy openness had a very strong influence on buffering both the maximum and minimum temperatures. More specifically, in both years, the forest lost its ability to buffer the maximum and minimum temperatures when the canopy openness exceeded roughly 22% and 15%, respectively. On average, the measured forest stands were not buffered as the summer average maximum and minimum temperature offsets in both years amounted to ⟨∆Tmax,tot⟩ = 0.21 ◦C and ⟨∆Tmin,tot⟩ = −0.28 ◦C in 2021 and ⟨∆Tmax,tot⟩ = 0.10 ◦C and ⟨∆Tmin,tot⟩ = −0.31 ◦C in 2022. However, the summers of 2021 and 2022 were climatically quite average, which could have influenced the correlation between the soil moisture and the air temperature. Overall, the results suggest that maintaining a canopy openness ≲ 22% is needed for microclimate buffering to occur in this Swedish coniferous forest during a climatically average summer

This dissertation proposes a learning-based, physics-aware soil moisture (SM) retrieval algorithm for NASA’s Cyclone Global Navigation Satellite System (CYGNSS) mission. The proposed methodology has been built upon the literature review, analyses, and findings from a number of published studies throughout the dissertation research. Namely, a Sig- nals of Opportunity Coherent Bistatic scattering model (SCoBi) has been first developed at MSU and then its simulator has been open-sourced. Simulated GNSS-Reflectometry (GNSS-R) analyses have been conducted by using SCoBi. Significant findings have been noted such that (1) Although the dominance of either the coherent reflections or incoher- ent scattering over land is a debate, we demonstrated that coherent reflections are stronger for flat and smooth surfaces covered by low-to-moderate vegetation canopy; (2) The influ- ence of several land geophysical parameters such as SM, vegetation water content (VWC), and surface roughness on the bistatic reflectivity was quantified, the dynamic ranges of reflectivity changes due to SM and VWC are much higher than the changes due to the surface roughness. Such findings of these analyses, combined with a comprehensive lit- erature survey, have led to the present inversion algorithm: Physics- and learning-based retrieval of soil moisture information from space-borne GNSS-R measurements that are taken by NASA’s CYGNSS mission. The study is the first work that proposes a machine learning-based, non-parametric, and non-linear regression algorithm for CYGNSS-based soil moisture estimation. The results over point-scale soil moisture observations demon- strate promising performance for applicability to large scales. Potential future work will be extension of the methodology to global scales by training the model with larger and diverse data sets.

The method for developing a soil moisture inversion algorithm using Radar data can be approached in two ways: the multiple-incident angle approach and the change detection method. This thesis discusses how these two methods can be used to predict surface soil moisture. In the multiple incident angle approach, surface roughness can be mapped, if multiple incident angle viewing is possible and if the surface roughness is assumed constant during data acquisitions. A backpropagation neural network (NN) is trained with the data set generated by the Integral Equation Method (IEM) model. The training data set includes possible combinations of backscatter obtained as a result of variation in dielectric constant within the period of data acquisitions. The inputs to the network are backscatter acquired at different incident angles. The outputs are correlation length and root mean square height (rms). Once the roughness is mapped using these outputs, dielectric constant can be determined. Three different data sets, (backscatter acquired from multiplerequencies, multiple-polarizations, and multiple-incident angles) are used to train the NN. The performance of the NN trained by the different data sets is compared. The next approach is the application of the change detection concept. In this approach, the relative change in dielectric constant over two different periods is determined from Radarsat data using a simplified algorithm. The vegetation backscatter contribution can be removed with the aid of multi-spectral data provided by Landsat. A method is proposed that minimizes the effect of incident angle on Radar backscatter by normalizing the acquired SAR images to a reference angle. A quantitative comparison of some of the existing soil moisture estimation algorithms is also made

Oriented strandboard (OSB) is a wood-based composite product with the largest market share for residential and commercial construction. OSB composite products have introduced variability in their physical and mechanical properties due to their raw material and process variation. Reliable in-line non-destructive evaluation (NDE) devices are needed to rapidly determine OSB panel product quality during and after the manufacturing process. Wood specific gravity (SG) and moisture content (MC) play an important role in the wood composite manufacturing process. A real-time after-press monitoring device for locating SG and MC variations can supply information needed to control and improve mat formation, hot press schedules, detect MC-related problems, reduce product variation, and perform final product quality inspection. No real-time non-contact NDE methods are available for simultaneous detection of MC and SG variation. In this research, the radio frequency (RF) scanning technique was used to evaluate the MC and SG of OSB. The numerical simulation method assisted in developing RF sensors to nondestructively evaluate MC and SG of OSB composite specimens. MC and SG prediction models were derived based on RF testing results. The model behavior between relative humidity conditioned method and oven-drying conditioning method were compared. The results indicated the RF scanning technique can be successfully used as a NDE tool to measure MC and SG of OSB panel products. Numerical simulation can help deciding RF sensor geometry successfully and accurately. The MC and SG of OSB can be predicted with the models developed with the procedure used in this study. The RF scanning results are not only influenced by material physical properties, but also influenced by their MC conditioning method, such as relative humidity conditioned method and oven-drying conditioning method.

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