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

No description available.

No description available.

No description available.

No description available.

No description available.

No description available.

No description available.