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Abstract. Agriculture plays a major role in eradicating poverty, promoting prosperity, and nourishing a projected 10 billion people by 2050 globally. In a changing climate, achieving optimal agricultural yields requires a deeper understanding of available natural resources and crops. This is especially important for places like the Navajo Nation, which faces significant challenges in food supply chain management due to various factors such as water demand, water quality, and insufficient information about land fertility and crops timings/seasons. Additionally, it is the largest Native American reservation in the U.S. It covers 27,425 square miles across Arizona, Utah, and New Mexico and has a population of 165,158 people, according to the 2020 census. Agriculture has been a key part of life in the Navajo Nation since the late 19th and early 20th centuries, playing a big role in the region’s development and stability. However, the lack of knowledge about decisions and actions during the crop growing season has resulted in lower crop productivity, as evidenced by the USDA statistical report for the Navajo Nation in 2012 and 2017. To support farmers by providing better decision-making and actionable insights, high-resolution, open-source Sentinel-2 satellite images are being used to develop advanced crop mapping techniques for identifying the spatial extent of various agricultural crops in the Navajo Nation. To address this, a collection of research papers was reviewed, leading to the development of a new methodology for analysing Sentinel-2 data from the 2017 and 2023 growing seasons within the Navajo Nation. The collected data was pre-processed by creating monthly median composites of surface reflectance to remove noise and enhance the results more accurately. After preprocessing, spectral indices were calculated from the spectral bands, including NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index), GCVI (Green Chlorophyll Vegetation Index), and LSWI (Land Surface Water Index), to differentiate the crops more precisely. The training datasets were obtained from the USDA’s Crop Data Layer (CDL) and split into 80% for training and 20% for validating the Random Forest supervised classification algorithm. The classification resulted in an accuracy of 80%. Finally, the accuracy of the results was compared with independent ground truth data. This research identifies notable discrepancies between the CDL data and the Navajo Nation agricultural census statistical report, particularly in estimating corn acreage for the Chinle and Fort Defiance agencies. Ultimately this approach information is used to provide actionable insights to Navajo Nation farmers. 

The hydro-mechanical behavior of unsaturated soil, particularly expansive soil, is influenced significantly by cyclic wetting and drying. Understanding the soil parameters is crucial when evaluating the performance of infrastructures constructed on expansive clay. As a result of extreme rainfall events, highway slopes containing highly expansive Yazoo clay in Mississippi, U.S., become vulnerable to volume change. The phenomenon creates perched water zones within the slopes and poses a risk of slope failure. The soil-water characteristic curve (SWCC) defines the relationship between water content and soil suction, which can be obtained from different laboratory procedures. However, conventional laboratory methods have some limitations. To address this, various analytical and predictive models have been developed, but they can only offer estimates based on soil characteristics and lack seasonal variations occurring in field conditions. Studying seasonal SWCC through field measurements can help understand soil responses to changing moisture conditions. The current study utilized field data from six highway slopes in Mississippi and classified the data into different seasons: spring, summer, and fall. After obtaining van Genuchten parameters from the fitted curve for each season, the finite element method was applied to evaluate the parameters for accurate numerical analysis of infrastructures containing expansive clay. The study observed the variations in flow parameters with seasonal change that cannot be achieved when data from only one season is considered. The findings underscore the importance of field instrumentation data for developing SWCC and the significance of seasonal flow parameters in infrastructure design. 

Quantum cascade lasers (QCLs) have emerged as promising candidates for generating chip-scale frequency combs in mid-infrared and terahertz wavelengths. In this work, we demonstrate frequency comb formation in ring terahertz QCLs using the injection of light from a distributed feedback (DFB) laser. The DFB design frequency is chosen to match the modes of the ring cavity (near 3.3 THz), and light from the DFB is injected into the ring QCL via a bus waveguide. By controlling the power and frequency of the optical injection, we show that combs can be selectively formed and controlled in the ring cavity. Numerical modeling suggests that this comb is primarily frequency-modulated in character, with the injection serving to trigger comb formation. We also show that the ring can be used as a filter to control the output of the DFB QCL, potentially being of interest in terahertz photonic integrated circuits. Our work demonstrates that waveguide couplers are a compelling approach for injecting and extracting radiation from ring terahertz combs and offer exciting possibilities for the generation of new comb states in terahertz, such as frequency-modulated waves, solitons, and more.