Land surface temperature (LST) is strongly influenced by landscape features as they change the thermal characteristics of the surface greatly. Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Built-up Index (NDBI), and Normalized Difference Bareness Index (NDBAI) correspond to vegetation cover, water bodies, impervious build-ups, and bare lands, respectively. These indices were utilized to demonstrate the relationship between multiple landscape features and LST using the spectral indices derived from images of Landsat 5 Thematic Mapper (TM), and Landsat 8 Operational Land Imager (OLI) of Sylhet Sadar Upazila (2000–2018). Google Earth Engine (GEE) cloud computing platform was used to filter, process, and analyze trends with logistic regression. LST and other spectral indices were calculated. Changes in LST (2000–2018) range from −6 °C to +4 °C in the study area. Because of higher vegetation cover and reserve forest, the north-eastern part of the study region had the greatest variations in LST. The spectral indices corresponding to landscape features have a considerable explanatory capacity for describing LST scenarios. The correlation of these indices with LST ranges from −0.52 (NDBI) to +0.57 (NDVI).

Wheat, being the third largest U.S. crop and the principal food grain, faces significant risks from climate extremes such as drought. This necessitates identifying and developing methods for early water-stress detection to prevent yield loss and improve water-use efficiency. This study investigates the potential of hyperspectral imaging to detect the early stages of drought stress in wheat. The goal is to utilize this technology as a tool for screening and selecting drought-tolerant wheat genotypes in breeding programs. Additionally, this research aims to systematically evaluate the effectiveness of various existing sensors and methods for detecting early stages of water stress. The experiment was conducted in a durum wheat experimental field trial in Maricopa, Arizona, in the spring of 2019 and included well-watered and water-limited treatments of a panel of 224 replicated durum wheat genotypes. Spectral indices derived from hyperspectral imagery were compared against other plant-level indicators of water stress such as Photosystem II (PSII) and relative water content (RWC) data derived from proximal sensors. Our findings showed a 12% drop in photosynthetic activity in the most affected genotypes when compared to the least affected. The Leaf Water Vegetation Index 1 (LWVI1) highlighted differences between drought-resistant and drought-susceptible genotypes. Drought-resistant genotypes retained 43.36% more water in leaves under well-watered conditions compared to water-limited conditions, while drought-susceptible genotypes retained only 15.69% more. The LWVI1 and LWVI2 indices, aligned with the RWC measurements, revealed a strong inverse correlation in the susceptible genotypes, underscoring their heightened sensitivity to water stress in earlier stages. Several genotypes previously classified based on their drought resistance showed spectral indices deviating from expectations. Results from this research can aid farmers in improving crop yields by informing early management practices. Moreover, this research offers wheat breeders insights into the selection of drought-tolerant genotypes, a requirement that is becoming increasingly important as weather patterns continue to change.

Assessing soil organic carbon (SOC) stocks is crucial for understanding the carbon sequestration potential of agroecosystems and for mitigating climate change. This study presents a novel method for assessing SOC and mineral content at various soil depths in sorghum crops using hyperspectral remote sensing. Conducted at Planthaven Farms, MO, the research encompassed 10 genotypes across 30 plots, yielding 180 soil samples from six depth intervals (0-150 cm) of bare soil. Chemical analyses determined the SOC and mineral levels, which were then compared to spectral data from HySpex indoor sensors. We utilized time-frequency analysis methods, including Discrete Wavelet Transformation (DWT), Continuous Wavelet Transformation (CWT), and Frame transformation along with traditional spectral transformations, specifically Fractional Derivatives and Continuum Removal. The analysis revealed the shortwave infrared (SWIR) region, particularly the 1800-2000 nm range, as having the strongest correlations with SOC content (with R ² exceeding 0.8). The visible near-infrared (VNIR) region also provided valuable insights. Models incorporating CWT achieved high accuracy (Test R2 exceeding 0.9), while Frame transformation achieved strong accuracy (Test R ² between 0.7-0.8) with fewer features. The Random Forest Regressor proved to be most robust, demonstrating superior accuracy and reduced overfitting compared to Support Vector Regression, Partial Least Squares Regression, and Deep Neural Network models. The models demonstrated the efficacy of hyperspectral data for SOC estimation, suggesting potential for future applications that integrate this data with above-ground biomass to improve SOC mapping across larger scales. This research offers a promising spectral transformation approach for effective carbon management and sustainable agriculture in a changing climate.