Hyperspectral imagining has been recently been used to obtain several water quality parameters in water bodies either inland or in oceans. Optical and thermal have proven that spatial and temporal information needed to track and understand trend changes for these water quality parameters will result in developing better management practices for improving water quality of water bodies. This paper will review water quality parameters Chlorophyll (Chl), Dissolved Organic Carbon (DOC), and Total Suspended Solids (TSS) obtained for the Sakonnet River in Narragansett Bay, Rhode Island using the AVIRIS Sensor. The AVIRIS Sensor should improve the assessment and the definition of locations and pollutant concentrations of point and non-point sources. It will provide for necessary monitoring data to follow the clean up efforts and locate the necessary water and wastewater infrastructure to eliminate these point and non-point sources. This hyperspectral application would enhance the evaluation by both point and non-point sources, improve upon and partially replace expenses, labor intensive field sampling, and allow for economical sampling and mapping of large geographical areas.
Precision farming relies on the cost effectiveness of collecting and interpreting data, which describes the variations of agricultural conditions such as crop stresses, nutrient deficiencies, water stresses, or pest infestation. Hyperspectral remote sensing from satellites and airborne sensors can be a way to obtain data needed to develop site-specific farming management strategies. The primary objective of the hyperspectral applications in precision farming is to provide farmers with a technology, which can detect specific crop conditions that can be used to program variable-rate applications. Applications of water, pesticides, and fertilizer can be tailored to the needs of the agricultural crops, based on the conditions reflected on the imagery. This paper presents an experimental study performed in Beltsville, Maryland for assessing the plant density and nutrient uptake of corn using a simple photographic method from a model airplane versus obtaining hyperspectral imagery from an airborne sensor. The hyperspectral sensor utilized in this study was the AISA sensor. These remote sensors can measure the temperature of plants; or to be more specific, they can measure how much energy plants emit at the visible and near-infrared wavelengths of the spectrum, such as water and vegetation.