A system for remote detection of fluid leaks from a natural gas or oil pipeline including a laser light source for detecting a methane leak while sweeping in multiple directions, a Midwave Infrared (MWIR) detector optically coupled with the laser light source and a controller operatively connected to the laser light source and the MWIR detector for aggregating data collected by the laser light source and the MWIR using a nuropmophic flow detection algorithm including computational fluid dynamic models.

A system, method, device and computer-readable medium for creating an ensemble model of water quality. The ensemble model is generated by determining a set of optimal component models for spectral regions of a body of water, and combining the optimal models. The optimal models can be based on remote sensing data, including satellite imagery. A K-fold partition approach or a global approach can be used to determine the optimal component models, and the optimal component models can be combined through spectral space partition rules to generate an ensemble model of water quality. The ensemble model not only has improved water quality prediction ability, but also has strong spatial and temporal extensibility. The spatial and temporal extensibility of the ensemble model is fundamentally important and desirable for long-term and large-scale remote sensing monitoring and assessment of water quality.

A method of determining whether to treat soybeans for soybean aphids, the method includes collecting at least one image of a soybean canopy using one or more remote sensing instruments and processing the image into spectral reflectance data and selecting from the spectral reflectance data optimal spectral wavelength bands. The selected reflectance data is classified into one of a plurality of classification groupings using a machine learned classification model. To treat or not treat the soybean canopy for aphids is determined based on the classification of the reflectance data into one of the class groupings.

A method of detecting a mineral or metal in ground includes capturing an image of a target region of the ground and processing the image to determine a location of a candidate rock or plant of interest that is known to be usable for the detection of a mineral or element of interest. Once located, electromagnetic radiation, such as a laser, is directed to the candidate rock or plant to create a light signature, such as by vaporization. The method analyzes the light signature for the presence of the mineral or metal in the target region and determines if the light signature indicates the mineral or metal is present in the ground. The light signature can be analyzed with a spectroscope to detect the mineral or element as an indication of the present of the mineral or element in the ground around or below the candidate rock or plant.

A method for correcting top-of-atmosphere reflectance data in high altitude imagery to a ground surface reflectance data. Transmission of light through Earth's atmosphere and its suspended load of aerosol particles degrades light within the visible through near infrared portion of the spectrum. This can severely affect the quality of the data recorded by orbiting Earth observation satellites. The method first measures the degree of atmospheric effects upon reflectance, then reverses these effects to deliver surface reflectance data and imagery cleaned of haze and thin clouds.

A soil analysis system that provides a field deployable device that is configured to remotely measure in situ soil suction through correlation with relative humidity at the soil surface.

Various embodiments are disclosed for a machine learning system for automatic genotype selection and performance evaluation using multi-source and spatiotemporal remote sensing data collected from an unmanned aerial system (UAS). A computing device may be configured to access images of a field having a first genotype and a second genotype of at least one crop or plant planted therein. The computing device may apply an image processing routine to the images to analyze the images of the field and determine characteristics of the first genotype and the second genotype of the at least one crop or plant planted in the field. The computing device may then apply a machine learning routine to forecast a first estimated yield of the first genotype and a second estimated yield of the second genotype using the identified characteristics of the first genotype and the second genotype.

A method of determining whether to treat soybeans for soybean aphids, the method includes collecting at least one image of a soybean canopy using one or more remote sensing instruments and processing the image into spectral reflectance data and selecting from the spectral reflectance data optimal spectral wavelength bands. The selected reflectance data is classified into one of a plurality of classification groupings using a machine learned classification model. To treat or not treat the soybean canopy for aphids is determined based on the classification of the reflectance data into one of the class groupings.

[Problem] To provide a spectral camera control device, a spectral camera control program, a spectral camera control system, an aircraft equipped with said system, and a spectral image capturing method, with which it is possible for each of a spatial resolution and an exposure time for spectral image capture to be set arbitrarily, and with which spatial distortion and displacement of the spectral image can be suppressed. [Solution] This spectral camera control device is installed together with a spectral camera 3 provided with a liquid crystal tunable filter 33 in an aircraft 1 capable of stationary flight, and causes the spectral camera 3 to capture an image in a snapshot mode each time the transmission wavelength of the liquid crystal tunable filter 33 is switched while the aircraft 1 is in stationary flight.

The present disclosure relates to methods and systems for obtaining image information of an organism including a set of optical data; calculating a growth index based on the set of optical data; and calculating an anticipated harvest time based on the growth index, where the image information includes at least one of: (a) visible image data obtained from an image sensor and non-visible image data obtained from the image sensor, and (b) a set of image data from at least two image capture devices, where the at least two image capture devices capture the set of image data from at least two positions.