A method for building and using soil models that determine soil properties from soil spectrum data is provided. In an embodiment, building soil model may be accomplished using soil spectrum data received via hyperspectral sensors from a land unit. A processor updates the soil spectrum data by removing interference signals from the soil spectrum data. Multiple ground sampling locations within the land unit are then determined based on the updated soil spectrum data. Soil property data are obtained from ground sampling at the ground sampling locations. Soil models that correlate the updated soil spectrum data with the soil property data are created based on the updated soil spectrum data and the soil property data. The soil models are sent to a storage for future use.

A system for in-situ measurement of an apparent spectrum of a water body includes a floating device, and an optical sensing and conduction device, an electronic measurement device, a control circuit, and a power supply device which are loaded on the floating device. The floating device includes a floating body ring and an optical probe mounting frame on the floating body ring in a direction perpendicular to a ring surface. The optical probe mounting frame includes a vertical mounting assembly and a horizontal connecting assembly. The horizontal connecting assembly is provided radially along the ring shape of the floating body ring. One end of the horizontal connecting assembly is connected to the vertical mounting assembly and the other end thereof is connected to the floating body ring.

A method for calibrating forecasts involving temperature, precipitation, and other weather related variables is provided. In an embodiment historical ensemble-based forecasts and historical observations are received by an agricultural intelligence computing system. Historical differences are determined between the forecasts and the observations corresponding to the forecasts and stored in the volatile memory of the agricultural intelligence computing system. The agricultural intelligence computing system receives current ensemble-based forecasts and a request for improved forecasts. The agricultural intelligence computing system retrieves the historical differences and uses a combination of the historical differences and the current ensemble-based forecasts to create probability distributions for the weather for each lead day. The agricultural intelligence computing system then samples from the probability distributions to create improved ensemble-based forecasts at the requested location.

A method for determining national crop yields during the growing season is provided. In an embodiment, a server computer system receives agricultural data records for a particular year that represent covariate data values related to plants at a specific geo-location at a specific time. The system aggregates the records to create geo-specific time series for a geo-location over a specified time. The system creates aggregated time series from a subset of the geo-specific time series. The system selects a representative feature from the aggregated time series and creates a covariate matrix for each specific geographic area in computer memory. The system determines a specific state crop yield for a specific year using linear regression to calculate the specific state crop yield from the covariate matrix. The system determines a national crop yield for the specific year using the sum of the state crop yields for the specific year nationally adjusted.

A method for determining national crop yields during the growing season using regional agricultural data is provided. In an embodiment, determining national crop yields during the growing season may be accomplished using a server computer system that receives, via a network, agricultural data records that are used to forecast a national crop yield for a particular year. Within the server computer system an agricultural time series module receives one or more agricultural data records that represent a type of covariate data value related to plants at a specific geo-location at a specific time. The agricultural time series module then aggregates the agricultural data records to create one or more geo-specific time series that represent a specific geo-location over a specified time. The agricultural time series module creates one or more aggregated time series that represent geographic areas from a subset of the one or more geo-specific time series. A crop yield estimating module selects a representative feature from the one or more aggregated time series and creates a covariate matrix for each specific geographic area in computer memory of the server computer system. The crop yield estimating module determines a specific state crop yield for a specific year by using a linear regression module to calculate the specific state crop yield from the covariate matrix that represents the specific state for that specific year. The crop estimation module determines a national crop yield for the specific year by using the distribution generation module to calculate the national crop yield for a specific year from the sum of the specific state crop yields for the specific year nationally adjusted using a national yield adjustment module.

Systems and methods spectrally and radiometrically calibrate an optical spectrum detected with a color-image sensor of an optical spectrometer. When the color-image sensor includes a Bayer filter, the red-peaked, green-peaked, and blue-peaked spectral responses of the color filters forming the Bayer filter may be used to identify unique spectral signatures in the red, green, and blue color channels. These spectral signatures may be used to associate calibration wavelengths to the pixel locations of the color-image sensor where the spectral signatures are observed. A fitted model may then be used to associate a wavelength to each pixel location of the color-image sensor. These systems and methods account for translational shifts of the optical spectrum on the color-image sensor induced by optical image stabilization, and thus may aid optical spectrometry utilizing a digital camera in a smartphone or tablet computer.

This invention relates to a method of and system for facilitating detection of a particular predetermined gas in a scene under observation. The gas in the scene is typically associated with a gas leak in equipment. To this end, the system comprises an infrared camera arrangement; a strobing illuminator device having a strobing frequency matched to a frame rate of the camera; and a processing arrangement. The processing arrangement is configured to store a prior frame obtained via the infrared camera arrangement; and compare a current frame with the stored prior frame and generate an output signal in response to said comparison. The system also comprises a display device configured to display an output image based at least on the output signal generated by the processing arrangement so as to facilitate detection of the particular predetermined gas, in use.

Gas quantity in a pixel may be estimated based on spectral matched filtering. Image data associated with a scene may be received. The scene may comprise a plurality of pixels. Next, a spectral matched filter may be constructed for a predetermined gas and based on the image data. A quantity of the predetermined gas may then be estimated in at least one of the plurality of pixels by applying the spectral matched filter to the image data.

A system includes a detector and a computing device communicatively coupled to the detector. The detector detects spatial or temporal spectral features of a light beam after transmission of the light beam through a turbulent or aberrated medium and generate a measurement signal indicative of the spectral feature. The computing device receives the measurement signal and a comparative signal indicative of a spectral feature of the light beam prior to or after transmission of the light beam through the medium. The computing device compares the measurement signal and the comparative signal and determines, based on the comparison of the measurement signal and the comparative signal, one or more values related to variations in refractive indices of the medium.

A method for building and using soil models that determine soil properties from soil spectrum data is provided. In an embodiment, building soil model may be accomplished using soil spectrum data received via hyperspectral sensors from a land unit. A processor updates the soil spectrum data by removing interference signals from the soil spectrum data. Multiple ground sampling locations within the land unit are then determined based on the updated soil spectrum data. Soil property data are obtained from ground sampling at the ground sampling locations. Soil models that correlate the updated soil spectrum data with the soil property data are created based on the updated soil spectrum data and the soil property data. The soil models are sent to a storage for future use.