Abstract: 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.

Abstract: A method for precisely applying chemicals targeted by digital maps developed from remotely sensed data, including: obtaining EOS data through a growing season of a crop growing in a field; processing the EOS data to reflectance values; removing error-inducing effects of atmospheric alteration from the processed EOS data; calculating from the processed EOS data a crop performance index that indicates one or more poor performing areas of the field; generating one or more maps of the crop performance index to allow a user to determine whether each of the one or more poor performing areas of the field are due to biological pests instead of topographic or soil constraints in discrete locations of the field; guiding the user to the one or more poor performing areas of the field using the one or more maps to allow the user to scout the one or more poor performing areas of the field to confirm and identify the biological pests; and providing guidance for a chemical application at the one or more poor performing areas

Abstract: The present art applies a unique conceptual model that uses image statistics of top-of-atmosphere reflectance to assess and then reverse atmospheric effects. The workflow first calibrates the method for an Earth observation satellite pre-operationally. Then, for operational conversion of each image of the satellite, the degree of atmospheric effect is mapped across an image to be converted and then reversed to deliver surface reflectance data for each pixel of the image. The resulting surface reflectance images have haze and thin clouds removed, are clarified for viewing and appropriate for automated analyses.

Abstract: A method for precisely applying chemicals targeted by digital maps developed from remotely sensed data, including: obtaining EOS data through a growing season of a crop growing in a field; processing the EOS data to reflectance values; removing error-inducing effects of atmospheric alteration from the processed EOS data; calculating from the processed EOS data a crop performance index that indicates one or more poor performing areas of the field; generating one or more maps of the crop performance index to allow a user to determine whether each of the one or more poor performing areas of the field are due to biological pests instead of topographic or soil constraints in discrete locations of the field; guiding the user to the one or more poor performing areas of the field using the one or more maps to allow the user to scout the one or more poor performing areas of the field to confirm and identify the biological pests; and providing guidance for a chemical application at the one or more poor performing areas

Abstract: 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.

Abstract: A method for precisely applying chemicals targeted by digital maps developed from remotely sensed data, including: obtaining EOS data through a growing season of a crop growing in a field; processing the EOS data to reflectance values, removing error-inducing effects of atmospheric alteration from the processed EOS data; calculating from the processed EOS data a crop performance index that indicates one or more poor performing area of the field; generating one or more maps of the crop performance index to allow a user to determine whether each of the one or more poor performing areas of the field are due to biological pests instead of topographic or soil constraints in discrete locations of the field; guiding the user to the one or more poor performing areas of the field using the one or more maps to allow the user to scout the one or more poor performing areas of the field to confirm and identify the biological pests; and providing guidance for a chemical application at the one or more poor performing areas

Abstract: Prevented planting occurs when fields cannot be planted during a time window due to wet conditions—this causes crop losses covered by insurance. Field visits to verify each prevented planting insurance claim seldom can be used to map the loss because fields too wet to plant are also too wet for loss adjusting. PP claims occur in wet years often in magnitudes straining crop loss-adjusting resources and preventing field visits to many claims. Statistical- and physical-based analysis employing weather, topography and satellite-based mapping of historic surface wetness rates the relative probability for PP claims throughout huge geographic regions. The method provides decision support that focuses adjuster attention to claims of low relative probability, possible fraud or unusual circumstances that require on-site documentation and establishes a safe threshold, above which claims can correctly be accepted as valid. The method provides digital documentation for the decision support process.

Abstract: A computer-implemented method of prescribing spatially-variable application rates of one or more nutrients for an agricultural field is disclosed.

Abstract: Some embodiments include a method. The method can include mapping a spatial pattern of yield for a crop of an agricultural field and prescribing spatially-variable application rates of one or more nutrients for the agricultural field. Other embodiments of related methods and systems are also disclosed.

Abstract: Prevented planting (PP), where wet conditions during the planting season prevent access to farmed fields, costs the United States an average of many hundreds of millions of dollars in crop losses each year. Crop insurance indemnifies most of the losses of PP on farmed land in the US. This system, method, and product uses Earth observation satellite data and geographic information system technology to provide automated PP crop-loss adjusting performed digitally to enhance crop loss adjusting efficiency, reduce loss-adjusting costs, assess crop losses across large geographic regions to forecast financial set asides to meet claim payouts, and parse data to all interested parties.

Abstract: A method for precisely applying chemicals targeted by digital maps developed from remotely sensed data, including: obtaining EOS data through a growing season of a crop growing in a field; processing the EOS data to reflectance values, removing error-inducing effects of atmospheric alteration from the processed EOS data; calculating from the processed EOS data a crop performance index that indicates one or more poor performing area of the field; generating one or more maps of the crop performance index to allow a user to determine whether each of the one or more poor performing areas of the field are due to biological pests instead of topographic or soil constraints in discrete locations of the field, guiding the user to the one or more poor performing areas of the field using the one or more maps to allow the user to scout the one or more poor performing areas of the field to confirm and identify the biological pests; and providing guidance for a chemical application at the one or more poor performing areas

Abstract: A method for precisely applying chemicals targeted by digital maps developed from remotely sensed data, including: obtaining EOS data through a growing season of a crop growing in a field; processing the EOS data to reflectance values; removing error-inducing effects of atmospheric alteration from the processed EOS data; calculating from the processed EOS data a crop performance index that indicates one or more poor performing areas of the field; generating one or more maps of the crop performance index to allow a user to determine whether each of the one or more poor performing areas of the field are due to biological pests instead of topographic or soil constraints in discrete locations of the field; guiding the user to the one or more poor performing areas of the field using the one or more maps to allow the user to scout the one or more poor performing areas of the field to confirm and identify the biological pests; and providing guidance for a chemical application at the one or more poor performing areas

Abstract: Some embodiments include a method. The method can include mapping a spatial pattern of yield for a crop of an agricultural field and prescribing spatially-variable application rates of one or more nutrients for the agricultural field. Other embodiments of related methods and systems are also disclosed.

Abstract: Crop insurance is a crucial support for United States (US) farmers that will benefit from technology, especially for adjusting crop losses. Earth observation satellite (EOS) data are sufficient in resolution and repeat coverage to assess variable crop loss across fields from a variety of causes including hail, drought, and insect pests and disease and permits highly accurate estimation of impacts. Combined with spatial data that define the outline of each field, an application of the present invention can be automated to assist in crop loss adjusting, to speed the process of loss adjusting and payment, and to document the loss adjustment analysis and results. These functions enhance efficiency and lower crop loss adjusting costs for the insurance provider. Through automation and use of EOS data, this technology can be applied across many thousands of square miles at a time.

Abstract: A field visit or other verification is required to verify each prevented planting PP (PP) claim after it is filed. No measurements pursuant to calculating crop loss are taken during this initial visit, only general information and photographs are collected to demonstrate claim validity. PP claims occur most often in very wet years with high claim density (claims/policies) that strains crop loss adjusting staff and causes significant costs to support what generally is no more than a picture of a soggy field and notes to that effect. Through the use of statistically and physically-based models the present invention provides estimates of the probability for PP claims throughout huge geographic regions potentially obviating nearly all field confirmation except for claims filed for conditions of low forecasted claim probability that have higher potential for insurance fraud.

Abstract: Prevented planting (PP), where wet conditions during the planting season prevent access to farmed fields, costs the United States an average of many hundreds of millions of dollars in crop losses each year. Crop insurance indemnifies most of the losses of PP on farmed land in the US. This system, method, and product uses Earth observation satellite data and geographic information system technology to provide automated PP crop-loss adjusting performed digitally to enhance crop loss adjusting efficiency, reduce loss-adjusting costs, assess crop losses across large geographic regions to forecast financial set asides to meet claim payouts, and parse data to all interested parties.

Abstract: A system and method to use remote sensing to estimate crop water use that is forecasted and is updated as weather and new satellite data become available. From these data the system and method uses a water accounting algorithm to prescribe irrigation differentially for regions of a field or for the entire field as an average. Irrigation prescription is delivered remotely through Internet technology.

Abstract: A brine membrane method for controlling dust on a saline lakebed is disclosed comprising the steps of creating or locating an existing wetting basin, depositing precipitated salts in the basin, and depositing a brine over the salts to form a brine membrane.

Abstract: A method for precisely applying chemicals targeted by digital maps developed from remotely sensed data, including: obtaining EOS data through a growing season of a crop growing in a field; processing the EOS data to reflectance values; removing error-inducing effects of atmospheric alteration from the processed EOS data; calculating from the processed EOS data a crop performance index that indicates one or more poor performing areas of the field; generating one or more maps of the crop performance index to allow a user to determine whether each of the one or more poor performing areas of the field are due to biological pests instead of topographic or soil constraints in discrete locations of the field; guiding the user to the one or more poor performing areas of the field using the one or more maps to allow the user to scout the one or more poor performing areas of the field to confirm and identify the biological pests; and providing guidance for a chemical application at the one or more poor performing areas

Abstract: A method for prescribing variable seed density planting. The method can include: obtaining first EOS data collected approximately on an estimated day (DOY?) during a past crop-growing season in which NDVI* data most closely resembles a spatial-yield pattern measured during harvest in the past crop-growing season; converting the first EOS data to first reflectance data and first NDVI data; calculating first NDVI* data on a per pixel basis for the first EOS data based on the first NDVI data using satellite scene statistics of the first EOS data; generating an NDVI* map for a first field using the first NDVI* data for the first EOS data; and generating a variable seed density prescription map using the NDVI* map. The variable seed density prescription map can be spatially defined. Other embodiments are provided.