Abstract: A computer-implemented method for predicting a cropland data layer (CDL) for a current year includes: retrieving a first set of records from a historical CDL database, where the first set corresponds to sampled areas of a region taken over a period for a number of years; retrieving a second set of records from a historical imagery database, where the second set corresponds to the sampled areas of the region, the period, and the number of years; employing the second set as inputs to train a deep learning network to generate the first set; retrieving a third set of records from a current imagery database, where the third set corresponds to a prescribed region, and where the third set corresponds to the time period and the current year; and using the third set as inputs and executing the trained deep learning network to generate a predicted CDL for the current year.

Abstract: A computer-implemented method for predicting a cropland data layer (CDL) for a current year includes: retrieving a first set of records from a historical CDL database, where the first set corresponds to sampled areas of a region taken over a period for a number of years; retrieving a second set of records from a historical imagery database, where the second set corresponds to the sampled areas of the region, the period, and the number of years; employing the second set as inputs to train a deep learning network to generate the first set; retrieving a third set of records from a current imagery database, where the third set corresponds to a prescribed region, and where the third set corresponds to the time period and the current year; and using the third set as inputs and executing the trained deep learning network to generate a predicted CDL for the current year.

Abstract: A method for monitoring regenerative management practices in agricultural parcels includes: determining a regenerative carbon footprint value for a parcel that comprises a difference of a regenerative carbon footprint and a baseline carbon footprint, where the baseline carbon footprint is derived by calculating greenhouse gas emissions based on simulating crop growth under current management practices and the regenerative carbon footprint is derived by calculating greenhouse gas emissions based on simulating crop growth under one or more regenerative management practices; for key dates corresponding to implementation and maintenance of each of the one or more regenerative management practices, processing and evaluating remotely sensed images against corresponding crop curves to determine compliance/noncompliance indicators that correspond each of the key dates; and storing the compliance/noncompliance indicators within a parcel database, where the compliance/noncompliance indicators are employed at a final da

Abstract: A method for agricultural land parcel valuation includes: accessing data for parcels within a prescribed region, the data comprising management practices, historical weather conditions, locations and topography, remote sense images, soil types, and crop types; assessing and ranking the management practices for each of the parcels; generating simulation inputs for the each of the parcels, where the simulation inputs comprise highest ranked management practices, the historical weather conditions, the locations and topography, the soil types, and the crop types; simulating crop growth for the each of the parcels over a prescribed number of previous years, where the simulating employs the simulation inputs provided by the generating; and employing selected outputs from the simulating to calculate agricultural metrics and a valuation corresponding to the each of the parcels, where the agricultural metrics include a productivity metric.

Abstract: A method for agricultural land parcel valuation includes: accessing data for parcels within a prescribed region, the data comprising management practices, historical weather conditions, locations and topography, remote sense images, soil types, and crop types; assessing and ranking the management practices for each of the parcels; generating simulation inputs for the each of the parcels, where the simulation inputs comprise highest ranked management practices, the historical weather conditions, the locations and topography, the soil types, and the crop types; simulating crop growth for the each of the parcels over a prescribed number of previous years, where the simulating employs the simulation inputs provided by the generating; and employing selected outputs from the simulating to calculate agricultural metrics and a valuation corresponding to the each of the parcels, where the agricultural metrics and the valuation for the each of the parcels are expressed relative to all of the parcels within the prescrib

Abstract: A method for verifying regenerative management practices in agricultural parcels includes: determining a regenerative carbon footprint value for a parcel that comprises a difference of a regenerative carbon footprint and a baseline carbon footprint, where the baseline carbon footprint is derived by calculating greenhouse gas emissions based on simulating crops under current management practices, and where the regenerative carbon footprint is derived by calculating greenhouse gas emissions based on simulating crops under regenerative management practices corresponding to a plan proposed by a grower; approving and publishing carbon credits according to the plan; for key dates corresponding to each of the regenerative management practices, processing remotely sensed images against corresponding crop curves to determine compliance/noncompliance indicators corresponding to the key dates; and storing the compliance/noncompliance indicators database, and determining at a verification date compliance with the plan.

Abstract: An automated method for search and generation of relevant search results includes: receiving both discrete parameters and freeform search characters input by a user; simultaneously searching a first freeform entry source and a second freeform entry source to obtain corresponding first and second suggested freeform entries; ranking the first suggested freeform entries according to first rules of relevancy to the user, and generating first ranked suggested freeform entries; ranking the second suggested freeform entries according to second rules of relevancy to the user, and generating second ranked suggested freeform entries; combining the first and second ranked freeform entries into a combined set of suggested freeform entries, and ranking the combined set of suggested freeform entries according to combined rules of relevancy to the user, and generating combined ranked suggested freeform entries; and transmitting the combined ranked suggested freeform entries to the user for selection of a desired type ahead

Abstract: A system for determining regenerative carbon footprint in agricultural parcels, including: a sequestration server, having: a management processor that builds first simulation inputs that correspond to baseline management practices, and that builds second simulation inputs that correspond to regenerative management practices; a crop simulation processor, that employs the first simulation inputs to simulate crop growth for a prescribed number of growing seasons to generate first outputs, and configured to employ the second simulation inputs to simulate crop growth for the prescribed number of growing seasons to generate second outputs; and a CO2E determination processor, that employs the first outputs and the second outputs to compute baseline annual carbon dioxide emissions and regenerative annual carbon dioxide emissions, and configured to subtract the baseline annual carbon dioxide emissions from the regenerative annual carbon dioxide emissions to yield a regenerative carbon footprint for the first agricultu

Abstract: A method for agricultural land parcel valuation includes: accessing data for parcels within a prescribed region, the data comprising management practices, historical weather conditions, locations and topography, remote sense images, soil types, and crop types; assessing and ranking the management practices for each of the parcels; generating simulation inputs for the each of the parcels, where the simulation inputs comprise highest ranked management practices, the historical weather conditions, the locations and topography, the soil types, and the crop types; simulating crop growth for the each of the parcels over a prescribed number of previous years, where the simulating employs the simulation inputs provided by the generating; and employing selected outputs from the simulating to calculate agricultural metrics and a valuation corresponding to the each of the parcels, where the agricultural metrics and the valuation for the each of the parcels are expressed relative to all of the parcels within the prescrib

Abstract: A method for agricultural land parcel valuation includes: accessing data for parcels within a prescribed region, the data comprising management practices, historical weather conditions, locations and topography, remote sense images, soil types, and crop types; assessing and ranking the management practices for each of the parcels; generating simulation inputs for the each of the parcels, where the simulation inputs comprise highest ranked management practices, the historical weather conditions, the locations and topography, the soil types, and the crop types; simulating crop growth for the each of the parcels over a prescribed number of previous years, where the simulating employs the simulation inputs provided by the generating; and employing selected outputs from the simulating to calculate agricultural metrics and a weighted valuation corresponding to the each of the parcels, where the agricultural metrics and the weighted valuation for the each of the parcels are expressed relative to all of the parcels w

Abstract: A method for agricultural land parcel valuation includes: accessing data for parcels within a prescribed region, the data comprising management practices, historical weather conditions, locations and topography, remote sense images, soil types, and crop types; assessing and ranking the management practices for each of the parcels; generating simulation inputs for the each of the parcels, where the simulation inputs comprise highest ranked management practices, the historical weather conditions, the locations and topography, the soil types, and the crop types; simulating crop growth for the each of the parcels over a prescribed number of previous years, where the simulating employs the simulation inputs provided by the generating; and employing selected outputs from the simulating to calculate agricultural metrics and a valuation corresponding to the each of the parcels, where the agricultural metrics include a sustainability metric.

Abstract: A method for agricultural land parcel valuation includes: accessing data for parcels within a prescribed region, the data comprising management practices, historical weather conditions, locations and topography, remote sense images, soil types, and crop types; assessing and ranking the management practices for each of the parcels; generating simulation inputs for the each of the parcels, where the simulation inputs comprise highest ranked management practices, the historical weather conditions, the locations and topography, the soil types, and the crop types; simulating crop growth for the each of the parcels over a prescribed number of previous years, where the simulating employs the simulation inputs provided by the generating; and employing selected outputs from the simulating to calculate agricultural metrics and a valuation corresponding to the each of the parcels, where the agricultural metrics include a stability metric.

Abstract: An automated method for search and generation of relevant search results includes: receiving freeform search characters input by a user; simultaneously searching a first freeform entry source and a second freeform entry source to obtain corresponding first suggested freeform entries and second suggested freeform entries; ranking the first suggested freeform entries according to first rules of relevancy to the user, and generating first ranked suggested freeform entries; ranking the second suggested freeform entries according to second rules of relevancy to the user, and generating second ranked suggested freeform entries; combining the first and second ranked suggested freeform entries into a combined set of suggested freeform entries, and ranking the combined set of suggested freeform entries according to combined rules of relevancy to the user, and generating combined ranked suggested freeform entries; and transmitting the combined ranked suggested freeform entries to the user for selection of a desired typ

Abstract: An automated method for search includes: receiving search characters and a viewport location on a displayed geographic area corresponding to an area of interest; simultaneously searching a first entry source and a second entry source to obtain corresponding first suggested entries and second suggested entries, where the first suggested entries correspond to geographic locations that are closer to the viewport location; ranking the first suggested entries according to first rules of relevancy, and generating first ranked suggested entries; ranking the second suggested entries according to second rules of relevancy, and generating second ranked suggested entries; combining the first and second ranked suggested entries into a combined set of suggested entries, and ranking the combined set of suggested entries according to combined rules of relevancy, and generating combined ranked suggested entries; and transmitting the combined ranked suggested entries to a user for selection of a desired type ahead entry.

Abstract: A computer-implemented method for predicting a cropland data layer (CDL) for a current year includes: retrieving a first set of records from a historical CDL database, where the first set corresponds to sampled areas of a region taken over a period for a number of years; retrieving a second set of records from a historical imagery database, where the second set corresponds to the sampled areas of the region, the period, and the number of years; employing the second set as inputs to train a deep learning network to generate the first set; retrieving a third set of records from a current imagery database, where the third set corresponds to a prescribed region, and where the third set corresponds to the time period and the current year; and using the third set as inputs and executing the trained deep learning network to generate a predicted CDL for the current year.

Abstract: A computer-implemented method for predicting a cropland data layer (CDL) for a current year includes: retrieving a first set of records from a historical CDL database, where the first set corresponds to sampled areas of a region taken over a period for a number of years; retrieving a second set of records from a historical imagery database, where the second set corresponds to the sampled areas of the region, the period, and the number of years; employing the second set as inputs to train a deep learning network to generate the first set; retrieving a third set of records from a current imagery database, where the third set corresponds to a prescribed region, and where the third set corresponds to the time period and the current year; and using the third set as inputs and executing the trained deep learning network to generate a predicted CDL for the current year.

Abstract: A computer-implemented method for predicting a cropland data layer (CDL) for a current year includes: retrieving a first set of records from a historical CDL database, where the first set corresponds to sampled areas of a region taken over a period for a number of years; retrieving a second set of records from a historical imagery database, where the second set corresponds to the sampled areas of the region, the period, and the number of years; employing the second set as inputs to train a deep learning network to generate the first set; retrieving a third set of records from a current imagery database, where the third set corresponds to a prescribed region, and where the third set corresponds to the time period and the current year; and using the third set as inputs and executing the trained deep learning network to generate a predicted CDL for the current year.

Abstract: A computer-implemented method for predicting a cropland data layer (CDL) for a current year includes: retrieving a first set of records from a historical CDL database, where the first set corresponds to sampled areas of a region taken over a period for a number of years; retrieving a second set of records from a historical imagery database, where the second set corresponds to the sampled areas of the region, the period, and the number of years; employing the second set as inputs to train a deep learning network to generate the first set; retrieving a third set of records from a current imagery database, where the third set corresponds to a prescribed region, and where the third set corresponds to the time period and the current year; and using the third set as inputs and executing the trained deep learning network to generate a predicted CDL for the current year.

Abstract: A computer-implemented method for predicting a cropland data layer (CDL) for a current year includes: retrieving a first set of records from a historical CDL database, where the first set corresponds to sampled areas of a region taken over a period for a number of years; retrieving a second set of records from a historical imagery database, where the second set corresponds to the sampled areas of the region, the period, and the number of years; employing the second set as inputs to train a deep learning network to generate the first set; retrieving a third set of records from a current imagery database, where the third set corresponds to a prescribed region, and where the third set corresponds to the time period and the current year; and using the third set as inputs and executing the trained deep learning network to generate a predicted CDL for the current year.

Abstract: A method for verifying regenerative management practices in agricultural parcels includes: determining a regenerative carbon footprint value for a parcel that comprises a difference of a regenerative carbon footprint and a baseline carbon footprint, where the baseline carbon footprint is derived by calculating greenhouse gas emissions based on simulating crops under current management practices, and where the regenerative carbon footprint is derived by calculating greenhouse gas emissions based on simulating crops under regenerative management practices corresponding to a plan proposed by a grower; approving and publishing carbon credits according to the plan; for key dates corresponding to each of the regenerative management practices, processing remotely sensed images against corresponding crop curves to determine compliance/noncompliance indicators corresponding to the key dates; and storing the compliance/noncompliance indicators database, and determining at a verification date compliance with the plan.