Statewide Crop Mapping

_Provisional Data Use Disclaimer:_ _This is a provisional release of statewide land use data for Water Year (WY) 2022. The provisional data is made available to provide immediate access for the convenience of interested persons. Importantly, land use classifications and field boundaries are not definitive and do not establish legal rights or define legal boundaries. While the Department believes the data to be reliable, the data is provisional and human or mechanical error remains a possibility. Therefore, the Department does not guarantee the accuracy, completeness, or timeliness of the information. Neither DWR nor any of the sources of the information shall be responsible for any errors or omissions, or for the use or results obtained from the use of this information._ The Department of Water Resources (DWR) Land Use Program is releasing the statewide land use data for Water Year (WY) 2022 as provisional, based on requests by data users and partner agencies. This provisional release is part of DWR’s ongoing commitment to provide comprehensive, accurate, and more frequent statewide land use data through remote sensing and field surveys for the California Water Plan, Groundwater Sustainability Agencies (GSAs), and other users throughout the state. Unlike all pre 2021 land use data releases, the 2022 provisional release is intended to get land use information out to users quickly, and the data has not been carefully reviewed or edited. The final and official version of the WY 2022 land use dataset is planned for release by the first quarter of 2025. Provisional data release highlights: • New data field “DataStatus” is filled out with “Provisional” to indicate that the data is provisional. When the final version is released, this filled will be populated with “Final”. • Metadata has been updated to indicate “provisional” in the following sections: Title, Tags, Summary, Description, and Distribution Format Version. • Statewide land use classification was completed at both crop class and subclass levels, mapping a total of 478,059 fields and 15,649,733 acres. This total mapped acreage includes multi-cropping, urban acreage, golf courses, and some non-irrigated cropland. • Accuracy statistics were calculated independently for each region as well as each level of legend. The overall accuracy for WY 2022 crop mapping statewide, as reported by Land IQ, was 98% at the DWR Crop Class legend level and 97% at the Subclass legend level. • The Subclass land use legend was expanded and refined to account for land use conditions more accurately. An irrigation type of “Not Irrigated” was included in the Mixed Pasture class to describe the variability in condition of Mixed Pasture. • Reference (ground truth) data collection routes were expanded in WY 2022 to collect data in all 10 California hydrologic regions, enabling Land IQ to perform accuracy assessments in each of the hydrologic regions for the first time. • A change to the methodology for assessing multi-cropping was implemented in WY 2022. All fields were reviewed for multi-cropping and percent cover was calculated on resulting fields, improving the capture of multi-cropped fields statewide.

The Department of Water Resources (DWR) Land Use Program is releasing the final and official version of statewide Water Year (WY) 2021 land use data. The statewide WY 2021 dataset was initially released as provisional on April 7, 2023, based on requests by data users and partner agencies. This final release is part of DWR’s ongoing commitment to provide comprehensive, accurate, and more frequent statewide land use data through remote sensing and field surveys. Precise field level data on the composition and distribution of irrigated crops and fallow lands are needed to accurately assess and manage competing water demands, including the critical needs of urban areas and ecological uses under drought conditions, at both community and regional scales. These data are critical for local agencies trying to manage groundwater resources within Sustainable Groundwater Management Act mandates, are used to assess economic impacts to agricultural production systems and the associated workforce, identify potential adverse impacts to rural water sources and native lands, and are used as a primary input data layer to publicly available water management and planning tools such as OpenET. For these purposes, as well as many others, a spatial mapping base layer is essential for effective decision-making and other applications. In response to this need for information, Land IQ LLC was contracted by DWR to develop a comprehensive and accurate spatial land use database first for the 2014, 2016, 2018, 2019, and 2020 WYs and now for the WY 2021, covering over 10.7 million acres of agriculture on a field scale and additional areas of urban extent. The primary objective of this effort was to produce a comprehensive and accurate spatial land use database with overall accuracies exceeding 95% using remote sensing, statistical, and temporal analysis methods. Agencies that use annually mapped land use data from DWR’s Land Use Program include the California Water Plan Group, where agricultural water cannot be accounted for without the land use data. Implementation of the 2014 Sustainable Groundwater Management Act by the Sustainable Groundwater Management Office (SGMO) requires agricultural land use information and mapping provided by the DWR Land Use Program land use surveys because most groundwater models incorporate this land use information to accurately determine the amount of groundwater withdrawals and deep percolation. Additionally, the land use data has become crucial for assessing drought impacts using fallowed land acreages that result from drought and is used by the State Water Resources Control Board for water rights determinations and assessments. Selected final data release highlights: • DWR Land Use Program staff reviewed the provisional dataset published in April 2023 and recommended changes that were implemented by Land IQ LLC, including updating peak maturity dates of crops. • DWR and Land IQ are now working on quantifying changes between the provisional and final WY 2021 datasets. • New columns were added to represent a new special condition for fallow rice fields infrastructure, and to capture sub-classifications not included in the DWR Remote Sensing Land Use Legend. • Updates were made to the metadata to reflect the new special condition for rice infrastructure and some more refined crop classes. Eucalyptus, Pecans, Sugar Beets, and Golf Courses were included while also breaking out Plums, Prunes, Apricots, Potatoes and Sweet Potatoes into their own crop classes.

Understanding the impacts of land use, crop location, acreage, and management practices on environmental attributes and resource management will be an integral step in the ability of GSAs to produce GSPs and implement projects to attain sustainability. For these purposes, as well as many others, a spatial mapping base layer is essential for effective decision-making and other applications. In response to this need for information, Land IQ was contracted by DWR to develop a comprehensive and accurate spatial land use database first for the 2014, 2016, 2018, and 2019 WYs and now for the 2020 WY, covering over 9.4 million acres of irrigated agriculture on a field scale and additional areas of urban extent. The primary objective of this effort was to produce a comprehensive and accurate spatial land use database with overall accuracies exceeding 95% using remote sensing, statistical, and temporal analysis methods.

The 2019 Crop Mapping dataset has been updated as of August 2022 and includes the following changes: - Slightly shifted Urban polygons were relocated to their original correct positions. - The following new rule has been included for ‘X’ Unclassified Fallow: “Unclassified Fallow is also used when indicating the planting of Alfalfa & Alfalfa Mixtures or Miscellaneous Grasses. In these scenarios Unclassified fallow would be Crop1, and Alfalfa & Alfalfa Mixtures or Miscellaneous Grasses would be Crop2.” - Some UniqueID’s that were accidentally duplicated have been corrected back to their original UniqueID’s. Land use data is critically important to the work of the Department of Water Resources (DWR) and other California agencies. Understanding the impacts of land use, crop location, acreage, and management practices on environmental attributes and resource management is an integral step in the ability of Groundwater Sustainability Agencies (GSAs) to produce Groundwater Sustainability Plans (GSPs) and implement projects to attain sustainability. Land IQ was contracted by DWR to develop a comprehensive and accurate spatial land use database for the 2019 water year (WY 2019). The primary objective of this effort was to produce a spatial land use database with accuracies exceeding 95% using remote sensing, statistical, and temporal analysis methods. This project is an extension of the 2014, 2016, and 2018 land use mapping, which classified over 14 million acres of land into irrigated agriculture and urban area. Unlike the 2014 and 2016 datasets, the WY 2018 and 2019 datasets include multi-cropping and incorporates DWR ground-truth data from Siskiyou, Modoc, Lassen and Shasta counties. Land IQ integrated crop production knowledge with detailed ground truth information and multiple satellite and aerial image resources to conduct remote sensing land use analysis at the field scale. Individual fields (boundaries of homogeneous crop types representing cropped area, rather than legal parcel boundaries) were classified using a crop category legend and a more specific crop type legend. A supervised classification algorithm using a random forest approach was used to classify delineated fields and was carried out county by county where training samples were available. Random forest approaches are currently some of the highest performing methods for data classification and regression. To determine frequency and seasonality of multiple-cropped fields, peak growth dates were determined for annual crops. Fields were attributed with DWR crop categories and included citrus/subtropical, deciduous fruits and nuts, field crops, grain and hay, idle, pasture, rice, truck crops, urban, vineyards, and young perennials. These categories represent aggregated groups of specific crop types in the Land IQ dataset. Accuracy was calculated for the crop mapping using both DWR and Land IQ crop legends. The overall accuracy result for the crop mapping statewide was 96.9% using the Land IQ legend and 98.1% using the DWR legend. Accuracy and error results varied among crop types. In particular, some less extensive crops that have very few validation samples may have a skewed accuracy result depending on the number and nature of validation sample points. DWR revised crops and conditions from the Land IQ classification were encoded using standard DWR land use codes added to feature attributes, and each modified classification is indicated by the value 'r' in the ‘DWR_REVISE' data field. Polygons drawn by DWR, not included in Land IQ dataset receive the 'n' code for new. Boundary change (i.e. DWR changed the boundary that LIQ delivered could be split boundary) indicated by 'b'. Each polygon classification is consistent with DWR attribute standards, however some of DWR's traditional attribute definitions are modified and extended to accommodate unavoidable constraints within remote-sensing classifications, or to make data more specific for DWR's water balance computation needs. The original Land IQ classifications reported for each polygon are preserved for comparison, and are also expressed as DWR standard attributes. Comments, problems, improvements, updates, or suggestions about local conditions or revisions in the final data set should be forwarded to the appropriate Regional Office Senior Land Use Supervisor. Revisions were made if: - DWR corrected the original crop classification based on local knowledge and analysis, -PARTIALLY IRRIGATED CROPS Crops irrigated for only part of their normal irrigation season were given the special condition of ‘X’, -In certain areas, DWR changed the irrigation status to irrigated or non-irrigated. Among those areas the special condition may have been changed to 'Partially Irrigated' based on image analysis and local knowledge, - young versus mature stages of perennial orchards and vineyards were identified (DWR added ‘Young’ to Special Condition attributes), - DWR determined that a field originally classified ‘Idle’ was actually cropped one or more times during the year, - the percent of cropped area was changed from the original acres reported by Land IQ (values indicated in DWR ‘Percent’ column), - DWR determined that the field boundary should have been split to better reflect separate crops within the same polygon and identified by a 'b' in the DWR_REVISED column, - The ‘Mixed’ was added to the MULTIUSE column refers to no boundary change, but percent of field is changed where more than one crop is found, - DWR identified a distinct early or late crop on the field before the main season crop (‘Double’ was added to the MULTIUSE column); if the 1st and 2nd sequential crops occupied different portions of the total field acreage, the area percentages were indicated for each crop). This dataset includes multicropped fields. If the field was determined to have more than one crop during the course of the water year, the order of the crops is sequential, beginning with Class 1. All single cropped fields will be placed in Class 2, so every polygon will have a crop in the Class 2 and CropType2 columns. In the case that a permanent crop was removed during the water year, the Class 2 crop will be the permanent crop followed by ‘X’ – Unclassified fallow in the Class 3 column. In the case of Intercropping, the main crop will be placed in the Class 2 column with the partial crop in the Class 3 column. A new column for the 2019 dataset is called ‘MAIN_CROP’. This column indicates which field Land IQ identified as the main season crop for the water year representing the crop grown during the dominant growing season for each county. The column ‘MAIN_CROP_DATE’, another addition to the 2019 dataset, indicates the NDVI peak date for this main season crop. Asterisks (* or **) in attribute table indicates no data have been collected for that specific attribute.

The 2019 Crop Mapping dataset has been updated as of August 2022 and includes the following changes: - Slightly shifted Urban polygons were relocated to their original correct positions. - The following new rule has been included for ‘X’ Unclassified Fallow: “Unclassified Fallow is also used when indicating the planting of Alfalfa & Alfalfa Mixtures or Miscellaneous Grasses. In these scenarios Unclassified fallow would be Crop1, and Alfalfa & Alfalfa Mixtures or Miscellaneous Grasses would be Crop2.” - Some UniqueID’s that were accidentally duplicated have been corrected back to their original UniqueID’s. Land use data is critically important to the work of the Department of Water Resources (DWR) and other California agencies. Understanding the impacts of land use, crop location, acreage, and management practices on environmental attributes and resource management is an integral step in the ability of Groundwater Sustainability Agencies (GSAs) to produce Groundwater Sustainability Plans (GSPs) and implement projects to attain sustainability. Land IQ was contracted by DWR to develop a comprehensive and accurate spatial land use database for the 2019 water year (WY 2019). The primary objective of this effort was to produce a spatial land use database with accuracies exceeding 95% using remote sensing, statistical, and temporal analysis methods. This project is an extension of the 2014, 2016, and 2018 land use mapping, which classified over 14 million acres of land into irrigated agriculture and urban area. Unlike the 2014 and 2016 datasets, the WY 2018 and 2019 datasets include multi-cropping and incorporates DWR ground-truth data from Siskiyou, Modoc, Lassen and Shasta counties. Land IQ integrated crop production knowledge with detailed ground truth information and multiple satellite and aerial image resources to conduct remote sensing land use analysis at the field scale. Individual fields (boundaries of homogeneous crop types representing cropped area, rather than legal parcel boundaries) were classified using a crop category legend and a more specific crop type legend. A supervised classification algorithm using a random forest approach was used to classify delineated fields and was carried out county by county where training samples were available. Random forest approaches are currently some of the highest performing methods for data classification and regression. To determine frequency and seasonality of multiple-cropped fields, peak growth dates were determined for annual crops. Fields were attributed with DWR crop categories and included citrus/subtropical, deciduous fruits and nuts, field crops, grain and hay, idle, pasture, rice, truck crops, urban, vineyards, and young perennials. These categories represent aggregated groups of specific crop types in the Land IQ dataset. Accuracy was calculated for the crop mapping using both DWR and Land IQ crop legends. The overall accuracy result for the crop mapping statewide was 96.9% using the Land IQ legend and 98.1% using the DWR legend. Accuracy and error results varied among crop types. In particular, some less extensive crops that have very few validation samples may have a skewed accuracy result depending on the number and nature of validation sample points. DWR revised crops and conditions from the Land IQ classification were encoded using standard DWR land use codes added to feature attributes, and each modified classification is indicated by the value 'r' in the ‘DWR_REVISE' data field. Polygons drawn by DWR, not included in Land IQ dataset receive the 'n' code for new. Boundary change (i.e. DWR changed the boundary that LIQ delivered could be split boundary) indicated by 'b'. Each polygon classification is consistent with DWR attribute standards, however some of DWR's traditional attribute definitions are modified and extended to accommodate unavoidable constraints within remote-sensing classifications, or to make data more specific for DWR's water balance computation needs. The original Land IQ classifications reported for each polygon are preserved for comparison, and are also expressed as DWR standard attributes. Comments, problems, improvements, updates, or suggestions about local conditions or revisions in the final data set should be forwarded to the appropriate Regional Office Senior Land Use Supervisor. Revisions were made if: - DWR corrected the original crop classification based on local knowledge and analysis, -PARTIALLY IRRIGATED CROPS Crops irrigated for only part of their normal irrigation season were given the special condition of ‘X’, -In certain areas, DWR changed the irrigation status to irrigated or non-irrigated. Among those areas the special condition may have been changed to 'Partially Irrigated' based on image analysis and local knowledge, - young versus mature stages of perennial orchards and vineyards were identified (DWR added ‘Young’ to Special Condition attributes), - DWR determined that a field originally classified ‘Idle’ was actually cropped one or more times during the year, - the percent of cropped area was changed from the original acres reported by Land IQ (values indicated in DWR ‘Percent’ column), - DWR determined that the field boundary should have been split to better reflect separate crops within the same polygon and identified by a 'b' in the DWR_REVISED column, - The ‘Mixed’ was added to the MULTIUSE column refers to no boundary change, but percent of field is changed where more than one crop is found, - DWR identified a distinct early or late crop on the field before the main season crop (‘Double’ was added to the MULTIUSE column); if the 1st and 2nd sequential crops occupied different portions of the total field acreage, the area percentages were indicated for each crop). This dataset includes multicropped fields. If the field was determined to have more than one crop during the course of the water year, the order of the crops is sequential, beginning with Class 1. All single cropped fields will be placed in Class 2, so every polygon will have a crop in the Class 2 and CropType2 columns. In the case that a permanent crop was removed during the water year, the Class 2 crop will be the permanent crop followed by ‘X’ – Unclassified fallow in the Class 3 column. In the case of Intercropping, the main crop will be placed in the Class 2 column with the partial crop in the Class 3 column. A new column for the 2019 dataset is called ‘MAIN_CROP’. This column indicates which field Land IQ identified as the main season crop for the water year representing the crop grown during the dominant growing season for each county. The column ‘MAIN_CROP_DATE’, another addition to the 2019 dataset, indicates the NDVI peak date for this main season crop. Asterisks (* or **) in attribute table indicates no data have been collected for that specific attribute.

Land use data is critically important to the work of the Department of Water Resources (DWR) and other California agencies. Understanding the impacts of land use, crop location, acreage, and management practices on environmental attributes and resource management is an integral step in the ability of Groundwater Sustainability Agencies (GSAs) to produce Groundwater Sustainability Plans (GSPs) and implement projects to attain sustainability. Land IQ was contracted by DWR to develop a comprehensive and accurate spatial land use database for the Water Year 2018, covering over 9.4 million acres of Irrigable agriculture on a field scale and additional areas of urban extent. The primary objective of this effort was to produce a spatial land use database with accuracies exceeding 95% using remote sensing, statistical, and temporal analysis methods. This project is an extension of the 2014 and 2016 land use mapping, which classified over 14 million acres of land into Irrigable agriculture and urban area. Unlike the 2014 and 2016 datasets, the Water Year 2018 dataset includes multi-cropping and incorporates ground-truth data from Siskiyou, Modoc, Lassen and Shasta counties. Land IQ integrated crop production knowledge with detailed ground truth information and multiple satellite and aerial image resources to conduct remote sensing land use analysis at the field scale. Individual fields (boundaries of homogeneous crop types representing true Irrigable area, rather than legal parcel boundaries) were classified using a crop category legend and a more specific crop type legend. A supervised classification algorithm using a random forest approach was used to classify delineated fields and was carried out county by county where training samples were available. Random forest approaches are currently some of the highest performing methods for data classification and regression. To determine frequency and seasonality of multiple-cropped fields, peak growth dates were determined for annual crops. Fields were attributed with DWR crop categories and included citrus/subtropical, deciduous fruits and nuts, field crops, grain and hay, idle, pasture, rice, truck crops, urban, vineyards, young perennials and wetland. These categories represent aggregated groups of specific crop types in the Land IQ dataset. Accuracy was calculated for the crop mapping using both DWR and Land IQ crop legends. The overall accuracy result for the crop mapping statewide was 96.5% using the Land IQ legend and 98.3% using the DWR legend. Accuracy and error results varied among crop types. In particular, some less extensive crops that have very few validation samples may have a skewed accuracy result depending on the number and nature of validation sample points. Revised crops and conditions were encoded using standard DWR land use codes added to feature attributes, and each modified classification is indicated by the value 'r' in the 'DWR_revised' data field. The value ‘n’ in the ‘DWR_REVISE’ data field indicates a Regional Office added a boundary and attributes where none was included in the Land IQ data set. Each polygon classification is consistent with DWR attribute standards, however some of DWR's traditional attribute definitions are modified and extended to accommodate unavoidable constraints within remote-sensing classifications, or to make data more specific for DWR's water balance computation needs. The original Land IQ classifications reported for each polygon are preserved for comparison, and are also expressed as DWR standard attributes. Comments, problems, improvements, updates, or suggestions about local conditions or revisions in the final data set should be forwarded to the appropriate Regional Office Senior Land Use Supervisor. Revisions were made if: - DWR corrected the original crop classification based on local knowledge and analysis, - young versus mature stages of perennial orchards and vineyards were identified (DWR added ‘Young’ to Special Condition attributes), - DWR determined that a field originally classified ‘Idle’ was actually cropped one or more times during the year, - the percent of cropped area was less than 100% of the original acres reported by Land IQ (values indicated in DWR ‘Percent’ column), - DWR determined that the field boundary should have been split to better reflect separate crops within the same polygon (‘Mixed’ was added to the MULTIUSE column; the crop classification and corresponding area percentages were indicated), - DWR determined that the crop was not irrigated. - DWR identified a distinct early or late crop on the field before the main season crop (‘Double’ was added to the MULTIUSE column); if the 1st and 2nd sequential crops occupied different portions of the total field acreage, the area percentages were indicated for each crop). DWR added Adjusted Day Of Year (ADOY) for peak NDVI date corresponding to CROPTYP category. The date received by Land IQ was delivered in a Julian date format (YYYYDDD) and was converted into the ADOY by DWR for statistical purposes. Land use boundaries delineated by Land IQ were not revised by DWR.

Land use data is critically important to the work of the Department of Water Resources (DWR) and other California agencies. Understanding the impacts of land use, crop location, acreage, and management practices on environmental attributes and resource management is an integral step in the ability of Groundwater Sustainability Agencies (GSAs) to produce Groundwater Sustainability Plans (GSPs) and implement projects to attain sustainability. Land IQ was contracted by DWR to develop a comprehensive and accurate spatial land use database for the Water Year 2018, covering over 9.4 million acres of Irrigable agriculture on a field scale and additional areas of urban extent. The primary objective of this effort was to produce a spatial land use database with accuracies exceeding 95% using remote sensing, statistical, and temporal analysis methods. This project is an extension of the 2014 and 2016 land use mapping, which classified over 14 million acres of land into Irrigable agriculture and urban area. Unlike the 2014 and 2016 datasets, the Water Year 2018 dataset includes multi-cropping and incorporates ground-truth data from Siskiyou, Modoc, Lassen and Shasta counties. Land IQ integrated crop production knowledge with detailed ground truth information and multiple satellite and aerial image resources to conduct remote sensing land use analysis at the field scale. Individual fields (boundaries of homogeneous crop types representing true Irrigable area, rather than legal parcel boundaries) were classified using a crop category legend and a more specific crop type legend. A supervised classification algorithm using a random forest approach was used to classify delineated fields and was carried out county by county where training samples were available. Random forest approaches are currently some of the highest performing methods for data classification and regression. To determine frequency and seasonality of multiple-cropped fields, peak growth dates were determined for annual crops. Fields were attributed with DWR crop categories and included citrus/subtropical, deciduous fruits and nuts, field crops, grain and hay, idle, pasture, rice, truck crops, urban, vineyards, young perennials and wetland. These categories represent aggregated groups of specific crop types in the Land IQ dataset. Accuracy was calculated for the crop mapping using both DWR and Land IQ crop legends. The overall accuracy result for the crop mapping statewide was 96.5% using the Land IQ legend and 98.3% using the DWR legend. Accuracy and error results varied among crop types. In particular, some less extensive crops that have very few validation samples may have a skewed accuracy result depending on the number and nature of validation sample points. Revised crops and conditions were encoded using standard DWR land use codes added to feature attributes, and each modified classification is indicated by the value 'r' in the 'DWR_revised' data field. The value ‘n’ in the ‘DWR_REVISE’ data field indicates a Regional Office added a boundary and attributes where none was included in the Land IQ data set. Each polygon classification is consistent with DWR attribute standards, however some of DWR's traditional attribute definitions are modified and extended to accommodate unavoidable constraints within remote-sensing classifications, or to make data more specific for DWR's water balance computation needs. The original Land IQ classifications reported for each polygon are preserved for comparison, and are also expressed as DWR standard attributes. Comments, problems, improvements, updates, or suggestions about local conditions or revisions in the final data set should be forwarded to the appropriate Regional Office Senior Land Use Supervisor. Revisions were made if: - DWR corrected the original crop classification based on local knowledge and analysis, - young versus mature stages of perennial orchards and vineyards were identified (DWR added ‘Young’ to Special Condition attributes), - DWR determined that a field originally classified ‘Idle’ was actually cropped one or more times during the year, - the percent of cropped area was less than 100% of the original acres reported by Land IQ (values indicated in DWR ‘Percent’ column), - DWR determined that the field boundary should have been split to better reflect separate crops within the same polygon (‘Mixed’ was added to the MULTIUSE column; the crop classification and corresponding area percentages were indicated), - DWR determined that the crop was not irrigated. - DWR identified a distinct early or late crop on the field before the main season crop (‘Double’ was added to the MULTIUSE column); if the 1st and 2nd sequential crops occupied different portions of the total field acreage, the area percentages were indicated for each crop). DWR added Adjusted Day Of Year (ADOY) for peak NDVI date corresponding to CROPTYP category. The date received by Land IQ was delivered in a Julian date format (YYYYDDD) and was converted into the ADOY by DWR for statistical purposes. Land use boundaries delineated by Land IQ were not revised by DWR.

This dataset represents the 2016 main season agricultural land use, wetlands, and urban boundaries for all 58 counties in California. This data was originally prepared by Land IQ, LLC and provided to the California Department of Water Resources (DWR) and other resource agencies involved in work and planning efforts across the state for current land use information. The Land IQ base data was reviewed, and in some cases revised, by DWR Regional Office Land Use staff using additional analyses based on a combination of aerial photography, remote sensing multi-spectral imagery, agronomic analysis and ground verification. Revised crops and conditions were encoded using standard DWR land use codes added to feature attributes, and each modified classification is indicated by the value 'r' in the 'DWR_revised' data field. Each polygon classification is consistent with DWR attribute standards, however some of DWR's traditional attribute definitions are modified and extended to accomodate unavoidable constraints within remote-sensing classifications, or to make data more specific for DWR's water balance computation needs. The original Land IQ classifications reported for each polygon are preserved for comparison, and are also expressed as DWR standard attributes. Comments, problems, improvements, updates, or suggestions about local conditions or revisions in the final data set should be forwarded to the appropriate Regional Office Senior Land Use Supervisor (see 'Contacts'). Revisions were made if: - DWR corrected the original crop classification based on local knowledge and analysis, - young versus mature stages of perennial orchards and vineyards were identified (DWR added ‘Young’ to Special Condition attributes), - DWR determined that a field originally classified ‘Idle’ was actually cropped one or more times during the year, - the percent of cropped area was less than 100% of the original acres reported by Land IQ (values indicated in DWR ‘Percent’ column), - DWR determined that the field boundary should have been split to better reflect separate crops within the same polygon (‘Mixed’ was added to the MULTIUSE column; the crop classification and corresponding area percentages were indicated), - DWR determined that the crop was not irrigated. - DWR identified a distinct early crop on the field before the main season crop (‘Double’ was added to the MULTIUSE column); if the 1st and 2nd sequential crops occupied different portions of the total field acreage, the area percentages were indicated for each crop). Land use boundaries were delineated by Land IQ from 2016 NAIP Imagery and were not revised by DWR.

This dataset represents the 2016 main season agricultural land use, wetlands, and urban boundaries for all 58 counties in California. This data was originally prepared by Land IQ, LLC and provided to the California Department of Water Resources (DWR) and other resource agencies involved in work and planning efforts across the state for current land use information. The Land IQ base data was reviewed, and in some cases revised, by DWR Regional Office Land Use staff using additional analyses based on a combination of aerial photography, remote sensing multi-spectral imagery, agronomic analysis and ground verification. Revised crops and conditions were encoded using standard DWR land use codes added to feature attributes, and each modified classification is indicated by the value 'r' in the 'DWR_revised' data field. Each polygon classification is consistent with DWR attribute standards, however some of DWR's traditional attribute definitions are modified and extended to accomodate unavoidable constraints within remote-sensing classifications, or to make data more specific for DWR's water balance computation needs. The original Land IQ classifications reported for each polygon are preserved for comparison, and are also expressed as DWR standard attributes. Comments, problems, improvements, updates, or suggestions about local conditions or revisions in the final data set should be forwarded to the appropriate Regional Office Senior Land Use Supervisor (see 'Contacts'). Revisions were made if: - DWR corrected the original crop classification based on local knowledge and analysis, - young versus mature stages of perennial orchards and vineyards were identified (DWR added ‘Young’ to Special Condition attributes), - DWR determined that a field originally classified ‘Idle’ was actually cropped one or more times during the year, - the percent of cropped area was less than 100% of the original acres reported by Land IQ (values indicated in DWR ‘Percent’ column), - DWR determined that the field boundary should have been split to better reflect separate crops within the same polygon (‘Mixed’ was added to the MULTIUSE column; the crop classification and corresponding area percentages were indicated), - DWR determined that the crop was not irrigated. - DWR identified a distinct early crop on the field before the main season crop (‘Double’ was added to the MULTIUSE column); if the 1st and 2nd sequential crops occupied different portions of the total field acreage, the area percentages were indicated for each crop). Land use boundaries were delineated by Land IQ from 2016 NAIP Imagery and were not revised by DWR.

This data is prepared by Land IQ, LLC and provided to the California Department of Water Resources (DWR) and other resource agencies involved in work and planning efforts across the state for current land use information. This dataset is meant to provide information for resource planning and assessments across multiple agencies and serves as a consistent base layer for a broad array of potential users and multiple end uses. This dataset presents the 2014 agricultural land use, managed wetlands, and urban boundaries for all 58 counties in California. This data is prepared by Land IQ, LLC and provided to the California Department of Water Resources (DWR) and other resource agencies involved in work and planning efforts across the state for current land use information. Delineated from 2014 NAIP Imagery. The data are derived from a combination of remote sensing and agronomic analysis and ground verification.

This data is prepared by Land IQ, LLC and provided to the California Department of Water Resources (DWR) and other resource agencies involved in work and planning efforts across the state for current land use information. This dataset is meant to provide information for resource planning and assessments across multiple agencies and serves as a consistent base layer for a broad array of potential users and multiple end uses. This dataset presents the 2014 agricultural land use, managed wetlands, and urban boundaries for all 58 counties in California. This data is prepared by Land IQ, LLC and provided to the California Department of Water Resources (DWR) and other resource agencies involved in work and planning efforts across the state for current land use information. Delineated from 2014 NAIP Imagery. The data are derived from a combination of remote sensing and agronomic analysis and ground verification.

The CADWR Land User Viewer allows local agencies and the public to easily access both statewide and existing DWR county land use survey datasets that have been collected over the last 30 years. Consistent, centralized land use data access improves coordination across the State and helps Groundwater Sustainability Agencies (GSAs) meet the requirements of the Sustainability Groundwater Management Act (SGMA) and the Groundwater Sustainability Plan (GSP) regulations. Application Use Disclaimer This application provides a graphic representation of land use for viewing purposes only. Land use classifications and field boundaries are not definitive and do not establish legal rights or define legal boundaries. The Department does not guarantee the accuracy, completeness, or timeliness of the information. Neither the Department of Water Resources nor any of the sources of the information shall be responsible for any errors or omissions, or for the use or results obtained from the use of this information.

The SGMA Data Viewer provides access to groundwater related datasets, including land use, that are organized by the requirements of SGMA and the Groundwater Sustainability Plan (GSP) regulations for the purpose of supporting GSP development and implementation.