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 2020 water year (WY 2020), 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 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, 2018 and 2019 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, 2019 and 2020 datasets include multi-cropping and incorporate 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 irrigated area, rather than legal parcel boundaries) were classified using a crop category legend and a more specific crop type legend. A supervised classification process 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 97.8% using the Land IQ legend (Land IQ Subclass) and 99.0% using the DWR legend (DWR Class). Accuracy and error results varied among crop types. 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 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’ or 'Unclassified' were 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 changed to better reflect the cropped area of the polygon and is identified by a 'b' in the DWR_REVISED 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 WY, 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 WY, 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 and 2020 datasets is called ‘MAIN_CROP’. This column indicates which field Land IQ identified as the main season crop for the WY representing the crop grown during the dominant growing season for each county. The column ‘MAIN_CROP_DATE’, another addition to the 2019 and 2020 datasets, indicates the Normalized Difference Vegetation Index (NDVI) peak date for this main season crop. The column 'EMRG_CROP' for 2019 and 2020 indicates an emerging crop at the end of the WY. Crops listed indicate that at the end of the WY, September 2020, crop activity was detected from a crop that reached peak NDVI in the following WY (2021 WY). This attribute is included to account for water use of crops that span multiple WYs and are not exclusive to a single WY. It is indicative of early crop growth and initial water use in the current WY, but a majority of crop development and water use in the following WY. Crops listed in the ‘EMRG_CROP’ attribute will also be captured as the first crop (not necessarily Crop 1) in the following WY (2021 WY). These crops are not included in the 2020 UCF_ATT code as their peak date occurred in the following WY. For the 2020 dataset new columns added are: 'YR_PLANTED' which represent the year orchard / grove was planted. 'SEN_CROP' indicates a senescing crop at the beginning of the WY. Crops listed indicate that at the beginning of the WY, October 2019, crop activity was detected from a crop that reached peak NDVI in the previous WY (2019 WY), thus was a senescing crop. This is included to account for water use of crop growth periods that span multiple WYs and are not exclusive to a WY. Crops listed in the ‘SEN_CROP’ attribute are also captured in the CROPTYP 1 through 4 sequence of the previous WY (2019 WY). These crops are not included in the 2020 UCF_ATT code as their peak NDVI occurred in the previous WY.

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 2020 water year (WY 2020), 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 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, 2018 and 2019 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, 2019 and 2020 datasets include multi-cropping and incorporate 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 irrigated area, rather than legal parcel boundaries) were classified using a crop category legend and a more specific crop type legend. A supervised classification process 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 97.8% using the Land IQ legend (Land IQ Subclass) and 99.0% using the DWR legend (DWR Class). Accuracy and error results varied among crop types. 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 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’ or 'Unclassified' were 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 changed to better reflect the cropped area of the polygon and is identified by a 'b' in the DWR_REVISED 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 WY, 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 WY, 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 and 2020 datasets is called ‘MAIN_CROP’. This column indicates which field Land IQ identified as the main season crop for the WY representing the crop grown during the dominant growing season for each county. The column ‘MAIN_CROP_DATE’, another addition to the 2019 and 2020 datasets, indicates the Normalized Difference Vegetation Index (NDVI) peak date for this main season crop. The column 'EMRG_CROP' for 2019 and 2020 indicates an emerging crop at the end of the WY. Crops listed indicate that at the end of the WY, September 2020, crop activity was detected from a crop that reached peak NDVI in the following WY (2021 WY). This attribute is included to account for water use of crops that span multiple WYs and are not exclusive to a single WY. It is indicative of early crop growth and initial water use in the current WY, but a majority of crop development and water use in the following WY. Crops listed in the ‘EMRG_CROP’ attribute will also be captured as the first crop (not necessarily Crop 1) in the following WY (2021 WY). These crops are not included in the 2020 UCF_ATT code as their peak date occurred in the following WY. For the 2020 dataset new columns added are: 'YR_PLANTED' which represent the year orchard / grove was planted. 'SEN_CROP' indicates a senescing crop at the beginning of the WY. Crops listed indicate that at the beginning of the WY, October 2019, crop activity was detected from a crop that reached peak NDVI in the previous WY (2019 WY), thus was a senescing crop. This is included to account for water use of crop growth periods that span multiple WYs and are not exclusive to a WY. Crops listed in the ‘SEN_CROP’ attribute are also captured in the CROPTYP 1 through 4 sequence of the previous WY (2019 WY). These crops are not included in the 2020 UCF_ATT code as their peak NDVI occurred in the previous WY.