This dataset contains shapefile boundaries for CA State, counties and places from the US Census Bureau's 2023 MAF/TIGER database. Current geography in the 2023 TIGER/Line Shapefiles generally reflects the boundaries of governmental units in effect as of January 1, 2023.

This dataset represents a summary of potential cropland inundation for the state of California applying high-frequency surface water map composites derived from two satellite remote sensing platforms (Landsat and Moderate Resolution Imaging Spectroradiometer [MODIS]) with high-quality cropland maps generated by the California Department of Water Resources (DWR). Using Google Earth Engine, we examined inundation dynamics in California croplands from 2003 –2020 by intersecting monthly surface water maps (n=216 months) with mapped locations of precipitation amounts, rice, field, truck (which comprises truck, nursery, and berry crops), deciduous (deciduous fruits and nuts), citrus (citrus and subtropical), vineyards, and young perennial crops. Surface water maps were produced using the Dynamic Surface Water Extent (DSWE) model, in which satellite image pixels are classified into different levels of detection confidence. Our analysis focused on calculating the monthly occurrence of “high confidence” water from each satellite collection across eight cropland types and 58 counties. The resulting tabular data have been joined to a county GIS shapefile covering the state of California. The file includes attributes summarizing each crop contained within the county boundaries along with a summary of how much cropland intersects past locations of cropland inundation, the relative percentage of cropland inundated, and the frequency of crop inundation. These summaries were generated using both the Landsat and MODIS water inundation maps, and are presented separately in the data release.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The boundaries for counties and equivalent entities are as of January 1, 2017, primarily as reported through the Census Bureau's Boundary and Annexation Survey (BAS).

Established in 1982, Government Code Section 65570 mandates FMMP to report biennially on even numbered years, the conversion of farmland and grazing land, and to provide maps and data to local government and the public. The Important Farmland survey area is based on Natural Resources Conservation Service (NRCS) modern soil surveys covering most non-governmental lands in California; 51 counties are fully or partially surveyed during the 2018 map update. Soil surveys specific to National Forests or other government land units are not surveyed. Beginning in 2002, SSURGO digital soil information was incorporated into the Riverside County Important Farmland data and the 2016 data began to incorporate the new NRCS Gridded Soil Survey (gSSURGO). Data subsequent to 2002 may have acreage and soil line differences due to incorporation of newer NRCS-SSURGO or gSSURGO editions.Prior to the availability of SSURGO or gSSURGO, soil information was hand-transferred from the paper soil surveys. Older versions of the data have not been modified. The land use minimum mapping unit of ten acres has not changed, but digital soil units of less than one acre occur in the gSSURGO-enhanced Important Farmland data. The data between 2002 and 2014 incorporates SSURGO and the interaction of land use and soil components resulted in units of less than ten acres for categories such as Other Land. The 2016 data incorporates gSSURGO and will no longer merge resulting polygons less than one acre for any map category for that and future updates. For more information on gSSURGO, contact the USDA-Natural Resources Conservation Service: https://www.nrcs.usda.govUSDA NRCS soil surveys CA638 (San Diego Area); CA678 (Orange County and Western Part of Riverside County); CA679 (Western Riverside Area); CA680 (Coachella Valley Area); CA681 (Palo Verde Area); AZ656 (Colorado River Indian Reservation, Parts of AZ and CA).Riverside County initial mapping year - 1984.USDA NRCS soil survey CA638 (San Diego Area) was added to the survey area in 2008.Geodetic Model: 1984 to 2012 - North American Datum of 1927; 2014 to 2018 - North American Datum of 1983.2018 county boundaries: California Department of Forestry and Fire Protection, Fire and Resource Assessment Program (FRAP) 2018 version (cnty 18_2) of California Counties GIS data. https://frap.fire.ca.gov2018 Imagery source: USDA Farm Service Agency - National Agricultural Imagery Program (NAIP); Summer 2018; True color; 1 meter resolution. https://www.fsa.usda.govGoogle Incorporated; Various dates; True color; Google Maps and Streetview. https://www.google.com/maps

Napa County has used a 2004 edition vegetation map produced using the Manual of California Vegetation classification system (Thorne et al. 2004) as one of the input layers for land use decision and policy. The county decided to update the map because of its utility. A University of California, Davis (UCD) group was engaged to produce the map. The earlier map used black and white digital orthophoto quadrangles from 1993, with a pixel resolution of 3 meters. This image was delineated using a heads up digitization technique produced by ASI (Aerial Services Incorporated). The resulting polygons were the provided vegetation and landcover attributes following the classification system used by California State Department of Fish and Wildlife mappers in the Manual of California Vegetation. That effort included a brief field campaign in which surveyors drove accessible roads and verified or corrected the dominant vegetation of polygons adjacent to roadways or visible using binoculars. There were no field relevé or rapid assessment plots conducted. This update version uses a 2016 edition of 1 meter color aerial imagery taken by the National Agriculture Imagery Program (NAIP) as the base imagery. In consultation with the county we decided to use similar methods to the previous mapping effort, in order to preserve the capacity to assess change in the county over time. This meant forgoing recent data and innovations in remote sensing such as the use LiDAR and Ecognition’s segmentation of imagery to delineate stands, which have been recently used in a concurrent project mapping of Sonoma County. The use of such technologies would have made it more difficult to track changes in landcover, because differences between publication dates would not be definitively attributable to either actual land cover change or to change in methodology. The overall cost of updating the map in the way was approximately 20% of the cost of the Sonoma vegetation mapping program.Therefore, we started with the original map, and on-screen inspections of the 2004 polygons to determine if change had occurred. If so, the boundaries and attributes were modified in this new edition of the map. We also used the time series of imagery available on Google Earth, to further inspect many edited polygons. While funding was not available to do field assessments, we incorporated field expertise and other map data from four projects that overlap with parts of Napa Count: the Angwin Experimental Forest; a 2014 vegetation map of the Knoxville area; agricultural rock piles were identified by Amber Manfree; and parts of a Sonoma Vegetation Map that used 2013 imagery.The Angwin Experimental Forest was mapped by Peter Lecourt from Pacific Union College. He identified several polygons of redwoods in what are potentially the eastern-most extent of that species. We reviewed those polygons with him and incorporated some of the data from his area into this map.The 2014 Knoxville Vegetation map was developed by California Department of Fish and Wildlife. It was made public in February of 2019, close to the end of this project. We reviewed the map, which covers part of the northeast portion of Napa County. We incorporated polygons and vegetation types for 18 vegetation types including the rare ones, we reviewed and incorporated some data for another 6 types, and we noted in comments the presence of another 5 types. There is a separate report specifically addressing the incorporation of this map to our map.Dr Amber Manfree has been conducting research on fire return intervals for parts of Napa County. In her research she identified that large piles of rocks are created when vineyards are put in. These are mapable features. She shared the locations of rock piles she identified, which we incorporated into the map. The Sonoma Vegetation Map mapped some distance into the western side of Napa County. We reviewed that map’s polygons for coast redwood. We then examined our imagery and the Google imagery to see if we could discern the whorled pattern of tree branches. Where we could, we amended or expanded redwood polygons in our map.The Vegetation classification systems used here follows California’s Manual of California Vegetation and the National Vegetation Classification System (MCV and NVCS). We started with the vegetation types listed in the 2004 map. We predominantly use the same set of species names, with modifications/additions particularly from the Knoxville map. The NVCS uses Alliance and Association as the two most taxonomically detailed levels. This map uses those levels. It also refers to vegetation types that have not been sampled in the field and that has 3-6 species and a site descriptor as Groups, which is the next more general level in the NVCS classification. We conducted 3 rounds of quality assessment/quality control exercises.

Site address points represent the location of site or service delivery addresses assigned by local governments.This data was initially created in 2017. The process involved using county Parcel data, field verification, commercial location PDFs provided by Public Safety, Community Development subdivision final maps, Google Street View, and Google Maps. The data continues to be updated by GIS and all new entries are input by Community Development.