Western Geographic Science Center

Data and Tools

Filter Total Items: 101
Date published: February 10, 2020

Implementation of a Surface Water Extent Model using Cloud-Based Remote Sensing - Code and Maps

This data release comprises the raster data files and code necessary to perform all analyses presented in the associated publication. The 16 TIF raster data files are classified surface water maps created using the Dynamic Surface Water Extent (DSWE) model implemented in Google Earth Engine using published technical documents. The 16 tiles cover the country of Cambodia, a flood-prone coun...

Date published: January 9, 2020

Liquid water content of coastal California fog events, San Mateo County

Liquid water content (LWC) measurements were collected during coastal fog events using specialized fog water collection units outfitted with six flat panel mesh collector frames. Simultaneous meteorological measurements were collected for four variables: wind, temperature, humidity, and solar radiation. The dataset includes ~12,000 records for two sites. Site one is in a grassl

Date published: December 2, 2019

Crop Water Productivity Studies for WaterSMART

Understanding how mitigation or adaptation strategies can be applied locally or regionally in response to climate change is a major issue for land managers. We propose a new and innovative approach to help optimize water use in California’s agricultural areas.

Date published: December 2, 2019

ACCA California (Wu & Thenkabail)

An automated cropland classification algorithm (ACCA) that is rule-based is illustrated here for the state of California, USA. 

Date published: December 2, 2019

An Automated Cropland Classification Algorithm (ACCA) for Tajikstan by Combining Landsat, MODIS, and Secondary Data

The overarching goal of this research was to develop and demonstrate an automated Cropland Classification Algorithm (ACCA) that will rapidly, routinely, and accurately classify agricultural cropland extent, areas, and characteristics (e.g., irrigated vs. rainfed) over large areas such as a country or a region through combination of multi-sensor remote sensing and secondary data.

Date published: December 2, 2019

ACCA Tajikistan (Wu & Thenkabail)

The automated cropland classification algorithm (ACCA) is written in ERDAS Modeler, and hence the algorithm file is available in .gmd format.

Date published: November 22, 2019

Phenology pattern data indicating recovery trajectories of ponderosa pine forests after high-severity fires

This tabular, machine-readable CSV file contains annual phenometrics at locations in ponderosa pine ecosystems across Arizona and New Mexico that experienced stand-clearing, high-severity fire. The locations represent areas of vegetative recovery towards pre-fire (coniferous/pine) vegetation communities or towards novel grassland, shrubland, or deciduous replacements. Each sample

Date published: October 17, 2019

Data supporting Landsat time series assessment of invasive annual grasses following energy development

To determine if invasive annual grasses increased around energy developments after the construction phase, we calculated an invasives index using Landsat TM and ETM+ imagery for a 34-year time period (1985-2018) and assessed trends for 1,755 wind turbines (from the U.S. Wind Turbine Database) installed between 1988 and 2013 in the southern California desert. The index uses the maxim

Date published: September 18, 2019

Global Hyperspectral Imaging Spectral-library of Agricultural crops for Conterminous United States

Thenkabail, P., I. Aneece. Global Hyperspectral Imaging Spectral-library of Agricultural crops for Conterminous United States V001. 2019, distributed by NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/Community/GHISA/GHISACONUS.001.

Date published: September 13, 2019

Monthly summaries of pixel counts in Joint Research Centre Monthly Water History v1.0 dataset in level-8 HUC in the greater Central Valley, California from 1984 to 2015

The dataset comprises a Landsat-derived assessment of monthly surface water area within the study area (California's greater Central Valley). The surface water estimates are supplied by the European Commission's Joint Research Centre (JRC) Monthly Water History, v1.0. The level of spatial aggregation is by level-8 hydrologic unit code (HUC).

Date published: September 13, 2019

Datasets for Integrating stream gage data and Landsat imagery to complete time-series of surface water extents in Central Valley, California

This data release comprises the data files and code necessary to perform all analyses presented in the associated publication. The *.csv data files are aggregations of water extent on the basis of the European Commission's Joint Research Centre (JRC) Monthly Water History database (v1.0) and the Dynamic Surface Water Extent (DSWE) algorithm. The shapefile dataset contains the st

Date published: September 13, 2019

Monthly summaries of pixel counts in Dynamic Surface Water Extent (DSWE) classes in level-8 HUCs in the greater Central Valley, California

The dataset comprises a Landsat-derived assessment of monthly surface water extent within the study area (California's greater Central Valley). The surface water dataset is based on the algorithm for the Dynamic Surface Water Extent (DSWE) (Jones, 2019), which was adapted to the Google Earth Engine JavaScript environment. The level of spatial aggregation is by level-8 hydrolog