Software

Welcome to Automated Regional Correlation Analysis for Hydrologic Record Imputation (ARCHI). ARCHI provides a workflow and interactive platform for regional-scale hydrologic record imputation. ARCHI allows users to aggregate, visualize, and impute records with irregular monitoring frequency at annual, seasonal, monthly, weekly, and daily timesteps. Missing data at “target” sites are...

In this tutorial, we show users how to mosaic and process overlapping Landsat scenes covering Las Vegas and the Grand Canyon on the Arizona-Nevada border. These scenes were carefully chosen based on their location and minimal cloud cover.

The Landsat Spatio Temporal Asset Catalog (STAC) API enables spatial querying of the Landsat data archive by allowing users to define areas of interest (AOI) using various GeoJSON objects (points, lineStrings, polygons, or multipolygons). These objects facilitate spatial filtering based on specific geometric shapes. The STAC intersects query filter allows users to retrieve items within a...

This quick guide shows users how to create a GeoJSON Area of Interest (AOI) file for querying in the Landsat SpatioTemporal Asset Catalog (STAC) API to access Landsat datasets.

This work demonstrates how to utilize the Pixel QA band in Landsat data to mask out pixels affected by clouds and snow specifically in the Vancouver, Canada area. This process involves decoding the QA bit flag, creating a mask for cloud- or snow-affected pixels, and applying it to the imported data.

The Landsat STAC catalog provides a versatile and user-friendly interface for accessing and exploring the USGS Landsat Archive. Understanding the characteristics and differences between the various Landsat data products is helpful for choosing suitable data for your research and applications. This tutorial focuses on filtering the Landsat STAC catalog for products based on various...

In this tutorial, we demonstrate the process of accessing Landsat data files through the STAC API and the AWS command-line tools, specifically the requester-pays functionality. Users can define their spatial and temporal parameters to query and download multiple Landsat data band files.

This tutorial will introduces the basics of selecting a subset of bands over a set area of interest (AOI) and time span, loading the desired cloud optimized geotiffs (COGs) into a Jupyter Notebook directly from the cloud, cropping the bands and creating a natural composite.

This tutorial demonstrates how to query using metadata specific to the various collections of Landsat data. We use the following Machine-to-Machine API filters: metadataValue, metadataBetween, metadataAnd, and metadataOr. Users can view queryable metadata after following one of these steps (1) Submitting a dataset-filters query and listing the results (2) Submitting a search on Earth...

This tutorial is designed to guide users through metadata filtering for Landsat-9 satellite data within the Landsat 8-9 Operational Land Imager and Thermal Infrared Sensor Collection 2 Level-2 dataset. The chosen location for use is Albemarle Sound, a large estuarine body of water in northeastern North Carolina, United States. The emphasis on metadata filtering is crucial, especially...

This notebook shows how to download Landsat Collection 2 bands, bundles, and band groups using the M2M API. In this context, "bands" refer to individual band files in TIF format, "bundles" include all files for a product(s) as .tar files, and "band groups" include downloading all files for a given product(s) as individual files (unpacked from the .tar file within a bundle). The area of...

This repository contains the code used to analyzed the water quality from a formalin study. We examined how formalin affected the total nitrogen and nitrite nitrogen in water that was passed through a biofilter.