Geology topics

Richard L. McCormick

Publications and source records attributed to Richard L. McCormick.

3 recordsLinked to original sources

USGS2025

Artificial neural network multilayer perceptron models to classify California’s crops using Harmonized Landsat Sentinel (HLS) data

Advances in remote sensing and machine learning are enhancing cropland classification, vital for global food and water security. We used multispectral Harmonized Landsat 8 Sentinel-2 (HLS) 30-m data in an artificial neural network (ANN) multi-layer perceptron (MLP) model to classify five crop classes (cotton, alfalfa, tree crops, grapes, and others) in California's Central Valley. The ANN MLP model, trained on 2021 data from the United States Department of Agriculture's Cropland Data Layer, was validated by classifying crops for an independent year, 2022. Across the five crop classes, the overall accuracy was 74%. Producer's and user's accuracies ranged from 65% to 87%, with cotton achieving the highest accuracies. The study highlights the potential of using deep learning with HLS time series data for accurate global crop classification.

California↗

USGS2024

Automated Cropland Fallow Algorithm (ACFA) for the Northern Great Plains of USA

Cropland fallowing is choosing not to plant a crop during a season when a crop is normally planted. It is an important component of many crop rotations and can improve soil moisture and health. Knowing which fields are fallow is critical to assess crop productivity and crop water productivity, needed for food security assessments. The annual spatial extent of cropland fallows is poorly understood within the United States (U.S.). The U.S. Department of Agriculture Cropland Data Layer does provide cropland fallow areas; however, at a significantly lower confidence than their cropland classes. This study developed a methodology to map cropland fallows within the Northern Great Plains region of the U.S. using an easily implementable decision tree algorithm leveraging training and validation data from wet (2019), normal (2015), and dry (2017) precipitation years to account for climatic variability. The decision trees automated cropland fallow algorithm (ACFA) was coded on a cloud platform utilizing remotely sensed, time-series data from the years 2010–2019 to separate cropland fallows from other land cover/land use classes. Overall accuracies varied between 96%-98%. Producer’s and user’s accuracies of cropland fallow class varied between 70-87%.

International Journal of Digital Earth↗

USGS2024

Machine learning and new-generation spaceborne hyperspectral data advance crop type mapping

Hyperspectral sensors provide near-continuous spectral data that can facilitate advancements in agricultural crop classification and characterization, which are important for addressing global food and water security issues. We investigated two new-generation hyperspectral sensors, Germany’s Deutsches Zentrum für Luft‐ und Raumfahrt Earth Sensing Imaging Spectrometer (DESIS) and Italy’s PRecursore IperSpettrale della Missione Applicativa (PRISMA), within California's Central Valley in August 2021 focusing on five irrigated agricultural crops (alfalfa, almonds, corn, grapes, and pistachios). With reference data from the U.S. Department of Agriculture Cropland Data Layer, we developed a spectral library of the crops and classified them using three machine learning algorithms (support vector machines [SVM], random forest [RF], and spectral angle mapper [SAM]) and two philosophies: 1. Full spectral analysis (FSA) and 2. Optimal hyperspectral narrowband (OHNB) analysis. For FSA, we used 59 DESIS four-bin product bands and 207 of 238 PRISMA bands. For OHNB analysis, 9 DESIS and 16 PRISMA nonredundant OHNBs for studying crops were selected. FSA achieved only 1% to 3% higher accuracies relative to OHNB analysis in most cases. SVM provided the best results, closely followed by RF. Using both DESIS and PRISMA image OHNBs in SVM for classification led to higher accuracy than using either image alone, with an overall accuracy of 99%, producer’s accuracies of 94% to 100%, and user's accuracies of 95% to 100%.

California↗