Geology topics

Steven B. Mirsky

Publications and source records attributed to Steven B. Mirsky.

4 recordsLinked to original sources

USGS2026

The impacts of cover crop biomass on satellite-based detectability of cover crops in Maryland

Cover crop adoption in the U.S. has increased over the past decades, increasing the need to quantify their performance and environmental benefits. While remote sensing (RS)-based approaches for detecting cover crop presence have been developed, there has been limited research on how cover crops with varied biomass and management practices influence detectability. Using unique field-level cover crop presence and biomass datasets in Maryland, U.S., we investigated how RS-based detectability changes for cover crops with varied aboveground biomass, planting, and termination dates. Specifically, we proposed a time-integrated satellite-based greenness feature from Harmonized Landsat-8 and Sentinel-2 (HLS) time series from 2017 to 2021 to estimate biomass of cover crops and evaluated their detectability using a phenology-based cover crop detection framework. The impacts of cover crop planting and termination dates on cover crop biomass were also analyzed. Our results demonstrated that Normalized Difference Vegetation Index (NDVI) estimated biomass with higher accuracy compared to other vegetation indices, and the time-integrated model estimated biomass with higher accuracy than the single-date “snapshot” linear model (R2 from 0.53 to 0.66 and RMSE from 922 kg/ha to 747 kg/ha). While the snapshot models were species sensitive, the time-integrated models showed strong robustness across different species. Detectability increased with cover crop biomass, as detected cover crops averaged 963.3 ±719.5 kg/ha compared to 297.2 ± 209.0 kg/ha for non-detected cover crops. Detection accuracy reached 96.1% for fields exceeding 500 kg/ha, compared with an overall accuracy of 62.7%. Earlier planting and later termination increased biomass and detectability, with biomass rising by 4.14 kg/ha/day (p < 0.01). This study demonstrates how management practices affect cover crop biomass and detectability via satellite time series and provides insights that can inform management of cover crops and monitoring of their effects on agroecosystems.

Maryland↗

USGS2025

Satellite assessment of winter cover crop and conservation tillage outcomes to support adaptive management in working landscapes

The use of winter cover crops and conservation tillage are agricultural practices promoted to reduce nutrient and sediment loss from cropland, improve soil health, increase infiltration, and support farm nutrient cycling and ecosystem services. However, environmental performance of these practices is variable in the working farm landscape. The Lower Chesapeake Bay research project within the USDA Long-Term Agroecosystem Research (LTAR) network has collaboratively developed satellite remote sensing algorithms to measure the performance and phenology of winter cover crops (aboveground biomass, nitrogen content, fractional cover, and emergence and termination dates) using no-cost Harmonized Landsat and Sentinel-2 multispectral satellite imagery. This research supports annual operational assessment of >28,000 fields per year in four states. Results document the impacts of agronomic management on conservation outcomes, support adaptive management of incentive payment structures, and can reduce the workload for conservation district staff by remotely verifying cover crop management. Additionally, super-spectral satellite applications have been developed to accurately map crop residue cover by measuring lignocellulose absorption in shortwave infrared wavelengths, producing a 7-year time series of tillage intensity maps for the Delmarva Peninsula. These remote sensing products can be used in decision support and modeling to estimate changes in nutrient, sediment, and carbon cycling resulting from conservation practice implementation in the working farm landscape. This manuscript provides an overview of remote sensing research findings and applications associated with the USDA LTAR and Conservation Effects Assessment Projects (CEAP), documenting a variety of previously published outcomes with update and expansion of techniques using additional unpublished data and analyses as appropriate.

Maryland↗

USGS2024

The LTAR cropland Common Experiment at Lower Chesapeake Bay

The Lower Chesapeake Bay (LCB) Long-Term Agroecosystem Research (LTAR) Common Experiment (CE) located in Beltsville, MD, focuses on research of concern to producers of the major regional crops, which are corn ( Zea mays L.), soybean [ Glycine max (L.) Merr.], wheat ( Triticum aestivum L.), and various forage species. Livestock production in the region includes broiler and laying chickens ( Gallus gallus domesticus L.) and dairy and beef cattle ( Bos taurus L.). The LCB region is among the most heavily populated in the United States. Urban development pressure is high for both farms and natural areas. The need to restore Chesapeake Bay water quality is a major influence on regional agricultural practices. Conservation practices such as cover cropping, no-till agriculture, and nutrient management planning are more common in the region compared to nationally. However, farmers still face management challenges implementing practices that address water quality and the rise of herbicide-resistant weeds. Researchers at the LCB site recognize the need to protect the Chesapeake and Delaware Bays and maintain farmer profitability. The LCB CE compares a 3-year crop rotation system featuring alternative crop management (cover crop intensification, crop rotation diversification, and integrated weed management [IWM]) with a prevailing 2-year system (no cover crops and no IWM), both under continuous no-tillage, to identify the optimal balance to promote the sustainability of regional cropping systems. The LTAR LCB site provides data-driven tools and solutions to support farmers in the mid-Atlantic region.

Delaware, Maryland, New Jersey, Pennsylvania, Virg↗

USGS2014

Remote sensing with simulated unmanned aircraft imagery for precision agriculture applications

An important application of unmanned aircraft systems (UAS) may be remote-sensing for precision agriculture, because of its ability to acquire images with very small pixel sizes from low altitude flights. The objective of this study was to compare information obtained from two different pixel sizes, one about a meter (the size of a small vegetation plot) and one about a millimeter. Cereal rye (Secale cereale) was planted at the Beltsville Agricultural Research Center for a winter cover crop with fall and spring fertilizer applications, which produced differences in biomass and leaf chlorophyll content. UAS imagery was simulated by placing a Fuji IS-Pro UVIR digital camera at 3-m height looking nadir. An external UV-IR cut filter was used to acquire true-color images; an external red cut filter was used to obtain color-infrared-like images with bands at near-infrared, green, and blue wavelengths. Plot-scale Green Normalized Difference Vegetation Index was correlated with dry aboveground biomass ( ${mbi {r}} = 0.58$ ), whereas the Triangular Greenness Index (TGI) was not correlated with chlorophyll content. We used the SamplePoint program to select 100 pixels systematically; we visually identified the cover type and acquired the digital numbers. The number of rye pixels in each image was better correlated with biomass ( ${mbi {r}} = 0.73$ ), and the average TGI from only leaf pixels was negatively correlated with chlorophyll content ( ${mbi {r}} = -0.72$ ). Thus, better information for crop requirements may be obtained using very small pixel sizes, but new algorithms based on computer vision are needed for analysis. It may not be necessary to geospatially register large numbers of photographs with very small pixel sizes. Instead, images could be analyzed as single plots along field transects.

IEEE Journal of Selected Topics in Applied Earth O↗