Geology topics

Research about Beltsville, Maryland

Source-linked reports with geographic coverage including Beltsville, Maryland.

5 recordsLinked to original sources

USGS2026

Mapping winter cover crops using remote sensing of crop phenology

Winter cover crops (WCC) are an effective agricultural conservation practice for improving soil health and water quality. Several U.S. states have established incentive programs to promote planting WCC between cash crop rotations. In recent years, the area planted with WCC has steadily increased due to these efforts, although the planting area varies among states. Since these programs are largely led by individual counties or state programs, there is no centralized data repository that quantifies the total WCC planted annually or their locations. Therefore, there is a practical need to identify fields that have been planted with WCC, as well as to quantify the total planted area both during and after the growing season. Furthermore, incentive programs often require verification that WCC were planted to release payments to participating growers in a timely manner. Satellite remote sensing time series can provide information on WCC growth status. However, it remains unclear what kind of WCC the remote sensing data detects. For example, separating WCC from weeds and winter commodity crops remains a challenge. Additionally, a binary classification of WCC presence and absence cannot accurately describe the category and performance of the WCC. This paper presents a new remote sensing phenology-based approach to map WCC likelihood. We assessed model predictions of WCC likelihood using WCC planting records and ground observations over fields at the Beltsville Agricultural Research Center (BARC) from 2018 to 2024. Regional WCC maps were assessed using the Maryland Department of Agriculture's records of WCC fields enrolled in the 2019 and 2020 incentive programs. Our results show that the balanced overall accuracy for WCC and non-WCC ranges from 75% to 85% but could exceed 90%, with limited training samples. However, the high accuracy of WCC corresponds with low accuracy for non-WCC due to confusion with weeds and perennial grasses. Early-season detection yields promising results, with overall accuracies comparable to late-season detection. The after-season detection can differentiate between winter cereal grain cash crops and incentive WCC based on the ending date of the growing season. With a small set of WCC samples and management information, the phenology-based mapping approach offers a rapid and scalable solution for mapping WCC over a large region during or after each WCC growing season.

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USGS2024

Intercomparison of same-day remote sensing data for measuring winter cover crop biophysical traits

Winter cover crops are planted during the fall to reduce nitrogen losses and soil erosion and improve soil health. Accurate estimations of winter cover crop performance and biophysical traits including biomass and fractional vegetative groundcover support accurate assessment of environmental benefits. We examined the comparability of measurements between ground-based and spaceborne sensors as well as between processing levels (e.g., surface vs. top-of-atmosphere reflectance) in estimating cover crop biophysical traits. This research examined the relationships between SPOT 5, Landsat 7, and WorldView-2 same-day paired satellite imagery and handheld multispectral proximal sensors on two days during the 2012–2013 winter cover crop season. We compared two processing levels from three satellites with spatially aggregated proximal data for red and green spectral bands as well as the normalized difference vegetation index (NDVI). We then compared NDVI estimated fractional green cover to in-situ photographs, and we derived cover crop biomass estimates from NDVI using existing calibration equations. We used slope and intercept contrasts to test whether estimates of biomass and fractional green cover differed statistically between sensors and processing levels. Compared to top-of-atmosphere imagery, surface reflectance imagery were more closely correlated with proximal sensors, with intercepts closer to zero, regression slopes nearer to the 1:1 line, and less variance between measured values. Additionally, surface reflectance NDVI derived from satellites showed strong agreement with passive handheld multispectral proximal sensor-sensor estimated fractional green cover and biomass (adj. R 2 = 0.96 and 0.95; RMSE = 4.76% and 259 kg ha−1, respectively). Although active handheld multispectral proximal sensor-sensor derived fractional green cover and biomass estimates showed high accuracies (R 2 = 0.96 and 0.96, respectively), they also demonstrated large intercept offsets (−25.5 and 4.51, respectively). Our results suggest that many passive multispectral remote sensing platforms may be used interchangeably to assess cover crop biophysical traits whereas SPOT 5 required an adjustment in NDVI intercept. Active sensors may require separate calibrations or intercept correction prior to combination with passive sensor data. Although surface reflectance products were highly correlated with proximal sensors, the standardized cloud mask failed to completely capture cloud shadows in Landsat 7, which dampened the signal of NIR and red bands in shadowed pixels.

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USGS2016

Assessment of trace element accumulation by earthworms in an orchard soil remediation study using soil amendments

This study assessed potential bioaccumulation of various trace elements in grasses and earthworms as a consequence of soil incorporation of organic amendments for in situ remediation of an orchard field soil contaminated with organochlorine and Pb pesticide residues. In this experiment, four organic amendments of differing total organic carbon content and quality (two types of composted manure, composted biosolids, and biochar) were added to a contaminated orchard field soil, planted with two types of grasses, and tested for their ability to reduce bioaccumulation of organochlorine pesticides and metals in earthworms. The experiment was carried out in 4-L soil microcosms in a controlled environment for 90 days. After 45 days of orchardgrass or perennial ryegrass growth, Lumbricus terrestris L. were introduced to the microcosms and exposed to the experimental soils for 45 days before the experiment was ended. Total trace element concentrations in the added organic amendments were below recommended safe levels and their phytoavailablity and earthworm availability remained low during a 90-day bioremediation study. At the end of the experiment, total tissue concentrations of Cu, Cd, Mn, Pb, and Zn in earthworms and grasses were below recommended safe levels. Total concentrations of Pb in test soil were similar to maximum background levels of Pb recorded in soils in the Eastern USA (100 mg kg −1 d.w.) because of previous application of orchard pesticides. Addition of aged dairy manure compost and presence of grasses was effective in reducing the accumulation of soil-derived Pb in earthworms, thus reducing the risk of soil Pb entry into wildlife food chains.

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USGS2015

Organic amendments for risk mitigation of organochlorine pesticide residues in old orchard soils

Performance of compost and biochar amendments for in situ risk mitigation of aged DDT, DDE and dieldrin residues in an old orchard soil was examined. The change in bioavailability of pesticide residues to Lumbricus terrestris L. relative to the unamended control soil was assessed using 4-L soil microcosms with and without plant cover in a 48-day experiment. The use of aged dairy manure compost and biosolids compost was found to be effective, especially in the planted treatments, at lowering the bioavailability factor (BAF) by 18–39%; however, BAF results for DDT in the unplanted soil treatments were unaffected or increased. The pine chip biochar utilized in this experiment was ineffective at lower the BAF of pesticides in the soil. The US EPA Soil Screening Level approach was used with our measured values. Addition of 10% of the aged dairy manure compost reduced the average hazard quotient values to below 1.0 for DDT + DDE and dieldrin. Results indicate this sustainable approach is appropriate to minimize risks to wildlife in areas of marginal organochlorine pesticide contamination. Application of this remediation approach has potential for use internationally in areas where historical pesticide contamination of soils remains a threat to wildlife populations.

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USGS2015

Evaluating the relationship between biomass, percent groundcover and remote sensing indices across six winter cover crop fields in Maryland, United States

Winter cover crops are an essential part of managing nutrient and sediment losses from agricultural lands. Cover crops lessen sedimentation by reducing erosion, and the accumulation of nitrogen in aboveground biomass results in reduced nutrient runoff. Winter cover crops are planted in the fall and are usually terminated in early spring, making them susceptible to senescence, frost burn, and leaf yellowing due to wintertime conditions. This study sought to determine to what extent remote sensing indices are capable of accurately estimating the percent groundcover and biomass of winter cover crops, and to analyze under what critical ranges these relationships are strong and under which conditions they break down. Cover crop growth on six fields planted to barley, rye, ryegrass, triticale or wheat was measured over the 2012–2013 winter growing season. Data collection included spectral reflectance measurements, aboveground biomass, and percent groundcover. Ten vegetation indices were evaluated using surface reflectance data from a 16-band CROPSCAN sensor. Restricting analysis to sampling dates before the onset of prolonged freezing temperatures and leaf yellowing resulted in increased estimation accuracy. There was a strong relationship between the normalized difference vegetation index (NDVI) and percent groundcover ( r 2 = 0.93) suggesting that date restrictions effectively eliminate yellowing vegetation from analysis. The triangular vegetation index (TVI) was most accurate in estimating high ranges of biomass ( r 2 = 0.86), while NDVI did not experience a clustering of values in the low and medium biomass ranges but saturated in the higher range (>1500 kg/ha). The results of this study show that accounting for index saturation, senescence, and frost burn on leaves can greatly increase the accuracy of estimates of percent groundcover and biomass for winter cover crops.

Maryland↗