Estimating the effect of winter cover crops on nitrogen leaching using cost-share enrollment data, satellite remote sensing, and Soil and Water Assessment Tool (SWAT) modeling

Hively, W. Dean; Lee, S.; Sadeghi, Ali; McCarty, Gregory W.; Lamb, Berton L.; Soroka, Alexander; Keppler, Jason; Yeo, In‐Young; Moglen, Glenn E.

doi:10.2489/jswc.75.3.362

Cited by 40 publications

(28 citation statements)

References 30 publications

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“…Another benefit is that cover crops can retain soil nitrogen from chemical nitrogen fertilizer for cash crops and reduce leaching of farmland nitrogen to the groundwater and watershed surface waters [5]. In Maryland and Delaware, winter cover crops have been identified as an essential component of watershed conservation implementation plans to 2 of 22 reduce nutrient and sediment losses from farmland [6,7]. Cost-share programs have been created to encourage the planting of cover crops for improving water quality [8].…”

Section: Introductionmentioning

confidence: 99%

Detecting Cover Crop End-Of-Season Using VENµS and Sentinel-2 Satellite Imagery

2020

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Cover crops are planted during the off-season to protect the soil and improve watershed management. The ability to map cover crop termination dates over agricultural landscapes is essential for quantifying conservation practice implementation, and enabling estimation of biomass accumulation during the active cover period. Remote sensing detection of end-of-season (termination) for cover crops has been limited by the lack of high spatial and temporal resolution observations and methods. In this paper, a new within-season termination (WIST) algorithm was developed to map cover crop termination dates using the Vegetation and Environment monitoring New Micro Satellite (VENµS) imagery (5 m, 2 days revisit). The WIST algorithm first detects the downward trend (senescent period) in the Normalized Difference Vegetation Index (NDVI) time-series and then refines the estimate to the two dates with the most rapid rate of decrease in NDVI during the senescent period. The WIST algorithm was assessed using farm operation records for experimental fields at the Beltsville Agricultural Research Center (BARC). The crop termination dates extracted from VENµS and Sentinel-2 time-series in 2019 and 2020 were compared to the recorded termination operation dates. The results show that the termination dates detected from the VENµS time-series (aggregated to 10 m) agree with the recorded harvest dates with a mean absolute difference of 2 days and uncertainty of 4 days. The operational Sentinel-2 time-series (10 m, 4–5 days revisit) also detected termination dates at BARC but had 7% missing and 10% false detections due to less frequent temporal observations. Near-real-time simulation using the VENµS time-series shows that the average lag times of termination detection are about 4 days for VENµS and 8 days for Sentinel-2, not including satellite data latency. The study demonstrates the potential for operational mapping of cover crop termination using high temporal and spatial resolution remote sensing data.

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Section: Introductionmentioning

confidence: 99%

Detecting Cover Crop End-Of-Season Using VENµS and Sentinel-2 Satellite Imagery

2020

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“…S3). Winter cover crops are widely implemented in this region to reduce nutrient loads and those crops are shown to increase the wintertime vegetation index (Hively et al, 2020). The omission of winter cover crops from our simulation resulted in low LAI relative to RS-LAI.…”

Section: Impacts Of Vegetation Data On Et Predictions and Predictive mentioning

confidence: 85%

Enhanced Watershed Modeling by Incorporating Remotely Sensed Evapotranspiration and Leaf Area Index

Lee

McCarty

Moglen

et al. 2021

Preprint

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Abstract. Remotely sensed evapotranspiration (RS-ET) products have been widely adopted as additional constraints on hydrologic modeling to enhance the model predictability while reducing predictive uncertainty. However, vegetation parameters, responsible for key time/space variation in evapotranspiration (ET), are often calibrated without the use of suitable constraints. Remotely sensed leaf area index (RS-LAI) products are increasingly available and provide an opportunity to assess vegetation dynamics and improve calibration of associated parameters. The goal of this study is to assess the Soil and Water Assessment Tool (SWAT) predictive uncertainty in estimates of ET using streamflow and two remotely sensed products (i.e., RS-ET and RS-LAI). We explore how the application of RS-ET and RS-LAI products contributes to 1) reducing the parameter uncertainty; 2) improving the model capacity to predict the spatial distribution of ET and LAI at the sub-watershed level; and 3) assessing the model predictions of ET and LAI at the basic modeling unit (i.e., the hydrologic response unit [HRU]) under the assumption that ET and LAI are related in croplands. Our results suggest that most of the parameter sets with acceptable performances for two constraints (i.e., streamflow and RS-ET; 12 parameter sets) are also acceptable for three constraints (i.e., streamflow, RS-ET, and RS-LAI; 11 parameter sets) at the watershed level. This finding is likely because both the ET simulation algorithm and the RS-ET products consider LAI as an input variable. Relative to the watershed-level assessment, the number of parameter sets that satisfactorily characterize spatial patterns of ET and LAI at the sub-watershed level are reduced from 11 to 6. Among the 11 parameter sets acceptable for three constraints (i.e., streamflow, RS-ET and RS-LAI) at the sub-watershed level, two parameter sets appear to provide high spatial and temporal consistency between ET and LAI at the HRU level. These results suggested that use of multiple remotely sensed products as model constraints enables model evaluations at finer scales – thereby constraining acceptable parameter sets and accurately representing the spatial characteristics of hydrologic variables. As such, this study highlights the potential of remotely sensed data to increase the predictability and utility of hydrologic models.

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“…Remote sensing can also be used to better constrain aboveground vegetation and processes represented in these models. For examples, high-resolution remote-sensing data have been used to improve the representation of land cover and land use in process-based models of nitrate leaching (Hively et al, 2020), and in empirical models of soil erosion (Phinzi & Ngetar, 2019).…”

Section: Non-point Pollution From Crop Productionmentioning

confidence: 99%