Location: Hydrology and Remote Sensing Laboratory2021 Annual Report
Objective 1: Develop and evaluate new methodologies and tools for characterizing spatiotemporal variability in land-surface water balance components from plot to global scales, integrating multi-sensor remote and in-situ measurement sources. Sub-objective 1.1: Improve representations of water and energy exchanges in structured agricultural environments, developed using in-situ measurements. Sub-objective 1.2: Improve multi-sensor tools for mapping water use over irrigated and rainfed crops, forests and rangelands. Sub-objective 1.3 Improve remote sensing tools for mapping regional and global soil moisture. Sub-objective 1.4: Develop new techniques for measuring soil moisture variability in situ and upscaling for validation of satellite retrievals. Sub-objective 1.5: Evaluate the terrestrial water budget at basin scale via the integration of remote sensing with ground observations. Objective 2: Develop remote sensing and modeling approaches for determining the timing and magnitude of agricultural drought and its impact on agroecosystems and onhe regional hydrology. Sub-objective 2.1: Improve early warning tools for identifying agricultural drought onset, severity and recovery at local to regional scales. Sub-objective 2.2: Improve techniques for assessing crop and rangeland phenology and condition and for forecasting yields. Sub-objective 2.3: Enhance understanding and monitoring of drought impacts on regional hydrologic components. Objective 3 (short): Assess the hydrologic status and trends within the Lower Chesapeake Bay Long-Term Agroecosystem Research site through measurements, remote sensing, and modeling. Sub-objective 3.1: Establish long-term data streams for the LCB LTAR project to examine agroecosystem status and trends. Sub-objective 3.2: Examine the effects of irrigation intensification within the LCB LTAR on trends in regional hydrology and nitrogen dynamics. Sub-objective 3.3: Improve prediction capability of SWAT in evaluating the effects of both natural riparian and restored wetlands on water quality. Sub-objective 3.4: Investigate sources and fate of nitrate in the LCB LTAR.
This project seeks to develop new tools for agricultural monitoring and management that integrate ground observations, remote sensing data and modeling frameworks. In specific, these multiscale tools will be used to address characterization of water supply (soil moisture), water demand (evapotranspiration), water quality drivers and drought impacts over agricultural landscapes.
This report documents progress for the fourth year of Project 8042-13610-028-00D (029)“Integrating Remote Sensing, Measurements and Modeling for Multi-Scale Assessment of Water Availability, Use, and Quality in Agroecosystems” which started in 2017. Substantial progress was made in all three objectives outlined in the project plan, all of which fall under NP 211. Under Objective 1, research activities focused on improving physical representations of agricultural water use and soil moisture dynamics, and moving remote sensing modeling and validation systems toward more operational status. In collaboration with partners at Princeton University, three distinct approaches for partitioning evapotranspiration (ET) into soil evaporation (E) and plant transpiration (T) were evaluated over irrigated croplands, including vineyards, using high-frequency measurements (Sub-objective 1.1). Findings suggest that the conditional eddy covariance method may provide the most robust partitioning results. Using these measurements, multiple studies were carried out to evaluate the capability of the remote sensing-based two-source energy balance (TSEB) model to accurately compute T and E using in situ inputs and very-high-resolution Unmanned aerial vehicle (UAV) imagery (Sub-objective 1.2). These studies indicate that the TSEB model can reliably partition ET when provided with accurate estimates of leaf area, with UAV images further enabling partitioning water use between a vine and interrow (cover crop) components. Finally, working in conjunction with partners at Utah State University and the University of California, Davis, a study combining both in-situ and airborne measurements was conducted to characterize the advective enhancement of ET due to local and regional scale advection. Additional studies that build on this work are planned. A regional 30-m resolution version of the TSEB model was successfully ported to Google Earth Engine for large area application and validation, looking toward the 60-month milestone of Sub-objective 1.2. The Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) project broke new ground in the production and delivery of high-resolution (30-m) root-zone soil moisture (RZSM) within irrigated vineyards, generated using a remote sensing data assimilation system (Sub-objective 1.5). Temporal tracking of RZSM is important for timing the onset of irrigation in the spring and providing the confidence necessary for producers to impose water-saving irrigation strategies. As part of ongoing GRAPEX activities, weekly deliveries of high-resolution ET and RZSM products were made during the 2020 growing season for two production vineyards operated by the E&J Gallo Winery. National and international soil moisture monitoring capabilities were advanced in FY21 using both in situ sensor networks and spaceborne imagery (Sub-objectives 1.3 and 1.4). A strategy was developed for a National Coordinated Soil Moisture Monitoring Network for improved drought monitoring, which will draw heavily from the Marena Oklahoma In Situ Sensor Testbed (MOISST). In addition, an international Good Practices document was developed to establish the criteria for soil moisture remote sensing product evaluation as a part of the Committee on Earth Observing Satellites Land Product Validation (CEOS-LPV) subgroup. The Soil Moisture Active Passive (SMAP) mission was shown to have the capability to monitor soil moisture in heavily vegetated regions, substantially increasing the regions for which SMAP can retrieve soil moisture estimates. Background work was conducted to advance remote sensing technology for agriculture using synthetic aperture radar for the NISAR mission (NASA ISRO Synthetic Aperture Radar), which will launch in 2023. Under Objective 2, progress was made toward advancing drought and phenology monitoring capabilities to finer spatial and temporal scales. Several manuscripts were submitted detailing investigations of flash drought: on understanding, atmospheric drivers, identifying useful indicators with a fast temporal response, and defining metrics for capturing both rapid intensification and severity components that characterize flash drought events. Previous work has shown that the remote sensing 4-km regional Evaporative Stress Index (ESI) product developed by HRSL Scientist has a good capacity for early flash drought detection. In FY21, a manuscript was submitted demonstrating the utility of higher (30-m) resolution ESI data over a forested landscape, linking water stress to tree mortality with a lag of 1-2 years (Sub-objective 2.1). High-resolution ESI data were also combined with the Normalized Difference Vegetation Index (NDVI) to assess crop yield variability (Sub-objective 2.2). The ET-based crop water stress index demonstrates good yield prediction ability, particularly when the index is corrected for variations in crop emergence date (as reported by NASS) and growing degree days from different years. The first results of the yield variability due to NDVI and water stress anomaly have been published, and further investigations over extended areas and years are in progress. Toward a remote sensing approach to obtain critical emergence date information, the within-season crop emergence (WISE) mapping approach was refined over LTAR sites in the Corn Belt states using the routine Harmonized Landsat and Sentinel-2 (HLS) dataset. Daily PhenoCam observations were used to extract crop emergence dates and used to validate the WISE algorithm. Crop emergence dates at the 30-m resolution over Corn Belt states (Iowa, Illinois, Indiana, and Minnesota) from 2017-2020 have been produced. In addition, the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) was used to generate daily NDVI time series from Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) data over the Central Plains Experimental Range (CPER) LTAR site from 2014-2019. Remote sensing phenology was generated from the daily NDVI time series and used to assess forage quality and animal performance during the grazing season. Utilizing newly available satellite-based soil moisture (SM) and evapotranspiration (ET) products, developed and evaluated under Objective 1, the project made important advances in our understanding of the coupling between SM and land surface water fluxes (e.g., ET and runoff; Sub-objective 2.3). These advances have exposed important biases in existing land surface models that impact the quality of long-term climate projections and short-term numerical weather forecasts that rely on these models. Most importantly, a general tendency for land surface models to over-couple SM and ET within the central United States was shown to be the cause of a persistent warm bias in short-term (12- to 24-hour) air temperature forecasts within the Corn Belt. Under Objective 3, strategic environmental data streams continued to be collected and analyzed at the Lower Chesapeake Bay (LCB) LTAR Network site (Sub-objective 3.1). These data will be used in process-based research and in models connecting agricultural water use and quality, land management, and air quality influences. Equipment, funded by NRCS, was purchased for two in situ phosphorus monitoring systems which will be installed at gage stations in the Choptank River watershed. A redesign of the LCB Long-Term Agroecosystem Research (LTAR) Common Experiment was also initiated. Progress was made toward constraining the Soil Water Assessment Tool (SWAT) in its representation of the spatial distribution of ET and temporal variability of streamflow (Sub-objective 3.2) utilizing remotely sensed ET products (from Sub-objective 1.2). Calibrating the SWAT model using remotely sensed ET produced more accurate representations of low-flow conditions, which are critical for assessing hydrologic connectivity and aquatic ecosystem health. Remotely sensed ET was also used to assess an enhanced version of the SWAT model that uses Richard’s equation to simulate soil moisture (RSWAT). RSWAT and the original SWAT were tested in the LCB LTAR site, and the performance of these two model versions was indistinguishable when using streamflow alone for model assessment; however, the accuracy of RSWAT modeling of evaporative losses was notably improved. The SWAT model was improved when coupled with the geographically isolated wetlands (GIW) and riparian wetland (RW) modules and was successfully tested in the LCB LTAR site (Sub-objective 3.3). Results showed that the GIW exerted significant impacts on streamflow in this region, while RWs had a little contribution. These findings suggest that both GIWs and RWs should be considered in wetland conservation plans to preserve water resources at the watershed scale. Follow-up studies further showed that using remote sensing data to improve the characterization of wetland-stream connectivity and representation of riparian wetlands enhanced the model performance for predicting wetland hydroperiod. Sampling continued at multiple LTAR Network sites for the Watershed Lag Time Project (WLTP), which uses MESA (a metabolite of the herbicide metolachlor) as a conservative tracer for nitrate transport; however, sampling was sporadic at many sites due to Covid restrictions (Sub-objective 3.4). An ARS Post doc was hired in May 2021 to analyze the effects of land-use, hydrology, and other environmental characteristics on lag time to address the 60-month milestone. A new project was initiated to evaluate the use of Miscanthus to protect waterways from phosphorus pollution in the LCB. NRCS provided additional funding for the WLTP and Miscanthus projects.
1. A 30-m leaf area index product in Google Earth Engine for monitoring crop condition and water use. Leaf area index (LAI) is a key biophysical parameter used for monitoring vegetation health and water use. Current LAI data products derived from satellite remote sensing data are typically generated at low spatial resolution (0.25 to 1 km), which is often too coarse for many agricultural applications at field scales. ARS scientists in Beltsville, Maryland, developed an operational approach to map LAI at 30-m resolution in Google Earth Engine (GEE). By leveraging the cloud computing power of GEE, long-term records of 30-m LAI can be generated with Landsat starting from the 1980s and covering the United States. Results show good agreement with ground measurements of LAI over various landscapes. The approach provides a feasible method for producing sub-field-scale LAI products for routine monitoring and retrospective analysis of crop condition and water use in the United States.
2. Improving numerical weather prediction in the central United States. Accurate short-term (< 48 hours) air temperature forecasts are valuable for a range of important agricultural management decisions; however, many weather forecast centers routinely overestimate summertime daily air temperature maximums in the central United States. Moreover, counter to expectations, the magnitude of this warm bias increases when these centers assimilate remotely sensed soil moisture retrievals into their land-surface models to improve their representation of surface soil water availability. ARS researchers in Beltsville, Maryland, have recently explained this (counterintuitive) tendency by showing that land-surface models used in numerical weather prediction tend to over-couple soil moisture and surface evapotranspiration in the summertime – in such a way that improving the representation of soil moisture in the model (via the assimilation of remotely sensed soil moisture products) can exacerbate pre-existing air temperature/evapotranspiration biases. Through this insight, ARS researchers have identified a valuable path forward for improving short-term numerical weather prediction within the central United States.
3. Development of a high-resolution soil moisture product from satellites. Soil moisture (SM) is a key indicator of crop health and developing agricultural drought. The SMAP (Soil Moisture Active Passive) satellite has proven to be an effective method of monitoring soil moisture content at fairly coarse resolution (36-km grid). With advanced downscaling techniques, it has been possible to reduce this gridding to 9 km, but this is still too coarse for many agricultural applications. ARS scientists in Beltsville, Marylad have developed a new technique that uses information about spatial variability land surface temperature and vegetation to produce 1-km soil moisture products of comparable accuracy to the original SMAP 36-km product. With these new high-resolution products, operational drought monitors can now design systems to optimally merge soil moisture information acquired from multiple sources (including ground-based observations) and maximize the probability of early drought detection.
4. A Good Practices document for soil moisture product validation. Satellites can be used to generate highly accurate maps of soil moisture at near-daily timesteps. While these products are very beneficial for agricultural applications, no consistent methods have been developed for determining the accuracy of different soil moisture products. ARS scientists in Beltsville, Maryland, worked with a team of remote sensing experts to develop a Soil Moisture Product Validation Good Practices Protocol document for the Committee on Earth Observing Satellites, bringing soil moisture to a Validation Stage of 3 (out of 4) by the standards established for this international body. The preparation of this document included over 50 scientists and academics working together toward formulating a common basis for soil moisture remote sensing product evaluation. This document provides a valuable resource for developing a standard platform for remote sensing evaluation for soil moisture, which is recognized as a critical parameter for agriculture.
5. Improved detection of wetland inundation below forest canopy. To best conserve wetlands and manage associated ecosystem services in the face of climate and land-use variation, wetlands must be routinely monitored to assess their extent and function. Wetland extent and function are largely driven by spatial and temporal patterns in inundation and soil moisture, which are difficult to map under forest vegetation. Commonly used lidar (Light Detection and Ranging) instrumentation uses reflections from a scanning laser to map topography and, as a byproduct, also collects maps of the intensity of laser light reflection. While these intensity data provide accurate information on forested wetland inundation when trees have lost their leaves, the presence of evergreen vegetation can interfere with the collection of inundation information. ARS scientists in Beltsville, Maryland, demonstrated a data processing approach to correct for the influence of evergreens on inundation mapping. Improved inundation maps will permit more accurate mapping of forested wetlands and allow better training of artificial intelligence procedures for assessing wetland ecosystem services provision in agricultural landscapes.
6. Modeling sediment diagenesis processes on riverbeds. Despite the widely recognized importance of aquatic processes for bridging gaps in the global carbon cycle, there is still a lack of understanding of riverbed processes' role in carbon flows and stocks in aquatic environments. ARS scientists in Beltsville, Marylad modified the USDA Soil Water Assessment Tool (SWAT) model to include two new modules that capture sediment dynamics for particulate and dissolved organic carbon and tested the revised model using a four-year observational dataset in a U.S. mid-Atlantic watershed. The new modules showed good agreement with observations and emphasize the importance of modeling these dynamics so that carbon fluxes and stocks are properly understood at the watershed scale. Findings from the revised SWAT model are useful to inform ecosystem services for watershed assessment and planning.
7. Using NASA earth observations and Google Earth Engine to map winter cover crop performance. The Maryland Cover Crop Program managed by the Maryland Department of Agriculture (MDA) incentivizes farmers to grow winter cover crops to reduce nutrient and sediment loss from farmland. ARS has collaborated with the MDA since 2006, developing remote sensing techniques to assess winter cover crop performance in Maryland. ARS scientists in Beltsville, Maryland developed Google Earth Engine (GEE) scripts to create composite seasonal satellite reflectance indices from Landsat and Sentinel 2 images covering the State of Maryland. They combined this information with MDA cost-share enrollment field boundary data to produce a winter and springtime evaluation of winter cover crop performance for all enrolled fields falling within three test counties on the Eastern Shore, and one test county in western Maryland. The tool can be modified for different seasonal cutoffs, utilize new sensors to capture phenology in winter and spring, and scale to larger regions for use in adaptive management of winter cover crops planted for environmental benefit. It is expected that this tool will now be used operationally by the MDA in the implementation of their ongoing winter cover crop program.
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