Location: Hydrology and Remote Sensing Laboratory2022 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 fifth year of Project 8042-13610-028-00D “Integrating Remote Sensing, Measurements and Modeling for Multi-Scale Assessment of Water Availability, Use, and Quality in Agroecosystems” which started in 2017. All Objectives outlined in the 2017 NP211 project plan have been completed. Under Objective 1, research activities in FY22 focused on synthesizing information derived from in situ observations and implementing near real-time modeling systems in support of operational decision making. Much of this work was conducted as part of the Grape Remote Sensing Atmospheric Profile Evapotranspiration eXperiment (GRAPEX), aimed at improving irrigation management in vineyards, as well as the sister Tree crop Remote sensing of Evapotranspiration eXperiment (T-REX) in irrigated almond orchards. In addition to ongoing data collection activities, significant progress was made toward developing in-situ methods to characterize land-atmosphere exchange in structured canopy systems. These include new spectral analysis methods to describe turbulent structure within and above vineyards affecting transport of heat, moisture, and trace gases, and field methods to determine the relative effects of local and regional scale advection on enhancement of evaporative water loss (Sub-objective 1.1). These field findings been integrated into a remote sensing-based evapotranspiration (ET) toolkit designed to inform irrigation decision making (Sub-objective 1.2). Output from the ET toolkit has been validated over three GRAPEX vineyard experimental sites, demonstrating good capabilities in characterizing heterogeneity in ET both within-field and over the surrounding landscape. Unmanned Aerial System imagery has been used to improve leaf area index retrievals for vineyards, a critical input for partitioning between interrow evaporation and canopy transpiration (Sub-objective 1.1). Additional satellite data sources (including Sentinel-2 surface reflectances) were integrated into the toolkit to improve temporal responsiveness to rapidly changing surface moisture and crop conditions. In FY22, the ET toolkit was applied in a pilot irrigation experiment over multiple high-value wine grape vineyards operated by E&J Gallo, with weekly 30-m resolution water use maps integrated into an existing irrigation scheduling dashboard. The data was supplemented by estimates of root-zone soil moisture from the Vineyard Data Assimilation System (VIDA) (Sub-objective 1.5). VIDA assimilates remotely sensed ET (from the ET toolkit) and surface soil moisture into a water balance modeling system adapted for the row structure and drip irrigation methods characteristic of California viticultural systems. Looking toward large-area applications, the core ET model was implemented in Google Earth Engine as part of the OpenET project, demonstrating good accuracy over irrigated cropping systems in the California Central Valley. A follow-on 2nd Special Issue in Irrigation Science “From vine to vineyard: The GRAPEX multi-scale experiment to improve irrigation management” has 20 manuscripts submitted with 19 currently accepted for publication. Soil moisture monitoring capabilities were advanced in FY22 using both in situ sensor networks and spaceborne imagery (Sub-objectives 1.3 and 1.4). The SMAPVEX22 field experiment series (starting from 2019) is currently underway, with data collection and analysis related to highly vegetated areas in the eastern United States. The data product from the Soil Moisture Active Passive (SMAP) satellite mission has been analyzed using measurements from the National Ecological Observing Network (NEON) with reasonable results. Synthetic Aperture Radar (SAR) has been applied to mapping of croplands with the goal of retrieving surface soil moisture at the field scale (an assimilation data source in VIDA). A 400-m resolution global soil moisture product was developed by downscaling 33-km SMAP data using a set of vegetation indices, yielding high accuracy in comparison with other lower resolution soil moisture products. Similar to Objective 1, research activities under Objective 2 have progressed in FY22 toward operational capacity. The Evaporative Stress Index (ESI) developed by Hydrology and Remote Sensing Laboratory (HRSL ) scientists is being distributed operationally over the U.S. at 4-km resolution by the National Oceanic and Atmospheric Administration (NOAA) in support of drought monitoring and model evaluation, and globally by National Aeronautics and Space Administration (NASA) at 5-km for crop monitoring and food security applications (Sub-objective 2.1). ESI is also being ingested into national drought monitors in Brazil and central Europe. Several papers were published in 2022 discussing use of ESI as a fast-response indicator of flash drought. In a new investigation, ESI and ET data at finer (30-m) resolution are being used as a metric of pasture health response to regenerative grazing management. Methods for mapping crop phenology using satellite remote sensing were also applied at large scale in FY22, and integrated with ESI, leaf area index (LAI) and vegetation index (VI) timeseries to improve yield estimation (Sub-objective 2.2). Crop emergence dates were mapped at 30-m resolution over five Corn Belt states from 2017-2020 using routine data from the Harmonized Landsat and Sentinel-2 (HLS) dataset and analyzed in comparison with in-situ and assessment from the National Agricultural Statistics Service (NASS). In addition, detailed crop growth stages from 2018-2020 were estimated using 5-m resolution Planet satellite data over the Beltsville Agricultural Research Center (BARC), Maryland, and assessed using field observations with good agreement. Crop phenology, LAI, VI, ET, and ESI remote sensing indicators have been collectively integrated into process-based and light-use efficiency crop growth models for yield estimation. Model output is being evaluated over experimental fields in BARC, with preliminary assessment indicating good spatial and temporal agreement. In support of yield modeling experiments, HRSL project and support scientists are developing an archival database of gridded yield maps for BARC production fields, derived from quality controlled and kriged harvester data. The database currently covers 2015-2021 and will be extended back to the beginning of the harvester record. These data will be curated for archive at the National Agricultural Library (NAL) and will provide a rich observational history for agricultural research at BARC. At larger scales, remotely sensed soil moisture and ET products were used to improve forecast models at short and climate timescales (Sub-objective 2.3). Using new remote sensing products and a novel statistical approach, systematic errors in the relationship between land surface model states (i.e., soil moisture) and water fluxes (i.e., runoff and ET) were identified in land surface models currently applied to both operational hydrologic forecasting and climate model projections. These errors were shown to significantly degrade the quality of streamflow forecasts and air temperature projections provided by these systems. Practical strategies for correcting these errors using only remote sensing data were tested and shown to be effective. Under Objective 3, strategic environmental data streams continued to be collected and analyzed at the Lower Chesapeake Bay (LCB) Long-Term Agroecosystem Research (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. Real time water quality data were collected at two United States Geological Survey (USGS) gage stations on the Choptank River Watershed, including evaluation of a new in situ total N and P sensor. The Lower Chesapeake Bay LTAR continues to provide real time BARC weather data to the NAL LTAR database. Phenocams associated with both BARC and Choptank flux towers provide hourly daylight images to the LTAR phenocam network. In addition, nitrate and tracer datasets are being generated for subwatersheds in the Choptank and Monocacy watersheds. Project scientists evaluated methods for calibrating 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 (Sub-objective 1.2) and LAI (Sub-objective 2.2) timeseries. Findings strongly suggest that use of multiple remotely sensed datasets holds great potential to reduce parameter uncertainty and to increase credibility of watershed modeling, particularly for characterizing spatial variability of hydrologic fluxes (e.g., ET) that are critically relevant to agricultural management (e.g., irrigation). A new wetland denitrification algorithm was incorporated into the SWAT model to improve the simulation of nitrate loading from the Choptank River Watershed (Sub-objective 3.3 and 3.4). Further improvements were made to the riparian and non-floodplain wetland modules within SWAT to demonstrate the important role of geographically isolated wetlands and riparian wetlands in altering the variability of downstream streamflow in the watershed. Variability of global climate model projections was found the most significant single source of uncertainty in model results, explaining 49% of the uncertainty associated with wetland water storage projections. We anticipate these improvements will benefit future applications of the SWAT model to support decision making related to agricultural sustainability. In addition, evaluation of a new remote sensing-based wetland functional assessment tool indicated the potential for functional characteristics to be incorporated in future iterations of ecosystem service assessment for agricultural landscapes.
1. A novel analysis method to characterize the vertical structure of turbulence. Improving irrigation management is critical to ensuring that water, already a scarce resource in California, is used effectively. But developing the tools needed to monitor water loss and manage irrigation in vineyards is complicated by their unique canopy structure and its effects on turbulent flow and exchange processes. To understand both the vertical structure of turbulence over vineyards, ARS scientists in Beltsville, Maryland, developed a novel analysis method that uses high-frequency measurements at multiple height to describe airflow within and above the vines. Using this new method, the direction of air flow was identified as a control on turbulent structure and exchange processes. This dependency on wind direction, which is a unique characteristic of highly structured canopies, provides an important new avenue for research efforts seeking to understand exchange fluxes from vineyards and develop enhanced remote sensing-based tools that will monitor vine water loss and improve irrigation management for vineyards. This work also provides a unique analysis technique that can be applied by scientific community to gain valuable insight into turbulent transport and exchange over diverse natural and agricultural landscapes.
3. Open access water-use information for western U.S. water management. Fresh water availability is a major challenge facing agriculture today, one which will only intensify as climate patterns continue to change and as competing water demands continue to grow. In the western US, an ongoing megadrought has caused major reservoirs to drop to historically low levels, resulting in emergency shortages affecting water rights, irrigation capacity, hydroelectric power production, as well as provision of ecosystem services. Finding sustainable methods for managing our freshwater resources into the future means that we need reliable ways to measure how water is being used today, from field to basin scales, and to get this information effectively into hands of the decision makers. Under the OpenET project, ARS scientists in Beltsville, MD implemented a satellite-based model of evapotranspiration (ET) on Google Earth Engine, contributing to an ensemble of 6 models estimating daily ET at 30-m resolution in near-real time over the 17 western states. Data from the ensemble average and individual models can be accessed through a web-based interface (openetdata.org), or through an automated programming interface for direct ingestion into existing water management toolkits. Current use cases include irrigation scheduling, groundwater planning, water accounting and allocation, and evaluation of water conservation measures (e.g., fallowing). This platform provides shared and open access to a trusted water use dataset at field scale that is spatially consistent across state boundaries, addressing a major data gap in water resource management.
5. Identifying the source of air temperature bias in climate model projections for the central United States. Despite increased scientific confidence in regional air temperature trends provided by climate model projections, such projections still contain residual biases. One well-known example of this is the tendency for retrospective climate model simulations to overestimate summertime daily maximum air temperature in the central United States. Past work has suggested that this bias is due to the mischaracterization of atmospheric features like cloud cover and convective rainfall. However, using a variety of remote sensing and ground data sets, ARS scientists in Beltsville, Maryland, have shown that the warm summertime bias in the central United States is much more likely to be associated with the inability of climate models to accurately capture hydrologic processes operating along the land surface – in particular, the correct relationship between soil moisture and land surface evapotranspiration. This result provides an important new direction for efforts to improve the use of climate model projections to describe the potential impacts of climate change on domestic agricultural production.
6. Large-area mapping of crop emergence within the crop growing season. Within-season estimates of crop emergence date generated at the field scale provide critical information for monitoring and modeling crop development and predicting yield at harvest. State or district-level emergence dates are reported by the National Agricultural Statistics Service (NASS) based on county observers, but to date, reliable spatially distributed estimates from remote sensing have only been produced retrospectively, after the growing season is over. ARS scientists in Beltsville, Maryland, have developed a novel within-season emergence mapping algorithm that detects crop emergence within 2-3 weeks, a timeframe useful for operational management and yield projections. The algorithm was implemented for efficient large-area application using the routine harmonized Landsat and Sentinel-2 (HLS) satellite image dataset. Results over five Corn Belt states (Iowa, Illinois, Indiana, Minnesota, and Nebraska) show that routine mapping of crop emergence within the growing season is feasible. The resulting multi-state green-up map, produced at 30-m resolution, provides information on spatial and temporal variability of crop emergence in response to climate, weather, management, economic, and other factors. This mapping technique will help NASS in crop monitoring as a valuable geospatial supplement to county-level observer data.
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