Wine grapes and almonds are some of the most important specialty crops produced in California and are largely irrigated. Acreage in these woody perennials continue to expand while at the same time, water availability in California poses a significant challenge to meeting the competing needs of agriculture, municipalities, and the environment. It is also an issue that is already reaching critical levels given the recent protracted California drought in 2012-2016 and the extreme drought in 2021 that saw historic drawdown of major reservoirs in California and the western US. With climate change, it is projected to become a still greater problem in the coming years. The need for monitoring crop water use or evapotranspiration (ET) is critical in maximizing water use efficiency for irrigated agriculture. The GRAPEX (Grape Remote sensing Atmospheric Profile Evapotranspiration eXperiment) project’s major goal has been to refine and apply a multi-scale remote sensing ET toolkit for mapping crop water use and crop stress for improved irrigation scheduling and water management in vineyards in the Central Valley of California. The plan is to provide the ET toolkit output to wineries and eventually to orchard growers throughout the state of California for improving water management and irrigation scheduling through the OpenET platform. Data sources, models, and technologies include earth observations from GOES, VIIRS, MODIS, Sentinel and Landsat satellites together with unmanned aerial vehicles (UAVs) applied to energy balance modeling techniques utilizing land surface temperature. We have also recently been evaluating a spectral-based ET approach using Sentinel-2 data for increasing the frequency of high resolution ET mapping. Model results have been validated with biophysical, soil moisture and micrometeorological measurement of fluxes from leaf to canopy to whole vineyard blocks at selected experimental vineyards. The results of GRAPEX and the application of the ET toolkit for irrigation scheduling will be discussed along with the successes and remaining issues that need to be resolved in order to have an operational earth observation system for irrigation management of perennial crops.
The world’s population is expected to increase by 2 billion people by 2050. To feed these additional people, a commensurate increase in food and water demands is expected. However, our current hydric and agricultural systems are already under unprecedented stress, which is projected to increase due to climate change. Remote sensing technologies such as satellite observations can help to optimize a farm’s resources and maximize yield precision when integrated into precision agriculture strategies. These strategies necessitate accurate and precise information on crop health and water use at high spatiotemporal resolutions (i.e., daily at less than 10 m). Nevertheless, until a few years ago, using satellite observations for precision agriculture was not feasible. There was a compromise between retrieval frequency and achievable spatial resolution. That is, it was possible to get either high spatial resolution images occasionally or frequent coarse spatial resolution observations. The development and mass launching of nanosatellites (i.e., CubeSats) that use off-the-shelf components has relaxed such constraints. Yet, no single satellite system has achieved the spatiotemporal resolution and data quality required to drive precision agriculture insights. Data fusion approaches can circumvent this limitation by leveraging the synergies between existing satellite platforms. In particular, Planet’s CubeSat constellation of over 180 satellites is well suited for closing the spatiotemporal resolution gap when combined with rigorously calibrated observations from traditional satellite platforms (e.g., Sentinel-2, Landsat 8, MODIS). Planet Fusion represents a novel data fusion approach for producing daily analysis-ready surface reflectance (SR) data (i.e., data that is ready to use for quantitative applications) at 3 m spatial resolution by leveraging multi-sensor (PlanetScope, Sentinel-2, Landsat 8, MODIS, VIIRS) data products. Here, we show the potential of combining Planet Fusion SR data with traditional Earth Observation data (e.g., Landsat, Sentinel, ECOSTRESS) to produce daily evaporation maps at 3 m spatial resolution in the context of precision agriculture. The results are evaluated using latent heat flux measurements from the AmeriFlux and FLUXNET eddy covariance network for a range of crop types (e.g., alfalfa, maize). The study demonstrates the advantages and additional information gained from the daily crop water use product compared with coarser spatiotemporal products (e.g., Landsat 8, Sentinel-2).
Earth Observation-based modeling of evapotranspiration, on a daily scale, requires high temporal revisit time. Although the integrated use of the Sentinel-2 and Landsat 8 sensors offer high temporal coverages (< 5 days), their utility in modeling evapotranspiration (ET) should be evaluated, taking into account their spectral and spatial differences.
In this study, an ET modeling framework, based on the combination equation models of Penman-Monteith (PM) and Shuttleworth-Wallace (SW) has been developed with special regard to sparse canopy conditions.
The combination equation models use spectral data in the visible, near-infrared, and shortwave infrared of Operational Land Imager (OLI) and MultiSpectral Instrument (MSI) sensors to infer the input data required for characterizing the crop surface in the calculation of evapotranspiration by means of a modified combination equation as proposed by [1]. This modified method is based on a modulation of surface resistances in the combination equation for ET by incorporating SWIR data in the assessment of the water status of the canopy and soil ensemble.
This approach is being used in the COALA project, funded by the Horizon 2020 program of the European Union with the aim of developing Copernicus Earth Observation-based information services for irrigation management in Australia, building on consolidated experience of past EU projects and existing operational irrigation advisory services (https://www.coalaproject.eu/).
To evaluate the impact of the different spectral and geometric characteristics of Sentinel-2 and Landsat-8 data, a set of coincident acquisitions have been processed respectively from SCIHUB (Level L2A) and USGS Earth Explorer (C2L2 and C2L1).
The surface parameters required as input in these models (hemispherical shortwave albedo, Leaf Area Index (LAI), and soil-canopy water status) have been properly derived from both datasets.
Particularly, the LAI data has been derived by using the last release of the Sentinel Application Platform toolbox (SNAP 8.0), where the “Biophysical Processor” tool provides three different sets of coefficients, specific for Sentinel-2A, Sentinel-2B, and Landsat-8. More specifically, LAI is estimated from the inversion of the radiative transfer model PROSPECT+SAILH based on Artificial Neural Network, thus taking full advantage of the spectral resolution [2].
Cross-comparison with flux tower observations, acquired over California vineyards during the USDA GRAPEX experiment (Grape Remote-sensing Atmospheric Profile and Evapotranspiration eXperiment) in California irrigated vineyards [3] has been done for evaluating the performance of the proposed approaches.
The current research envisages new operational perspectives in the utilization of the virtual constellation composed by the two Sentinel-2 and the two Landsat platforms, also considering the availability of the Harmonized Landsat and Sentinel-2 (HLS v2.0) dataset.
[1] D’Urso G., Falanga Bolognesi S., Kustas W.P., Knipper K.R., Anderson M.C., Alsina M.M., Hain C.R., Alfieri J.G., Prueger J.H., Gao F., McKee L.G., De Michele C., McElrone, A.J., Bambach-Ortiz, N., Sanchez L.A., & Belfiore O.R. (2021). Determining Evapotranspiration by Using Combination Equation Models with Sentinel-2 Data and Comparison with Thermal-Based Energy Balance in a California Irrigated Vineyard. Remote Sensing, 13(18), 3720.
[2] Weiss, M.; Baret, F. S2ToolBox Level 2 Products: LAI, FAPAR, FCOVER.
[3] USDA. GRAPEX. Available online: https://www.ars.usda.gov/northeast-area/beltsville-md-barc/beltsville-agricultural-research-center/hydrology-and-remote-sensing-laboratory/docs/grapex/grapex-home/
WaterSENSE: Water Use Monitoring and Assessment Services
The Murray-Darling Basin in Australia, the initial focus for the H2020 WaterSENSE project, produces 39% of the country’s agricultural output, uses as much as 66% of its water for irrigated agriculture, has over 9200 irrigation businesses and an agriculture industry worth AUD 24 billion annually (15 billion euro). The freshwater systems of the Darling were listed as drought-endangered in 2018 , with a significant estimated loss in production and the loss of 6000 jobs. Failure to address excess diversions upstream resolutely threatens the viability of the river, the fish, and the communities that depend on the river for their livelihoods and wellbeing .
Lack of capacity to proactively monitor and identify landscape and hydrological changes, including allegations of water theft, have recently made headline news across Australia. This has triggered a comprehensive set of reforms in operational improvements for water management across New South Wales and renewed efforts to improve water availability and use management, regulation, compliance and enforcement.
Unfortunately, the massive areal coverage makes this monitoring challenging and there is limited access to data and tools that allow for the state-wide operational monitoring of water consumption. Traditional methods to monitor compliance (e.g. installation of water meters) take years to implement and involve high costs and human resources to maintain. There is therefore an urgent need for accurate, inexpensive, and rapid monitoring tools to get an improved insight in the water use.
WaterSENSE addresses this challenge by developing water-monitoring capabilities able to support effective water management in areas ranging from irrigated fields and districts to entire river basins. The goal of WaterSENSE is to develop a modular, operational, water-monitoring system built on Copernicus Earth Observation data in order to provide water managers with a toolbox of reliable and actionable information on water availability and water use in support of sustainable water management and transparency across the entire water value chain.
Water Use Monitoring and Auditing Services (WUMAS)
Central to WaterSense is the Water Use Monitoring and Auditing Services (WUMAS). It provides water use monitoring information at different time and spatial resolutions, based on Copernicus EO data, hydrological models and local data. Its modular design and configurability to user needs and circumstances sets the stage for our research and innovation action. Our research is focused on different technology elements and its integration into a single system. The information is made available in online dashboards through the HydroNET platform, a SaaS decision support system for water managers, where it compares the estimated irrigated water use with water permits.
During the session we will present our EO related research activities that are focused on providing insight into irrigated water use. Based on satellite information and (in-situ) weather data, high-resolution (10 m) remote sensing-based evapotranspiration (ET) data is calculated using the ETLook energy balance model. This data is used in a novel way to estimate irrigation water use by comparing the ET of irrigated agricultural pixels to the weighted average ET of a subset of natural Hydrological Similar Pixels. The accuracy of the results depends also on the accuracy of other (EO based) information source. We will present our research involving EO data in relation to:
• Improved rainfall and weather information
• Irrigated land use detection
• Farm dam volume change indication and estimation
We will show the demonstration results of the service, which takes place from January until December 2022 covering the Namoi Catchment in New South Wales, and our experience with parts of the service in the Netherlands and South Africa.
References:
Bastiaanssen. W. G. M., Cheema. M. J. M., Immerzeel. W. W., Miltenburg. I. J., and Pelgrum. H. (2012). Surface energy balance and actual evapotranspiration of the transboundary Indus Basin estimated from satellite measurements and the ETLook model. Water Resources Research 48, no. 11.
Brombacher, J., Rezende de Oliveira Silva, I., Degen, J., Pelgrum, H. (submitted) A Novel Evapotranspiration Based Irrigation Quantification Method Using the Hydrological Similar Pixels Algorithm
Einfalt, T., Lobbrecht, A. (2012). Compositing international radar data using a weight-based scheme. IAHS Publ. 351, p.20 - 25.
FAO (2018). WaPOR Database Methodology: Level 1. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. Licence: CC BY-NC-SA 3.0 IGO.
FAO and IHE Delft. (2019). WaPOR quality assessment. Technical report on the data quality of the WaPOR FAO database version 1.0. Rome. 134 pp
Fuentes, I., Scalzo, R., Vervoort, R.W. (2021). Volume and uncertainty estimates of on-farm reservoirs using surface reflectance and LiDAR data. Environmental Modelling & Software Volume 143, September 2021, 105095
Hunink. J.E., Contreras. S., Soto-García. M., Martin-Gorriz. B., Martinez-Álvarez. V., Baille. A. (2015). Estimating groundwater use patterns of perennial and seasonal crops in a Mediterranean irrigation scheme, using remote sensing. Agricultural Water Management, Volume 162, Pages 47-56.
Lobbrecht, A., Einfalt, T., Reichard, L., Poortinga, I. (2012) Decision support for urban drainage using radar data of HydroNET-SCOUT. IAHS Publ. 351, p.626 - 631.
Pelgrum. H., Miltenburg. I.J., Cheema. M.J.M., Klaasse. A., Bastiaanssen. W.G.M. (2011). ETLook: A novel continental evapotranspiration algorithm. Remote Sensing and Hydrology, Jackson Hole, Wyoming, USA.
Strehz, A., Einfalt, T., Alderlieste, M. (2019) HydroNET-SCOUT – Ein Webportal zum Zugriff auf qualitätsgeprüfte Niederschlagsdaten. Tag der Hydrologie 28./29.3.2019, Karlsruhe.
van Eekelen. M.W., Bastiaanssen. W.G.M., Jarmain. C, Jackson. B., Ferreira. F., van der Zaag. P., Saraiva Okello. A., Bosch. J., Dye. P., Bastidas-Obando. E., Dost. R.J.J., Luxemburg. W.M.J. (2015). A novel approach to estimate direct and indirect water withdrawals from satellite measurements: A case study from the Incomati basin. Agriculture, Ecosystems & Environment, Volume 200, Pages 126-142.
Yang. Y., Guan. H., Long. D., Liu. B., Qin. G., Qin. J., Batelaan. O. (2015). Estimation of Surface Soil Moisture from Thermal Infrared Remote Sensing Using an Improved Trapezoid Method. Remote Sens. 2015, 7, 8250-8270.
Tensions for water resources between competing water usages are a common issue in many regions of the world. Agricultural use accounts for a large share of the water demand and is key for food security and socio-economic stability in rural areas. Hence, ensuring an efficient use of irrigation water is a common goal for water policies. On the other hand, managing irrigation in farms is not a trivial task since the water requirements by crops are site-specific and vary in time. In this context, the availability of EO data opens the opportunity to develop tools for the supervision and management of irrigation, scalable from farms to districts and basins. Time series of observed biophysical parameters of the vegetation and estimates of actual crop evapotranspiration (ETa) are promising resources for these applications. Assimilation of those data on models of crop development and soil water balance would enhance their ability for assessing irrigation performance and for making management decisions. Here we describe an approach based on digital twins that assimilate EO data and simulate the water balance of the soil-crop system at each individual plot. The goal is to obtain a dynamic view of irrigation performance scaling from individual plots to the basin, quantifying at real time the progress of crop growth and seasonal water balance, including forecasts of the forthcoming water demand. This approach is being implemented in the lower Ter River basin, on an area of 675 km2 covering 41 municipalities. A separate digital twin was defined for each of over 17000 agricultural plots listed in the Land Parcel Identification System. On each of these digital twins, the agricultural scenario was set according to open data of EU CAP’s Single Farm Payment and a soil map of the area. This included the list of crops declared from 2015 to 2021, the irrigation method and the soil class. From these basic categoric data, more detailed parameters of the crop, soil and irrigation method were assigned according to the description of actual agricultural scenarios on the area. The development of the crop and its soil water balance at each individual plot is simulated at real time, using a customized model based in a rationale similar to FAO’s AquaCrop, with additional adaptations to permanent crops, localized irrigation and discontinuous canopies. Simulations are updated every day, using online weather data from the Meteorological Service of Catalonia. In parallel, as soon as new Sentinel-2 images are available, fAPAR and LAI are computed through the Biophysical Processor available in the SNAP software and these parameters are assimilated in the model. The output are maps and time series with the estimated ETa, irrigation and available soil water at each plot. The maps are updated daily. Time series cover the whole year, on a week basis, including the forecasts for the remaining part of the year.
The conflicting use of water is becoming more and more evident, also in regions that are traditionally rich in water. With the world’s population projected to increase to 8.5 billion by 2030, the simultaneous growth in income will imply a substantial increase in demand for both water and food. Climate change impacts will further stress the water availability enhancing also its conflictual use. The agricultural sector is the biggest and least efficient water user, accounts for around 24% of total water use in Europe, peaking at 80% in the southern regions.
The objective of this study is then to improve farm and irrigation district water use efficiency developing an operational procedure for parsimonious irrigation, optimizing the irrigation water use and relative water productivity, according to different agronomic practices supporting different level of water users.
The SMARTIES optimization irrigation strategy, based on soil moisture (SM) and crop stress thresholds, has been implemented the Chiese (North Italy) and Capitanata (South Italy) Irrigation Consortia. The system is based on the energy-water balance model FEST-EWB (Flash–flood Event–based Spatially–distributed rainfall–runoff Transformation- Energy Water Balance model), which is a pixel wise calibrated model with remotely-sensed land surface temperature (LST), with mean areal absolute errors of about 3 °C, and then validated against local measured SM and latent heat flux (LE) with RMSE values of about 0.07 and 40 Wm−2.
Optimized irrigation volumes are assessed based on a soil moisture thresholds criterion, allowing to reduce the passages over the field capacity threshold reducing the percolation flux with a saving of irrigation volume without affecting evapotranspiration and so that the crop production. The implemented strategy has shown a significative irrigation water saving, also in this area where a traditional careful use of water is assessed.
The effect of the optimization strategy has been evaluated on the reductions of irrigation volumes and timing, from about 500 mm over the crop season in the Capitanata area to about 1000 mm in the Chiese district, as well as on the cumulated drainage and ET fluxes. The irrigation water use efficiency (IWUE) indicator is found to be higher when applying the SIM strategy than with the traditional irrigation strategy: of about 35 % for tomatoes fields in the South of Italy and of 80 % for maize fields in the North of Italy.
The activity is part of the European projects RET-SIF (www.retsif.polimi.it) and SMARTIES (www.smarties.polimi.it).