What We do
Novel Approach for Irrigation and Fertilizing Recommendations by Remote Sensing Data Assimilation to a Mechanistic Crop Model that Predicts Crop Response to Management and Environmental Conditions
2023-2025, Role: PI. Funding Agency: Chief Scientist of the Ministry of Agriculture (705,000 ILS)
What is the best way to assimilate remote sensing data to improve crop growth simulation in order to get more accurate yield predictions and make better management decisions throughout the growing season? Our hypothesis in this study is that combining remote sensing spectral measurements with eddy covariance flux measurements will create a more robust coupling between a crop model and a canopy radiative transfer model to monitor crop stress. Moreover, this coupling is expected to facilitate stressor identification and serve as a basis for a decision support system. Uncertainty propagation analysis for the coupled model is expected to inform on the best time for measurements to be performed in order to minimize the prediction uncertainty.
In collaboration with Prof. Rafi Linker (Technion)
Olive Canopy Management Using Airborne Sensors Synergy Under Climate Uncertainty
2023-2025, Role: PI. Funding Agency: Chief Scientist of the Ministry of Agriculture (600,000 ILS)
Trees rely on soil water storage in traditional rainfed settings, and pruning is the main management practice to reduce the canopy volume and limit tree water consumption. Insufficient pruning can lead to a depletion of the available water early in the season and result in reduced yield quality and quantity. Therefore, growers must adapt the canopy volume to the available water, particularly in drought years. Climate variability poses a significant challenge to orchard management, specifically because of inter-annual changes in rainfall amount and timing, along with the variations in the evaporative demand that result in unstable water consumption. This study aims to improve the olive oil yield in traditional olive orchards by empirically determining the optimal amount of required pruning in order to match it with water availability.
Achieving this aim will provide farmers with technological means to reach the optimal yield of their plot while accounting for the planting density and canopy volume. The main objective of our research is to combine an advanced method for estimating the tree canopy volume with a method to estimate the tree water stress level using remote sensing technology. By linking water stress and canopy volume with yield quality and quantity, we can devise recommendations to the growers to replace some of the lost traditional knowledge to deal with continuous pressure to grow crops with limited water supplies and climate uncertainty.
It is expected to increase the profitability of both traditional and modern orchards and help growers achieve higher water use efficiency.
In collaboration with Dr. Yafit Cohen and Prof. Arnon Dag.
A Prototype High-Resolution Data-Assimilation System and Reanalysis for Israel
2022-2025, Role: CI. Funding Agency: Ministry of Science and Technology (750,000 ILS)
Climate change is already here, but our understanding of its local impacts in Israel is still lacking. Although large networks of in-situ observations cover Israel, and there is an increasing amount of information coming from satellites, there are still spatial and temporal gaps that are not expected to be solved in the coming decades. The problem is more pronounced in Israel than in other locations due to its complex terrain and high climate variability. These characteristics necessitate more observations (relative to other regions) to reliably sample the regional variability and allow for regular temporal and spatial data interpolation. Reanalysis datasets are becoming more popular in the last few decades due to
their regularity in space and time, which is achieved by combining observations with model outputs using a predefined data-assimilation method. However, current reanalysis products are still too coarse to represent the high climate variability in Israel, and therefore, their use is limited. The proposed research will use up-to-date data assimilation practices and recently developed machine learning algorithms to generate a prototype high-resolution convectionpermitting
ensemble-based data-assimilation system and a reanalysis product for Israel. Future application of such a system will allow to study of recent changes in the local climate conditions, initialize future improved weather and climate forecasts, and force various physical, biological, and ecological models. Also, it will improve the ability of policymakers, stakeholders, and, more specifically, farmers to deal with the changing climate.
In collaboration with Dr. Udi Strobach
Near Real-Time Crop Mapping for Israel
2022-2025, Role: PI. Funding Agency: Ministry of Agriculture (1,200,000 ILS)
Up-to-date crop inventory is essential for efficient agricultural management, decision-making, improving public services to farmers, and smart management of resources. This task is crucial when food security is under threat as the population grows and climate variability is expected to rise. This is also critical for the upcoming agricultural reform in Israel to track changes and successfully navigate new agricultural policies.
In collaboration with Dr. Tarin Paz-Kagan
Developing a system for providing dynamic, high-resolution irrigation and fertilization recommendations for Israel
2022-2024, Role: CI. Funding Agency: Chief Scientist of the Ministry of Agriculture (699,000 ILS)
This study aims to develop a dynamic and continuous decision support system for irrigation and fertilization for specific growers and crops. This system could be assimilated into the Ministry of Agriculture web services at the end of the project. The specific objectives include: 1) improving four-day predictions of the potential evaporation and precipitation; 2) enhancing crop growth and crop coefficient estimations from earth observation data; 3) Assimilating the above mentioned products into a model that simulates the subsurface flow of water and solubles in order to derive irrigation recommendations; and 4) introducing an objective function to determine the desired crop stress-level, water and nutrient use efficiency, and potential environmental pollution that could result from over-irrigation vs. optimal irrigation while considering the input variables uncertainty.
in collaboration with Dr. Eran Tas (HUJI), and Prof. Naftali Lazarovitch (BGU)
Organic Soil function as source or sink for atmospheric carbon
2022-2024, Role: CI. Funding Agency: Chief Scientist of the Ministry of Agriculture (609,000 ILS)
This study aims to describe and quantify the main processes that affect the carbon cycle in peatlands in the Hula Valley under combined agricultural-hydrological management. The peatland contribution to carbon emissions will be assessed under the current operational conditions to determine whether these soils are a source or sink for carbon as a function of the water table levels. This study will include greenhouse gas flux measurements from the local scale to the regional scale, along side a study of the soil profiles and characterization of the main geochemical processes in the carbon system along these profiles.
in collaboration with Dr. Rotem Golan (ARO)
Using remote sensing for irrigation management: models calibration and testing via irrigation trials
2021-2023, Role: PI. Funding Agency: Chief Scientist of the Ministry of Agriculture (623,000 ILS)
This study aims to develop an irrigation management model for processing tomatoes using a synergic combination of public domain satellite imagery and Unmanned Aerial Vehicle (UAV) imagery. The crop coefficient will be estimated from time series of evapotranspiration measurements. In parallel, the desired crop stress curve will be calibrated against yield for optimization. Subsequently, we will perform an irrigation experiment in a commercial scale to test this combination of information sources and determine its water management potential agains the best-practived irrigation method.
in collaboration with Dr. Josef Tanny (ARO), and Dr. Yafit Cohen (ARO).
Development of a spatial decision support model to reduce agricultural soil erosion at a watershed scale using remote sensing tools
2020-2023, Role: CI. Funding Agency: Chief Scientist of the Ministry of Agriculture, (450,000 ILS)
The economic cost of soil deterioration and erosion is huge. In addition to the loss of fertile soil for agriculture, eroded soil negatively impacts adjacent fields, and clogs drainage channels. This study will use LIDAR and multispectral UAV imagery together with SAR to improve erosion estimations. In turn, it will serve as infrastructure to a decision support system that will help reduce soil erosion by estimating the effectivity of different cropping methods along the drainage basin.
In collaboration with Tarin Paz Kagan, Eli Argaman, Shmuel Assouline
Past Research
Estimating the national open-space agricultural water consumption
2019-2021, Role: PI. Funding Agency: Israel Water Authority (360,000 ILS)
Can we perform a daily water consumption estimation of the main crops in Israel based on satellite imagery? Nowadays, national water resources management relies heavily on water meter data. These data are often aggregated over long time periods for settlement consumption. This makes it very difficult to understand the spatial water consumption and to separate the consumption by different crops in open fields, orchards, growth houses, horticulture, etc. The aim of this research is to develop a model based on meteorological data and remote sensing in order to estimate water consumption in every field at a high temporal resolution. The study will be used to produce an operational tool to estimate the daily, long-term national water consumption of specific crops. This addition to the water resource management toolbox will facilitate agricultural water management, monitoring, and planning.
In collaboration with Uri Hochberg (ARO, Volcani).
Estimating crop water consumption based on UAV imagery
2019-2021, Role: PI. Funding Agency: Chief Scientist of the Ministry of Agriculture, IAI (900,000 ILS)
The purpose of this study is to develop a model to accurately estimate the water consumption of processing tomatoes using a VIS-NIR multispectral camera onboard a UAV as a means to improve the irrigation water-use efficiency, profitability, and sustainability in conventional field crops.
In collaboration with Josef Tanny (ARO, Volcani) and Israel Aerospace Industries.
Empowering big-data driven farming
2019-2021, Role: PI. Funding Agency: Ministry of Science and Technology (1,200,000 ILS)
In today’s digital age, large volumes of meteorological data and satellite imagery are being collected. The water consumption of field crops can be estimated based on these datasets, and irrigation management tools can be developed. Still, the great challenge is to fuse together this mass amount of data to produce irrigation recommendations for large areas.
In collaboration with Josef Tanny (ARO, Volcani)Dr. Eyal Brill (HIT)
Using the shortwave infrared (SWIR) sensor onboard BGUSAT to monitor land phenomena such as fires, desertification, and dryland agriculture.
2018, Role: PI. Funding Agency: Ministry of Science and Technology (200,000 ILS)
Nano-satellites are relatively inexpensive to construct and launch into low earth orbit. The BGUSAT is a nanosatellite carrying a payload of a one spectral band shortwave infrared (SWIR) camera at 1.55-1.7 µm. This band was expected to penetrate smoke, be sensitive to surface albedo, vegetation moisture content, fire-affected areas, and to active fires. This study demonstrates that basic scientific tasks can be performed using relatively modest means.
In collaboration with Arnon Karnieli (BGU).
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Machine learning-based estimation of actual evapotranspiration to support irrigation management
2019-2020, Role: PI. Funding Agency: Ministry of Science and Technology (80,000 EUR)
Agricultural water management plays a key role in economic and social development worldwide. The actual crop evapotranspiration (ET) determines the crop water requirements and therefore its estimation is important for efficient irrigation management. The eddy covariance (EC) method directly measures ET, which, in turn, is used to calibrate and validate simplified ET models to be used by growers for day-to-day irrigation management. This study will address two of the principal challenges of measuring ET continuously: 1) EC sensors often malfunction, which leads to data gaps. Besides, subjecting the data to quality control criteria adds additional gaps in the data; 2) EC systems are expensive and require highly skilled personnel for their operation and data processing. Therefore, it would be more feasible for farmers to estimate ET using models derived from a standard meteorological station.
The aim of this research is to develop robust algorithms to estimate actual crop ET using data from simple sensors that are commonly included in standard meteorological stations. These models would be applied during data gaps in the time-series of EC measurements when data from standard meteorological stations are available. These models would also be extended to situations where the EC system is absent and all we have to rely on to estimate ET is a standard meteorological station. This development could be the basis for an irrigation decision aid to farms at all scales, from smallholder farmers to large scale growers. In addition, this could be useful for research scientists that use EC systems on flux towers to monitor various ecosystems, from intensive agricultural systems to pasture land and natural habitats.
From the data science perspective, the scientific contribution will be the evaluation of a new robust method to estimate ET and a quantification of the expected precision as a function of the training data. From the technology side, the integration of this development in routine operation of flux towers would facilitate intercalibration of ET data with remote sensing and meteorological measurements to create irrigation management tools. Additionally, developing the technology into a mobile application will demonstrate the feasibility of the approach for wide-spread use by farmers.
In collaboration with Josef Tanny (ARO, Volcani) and Cedric Pradalier (Georgia Tech Lorrain)
Developing a model to estimate crop water requirements using satellite remote sensing
2017-2020, Role: PI. Funding Agency: Ministry of Science and Technology (600,000 ILS)
Soil and crop status and parameters change throughout the growing season. Monitoring these changes can facilitate practices that increase agricultural productivity and sustainability. In particular, estimating the crop water consumption is necessary for efficient irrigation management. This can be achieved either by various types of ground measurements, or by remote sensing. However, in practice, the use of satellite remote sensing to estimate crop water consumption has been limited due to lack of images at a spatial resolution enabling to capture the within-field variability of crop conditions at a sufficiently high frequency needed for irrigation management. This low temporal resolution limitation is further aggravated due to cloud coverage during satellite overpass. With the newly launched Venµs (a revisit period of 2 days and a 10 m ground resolution), and Sentinel-2 (a revisit period of 5 days and a 10 m ground resolution), we could get past these basic limitations for the first time.
In addition, we propose to develop complementary processing methods to integrate the cloud penetrating synthetic aperture radar (SAR) from the newly launched Sentinel-1 in order to estimate crop water consumption on days with cloud coverage. Therefore, the overarching aim of this research is to harness the high temporal and spatial resolution of Venµs, Sentinel-2, and Sentinel-1 to assess crop water consumption.
In collaboration with Josef Tanny (ARO, Volcani).
