Space to Soil: How Satellites are Revolutionizing Agriculture!!

Remote sensing data using satellites is a necessity, not a choice, for decision-making by the agriculture and food ecosystem stakeholders. The world needs to keep an eye on the agriculture value chains, from farms to the fork, more than ever. Disruptions due to pandemics, wars, and climate change are not just side notes but the Big Picture today. The majority of agricultural commodities saw extreme volatility, driven by supply shortages, demand imbalances, climate disruptions, transportation bottlenecks, and geopolitical issues. There has been a large-scale shift in cropping patterns globally because of environmental and other issues. It is time we make our Agri commodity value chains more predictable.

Learning from History: The Great Grain Robbery of 1972

In the 1970s, sophisticated agricultural monitoring was hardly available and limited to large developed nations like the U.S. and the U.S.S.R, two key actors in "The Great Grain Robbery." According to the National Geospatial-Intelligence Agency of the U.S., despite using satellites to photograph grain-growing areas, the resolution was not clear enough to reveal much information on the health of crops, leaving the probable outcomes of Russian harvests opaque to U.S. intelligence.

In 1972, the U.S. and the U.S.S.R, while in the middle of the Cold War, were still trading. Crop failure was not unusual in the breadbasket areas of the U.S.S.R due to weather, and the U.S.S.R often turned to foreign commodity markets to make up the difference. In July 1972, the U.S.S.R began buying up foreign wheat, purchasing 10 million tons from U.S. brokers by August. Despite reports of crop failures in the U.S.S.R and elsewhere, the U.S. government failed to appreciate the significance of the global grain shortage and its effects on the U.S. economy.

The U.S. government wasted $300 million in public funds and lost another $300 million in potential revenue by unwittingly subsidizing the U.S.S.R wheat purchases. The shortage in Russia was part of a worldwide grain production shortage that almost wiped out international stockpiles. The U.S. government did not recognize this due to a lack of a comprehensive view of agricultural output worldwide.

The Need for Satellite-Based Remote Sensing in Agriculture

Leaving out the politics behind The Great Grain Robbery, we need to focus on the need for a comprehensive view of agricultural production. It is crucial for nations individually and the world collectively to be prepared for shocks, as seen in the past and unfolding today. The world needs satellite-based remote sensing in agriculture more than ever.

How Satellites Make Agriculture More Efficient and Predictable

In generic terms, satellite data comprises signals sent by crops, soil, water, and more, at different wavelengths of light. Processing satellite data can let us know if crops, soil, and water are facing any problems because of climate change, pests, and other instances. With satellite-captured data, one can estimate variables like soil moisture, vegetation indices, evapotranspiration, crop health, yield, and crop water usage.

• Farmers: Can put favorable management techniques in place to minimize inputs, maximize outputs, and eliminate waste.
• Governments: Can make policies to ensure food security, adequate realization to farmers, and better incentivization for different value chain players.
• Insurance Companies and Agriculture Finance Players: Can underwrite risks more efficiently and handle the settlement of claims more transparently.
• Agri Commodity Traders and Processors: Can plan their procurement strategies better.
• Agri Input Producers: Can forecast demands for their products and ensure their availability.

Benefits of Using Satellites for Agriculture

Satellites offer several advantages due to their inherent capability of acting as an eye in the sky with the possibility of covering every corner of the Earth:

• Timely and Objective Coverage: Provides actionable insights from local to global levels.
• Observing Inaccessible Areas: Enables better planning and effective provision of extension support.
• Periodic Plant Growth Monitoring: Estimates crop productivity at micro and macro levels.
• Soil Moisture and Irrigation Requirements: Assesses needs for individual farms or regions and visibility of water tables and local water bodies.
• Soil and Agro-Climatic Conditions: Identifies conditions to plan better varieties to cultivate and optimize agricultural practices and inputs.
• Crop Yields and Health Forecasting: Provides early warnings of pest attacks and crop diseases.
• Maximizing Yields: Reduces energy consumption and avoids waste of farm inputs (water, fertilizer, and pesticide) through optimization.

Limitations of Satellite Use in Agriculture

While satellite technology has evolved significantly since Sputnik 1 was launched in 1957, there are still a few limitations on economic and technological fronts:

• High Resolution Challenges: Difficult to obtain high spectral, spatial, and temporal resolution with the same instrument. Collating data from different instruments is tedious and costly.
• Cloud and Vegetative Cover Penetration: Optical sensors cannot penetrate clouds or vegetative cover, leading to data gaps or decreased utility.
• Coarse Resolution Data: Provides a synoptic view but the spatial resolution is too coarse for field-level assessments.
• Frequency of Satellite Passes: Many satellites only pass over the same spot every 3-5 days or sometimes as seldom as every 16+ days.
• Limited Bands in Multispectral Instruments: Observes reflected and emitted light in broad wavelength ranges for a particular band with a limited number of bands.
• Data Variety and Volume: Large amounts of data exist in various file formats, sizes, and from multiple sources.

Overcoming Limitations with Modern Techniques

Modern techniques complement satellite data to overcome some limitations:

• Artificial Intelligence & Machine Learning: Enhance data processing and analysis.
• Calibration & Ground Truthing Using IoT Sensors: Improve accuracy and reliability.
• Big Data Analysis & Simulation Tools: Enable comprehensive and actionable insights.

Satellite Platforms and Features for Agriculture

Various national and multilateral agencies, including NASA, ESA, CNSA, and ISRO, are at the forefront of providing satellite data for agriculture. Private players like Planet Labs, DigitalGlobe, EarthDaily Analytics, and EOS Data Analytics are also enhancing the application of satellite-based remote sensing.

Key Satellite Sensors and Their Uses

• Land Surface Reflectance (LSR): Measures greenness of vegetation, useful for determining phenological transition dates and generating insights like Vegetation Indices (VIs) and Leaf Area Index (LAI).
• Evapotranspiration (ET): Provides spatially distributed regional ET information, useful for monitoring water availability, drought conditions, and crop production.
• Land Surface Temperature (LST): Shows temperature of the land surface, useful for monitoring changes in weather and climate patterns, and evaluating water requirements.
• Precipitation: Measures intensity and variability of rain and snow, providing frequent and accurate observations and measurements.
• Soil Moisture (SM): Important for modeling interactions between land and the atmosphere, helping in accurate weather and climate predictions.
• Vegetation Greenness/Index (VG/VI): Based on absorption and reflection of visible and near-infrared wavelengths, provides information about plant health and productivity.
• Synthetic Aperture Radar (SAR): Active data collection that provides high-resolution data, useful for monitoring surface characteristics like structure and moisture.

Practical Application of Satellite-Based Remote Sensing in Agriculture

Crop Monitoring

Satellites enable phenology, crop area, crop type, crop condition, yield, irrigated landscape, flood, drought, frost monitoring, and accurate reporting of agricultural statistics.

Crop Forecasting

Accurate forecasting of yield or shortfalls in crop production and food supply per region and country.

Disaster Management

Monitors food security in high-risk regions worldwide by providing early warning of famine, enabling timely mobilization of international response in food aid.

Case Studies of Top Agriculture Remote Sensing Companies

PlanetDAO

Overview: Planet Labs operates a fleet of small satellites known as "Doves" that capture high-resolution imagery of the Earth’s surface.

Application in Agriculture:

• Crop Monitoring: Provides daily updates on crop health, allowing farmers to make informed decisions on irrigation, fertilization, and pest management.
• Yield Prediction: Helps predict crop yields by analyzing vegetation indices and other relevant data.

Case Study: In 2020, Planet Labs partnered with Corteva Agriscience to provide satellite imagery for real-time crop monitoring, which helped farmers in the U.S. Midwest manage their fields more efficiently, resulting in improved crop yields and reduced input costs.

DigitalGlobe ( Maxar Technologies )

Overview: DigitalGlobe, a subsidiary of Maxar Technologies, provides high-resolution Earth imagery and geospatial solutions.

Application in Agriculture:

• Precision Agriculture: Offers detailed imagery that helps in precise application of inputs like water, fertilizer, and pesticides.
• Disaster Response: Provides imagery for assessing damage from natural disasters like floods and droughts, aiding in rapid response and recovery.

Case Study: During the 2019 floods in the Midwest United States, DigitalGlobe’s satellite imagery was used to assess crop damage and guide the deployment of resources for recovery, helping farmers to quickly resume agricultural activities.

EarthDaily Analytics

Overview: EarthDaily Analytics focuses on high-resolution imagery and data analytics for various applications, including agriculture.

Application in Agriculture:

• Crop Health Monitoring: Uses machine learning algorithms to analyze satellite imagery and provide insights into crop health and stress factors.
• Sustainability Tracking: Monitors land use and sustainability practices to ensure environmentally friendly farming.

Case Study: In 2021, EarthDaily Analytics collaborated with the Government of Brazil to monitor deforestation and its impact on agriculture. The project helped in implementing policies to promote sustainable farming practices, reducing deforestation rates and improving agricultural productivity.

EOS DATA ANALYTICS

Overview: EOS Data Analytics provides geospatial solutions with a focus on agricultural monitoring and analysis.

Application in Agriculture:

• Soil Moisture Monitoring: Uses satellite data to assess soil moisture levels, aiding in optimal irrigation management.
• Crop Disease Prediction: Analyzes vegetation indices and other data to predict and mitigate crop diseases.

Case Study: In 2022, EOS Data Analytics partnered with the Ukrainian Ministry of Agriculture to provide real-time soil moisture data, helping farmers optimize irrigation practices and conserve water, leading to increased crop yields and reduced water usage.

Conclusion

The integration of satellite-based remote sensing in agriculture is not just a technological advancement but a necessity in today's world. It offers a comprehensive and real-time view of agricultural activities, enabling better decision-making, enhancing efficiency, and ensuring sustainability. By learning from historical events like The Great Grain Robbery and leveraging modern satellite technology, we can build a more resilient and predictable agricultural value chain.

About HnyB

HnyB is a boutique consulting company specializing in the dynamic intersection of Agriculture, Technology, and Economics. With a focus on the agriculture sector, HnyB provides expert guidance and strategic advice to various stakeholders within the agriculture ecosystem. Their services encompass a wide range of strategic areas, helping clients navigate the complex landscape of modern agriculture. By leveraging their expertise at the intersection of these fields, HnyB plays a vital role in shaping the future of agriculture, optimizing technology adoption, and driving economic growth in the agricultural sector.

About the Author

帕里克迪帕克 is a serial entrepreneur, investor, and ecosystem builder in the agriculture technology domain. With 25 years of diverse experience working across 34 countries on various projects, he has been honored as a Top 10 Agropreneur 2019 by Future Agro Challenge, Greece, and a Technology Pioneer 2018 by the World Economic Forum, Switzerland. He advises various private, public, and multilateral organizations in the agriculture and technology domain.