Using Satellite Imagery Datasets in the Agriculture Sector

The sheer amount of low-earth objects orbiting the earth is very telling.

Low Earth Orbit Visualization (LEOV) showing the 19204 objects deployed for remote sensing and other applications — Screenshot from Leo Labs Platform taken on 23–02–2022 02:04 UTC

This blog assesses how satellite imagery datasets and their derived products are being used to make high-quality research outcomes by public sector organizations with a goal to support policy- and decision-making in the Agriculture sector. The analysis is based on a systematic literature review of 159 Scopus indexed journal publications on this topic.


Dataset downloaded from Scopus

Key Observations

High-resolution (500 m grid cell size) map of Soil Erodibility estimated as K-factor in the European Union (Source — Soil erodibility in Europe: A high-resolution dataset based on LUCAS)
Crop Health Factor Maps showing its distribution among the administrative units across different years (Source — Paddy crop insurance using a satellite-based composite index of crop performance)
Schema shows the water balance of the Badra Basin in Iraq (Source — Estimating the Volume of Sediments and Assessing the Water Balance of the Badra Basin, Eastern Iraq, Using Swat Model and Remote Sensing Data)
The global land cover map in 2020 under different classification systems (Source — iMap World 1.0)
ESRI 2020 Land Cover Map (Source — ARCGIS App)
a Water footprint, b energy footprint, c carbon footprint, and d water sustainability in irrigated agriculture of the North China Plain (Source — Impacts of irrigated agriculture on food–energy–water–CO2 nexus across metacoupled systems)
The peak power demand for irrigation water pumping in a specific scenario for different months (Source - A GIS-Based Approach to Estimate Electricity Requirements for Small-Scale Groundwater Irrigation)
Collect Earth Tool
The protected area forest lifetimegreen map”

Use Cases

Access to Information for Agricultural Production and Management

  • Crop Productivity and Yield form the foundation for agricultural production and management for smallholder farmers. Spatial distribution of crops, Yield monitoring, … are some of the key parameters to derive important information about crop productivity and food security. There are numerous studies proposing methods to integrate satellite imagery sources for calculating these parameters. For example, this study from China proposes a method based on discrete grids with machine learning to integrate GaoFen-1 and Sentinel-2 imagery for crop classification.
Crop mapping via different satellite data in various planting patterns (Source — Large-scale crop mapping from multi-source optical satellite imageries using machine learning with discrete grids)
Vegetable map based on the combined defuzzification (Source — Vegetable mapping using the fuzzy classification of Dynamic Time Warping distances from time series of Sentinel-1A images)
  • Crop Planning — Predicting crop maturity dates is important for improving crop harvest planning and grain quality. In a study from China, a data assimilation framework incorporating the leaf area index (LAI) product from Moderate Resolution Imaging Spectroradiometer (MODIS) into a World Food Studies (WOFOST) model was proposed to predict the maturity dates of winter wheat in Henan province, China.
Prediction results of maturity date for different forecasting nodes. (Source — Prediction of Winter Wheat Maturity Dates through Assimilating Remotely Sensed Leaf Area Index into Crop Growth Model)

Sustainability and Climate Resilience

  • Data-Powered Positive Deviance — The Positive Deviance approach is focused on the identification and scaling of strategies undertaken by positive deviants (PDs), which refer to individuals or communities that use uncommon practices that enable them to achieve better outcomes than their peers, despite having similar conditions and resources. Although the approach has had success, the scaling of successes achieved across diverse geographies and large populations has presented numerous challenges, part of which can be attributed to the conventional qualitative and quantitative approaches employed, namely interviews and surveys. Recent developments in technologies and the availability of big data have also presented new possibilities for the PD approach, whereby big data can be harnessed to fill those Spatio-temporal information gaps. In this publication, Identifying Potential Positive Deviants (PDs) Across Rice Producing Areas in Indonesia, an independent time series-based validation approach was developed entirely using EO data to confirm the detection of significant differences in terms of productivity proxies between positive deviants and the rest of the samples.
  • Water Management — This work aims to evaluate the sustainability of water management for agriculture in a specific territory through the creation of a synthetic index resulting from the aggregation of multiple indices (environmental, economic, and social). The resulting synthetic index can be used to set sustainability standards and to guide the choices mandated by the Common Agricultural Policy 2023–2027. In this work, a Multiple Criteria Decision Analysis (MCDA) method facilitates a complex process such as establishing a degree of sustainability in a certain area and, therefore, provides support to national or regional policies and communities. The integration of MCDA and GIS increases the efficiency of the support activity. A case study is presented evaluating the level of sustainability in the Irrigation and Reclamation Consortium of Piacenza and Emilia Centrale
  • Monitoring Soil Parameters — Prediction of soil moisture is important for determining when to irrigate and how much water to apply, to avoid problems associated with over-and under-watering. Satellite imagery sources such as Sentinel-1 can be used to predict soil moisture content, through the use of architectures such as the convolutional neural network (CNN).
  • Early-warning systems — Drought prediction models have been effective in estimating future agricultural drought by using meteorological data. When coupled with satellite-derived indicators such as the Vegetation Condition Index, it has been shown that the accuracy of these models increases significantly. These models can help establish early-warning systems that help reduce disaster risk and prevent environmental and socio-economic damage.
Prediction of diagnostic drought prediction model (DDPM) vegetation condition index (VCI) in August 2008, an example year for severe conditions, and its intermediate results. (Source — Development of earth observational diagnostic drought prediction model for regional error calibration)
  • Cropping Intensity Dynamics — Cropping intensity (CI) in cultivated land, helps in understanding the intensified utilization of cultivated land which is critical information for ensuring food security and realizing agricultural sustainability. This study from China demonstrates how to monitor cropping intensity dynamics from 1982 to 2018 using Global Land Surface Satellite Products at large scales.
Percentage areas of cultivating and where different cropping systems were used in NCP, China from 1982 to 2018 (Source — Monitoring Cropping Intensity Dynamics across the North China Plain from 1982 to 2018 Using GLASS LAI Products)

Access to Finance, and Markets

  • Agricultural finance institutions provide the agri-food industry with credit and insurance services, as well as related services such as re-insurance and decision support services to commodities and derivatives traders. They face challenges due to the lack of availability of standardized and up-to-date datasets. For example, insurance contracts such as area-yield insurance (guaranteeing a certain percentage of normal yield over an insured area) require highly reliable, near real-time yield estimates. Satellite-derived indicators play a key role in bridging this data gap in the financing process. For example, this paper introduces a “satellite-derived crop health index” as an alternative to yield data in such an insurance model.
  • Research is also able to identify trends and insights from market-oriented practices in urban and peri-urban agricultural (UPA) practices as demonstrated by the paper from Kenya, where spatiotemporal dynamics of UPA and market-oriented farmer’s responses to changing socio-spatial circumstances in two rapidly growing Kenyan cities: Nyeri and Nakuru were analyzed. Another work from Argentina proposes the calculation of a crop proximity index using satellite information to assess and monitor peri-urban agro-industrial activity.
Maps of LULC in Nyeri and Nakuru in 2019 and zonal statistics (1 km rings) on relative distributions of the five LULC classes along the urban-rural continuum in 2010 and 2019. (Source — Continuity under change)
  • A greenhouse is an important land-use type, which can effectively improve agricultural production conditions and increase crop yields. The human population is increasing as well as the demand for food and agricultural products, which is expected to grow by 70–100% of the total production of 2010 by 2050 (Godfray et al., 2010). In the context of globalization, many countries, such as Mexico, are reorienting their agriculture towards exports. To achieve these goals, global annual agricultural expansion and improvements in production technology are expected (Tilman, Balzer, Hill, & Befort, 2011). One of the most common technologies is Protected Agriculture (PA) which consists of infrastructure that modifies the natural environment, to protect the crops from external factors and enhance growth (Jensen & Malter, 1995). These include mulching, controlled environment agriculture, hydroponics, greenhouses, tunnels, etc. The publication Rapid Mapping of Large-Scale Greenhouse Based on Integrated Learning Algorithm and Google Earth Engine demonstrates how this infrastructure type can be classified from Landsat images.
Spatial Distribution of Greenhouses is one of the provinces in China (Source — Landsat-Derived Annual Maps of Agricultural Greenhouse in Shandong Province, China from 1989 to 2018)


  • Access to Information for Agricultural Production and Management
  • Sustainability and Climate Resilience
  • Access to Finance and Markets



Lead Data Scientist | CTO at Analytics for a Better World | Public Sector Consultant

Love podcasts or audiobooks? Learn on the go with our new app.

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store