Agricultural Yield Estimation and Forecast with Remote Sensing and GIS
[Contact: Prof. Raghavendra Jana, r.jana@skoltech.ru]
Accurate estimation and forecast of agricultural yield are essential for effective decision-making in agriculture, including crop planning, resource allocation, market forecasting, and policy formulation. Traditionally, yield estimation has relied on labor-intensive field surveys and statistical methods, which are often limited in their spatial and temporal resolution. In recent decades, advances in remote sensing and geographic information systems (GIS) have revolutionized the way we monitor and analyze agricultural landscapes, offering a wealth of opportunities for improving yield estimation accuracy and efficiency.
Remote sensing technologies, such as satellite imagery and airborne sensors, provide valuable data on crop growth, health, and spatial distribution at various scales. Vegetation indices derived from remote sensing data, such as the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), represent crop biomass and productivity, enabling the estimation of yield with high spatial and temporal resolution. Furthermore, GIS allows for the integration of remote sensing data with other geospatial information, such as soil properties, weather data, and land use/land cover maps, to develop comprehensive models for yield estimation and forecast.
In the context of Russian agriculture, where vast territories are devoted to crop cultivation, the adoption of remote sensing and GIS-based approaches for yield estimation and forecast could significantly enhance agricultural productivity and sustainability. By providing timely and spatially distributed information on crop growth and yield across large agricultural landscapes, these technologies can enable farmers, researchers, and policymakers to make more informed decisions regarding crop management, input optimization, and land use planning. However, there remains a need for research to develop and validate robust methodologies for yield estimation tailored to the specific conditions of Russian agricultural systems, taking into account the diverse environmental factors influencing crop production.
Objectives:
This project aims to:
1. Develop a remote sensing-based framework for mapping and monitoring agricultural land use and crop types.
2. Investigate the use of satellite-derived vegetation indices and biophysical parameters for estimating crop yield.
3. Integrate ground-truth data and computational algorithms to calibrate and validate remote sensing-based yield estimation and forecast models.
4. Explore the potential of combining remote sensing data with GIS-based spatial analysis techniques to assess the spatial variability of crop yield within agricultural fields and across landscapes.
Methodology Overview:
The project will involve acquiring and preprocessing high-resolution satellite imagery covering target agricultural regions. Land use classification techniques will be applied to delineate agricultural classes and identify crop types. Satellite-derived vegetation indices will then be utilized to monitor crop growth and estimate yield. Machine learning algorithms, trained with ground-truth yield data, will develop robust models for yield estimation. Finally, GIS tools will analyze spatial variability of yield within fields and generate maps for precision agriculture decision support.
Accurate estimation and forecast of agricultural yield are essential for effective decision-making in agriculture, including crop planning, resource allocation, market forecasting, and policy formulation. Traditionally, yield estimation has relied on labor-intensive field surveys and statistical methods, which are often limited in their spatial and temporal resolution. In recent decades, advances in remote sensing and geographic information systems (GIS) have revolutionized the way we monitor and analyze agricultural landscapes, offering a wealth of opportunities for improving yield estimation accuracy and efficiency.
Remote sensing technologies, such as satellite imagery and airborne sensors, provide valuable data on crop growth, health, and spatial distribution at various scales. Vegetation indices derived from remote sensing data, such as the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), represent crop biomass and productivity, enabling the estimation of yield with high spatial and temporal resolution. Furthermore, GIS allows for the integration of remote sensing data with other geospatial information, such as soil properties, weather data, and land use/land cover maps, to develop comprehensive models for yield estimation and forecast.
In the context of Russian agriculture, where vast territories are devoted to crop cultivation, the adoption of remote sensing and GIS-based approaches for yield estimation and forecast could significantly enhance agricultural productivity and sustainability. By providing timely and spatially distributed information on crop growth and yield across large agricultural landscapes, these technologies can enable farmers, researchers, and policymakers to make more informed decisions regarding crop management, input optimization, and land use planning. However, there remains a need for research to develop and validate robust methodologies for yield estimation tailored to the specific conditions of Russian agricultural systems, taking into account the diverse environmental factors influencing crop production.
Objectives:
This project aims to:
1. Develop a remote sensing-based framework for mapping and monitoring agricultural land use and crop types.
2. Investigate the use of satellite-derived vegetation indices and biophysical parameters for estimating crop yield.
3. Integrate ground-truth data and computational algorithms to calibrate and validate remote sensing-based yield estimation and forecast models.
4. Explore the potential of combining remote sensing data with GIS-based spatial analysis techniques to assess the spatial variability of crop yield within agricultural fields and across landscapes.
Methodology Overview:
The project will involve acquiring and preprocessing high-resolution satellite imagery covering target agricultural regions. Land use classification techniques will be applied to delineate agricultural classes and identify crop types. Satellite-derived vegetation indices will then be utilized to monitor crop growth and estimate yield. Machine learning algorithms, trained with ground-truth yield data, will develop robust models for yield estimation. Finally, GIS tools will analyze spatial variability of yield within fields and generate maps for precision agriculture decision support.