Application of Remote Sensing in Crop Monitoring and Forecasting

Background: Traditional crop monitoring methods rely heavily on ground-based surveys and manual field assessments, which are time-consuming, labor-intensive, and limited in spatial coverage. With increasing global food security concerns and the need for precision agriculture, there is a critical demand for efficient, large-scale monitoring systems that can provide timely and accurate information about crop health, growth patterns, and yield predictions across diverse agricultural landscapes.

Objectives: This study aims to evaluate the effectiveness of satellite-based remote sensing technologies for real-time crop monitoring and yield forecasting. The primary objectives include: (1) assessing crop health and stress conditions using multispectral imagery, (2) developing predictive models for yield estimation, and (3) creating an automated monitoring framework that can be implemented across different crop types and geographical regions.

Methods: The research utilized multi-temporal Sentinel-2 and Landsat-8 satellite imagery combined with ground-truth data collected from 150 agricultural fields across three growing seasons (2021-2023). Vegetation indices including NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index), and SAVI (Soil-Adjusted Vegetation Index) were calculated to assess crop vigor and phenological stages. Machine learning algorithms, including Random Forest and Support Vector Regression, were employed to develop yield prediction models. Ground-based measurements of crop biophysical parameters, weather data, and final harvest yields were integrated to validate remote sensing observations.

Key Results: The remote sensing approach demonstrated 92?curacy in identifying crop stress conditions compared to ground-based assessments, with early detection capabilities up to 3-4 weeks before visual symptoms appeared. Yield prediction models achieved a coefficient of determination (R²) of 0.87 when combining spectral indices with meteorological data, representing a significant improvement over traditional forecasting methods. The automated monitoring system successfully tracked crop development stages with 89% temporal accuracy and reduced field survey requirements by approximately 75%.

Conclusion & Implications: Remote sensing technologies offer a robust and scalable solution for modern crop monitoring and yield forecasting, providing farmers and agricultural stakeholders with timely, accurate information for decision-making. The high accuracy rates in stress detection and yield prediction demonstrate the potential for implementing these methods in precision agriculture systems. This approach can significantly enhance food security planning, optimize resource allocation, and support sustainable agricultural practices. Future applications should focus on integrating real-time data streams and expanding the framework to include emerging crops and climate-sensitive regions, ultimately contributing to more resilient and productive agricultural systems.