Remote sensing of maize intercropping – a multi-sensor approach
Mbali Mahlayeye is a PhD student in the Department of Natural Resources. Promotors are prof.dr. A.D. Nelson and dr. R. Darvishzadeh Varchehi from the Faculty ITC.
Cropping patterns, particularly intercropping - cultivating two or more crops within the same field, are prevalent in smallholder farming systems, enabling farmers to optimize land use, improve soil fertility, and increase resilience to environmental pressures such as pests, diseases, and climate extremes. When implemented effectively, intercropping promotes biodiversity and stabilizes yields. However, the diversity of crops and their interactions in intercropped fields make monitoring crop growth and estimating crop yields challenging. The dynamic and diverse nature of these practices often goes unreported in agricultural statistics, making them even more challenging to identify and document accurately.
Traditional methods, such as field surveys and manual crop assessments, often lack the precision needed to capture detailed variations in crop types, growth and health within these cropping patterns. Further, the capturing methods can be time-consuming, labor-intensive, and prone to human-error and prone to missing important aspects of crop growth. While remote sensing offer a more efficient way to map and monitor crops, the mapping of cropping patterns is often generalized. The fields are often classified as broad cropland categories, or identified only by the most dominant crop in the field. This oversimplification result in the loss of critical information regarding the location of various types of cropping patterns, as a result, key data needed for improving yields and managing resources effectively are often overlooked. Therefore, using both remote sensing data and field data to explore and understand the dynamics of these cropping patterns is crucial for eventually optimizing cropping practices and boosting crop productivity.
The small field sizes and variability in cropping practices of diverse agricultural landscapes requires high spatial and high temporal resolution data for discrimination and monitoring of cropping patterns. Remote sensing plays a key role in monitoring the spatial and temporal variability of growth phases in cropping patterns. This thesis demonstrates the spatio-temporal and spectral variability of cropping patterns using imagery data from Sentinel-2, PlanetScope, and DESIS hyperspectral sensors. The aim was to enhance our understanding of how to monitor and distinguish the complex cropping patterns, particularly those involving intercropping.
This thesis addressed three key aspects of monitoring and discriminating cropping patterns using remote sensing data:
- Temporal Variability: Using Sentinel-2 data, we analyzed annual cropping patterns and identified critical crop growth phases for distinguishing intercropping, emphasizing the importance of temporal resolution.
- Spectral Variability: Key spectral regions were examined using Sentinel-2 and DESIS hyperspectral data to improve cropping pattern discrimination.
- Spatial Variability: PlanetScope and Sentinel-2 were used to assess spatial differences within intercropping patterns, highlighting the role of spatial resolution.
The study faced several challenges, including limited field data, diverse cropping practices, and small field sizes, all of which impacted classification accuracy. The complexity of intercropped fields and their dynamic interactions throughout the crop growth cycle further contributed to these difficulties. Despite these challenges, the findings highlight the potential of remote sensing for improving cropping pattern classification in smallholder farming systems, particularly through the careful selection of sensors to enhance discrimination accuracy.
