This chapter highlights time series image processing for accurate agriculture characterization through two Moroccan experiences. The first case aims at crop mapping. A new classification approach based on multiple classifiers combination (MCC) was developed and applied to multi-temporal enhanced vegetation index (EVI) bands. The whole process is performed in three stages: (1) Landsat data preparation and multi-temporal staked EVI image extraction, (2) MCC construction from six advanced and supervised classifiers, and (3) stacked EVI image classification using the build-up MCC. Some post-classification contextual rules were also added in order to optimize the crops classification and the final parcel shape. In the second case, a post-classification change detection process was implemented to detect changes in forest area. Many classification schemes with different vegetation and texture indices were investigated. The two experiences are cost-effective, reproducible, and transferable. Consequently, they can regularly be used to produce up-to-date land use maps.

The recent advances in geoscience technologies and earth observation tools have evolved in recent years in a fast cadence. Since 1980 with the apparition of Landsat mission firstly named the Earth Resources Technology Satellite, with 80 m spatial resolution; the ability to capture finest details was limited. The emergence of new concepts in agriculture like digital agriculture and precision agriculture (PA) was challenging the capacity of satellite images to capture the variation within the field. The domain that was firstly more dedicated to aerial and handheld remote sensing is now accessible to satellite remote sensing thanks to recent advancements that had provided more satisfactory spatial resolution reaching 0.3 m in a daily revisit frequency. Different providers have launched commercial, very high-resolution nanosatellite constellations to respond to more precise needs, and increasing the availability of remotely sensed images expands the horizon of our choices of imagery sources. The present work intends to compare spatial, temporal, spectral, and radiometric resolution, considering the farmer's requirements and precision agriculture application's requirements nowadays available and the future generation satellite missions, and benchmark the major satellite images providers. It exposes the characteristics and future missions to facilitate adequate choice. The criterion of selecting the appropriate sensors among the considerable amount of providers was limited to optical data, with less than 10 m spatial resolution and frequent revisit time. The offer of imagery is majorly commercial missions; the available open and accessible alternative is limited in the sentinel mission with 10 m spatial resolution.

This chapter highlights time series image processing for accurate agriculture characterization through two Moroccan experiences. The first case aims at crop mapping. A new classification approach based on multiple classifiers combination (MCC) was developed and applied to multi-temporal enhanced vegetation index (EVI) bands. The whole process is performed in three stages: (1) Landsat data preparation and multi-temporal staked EVI image extraction, (2) MCC construction from six advanced and supervised classifiers, and (3) stacked EVI image classification using the build-up MCC. Some post-classification contextual rules were also added in order to optimize the crops classification and the final parcel shape. In the second case, a post-classification change detection process was implemented to detect changes in forest area. Many classification schemes with different vegetation and texture indices were investigated. The two experiences are cost-effective, reproducible, and transferable. Consequently, they can regularly be used to produce up-to-date land use maps.