LAI, yield and crop calendar information was collected from rice and maize sample fields. This dataset contains the measurements at locations within the fields and the averages per study field. The ground data was used for calibration and validation of phenology, detection, LAI, and yield estimation from the EO data. In addition to the ground datasets, LAI, crop phenometrics and yield were derived from the Landsat-MODIS data fusion and MODIS datasets. The processing procedure to derive these outputs are outlined in the four manuscripts published in this PhD thesis and a brief description is included in the readme.

The project includes four research chapters published in ISI journals 1. Sisheber, B., M. Marshall, D. Ayalew & A. Nelson (2022) Tracking crop phenology in a highly dynamic landscape with knowledge-based Landsat–MODIS data fusion. International Journal of Applied Earth Observation and Geoinformation, 106, 102670. 2. Sisheber, B., M. Marshall, D. Mengistu & A. Nelson (2023) Detecting the long-term spatiotemporal crop phenology changes in a highly fragmented agricultural landscape. Agricultural and Forest Meteorology, 340, 109601. 3. Sisheber, B., Marshall, M., Mengistu, D., and Nelson, A. (2023). Assimilation of Earth Observation data for crop yield estimation in smallholder agricultural systems. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 1-17, 10.1109/jstars.2023.3329237 4. Sisheber, B., M. Marshall, D. Mengistu & A. Nelson. The influence of temporal resolution on crop yield estimation with Earth Observation data assimilation (under review).

LAI was collected using AccuPar ceptometer to estimate LAI from fused and MODIS data Crop yield was measured through crop cut method and farmers information to validate crop yield modelling Crop sowing harvest dates was collected from farmers to calibate and validate phenology detection from Landsat-MODIS fused data