International Journal of Applied Earth Observation and Geoinformation Improving crop yield prediction in Sweden using satellite remote sensi
International Journal of Applied Earth Observation and Geoinformation Improving crop yield prediction in Sweden using satellite remote sensing and the ecosystem model LPJ-GUESS. 134(104183)
Remote sensing observations and artificial intelligence algorithms have emerged as key components for crop yield estimation at various scale
Remote sensing observations and artificial intelligence algorithms have emerged as key components for crop yield estimation at various scales during the past decades. However, the utilization of multi-source satellite data and machine learning for estimating aggregated crop yield at the regional level in Europe has been only scarcely explored. Our study aims to bridge this research gap by focusing on the district-level spring barley yield estimation in southern Sweden from 2017 to 2022. We developed an estimation method with the random forest (RF) approach using four satellite-derived products along with two climate variables. These variables were used individually and in combinations as inputs for the RF approach. The results showed that vegetation indices (VIs) outperformed solar-induced chlorophyll fluorescence (SIF) in barley yield estimation, while combining VIs and SIF variables achieved the highest model performance (R2 = 0.77, RMSE = 488 kg/ha). The inclusion of climate variables generally had little added contributions to the model performance. Importantly, barley yield prediction could be achieved two months prior to harvest, using monthly VIs and SIF data from April and May. Our study demonstrated the feasibility of using freely accessible satellite data and the machine learning approach for estimating crop yield at the pan-European regional level. We expect that our proposed methodology can be extended to different crop types and regional-scale crop yield estimation in Europe, benefiting national and local authorities in making agricultural productivity decisions.
Lund University, Faculty of Science, Dept of Physical Geography and Ecosystem Science, Lunds universitet, Naturvetenskapliga fakulteten, Institutionen för naturgeografi och ekosystemvetenskap, Originator, Lund University, Profile areas and other strong research environments, Strategic research areas (SRA), MERGE: ModElling the Regional and Global Earth system, Lunds universitet, Profilområden och andra starka forskningsmiljöer, Strategiska forskningsområden (SFO), MERGE: ModElling the Regional and Global Earth system, Originator, Lund University, Profile areas and other strong research environments, Strategic research areas (SRA), BECC: Biodiversity and Ecosystem services in a Changing Climate, Lunds universitet, Profilområden och andra starka forskningsmiljöer, Strategiska forskningsområden (SFO), BECC: Biodiversity and Ecosystem services in a Changing Climate, Originator, Lund University, Profile areas and other strong research environments, Lund University Profile areas, LU Profile Area: Nature-based future solutions, Lunds universitet, Profilområden och andra starka forskningsmiljöer, Lunds universitets profilområden, LU profilområde: Naturbaserade framtidslösningar, Originator, Lund University, Profile areas and other strong research environments, Strategic research areas (SRA), eSSENCE: The e-Science Collaboration, Lunds universitet, Profilområden och andra starka forskningsmiljöer, Strategiska forskningsområden (SFO), eSSENCE: The e-Science Collaboration, Originator
