Within-field Spatial Heterogeneity of Crop Growth from Multi-year Green Area Index Time Series Analysis
(2024) EO FOR AGRICULTURE UNDER PRESSURE — Location: ESA-ESRIN FRASCATI, ITALY (20.May.2024)
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- Advances in field-based plant phenotyping, ranging from low-cost handheld devices to extensive satellite imagery, are opening new avenues for understanding and optimising plant responses to environmental factors. The scientific challenge is to scale-up such observational capabilities from in-orbit systems. The present study aims to characterise the within-field spatial and temporal variability of environmental conditions affecting crop growth. After pre-processing the Sentinel-2 data with the open source system Sen4Stat, the Green Area Index (GAI) was retrieved by inversion of a radiative transfer model, i.e. a locally tuned BV-Net algorithm. For each year of Sentinel-2 acquisition (2017-2023) and each parcel of the study area (Wallonia), the GAI time series was used to (1) infer the growing season of the main crop and (2) produce a spatial indicator of vegetation growth heterogeneity based on this in-season time series. The resulting maps effectively capture the homogeneity or heterogeneity observed in the GAI profiles. The evaluation of the seasonal maps included an analysis of agronomic factors such as crop rotation, agrometeorological data, soil maps and slope, which helped to interpret the sources of heterogeneity within fields. Despite significant variation in the seasonal maps between years, discernible patterns emerged, highlighting similarities in conditions or crops between years. An important finding is that certain fields, identified as spatially homogeneous on the basis of soil characteristics, exhibit heterogeneity in vegetation growth. Conversely, fields that appear to have strong spatial heterogeneity based on the soil map may either have a fair degree of homogeneity in vegetation growth, or a pattern of heterogeneity that differs from the soil map. The versatility of the method extends its applicability to different agricultural settings and to any type of crop. The resulting maps could guide dynamic agricultural practices towards greater sustainability, including irrigation, fertilisation and spraying management, and could also provide opportunities for new soil sampling designs and targeted in-field phenotyping.
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Kenda, T., Champagne, C., Draye, X., & Defourny, P. (2024). Within-field Spatial Heterogeneity of Crop Growth from Multi-year Green Area Index Time Series Analysis. EO FOR AGRICULTURE UNDER PRESSURE, ESA-ESRIN FRASCATI, ITALY. https://hdl.handle.net/2078.5/242055