| Title: |
Estimating spatial and temporal variability of crop growth by radiation-driven models based on satellite data assimilation |
| Authors: |
Perfetto Marco; Alessi Nicola; Nutini Francesco; Boschetti Mirco; Perego Alessia; Ferrarini Andrea; Ragaglini Giorgio |
| Contributors: |
M. Perfetto; N. Alessi; F. Nutini; M. Boschetti; A. Perego; A. Ferrarini; G. Ragaglini |
| Publisher Information: |
iCROPM |
| Publication Year: |
2026 |
| Collection: |
The University of Milan: Archivio Istituzionale della Ricerca (AIR) |
| Subject Terms: |
crop modelling; remote sensing; LAI; precision agriculture; production efficiency model; sensitivity analysis; Settore AGRI-02/A - Agronomia e coltivazioni erbacee |
| Description: |
Precision agriculture aims to improve field management by accounting for spatial variability in dynamic cropping systems (CS). To support farmers in optimizing crop management, it has become important to develop tools that capture both spatial and temporal heterogeneity within crop production. Mechanistic crop models simulate aboveground biomass (AGB) accumulation by explicitly representing physiological and environmental processes linking absorbed solar radiation, transpiration, and nutrient uptake. However, these process-based models typically require site- and cultivar-specific calibration and often fail to capture fine-scale variability. Assimilating remote sensing (RS) data into radiation-driven Production Efficiency Models (PEM; Monteith, 1977; McCallum et al., 2009), offers a promising solution by providing spatio-temporal explicit predictions of biomass growth, and thus crop requirements or responses. A PEM was developed and implemented in a PostGIS database to run at a daily time steps and with Sentinel-2 (S2) resolution (10 x 10 m). Leaf Area Index (LAI), derived from the S2 biophysical processor (Weiss et al., 2020), was assimilated daily to estimate the fraction of intercepted solar radiation by crops at pixel level via the Lambert–Beer law. The PEM was calibrated and evaluated for wheat and maize grown in the Po Valley (northern Italy) using two independent datasets. Calibration relied on 120 AGB observations (2022-2023) from five different sites with different management practices, while evaluation was carried out on 312 observations (2025) from four additional sites. Although the temporal and spatial dynamics of LAI should implicitly reflect the effects of limiting factors, a development stage-dependent Morris sensitivity analysis (SA) was conducted to assess how variations in LAI dynamics, air temperature, and senescence affect simulated AGB. The simulations performed for each S2 pixel provide reliable estimates of AGB. On evaluation, the model achieved a relative Root Mean Square Error of 0.23 ... |
| Document Type: |
book part |
| Language: |
English |
| Relation: |
ispartofbook:Book of Abstracts - Third International Crop Modelling Symposium; Crop modelling for agriculture and food security under global change; firstpage:73; lastpage:74; numberofpages:2; https://hdl.handle.net/2434/1223355 |
| Availability: |
https://hdl.handle.net/2434/1223355 |
| Rights: |
info:eu-repo/semantics/closedAccess ; license:Nessuna licenza ; license uri:iris.PRI01 |
| Accession Number: |
edsbas.21ECB831 |
| Database: |
BASE |