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. 2025 Apr 4;15(1):11582.
doi: 10.1038/s41598-025-95109-4.

Evaluation of crop phenology using remote sensing and decision support system for agrotechnology transfer

Naz Ul Amin  1 , Fakhrul Islam  2   3   4 , Muhammad Umar  1 , Waqas Muhammad  1 , Siddiq Ur Rahman  5 , Abdel-Rhman Z Gaafar  6 , Tawaf Ali Shah  7 , Musaab Dauelbait  8 , Mohammed Bourhia  9
Affiliations

Evaluation of crop phenology using remote sensing and decision support system for agrotechnology transfer

Naz Ul Amin et al. Sci Rep. .

Abstract

The decision support system for agro-technology transfer (DSSAT) is a worldwide crop modeling platform used for crops growth, yield, leaf area index (LAI), and biomass estimation under varying climatic, soil and management conditions. This study integrates DSSAT with satellite remote sensing (RS) data to estimates canopy state variables like LAI and biomass. For LAI estimation, Moderate Resolution Imaging Spectroradiometer (MODIS) product (MCD15A3H for LAI and MOD17A2 / MOD17A3 products for biomass) are used. Field data for Sheikhupura district is provided by National Agriculture Research Council (NARC) and used for the calibration and validation of the model. The results indicate strong agreement between the DSSAT and RS derived estimates. Correlation coefficients (R²) for LAI varied from 0.82 to 0.90, while for biomass ranged from 0.92 to 0.99 over two farms and two growing seasons (2012-2014). The index of agreement (D-index) ranged from 0.79 to 0.96 across the two farms and two growing seasons (2012-2014) affirming the model's durability. However, the biomass estimated from RS data is underestimated due to saturation phenomenon in the optical RS. The performance metrics, comprising the coefficient of residual mass (CRM) and normalized root mean square error (nRMSE), further substantiate the approach utilized. This study will help decision and policymakers and researchers to apply geospatial techniques for the sustainable agriculture practices.

Keywords: Agro technology; DSSAT; GEE; Pakistan; Sheikhupura.

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Conflict of interest statement

Declarations. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Geographic map of the Sheikhupura District of Punjab, Pakistan. Source: For the creation of this Map shape file of Pakistan is downloaded from https://pakistangis.org/vector-datasets/, Digital Elevation Model (DEM) data downloaded from https://earthexplorer.usgs.gov/ site. The data has been processed using QGIS 3.4 version which is open-source software and downloaded from https://qgis.org/ site.
Fig. 2
Fig. 2
Geographic map of the Sheikhupura District of Punjab, Pakistan showing mixed pixels distribtion of the of the area. Source: For the creation of this map shape file is downloaded from https://pakistangis.org/vector-datasets/. Values is extraced in GEE .Finaal map layout is process in QGIS 3.4 version which is open-source software and downloaded from https://qgis.org/ site.
Fig. 3
Fig. 3
Methodological flow of current research.
Fig. 4
Fig. 4
Shows the corealtion between of LAI estimated by DSSAT model.
Fig. 5
Fig. 5
Shows the correlation between DSSAT and MODIS LAI estimates at Shahbaz farm.
Fig. 6
Fig. 6
Correlation between DSSAT and MODIS LAI estimates at Shahbaz farm.
Fig. 7
Fig. 7
Shows the correlation between DSSAT and MODIS LAI estimates at Rattaber farm.
Fig. 8
Fig. 8
Shows the correlation between DSSAT and MODIS LAI estimates at Rattaber farm.
Fig. 9
Fig. 9
Shows the corelation of DSSAT model-based biomass from sowing to maturity.
Fig. 10
Fig. 10
Shows the corelation between MODIS-derived biomass with time phase.
Fig. 11
Fig. 11
Shows the correlation between DSSAT and MODIS Biomass estimates at Shahbaz farm.
Fig. 12
Fig. 12
Shows the correlation between DSSAT and MODIS Biomass estimates at Shahbaz farm.
Fig. 13
Fig. 13
Shows the correlation between DSSAT and MODIS Biomass estimates at Rattaber farm.
Fig. 14
Fig. 14
Shows the correlation between DSSAT and MODIS Biomass estimates at Rattaber farm.
Fig. 15
Fig. 15
Show the relationship between DSSAT CERES wheat model and observed yield.
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