Integrating remotely sensed leaf area index and leaf nitrogen accumulation with RiceGrow model based on particle swarm optimization algorithm for rice grain yield assessment

Abstract

Abstract. A regional rice (Oryza sativa) grain yield prediction technique was proposed by integration of ground-based and spaceborne remote sensing (RS) data with the rice growth model (RiceGrow) through a new particle swarm optimization (PSO) algorithm. Based on an initialization/parameterization strategy (calibration), two agronomic indicators, leaf area index (LAI) and leaf nitrogen accumulation (LNA) remotely sensed by field spectra and satellite images, were combined to serve as an external assimilation parameter and integrated with the RiceGrow model for inversion of three model management parameters, including sowing date, sowing rate, and nitrogen rate. Rice grain yield was then predicted by inputting these optimized parameters into the reinitialized model. PSO was used for the parameterization and regionalization of the integrated model and compared with the shuffled complex evolutionUniversity of Arizona (SCE-UA) optimization algorithm. The test results showed that LAI together with LNA as the integrated parameter performed better than each alone for crop model parameter initialization. PSO also performed better than SCE-UA in terms of running efficiency and assimilation results, indicating that PSO is a reliable optimization method for assimilating RS information and the crop growth model. The integrated model also had improved precision for predicting rice grain yield. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

Assimilation of Two Variables Derived from Hyperspectral Data into the DSSAT-CERES Model for Grain Yield and Quality Estimation

“…Ma [20] selected the Soil-water-atmosphere-plant environment (SWAP) model and the Moderate Resolution Imaging Spectroradiometer (MODIS) products to assimilate evapotranspiration and LAI and suggested that this could achieve a better yield estimation of winter wheat than the use of just one variable. Wang et al [18] confirmed that the use of LAI together with leaf N accumulation as assimilation variables resulted in a better estimation of winter wheat yield than using each variable alone for model parameter initialization.…”

“…The primary reason for this is that LAI is a key variable for crop growth monitoring and yield prediction [58], and CNA is an important indicator of the N status of wheat and significantly affects photosynthetic production and grain yield and quality [59]. Various crop state variables are independent of each other, though they interact with each other [18,60]. Therefore, the SVLAI + CNA method obtained a greater robustness of the DSSAT-CERES model than the single variable methods.…”

“…In such studies, the assimilated variable of the model had a reliable accuracy, but other agronomic state variables did not [18]. Thorp et al [19] linked the PROSPECT + SAIL (PROSAIL) radiative transfer model and Decision Support System for Agrotechnology Transfer (DSSAT) crop growth models (DSSAT-PROSAIL) with LAI and plant nitrogen (N) content to estimate the LAI, canopy biomass, plant N content, and yield of durum wheat and demonstrated that inverting the DSSAT-PROSAIL model offered better estimates of crop biophysical properties.…”

Development of a rice simulation model for remote-sensing (SIMRIW-RS)

“…Although rice simulation models are usually considered effective tools for the evaluation of rice production, previous models have been developed based on weather rice production relationships or field-scale observations (Horie et al, 1995;Bouman, 2001;Timsina and Humphreys, 2006;Yoshida and Horie, 2010). Accordingly, a combination with remotesensing has been explored to apply rice simulation models on a regional scale (Inoue et al, 1998;Oki et al, 2013;Wang et al, 2014).…”

Evaluation of Three Techniques for Correcting the Spatial Scaling Bias of Leaf Area Index

“…Leaf area index (LAI), defined as half the total foliage area per unit ground horizontal surface area [1], is an essential biophysical property of vegetation for environmental assessment [2,3], crop growth monitoring [4,5] and yield prediction [6][7][8]. The accurate monitoring of LAI on multiple scales has become a key technique in those applications.…”