Estimating FAPAR of Rice Growth Period Using Radiation Transfer Model Coupled with the WOFOST Model for Analyzing Heavy Metal Stress

Abstract

Timely assessment of crop growth conditions under heavy metal pollution is of great significance for agricultural decision-making and estimation of crop productivity. The object of this study is to assess the effects of heavy metal stress on physiological functions of rice through the spatial-temporal analysis of the fraction of absorbed photosynthetically active radiation (FAPAR). The calculation of daily FAPAR is conducted based on a coupled model consisting of the leaf-canopy radiative transfer model and World Food Study Model (WOFOST). These two models are connected by leaf area index (LAI) and a fraction of diffused incoming solar radiation (SKYL) in the rice growth period. The input parameters of the coupled model are obtained from measured data and GF-1 images. Meanwhile, in order to improve accuracy of FAPAR, the crop growth model is optimized by data assimilation. The validation result shows that the correlation between the simulated FAPAR and the measured data is strong in the rice growth period, with the correlation coefficients being above 7.5 for two areas. The discrepancy of FAPAR between two areas of different stress levels is visualized by spatial-temporal analysis. FAPAR discrepancy starts to appear in the jointing-booting period and experiences a gradual rise, reaching its maximum in the heading-flowering stage. This study suggests that the coupled model, consisting of the leaf-canopy radiative transfer model and the WOFOST model, is able to accurately simulate daily FAPAR during crop growth period and FAPAR can be used as a potential indicator to reflect the impact of heavy metal stress on crop growth.

An approach for heavy metal pollution detected from spatio-temporal stability of stress in rice using satellite images

“…This simulation can be implemented at the potential productivity level on the basis of the main factors that limit crop growth, such as the solar radiation and temperature. The input parameters of the WOFOST model were set on the basis of values reported in the literature of this study area (Jin et al, 2015;Huang et al, 2016;Liu et al, 2016a, b, c;Zhou et al, 2017). The primary parameter values used are shown in Table S5.…”

“…The primary parameter values used are shown in Table S5. The values of other parameters in the PROSAIL model were derived from LOPEX (Ceccato et al, 2001;Hosgood et al, 1994;Zhou et al, 2017) and remote sensing data (Table S4). It should be noted that the growth of healthy rice in a given year at any site within this area is expected to be the same because they experienced the same meteorological conditions.…”

Estimation and Spatial Analysis of Heavy Metals in Metal Tailing Pond Based on Improved PLS With Multiple Factors

“…As many studies indicate, the heavy metal content in soil can be retrieved indirectly by studying the stress that vegetation and soil components are under due to heavy metal exposure. This is because vegetation is affected by heavy metals in the soil during growth, leading to significant changes in the reflection spectrum [33][34][35][36][37]. By adding vegetation and soil indices obtained from different band combinations as additional modeling factors, the accuracy of the model will be improved, and more accurate results will be obtained.…”

Detection of Rice Phenological Variations under Heavy Metal Stress by Means of Blended Landsat and MODIS Image Time Series

“…A number of agronomy studies have exhibited the variations in spectral properties, pigment content, dry matter, photosynthesis, and transpiration of plants under heavy metal stress [4][5][6]. Based on these symptoms, researchers have made attempts to (1) extract the characteristics of multispectral and hyperspectral signatures [7][8][9]; (2) retrieve physiological and biochemical parameters [10,11]; and (3) integrate remote sensing data and crop growth models [12][13][14][15] to detect heavy metal contamination in agricultural soils.…”