Evaluating Heavy-Metal Stress Levels in Rice Using a Theoretical Model of Canopy-Air Temperature and Leaf Area Index Based on Remote Sensing

Jin, Ming; Liu, Xiangnan; Zhang, Biyao

doi:10.1109/jstars.2017.2669204

Cited by 22 publications

(17 citation statements)

References 46 publications

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“…In non-destructive methods, LAI determination can be performed by optical instruments, which allows to evaluate the same plant throughout the growing season, and also by satellite images to larger Rev. Ceres, Viçosa, v. 66, n.3, p. 191-199, mai/jun, 2019 areas (Jin et al, 2017, Hirooka et al, 2018. For some species, such as forest, the instrument-determined LAI is smaller than actual LAI measured directly in the plant (Stenberg et al, 1994;White et al, 2000).…”

Section: Introductionmentioning

confidence: 99%

Can leaf area in rice be defined by a mathematical model?

et al. 2019

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The goal of this study was to define an empirical model to calculate the leaf area in rice from linear leaf measure in genotypes used by farmers in Brazil. Through the leaf dimensions it is possible to identify the final crop yield from the LAI. Therefore, the leaves shape is closely related to the production of photoassimilates that will be converted into grain yield. Field experiments were carried out in four counties of Rio Grande do Sul with twelve-three varieties of rice in four growing seasons. We measured the length and width of leaves to construct the model. The relationship between leaf area and linear dimensions was shaped using a linear model for each genotype, and general model grouping all genotypes. The model accuracy was measure following statistics: Root Mean Square Error, BIAS, modified index of agreement and coefficient r. The non-destructive method for individual leaves was appropriate for estimating the leaf area in rice. Moreover, the general equation was estimated and can be used for all modern genotypes of rice in Brazil.

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Section: Introductionmentioning

confidence: 99%

Can leaf area in rice be defined by a mathematical model?

et al. 2019

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“…Recently, remote sensing technology has provided a cheap, rapid, and environmentally friendly alternative for estimating heavy metal stress [ 4 , 6 ]. Numerous studies have proved that remote sensing technology has been used successfully to monitor heavy metal stress levels in plants [ 6 , 7 , 8 , 9 , 10 , 11 ]. Heavy metal stress can negatively influence the growth of plants; for example, it can reduce canopy chlorophyll content and change cellular structure [ 12 , 13 , 14 , 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

A New Vegetation Index Based on Multitemporal Sentinel-2 Images for Discriminating Heavy Metal Stress Levels in Rice

Zhang

Liu

et al. 2018

Sensors

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Heavy metal stress in crops is a worldwide problem that requires accurate and timely monitoring. This study aimed to improve the accuracy of monitoring heavy metal stress levels in rice by using multiple Sentinel-2 images. The selected study areas are in Zhuzhou City, Hunan Province, China. Six Sentinel-2 images were acquired in 2017, and heavy metal concentrations in soil were measured. A novel vegetation index called heavy metal stress sensitive index (HMSSI) was proposed. HMSSI is the ratio between two red-edge spectral indices, namely the red-edge chlorophyll index (CIred-edge) and the plant senescence reflectance index (PSRI). To demonstrate the capability of HMSSI, the performances of CIred-edge and PSRI in discriminating heavy metal stress levels were compared with that of HMSSI at different growth stages. Random forest (RF) was used to establish a multitemporal monitoring model to detect heavy metal stress levels in rice based on HMSSI at different growth stages. Results show that HMSSI is more sensitive to heavy metal stress than CIred-edge and PSRI at different growth stages. The performance of a multitemporal monitoring model combining the whole growth stage images was better than any other single growth stage in distinguishing heavy metal stress levels. Therefore, HMSSI can be regarded as an indicator for monitoring heavy metal stress levels with a multitemporal monitoring model.

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“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Liu

et al. 2018

Remote Sensing

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Monitoring phenological changes of crops through remote sensing methods is becoming a new perspective in assessing heavy metal contamination in agricultural farmlands. This paper proposes a method that combines the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI) to detect heavy metal stress-induced variations in satellite-derived rice phenology. First, we applied the enhanced spatial and temporal adaptive reflectance fusion model to obtain the NDVI and NDWI time series for the NDVI–NDWI phase–space construction. Then, six specific rice phenometrics were derived from the NDVI and the phase–space, respectively. Last, we introduced a relative phenophase index (RPI), which characterizes the relative change of the phenometrics to identify the rice paddies under heavy metal stress. The results indicated that satellite-derived rice phenometrics are generally influenced by human and natural factors (e.g., transplanting date, air temperature, and solar radiation), while the RPI showed weak correlations with all of these variables. In the determination of heavy metal stress, the NDVI- and phase–space-based RPIs of unstressed rice both show significantly (p < 0.001) higher values than those of stressed rice, while the phase–space-based RPI shows more apparent statistical difference between the stressed and unstressed rice compared to the NDVI-based one. Our work proved the capability of the phase–space-based method as well as the RPI in the discrimination of regional heavy metal pollution in rice fields.

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Evaluating Heavy-Metal Stress Levels in Rice Using a Theoretical Model of Canopy-Air Temperature and Leaf Area Index Based on Remote Sensing

Cited by 22 publications

References 46 publications

Can leaf area in rice be defined by a mathematical model?

Can leaf area in rice be defined by a mathematical model?

A New Vegetation Index Based on Multitemporal Sentinel-2 Images for Discriminating Heavy Metal Stress Levels in Rice

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

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