Monitoring model of corn lodging based on Sentinel-1 radar image

Han, Dong; Yang, Hao; Yang, Guijun; Qiu, Chunjuan

doi:10.1109/bigsardata.2017.8124928

Cited by 28 publications

(30 citation statements)

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“…Traditional field phenotyping relies on ground investigation using manpower measurement, which is expensive, laborious, and inefficient, especially when thousands of plots are being evaluated in a breeding program [7,8], and it is not applicable to large scale and repeatable surveys [9]. With the richness of platforms and sensors, remote sensing technology provides a feasible, reliable, and valid approach to identify and analyze crop lodging [6,10,11].…”

Section: Introductionmentioning

confidence: 99%

“…Yang et al [13] reported that polarimetric features extracted from synthetic aperture radar (SAR) imagery were used to monitor wheat lodging. Han et al [9] reported that the ratio of the natural height to the plant height extracted from SAR imagery was used as a standard for distinguishing the degree of lodging. Liu et al [14] and Li et al [15] reported that spectral features were utilized to estimate the lodging grade by using multispectral sensors.…”

Section: Introductionmentioning

confidence: 99%

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Quantitative Identification of Maize Lodging-Causing Feature Factors Using Unmanned Aerial Vehicle Images and a Nomogram Computation

et al. 2018

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Maize (zee mays L.) is one of the most important grain crops in China. Lodging is a natural disaster that can cause significant yield losses and threaten food security. Lodging identification and analysis contributes to evaluate disaster losses and cultivates lodging-resistant maize varieties. In this study, we collected visible and multispectral images with an unmanned aerial vehicle (UAV), and introduce a comprehensive methodology and workflow to extract lodging features from UAV imagery. We use statistical methods to screen several potential feature factors (e.g., texture, canopy structure, spectral characteristics, and terrain), and construct two nomograms (i.e., Model-1 and Model-2) with better validation performance based on selected feature factors. Model-2 was superior to Model-1 in term of its discrimination ability, but had an over-fitting phenomenon when the predicted probability of lodging went from 0.2 to 0.4. The results show that the nomogram could not only predict the occurrence probability of lodging, but also explore the underlying association between maize lodging and the selected feature factors. Compared with spectral features, terrain features, texture features, canopy cover, and genetic background, canopy structural features were more conclusive in discriminating whether maize lodging occurs at the plot scale. Using nomogram analysis, we identified protective factors (i.e., normalized difference vegetation index, NDVI and canopy elevation relief ratio, CRR) and risk factors (i.e., Hcv1) related to maize lodging, and also found a problem of terrain spatial variability that is easily overlooked in lodging-resistant breeding trials.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantitative Identification of Maize Lodging-Causing Feature Factors Using Unmanned Aerial Vehicle Images and a Nomogram Computation

et al. 2018

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“…(For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) the potential of C-band for studying lodging in "narrow-leaf" crops such as wheat have been made by Yang et al (2015) and Han et al (2017). These results are summarized in Fig.…”

Section: Important Wavelength Regions and Remote Sensing Parametersmentioning

confidence: 97%

“…While a majority of the studies have exploited the advanced capabilities of RADARSAT-2 satellite data (a single platform, commercial data source with limited revisit frequency), we found only one study that used freely accessible satellite data with a high temporal resolution to map lodging severity. Recently, Han et al (2017) built a quantitative lodging classification model for maize using height information derived from Sentinel-1 data. It is a first step towards the use of satellite data for quantitative modelling of lodging stage.…”

Section: Spaceborne Platformsmentioning

confidence: 99%

“…RADAR-SAT-2, with its ability to acquire data in quad-polarizations (HH, HV, VH, VV), reduces the need to acquire data over several dates. Moreover, data from recently launched SAR missions such as L-band ALOS-2 (2014) and C-band Sentinel-1A andB (2014, 2016) have not yet been explored for lodging assessment (except Han et al, 2017). Although the limited feature space of Sentinel-1 data (dual-pol data) may not characterize the heterogeneous patterns of lodging in the same way as RADARSAT-2, free data access and high temporal resolution make it particularly relevant for developing operational applications.…”

Section: Outlook On Remote Sensing Sensors and Platformsmentioning

confidence: 99%

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Remote sensing-based crop lodging assessment: Current status and perspectives

Chauhan

Darvishzadeh

Boschetti

et al. 2019

ISPRS Journal of Photogrammetry and Remote Sensing

100

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Rapid and quantitative assessment of crop lodging is important for understanding the causes of the phenomena, improving crop management, making better production and supporting loss estimates in general. Accurate information on the location and timing of crop lodging is valuable for farmers, agronomists, insurance loss adjusters, and policymakers. Lodging studies can be performed to assess the impact of lodging events or to model the risk of occurrence, both of which rely on information that can be acquired by field observations, from meteorological data and from remote sensing (RS). While studies applying RS data to assess crop lodging dates back three decades, there has been no comprehensive review of the status, potential, current approaches, and challenges in this domain. In this position paper, we review the trends in field/lab-based and RS-based studies for crop lodging assessment and discuss the strengths and weaknesses of current approaches. Theoretical background on crop lodging is presented, and the scope of RS in assessing plant characteristics associated with lodging is reviewed and discussed. The review focuses on RS-based studies, grouping them according to the platform deployed (i.e., ground-based, airborne and spaceborne), with an emphasis on analyzing the pros and cons of the technology. Finally, the challenges, research gaps, perspectives for future research, and an outlook on new sensors and platforms are presented to provide state-of-the-art and future scenarios of RS in lodging assessment. Our review reveals that the use of RS techniques in crop lodging assessment is still in an experimental stage. However, there is increasing interest within the RS scientific community (based on the increased rate of publications over time) to investigate its use for crop lodging detection and risk mapping. The existing satellitebased lodging assessment studies are very few, and the operational application of the current approaches over large spatial extents seems to be the biggest challenge. We identify opportunities for future studies that can develop quantitative models for estimating lodging severity and mapping lodging risk using RS data. 1.2. The role of remote sensing The past few decades have witnessed considerable growth in the use of sensors on-board Earth-Observation (EO) systems for agricultural monitoring applications. Today, crop biophysical properties such as leaf

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Determination of Spring Barley Lodging Area with Help of Unmanned Aerial Vehicle

Курбанов

Захарова

2021

Agriculture Digitalization and Organic Production

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Monitoring model of corn lodging based on Sentinel-1 radar image

Cited by 28 publications

References 3 publications

Quantitative Identification of Maize Lodging-Causing Feature Factors Using Unmanned Aerial Vehicle Images and a Nomogram Computation

Quantitative Identification of Maize Lodging-Causing Feature Factors Using Unmanned Aerial Vehicle Images and a Nomogram Computation

Remote sensing-based crop lodging assessment: Current status and perspectives

Determination of Spring Barley Lodging Area with Help of Unmanned Aerial Vehicle

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