Downscaling Solar-Induced Chlorophyll Fluorescence Based on Convolutional Neural Network Method to Monitor Agricultural Drought

Zhang, Zhaoxu; Xu, Wei; Qin, Qiming; Long, Zehao

doi:10.1109/tgrs.2020.2999371

Cited by 32 publications

(12 citation statements)

References 76 publications

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“…Under the understanding the mechanism of SIF response to drought, SIF dataset can be used as a factor of model input or drought monitoring index (e.g., scaled SIF index). For the phenomenon of the coarse resolution, appropriate SIF downscaling method is important to research agricultural drought [38], [41], [70].…”

Section: E the Limitation Of Sif For Drought Monitoringmentioning

confidence: 99%

Monitoring and Assessment of Agricultural Drought Based on Solar-Induced Chlorophyll Fluorescence During Growing Season in North China Plain

Zhang

Qin

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Drought is a frequent global phenomenon that has the most significant influence on agriculture. Solar-induced chlorophyll fluorescence (SIF) is a by-product of photosynthesis that can be used to monitor vegetation growth and agricultural drought. This study aims to monitor and assess monthly agricultural drought using SIF data with 0.05-degree spatial resolution. The scaled SIF index was calculated during the cropgrowing season (March-October, 2000-2017) in agricultural areas of North China Plain (NCP), and the monthly agricultural drought spatial distribution and severity were mapped. Results indicated that NCP experienced mild to severe drought during the study period, the severe drought (proportion more than 50%) affected

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Section: E the Limitation Of Sif For Drought Monitoringmentioning

confidence: 99%

Monitoring and Assessment of Agricultural Drought Based on Solar-Induced Chlorophyll Fluorescence During Growing Season in North China Plain

Zhang

Qin

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…The research area is located in Henan Province, China, and extends from 31°23 N-36°22 N, 110°21 E-116°39 E [5]. Fig.…”

Section: A Study Areamentioning

confidence: 99%

“…The daily soil moisture data were obtained from the European Space Agency (ESA CCI) from 2000 to 2018. 5 The monthly soil moisture results were calculated by the maximum value synthesis method.…”

Section: B Datamentioning

confidence: 99%

Establishment of a Comprehensive Drought Monitoring Index Based on Multisource Remote Sensing Data and Agricultural Drought Monitoring

Zhang

Shi

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The occurrence of drought is a complex process and is caused by the interaction of multiple drought-causing factors. The construction of traditional drought models and indexes seldom considers multiple drought-causing factors. This study integrated the precipitation, soil water and heat balance, and crop growth during drought. From the beginning of the process of agricultural drought, the atmosphere, soil, and crops that characterize drought are considered, through the principal component analysis method to construct a comprehensive drought monitoring index (CDMI). This index was verified by using the areas covered by drought, areas affected by drought, relative soil moisture, and crop yield. The annual average CDMI had negative correlations with areas covered and affected by drought. The correlation coefficients were -0.68 and -0.73. Moreover, the CDMI value had positive correlations with relative soil moisture and crop yield. The maximum correlation coefficient between CDMI and relative soil moisture was 0.91, and the correlation coefficient with maize yield was 0.52. Subsequently, the CDMI was applied to long-term drought monitoring in agricultural areas during the summer maize growing season (June to September) in Henan Province. Results showed that the most severe years of agricultural drought in

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“…Starting with a study by Guanter et al (2007) that reported the first space-based SIF observations on board the ENVIronmental SATellite (ENVISAT) and continuing with SIF measurements acquired by the Japanese GOSAT mission launched in 2009 (Frankenberg et al, 2018), indices related to SIF have been evolved and refined in order to better manage environmental factors, such as soil moisture. For instance, SIF has been reported to show a significant reduction under serious drought (Liu et al, 2018), and the resulting downscaled SIF value combined with land surface temperature (LST) data was used to develop the temperature fluorescence dryness index (TFDI), which is able to show enhanced spatial details and is suitable to be used in agricultural drought condition studies (Zhang et al, 2020).…”

Section: Independent and Dependent Variablesmentioning

confidence: 99%

Analysis of designs used in monitoring crop growth based on remote sensing methods

Dobrotă¹,

Carpa²,

Butiuc-Keul³

2021

Turk J Agric For

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Choosing appropriate designs and methods for monitoring crop growth is a challenging process of major importance. Remote sensing from space and manned or unmanned airborne operations are used to measure crop reflectance and a wide variety of other agricultural parameters. While some experiments use only a few, specific methods and designs and organize the results in lists of evidence, other experiments use a wider range of techniques to create a more credible and comprehensive assessment of crop yield. Particular situations related to the available resources in terms of data collection and expertise in addition to the intended use of the results may require specific designs or a combination of methods and design. This review intended to explore the challenges and document a range of possible approaches for remote evaluation of crop growth. The scope of the analysis was designed to provide information on methods that work under different circumstances and why and how effective a particular method is an approach to monitoring crop growth. Considering the agricultural ecosystems as complex systems and the working methodology as having a high degree of complexity, we propose an approach suitable for complex systems. New sets of models and methods need to be developed for approaching complex systems, which are characterized by self-organization, nonlinearity, ongoing adaptation, and networking.

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Downscaling Solar-Induced Chlorophyll Fluorescence Based on Convolutional Neural Network Method to Monitor Agricultural Drought

Cited by 32 publications

References 76 publications

Monitoring and Assessment of Agricultural Drought Based on Solar-Induced Chlorophyll Fluorescence During Growing Season in North China Plain

Monitoring and Assessment of Agricultural Drought Based on Solar-Induced Chlorophyll Fluorescence During Growing Season in North China Plain

Establishment of a Comprehensive Drought Monitoring Index Based on Multisource Remote Sensing Data and Agricultural Drought Monitoring

Analysis of designs used in monitoring crop growth based on remote sensing methods

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