Identifying rice stress on a regional scale from multi-temporal satellite images using a Bayesian method

Meiling Liu (Corresponding Author), Tiejun Wang, A.K. Skidmore, Xiangnan Liu, Mengmeng Li

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Crops are prone to various types of stress, such as caused by heavy metals, drought and pest/disease, during their life cycle. Heavy metal stress in crops poses a serious threat to crop quality and human health. However, differentiating between heavy metal and non-heavy metal stress presents a great challenge, since responses to environmental stress in crops are complex and uncertain, with different stressors possibly triggering similar canopy reflectance responses. This study aims to infer the occurrence probability of heavy metal stress (i.e., Cd stress) on a regional scale by integrating satellite-derived vegetation index and spatio-temporal characteristics of different stressors with a Bayesian method. The study area is located in the Hunan Province, China. Seven scenes of Sentinel-2 satellite images from 2016 to 2017 were collected, as well as Cd concentrations in the soil. First, the probability of rice being stressed was screened using the normalized difference red-edge index (NDRE) at all the growth stages of rice. Further, the stressed rice was used as input, along with the coefficients of spatio-temporal variation (CSTV) derived from NDRE, for a Bayesian method to infer rice exposed to Cd pollution. The results demonstrated that NDRE was a sensitive indicator for assessing stress levels in rice crops. The CSTV with a threshold of 2.7 successfully detected rice under Cd as well as abrupt stress on a regional scale. A high map accuracy for Cd induced stress in rice was achieved with an accuracy of 81.57%. This study suggests that vegetation index obtained from satellite images can assist in capturing crop stress, and that the used Bayesian method can be very useful for distinguishing a specific stressor in crops by incorporating temporal-spatial characteristic of different stressors in crops into satellite-derived vegetation index.
Original languageEnglish
Pages (from-to)488-498
Number of pages11
JournalEnvironmental Pollution
Volume247
Issue numberApril
DOIs
Publication statusPublished - 1 Apr 2019

Fingerprint

Bayes Theorem
Crops
Satellites
Heavy Metals
Heavy metals
Droughts
Oryza
Life Cycle Stages
Drought
China
Soil
Metals
Life cycle
Pollution
Health
Growth
Soils

Keywords

  • ITC-ISI-JOURNAL-ARTICLE
  • Coefficients of spatio-temporal variation
  • Bayesian method
  • Sentinel-2 images
  • Heavy metal stress

Cite this

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title = "Identifying rice stress on a regional scale from multi-temporal satellite images using a Bayesian method",
abstract = "Crops are prone to various types of stress, such as caused by heavy metals, drought and pest/disease, during their life cycle. Heavy metal stress in crops poses a serious threat to crop quality and human health. However, differentiating between heavy metal and non-heavy metal stress presents a great challenge, since responses to environmental stress in crops are complex and uncertain, with different stressors possibly triggering similar canopy reflectance responses. This study aims to infer the occurrence probability of heavy metal stress (i.e., Cd stress) on a regional scale by integrating satellite-derived vegetation index and spatio-temporal characteristics of different stressors with a Bayesian method. The study area is located in the Hunan Province, China. Seven scenes of Sentinel-2 satellite images from 2016 to 2017 were collected, as well as Cd concentrations in the soil. First, the probability of rice being stressed was screened using the normalized difference red-edge index (NDRE) at all the growth stages of rice. Further, the stressed rice was used as input, along with the coefficients of spatio-temporal variation (CSTV) derived from NDRE, for a Bayesian method to infer rice exposed to Cd pollution. The results demonstrated that NDRE was a sensitive indicator for assessing stress levels in rice crops. The CSTV with a threshold of 2.7 successfully detected rice under Cd as well as abrupt stress on a regional scale. A high map accuracy for Cd induced stress in rice was achieved with an accuracy of 81.57{\%}. This study suggests that vegetation index obtained from satellite images can assist in capturing crop stress, and that the used Bayesian method can be very useful for distinguishing a specific stressor in crops by incorporating temporal-spatial characteristic of different stressors in crops into satellite-derived vegetation index.",
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Identifying rice stress on a regional scale from multi-temporal satellite images using a Bayesian method. / Liu, Meiling (Corresponding Author); Wang, Tiejun; Skidmore, A.K.; Liu, Xiangnan; Li, Mengmeng.

In: Environmental Pollution, Vol. 247, No. April, 01.04.2019, p. 488-498.

Research output: Contribution to journalArticleAcademicpeer-review

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AU - Liu, Xiangnan

AU - Li, Mengmeng

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