Identifying rice stress on a regional scale from multi-temporal satellite images using a Bayesian method
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...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Liu, Meiling [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019transfer abstract |
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| Schlagwörter: |
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| Umfang: |
11 |
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| Übergeordnetes Werk: |
Enthalten in: Structural failure performance of the encased functionally graded porous cylinder consolidated by graphene platelet under uniform radial loading - Li, Zhaochao ELSEVIER, 2019, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:247 ; year:2019 ; pages:488-498 ; extent:11 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.envpol.2019.01.024 |
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ELV045901066 |
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| 520 | |a 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. | ||
| 520 | |a 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. | ||
| 650 | 7 | |a Coefficients of spatio-temporal variation |2 Elsevier | |
| 650 | 7 | |a Bayesian method |2 Elsevier | |
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| 650 | 7 | |a Heavy metal stress |2 Elsevier | |
| 700 | 1 | |a Wang, Tiejun |4 oth | |
| 700 | 1 | |a Skidmore, Andrew K. |4 oth | |
| 700 | 1 | |a Liu, Xiangnan |4 oth | |
| 700 | 1 | |a Li, Mengmeng |4 oth | |
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10.1016/j.envpol.2019.01.024 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000855.pica (DE-627)ELV045901066 (ELSEVIER)S0269-7491(18)33981-2 DE-627 ger DE-627 rakwb eng 690 VZ 50.31 bkl 56.11 bkl Liu, Meiling verfasserin aut Identifying rice stress on a regional scale from multi-temporal satellite images using a Bayesian method 2019transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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. 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. Coefficients of spatio-temporal variation Elsevier Bayesian method Elsevier Sentinel-2 images Elsevier Heavy metal stress Elsevier Wang, Tiejun oth Skidmore, Andrew K. oth Liu, Xiangnan oth Li, Mengmeng oth Enthalten in Elsevier Science Li, Zhaochao ELSEVIER Structural failure performance of the encased functionally graded porous cylinder consolidated by graphene platelet under uniform radial loading 2019 Amsterdam [u.a.] (DE-627)ELV00327988X volume:247 year:2019 pages:488-498 extent:11 https://doi.org/10.1016/j.envpol.2019.01.024 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 50.31 Technische Mechanik VZ 56.11 Baukonstruktion VZ AR 247 2019 488-498 11 |
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10.1016/j.envpol.2019.01.024 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000855.pica (DE-627)ELV045901066 (ELSEVIER)S0269-7491(18)33981-2 DE-627 ger DE-627 rakwb eng 690 VZ 50.31 bkl 56.11 bkl Liu, Meiling verfasserin aut Identifying rice stress on a regional scale from multi-temporal satellite images using a Bayesian method 2019transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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. 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. Coefficients of spatio-temporal variation Elsevier Bayesian method Elsevier Sentinel-2 images Elsevier Heavy metal stress Elsevier Wang, Tiejun oth Skidmore, Andrew K. oth Liu, Xiangnan oth Li, Mengmeng oth Enthalten in Elsevier Science Li, Zhaochao ELSEVIER Structural failure performance of the encased functionally graded porous cylinder consolidated by graphene platelet under uniform radial loading 2019 Amsterdam [u.a.] (DE-627)ELV00327988X volume:247 year:2019 pages:488-498 extent:11 https://doi.org/10.1016/j.envpol.2019.01.024 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 50.31 Technische Mechanik VZ 56.11 Baukonstruktion VZ AR 247 2019 488-498 11 |
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10.1016/j.envpol.2019.01.024 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000855.pica (DE-627)ELV045901066 (ELSEVIER)S0269-7491(18)33981-2 DE-627 ger DE-627 rakwb eng 690 VZ 50.31 bkl 56.11 bkl Liu, Meiling verfasserin aut Identifying rice stress on a regional scale from multi-temporal satellite images using a Bayesian method 2019transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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. 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. Coefficients of spatio-temporal variation Elsevier Bayesian method Elsevier Sentinel-2 images Elsevier Heavy metal stress Elsevier Wang, Tiejun oth Skidmore, Andrew K. oth Liu, Xiangnan oth Li, Mengmeng oth Enthalten in Elsevier Science Li, Zhaochao ELSEVIER Structural failure performance of the encased functionally graded porous cylinder consolidated by graphene platelet under uniform radial loading 2019 Amsterdam [u.a.] (DE-627)ELV00327988X volume:247 year:2019 pages:488-498 extent:11 https://doi.org/10.1016/j.envpol.2019.01.024 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 50.31 Technische Mechanik VZ 56.11 Baukonstruktion VZ AR 247 2019 488-498 11 |
| allfieldsGer |
10.1016/j.envpol.2019.01.024 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000855.pica (DE-627)ELV045901066 (ELSEVIER)S0269-7491(18)33981-2 DE-627 ger DE-627 rakwb eng 690 VZ 50.31 bkl 56.11 bkl Liu, Meiling verfasserin aut Identifying rice stress on a regional scale from multi-temporal satellite images using a Bayesian method 2019transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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. 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. Coefficients of spatio-temporal variation Elsevier Bayesian method Elsevier Sentinel-2 images Elsevier Heavy metal stress Elsevier Wang, Tiejun oth Skidmore, Andrew K. oth Liu, Xiangnan oth Li, Mengmeng oth Enthalten in Elsevier Science Li, Zhaochao ELSEVIER Structural failure performance of the encased functionally graded porous cylinder consolidated by graphene platelet under uniform radial loading 2019 Amsterdam [u.a.] (DE-627)ELV00327988X volume:247 year:2019 pages:488-498 extent:11 https://doi.org/10.1016/j.envpol.2019.01.024 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 50.31 Technische Mechanik VZ 56.11 Baukonstruktion VZ AR 247 2019 488-498 11 |
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10.1016/j.envpol.2019.01.024 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000855.pica (DE-627)ELV045901066 (ELSEVIER)S0269-7491(18)33981-2 DE-627 ger DE-627 rakwb eng 690 VZ 50.31 bkl 56.11 bkl Liu, Meiling verfasserin aut Identifying rice stress on a regional scale from multi-temporal satellite images using a Bayesian method 2019transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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. 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. Coefficients of spatio-temporal variation Elsevier Bayesian method Elsevier Sentinel-2 images Elsevier Heavy metal stress Elsevier Wang, Tiejun oth Skidmore, Andrew K. oth Liu, Xiangnan oth Li, Mengmeng oth Enthalten in Elsevier Science Li, Zhaochao ELSEVIER Structural failure performance of the encased functionally graded porous cylinder consolidated by graphene platelet under uniform radial loading 2019 Amsterdam [u.a.] (DE-627)ELV00327988X volume:247 year:2019 pages:488-498 extent:11 https://doi.org/10.1016/j.envpol.2019.01.024 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 50.31 Technische Mechanik VZ 56.11 Baukonstruktion VZ AR 247 2019 488-498 11 |
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identifying rice stress on a regional scale from multi-temporal satellite images using a bayesian method |
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Identifying rice stress on a regional scale from multi-temporal satellite images using a Bayesian method |
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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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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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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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