Response times of remote sensing measured sun-induced chlorophyll fluorescence, surface temperature and vegetation indices to evolving soil water limitation in a crop canopy

Damm, Alexander; Cogliati, Sergio; Colombo, R.; Fritsche, Liv; Genangeli, Andrea; Genesio, L.; Hanuš, Jan; Peressotti, Alessandro; Rademske, Patrick; Rascher, Uwe; Schuettemeyer, Dirk; Siegmann, Bastian; Sturm, J. W.; Miglietta, F.

doi:10.1016/j.rse.2022.112957

Cited by 31 publications

(17 citation statements)

References 53 publications

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“…For instance, Zarco-Tejada et al (2021) obtained several spectral traits from multisensor data and used the derived spectral fingerprints for distinguishing different diseases. Another study by Damm et al (2022) investigated temporal sensitivity of multi-sensor derived vegetation information for evolving water stress. Since such kinds of studies are still scarce, we advocate more research aiming to obtain a holistic view of plants under biotic and abiotic stress through synergistic usage of spectral information from multiple sensors or domains.…”

Section: Combined Acquisition and Analysis Of Multi-sensor Datamentioning

confidence: 99%

“…Together with improvements in computing power and machine learning (ML), unprecedented possibilities are offered for precision agriculture and other agricultural applications, going along with major improvements in crop stress detection. The most common crop stress type analysed by means of RS techniques is drought, i.e., water deficit stress Damm et al, 2022;Gago et al, 2015;Gerhards et al, 2016;Govender et al, 2009;Ihuoma and Madramootoo, 2017;Parkash and Singh, 2020;Virnodkar et al, 2020). Also, other stress factors have been studied with RS, such as insects (Herrmann et al, 2017) and pathogens (Gold et al, 2020;Herrmann et al, 2018;Mahlein, 2016;Sishodia et al, 2020) nutrient deficiency (Baret et al, 2007;Herrmann et al, 2010;Mahajan et al, 2021) or soil contamination (Gholizadeh and Kopacková, 2019).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review

Berger

Machwitz

Kycko

et al. 2022

Remote Sensing of Environment

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Section: Combined Acquisition and Analysis Of Multi-sensor Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review

Berger

Machwitz

Kycko

et al. 2022

Remote Sensing of Environment

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“…Chlorophyll is a green pigment that is essential in perceiving light for photosynthetic activities. Chlorophyll content in plants is greatly influenced by environmental factors such as soil nutrients, temperature, light intensity, and water availability (Du et al, 2017;Naznin et al, 2019;Ajeng et al, 2020;Damm et al, 2022), which indirectly affect plant growth and productivity. The reduction of chlorophyll content due to abiotic stresses could be associated with chloroplast degradation through autophagy or chloroplast vesiculation (Jiang et al, 2020) and enhanced chlorophyllase activities that promote the degradation of chlorophyll (Sakuraba et al, 2014;Sharma et al, 2020).…”

Section: Chlorophyll Contentmentioning

confidence: 99%

The Effects of Submergence on Selected Malaysian Rice Varieties

Sukiran

Karso

Razemin

et al. 2022

MAB

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Various varieties have been developed in Malaysia, mainly to improve rice response to environmental changes, pests, and diseases, as well as to increase rice productivity under stressful conditions. Despite being semi-aquatic plants, rice is intolerant to complete submergence for a long period. This study was conducted to evaluate the response of seven Malaysian rice varieties at the vegetative stage under submergence stress. Two-week-old rice seedlings were submerged for 14 days, and the changes in plant height, chlorophyll content, and soluble sugar content were determined. The survival percentage of these varieties was observed after 14 days of de-submergence, where UKMRC2 and MR220CL possessed high survivability (90% & 60%, respectively). After submergence, all varieties showed height increment and reduced chlorophyll and soluble sugar contents. Based on our analyses, UKMRC2 performed better than other varieties, although slightly less than IR64-Sub1. It was confirmed that UKMRC2 is the submergence-tolerant variety, and its response to underwater germination was also determined. Our result showed that UKMRC2 might possess tolerance to anaerobic germination conditions, and more studies are needed to understand its molecular mechanism for submergence. In conclusion, many varieties used were susceptible to submergence, and the development of more submergence-tolerant varieties is crucial for Malaysia’s food security sustainability.

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“…Vegetation indices were found to be more effective at extracting various characteristics of field crops. as these are developed by using different combinations of satellite bands that were found to be sensitive to vegetation [41]- [43]. On the other hand, employing reflectance from individual bands of distinct wavelength as a metric for estimation of the biochemical variable of the crop plants are not very productive because each wavelength has its measurement error, which increases towards both water absorption wavebands and longer wavelengths so it can only be utilized to have the initial and overall idea about the crop condition [12], [44].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Machine Learning Enabled Non-Destructive Technique for Remote Monitoring of Sugarcane Crop Health

et al. 2022

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Crop health can be predicted based on various biochemical variables of crops, which include chlorophyll, phenol, carbohydrate, lipid, protein, hydrogen peroxide, and proline as these variables play a critical role in maintaining the intricate phytochemistry of crop plants. In-situ monitoring of the abovementioned variables is very cumbersome and laborious, so it is atrociously needed to identify some alternatives to monitor these variables in crop plants. Assessing these variables using satellite data may be a good choice provided it has a high spatial and temporal resolution. Sentinel-2 satellite sensor contains VNIR/SWIR spectral region, two red-edge bands, and also has good spatial as well as temporal resolution so it may be the finest option. Precise information of the field is required for the development of a retrieval algorithm for which the drone data is used, as it is highly accurate ground truth reference data. In previous studies, the researchers have primarily focused on the monitoring of biophysical and morphological parameters of crop plants like leaf area index, plant height, and stomatal conductance using these spectral features. Because monitoring biochemical variables of crop plants using satellite derivatives is still a difficult undertaking for academics, just a few studies have been published. As a result, in this study, an attempt is made to establish a methodology for monitoring sugarcane crop biochemical characteristics utilising satellite-derived variables. Satellite derivatives, i.e., vegetation indices are extracted using Sentinel-2 data while biochemical variables of the crop (as mentioned above) are analyzed using leaf samples in the laboratory using optimized protocols. Subsequently, a Machine learning-based Gaussian process regression model is developed for all the biochemical variables using the different combinations of vegetation indices. The developed model showed promising results with R 2 greater than 0.7 and NRMSE less than 0.2 thus holding good potential for effective monitoring the crop health condition remotely.INDEX TERMS Crop health, non-destructive technique, remote sensing, vegetation indices, biochemical variables.

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Response times of remote sensing measured sun-induced chlorophyll fluorescence, surface temperature and vegetation indices to evolving soil water limitation in a crop canopy

Cited by 31 publications

References 53 publications

Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review

Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review

The Effects of Submergence on Selected Malaysian Rice Varieties

An Efficient Machine Learning Enabled Non-Destructive Technique for Remote Monitoring of Sugarcane Crop Health

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