scholarly journals Canopy-Level Photochemical Reflectance Index from Hyperspectral Remote Sensing and Leaf-Level Non-Photochemical Quenching as Early Indicators of Water Stress in Maize

2017 ◽  
Vol 9 (8) ◽  
pp. 794 ◽  
Author(s):  
Shuren Chou ◽  
Jing Chen ◽  
Hua Yu ◽  
Bin Chen ◽  
Xiuying Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
Hyperspectral Remote Sensing ◽  
Photochemical Quenching ◽  
Photochemical Reflectance Index ◽  
Leaf Level ◽  
Non Photochemical Quenching ◽  
Early Indicators

scholarly journals Relation of Photochemical Reflectance Indices Based on Different Wavelengths to the Parameters of Light Reactions in Photosystems I and II in Pea Plants

2020 ◽  
Vol 12 (8) ◽  
pp. 1312
Author(s):  
Ekaterina Sukhova ◽  
Vladimir Sukhov
Keyword(s):  
Remote Sensing ◽  
Pulse Amplitude ◽  
Ecological Monitoring ◽  
Photochemical Quenching ◽  
Photosynthetic Parameters ◽  
Pam Fluorometer ◽  
Physiological Processes ◽  
Non Photochemical Quenching ◽  
Induced Changes ◽  
Reflectance Indices

Measurement and analysis of the numerous reflectance indices of plants is an effective approach for the remote sensing of plant physiological processes in agriculture and ecological monitoring. A photochemical reflectance index (PRI) plays an important role in this kind of remote sensing because it can be related to early changes in photosynthetic processes under the action of stressors (excess light, changes in temperature, drought, etc.). In particular, we previously showed that light-induced changes in PRIs could be strongly related to the energy-dependent component of the non-photochemical quenching in photosystem II. The aim of the present work was to undertake comparative analysis of the efficiency of using light-induced changes in PRIs (ΔPRIs) based on different wavelengths for the estimation of the parameters of photosynthetic light reactions (including the parameters of photosystem I). Pea plants were used in the investigation; the photosynthetic parameters were measured using the pulse-amplitude-modulated (PAM) fluorometer Dual-PAM-100 and the intensities of the reflected light were measured using the spectrometer S100. The ΔPRIs were calculated as ΔPRI(band,570), where the band was 531 nm for the typical PRI and 515, 525, 535, 545, or 555 nm for modified PRIs; 570 nm was the reference wavelength for all PRIs. There were several important results: (1) ∆PRI(525,570), ∆PRI(531,570), ∆PRI(535,570), and ∆PRI(545,570) could be used for estimation of most of the photosynthetic parameters under light only or under dark only conditions. (2) The combination of dark and light conditions decreased the efficiency of ∆PRIs for the estimation of the photosynthetic parameters; ∆PRI(535,570) and ∆PRI(545,570) had maximal efficiency under these conditions. (3) ∆PRI(515,570) and ∆PRI(525,570) mainly included the slow-relaxing component of PRI; in contrast, ∆PRI(531,570), ∆PRI(535,570), ∆PRI(545,570), and ∆PRI(555,570) mainly included the fast-relaxing component of PRI. These components were probably caused by different mechanisms.

Long-term effects of water stress on hyperspectral remote sensing indicators in young radiata pine

2021 ◽  
Vol 502 ◽  
pp. 119707
Author(s):  
Michael S. Watt ◽  
Ellen Mae C. Leonardo ◽  
Honey Jane C. Estarija ◽  
Peter Massam ◽  
Dilshan de Silva ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
Radiata Pine ◽  
Hyperspectral Remote Sensing ◽  
Long Term Effects

scholarly journals Leaf rolling in maize crops: from leaf scoring to canopy level measurements for phenotyping

2017 ◽  
Author(s):  
F. Baret ◽  
S. Madec ◽  
K. Irfan ◽  
J. Lopez ◽  
A. Comar ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
High Throughput ◽  
Canopy Structure ◽  
Leaf Rolling ◽  
Hemispherical Photography ◽  
Structure Changes ◽  
Field Phenotyping ◽  
Leaf Level ◽  
Intercepted Radiation

AbstractLeaf rolling in maize crops is one of the main plant reactions to water stress that may be visually scored in the field. However, the leaf scoring did not reach the high-throughput desired by breeders for efficient phenotyping. This study investigates the relationship between leaf rolling score and the induced canopy structure changes that may be accessed by high-throughput remote sensing techniques.Results gathered over a field phenotyping platform run in 2015 and 2016 show that leaf starts to roll for the water stressed conditions around 9:00 and reaches its maximum around 15:00. Conversely, genotypes conducted under well watered conditions do not show any significant rolling during the same day. Leaf level rolling was very strongly correlated to canopy structure changes as described by the fraction of intercepted radiation fIPARWS derived from digital hemispherical photography. The changes in fIPARWS were stronly correlated (R2=0.86, n=50) to the leaf level rolling visual score. Further, a very good consistency of the genotype ranking of the fIPARWS changes during the day was found (ρ=0.62). This study demonstrating the strong coordination between leaf level rolling and its impact on canopy structure changes poses the basis for new high-throughput remote sensing methods to quantify this water stress trait.HighlighThe diurnal dynamics of leaf rolling scored visually is strongly related to canopy structure changes that can be documented using Digital hemispherical photography. Consequences for high-throughput field phenotyping are discussed

scholarly journals Challenges and Future Perspectives of Multi-/Hyperspectral Thermal Infrared Remote Sensing for Crop Water-Stress Detection: A Review

2019 ◽  
Vol 11 (10) ◽  
pp. 1240 ◽  
Author(s):  
Max Gerhards ◽  
Martin Schlerf ◽  
Kaniska Mallick ◽  
Thomas Udelhoven
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
Water Status ◽  
Thermal Infrared ◽  
Hyperspectral Remote Sensing ◽  
Plant Responses ◽  
Plant Water ◽  
Stress Detection ◽  
Crop Water ◽  
Crop Water Stress

Thermal infrared (TIR) multi-/hyperspectral and sun-induced fluorescence (SIF) approaches together with classic solar-reflective (visible, near-, and shortwave infrared reflectance (VNIR)/SWIR) hyperspectral remote sensing form the latest state-of-the-art techniques for the detection of crop water stress. Each of these three domains requires dedicated sensor technology currently in place for ground and airborne applications and either have satellite concepts under development (e.g., HySPIRI/SBG (Surface Biology and Geology), Sentinel-8, HiTeSEM in the TIR) or are subject to satellite missions recently launched or scheduled within the next years (i.e., EnMAP and PRISMA (PRecursore IperSpettrale della Missione Applicativa, launched on March 2019) in the VNIR/SWIR, Fluorescence Explorer (FLEX) in the SIF). Identification of plant water stress or drought is of utmost importance to guarantee global water and food supply. Therefore, knowledge of crop water status over large farmland areas bears large potential for optimizing agricultural water use. As plant responses to water stress are numerous and complex, their physiological consequences affect the electromagnetic signal in different spectral domains. This review paper summarizes the importance of water stress-related applications and the plant responses to water stress, followed by a concise review of water-stress detection through remote sensing, focusing on TIR without neglecting the comparison to other spectral domains (i.e., VNIR/SWIR and SIF) and multi-sensor approaches. Current and planned sensors at ground, airborne, and satellite level for the TIR as well as a selection of commonly used indices and approaches for water-stress detection using the main multi-/hyperspectral remote sensing imaging techniques are reviewed. Several important challenges are discussed that occur when using spectral emissivity, temperature-based indices, and physically-based approaches for water-stress detection in the TIR spectral domain. Furthermore, challenges with data processing and the perspectives for future satellite missions in the TIR are critically examined. In conclusion, information from multi-/hyperspectral TIR together with those from VNIR/SWIR and SIF sensors within a multi-sensor approach can provide profound insights to actual plant (water) status and the rationale of physiological and biochemical changes. Synergistic sensor use will open new avenues for scientists to study plant functioning and the response to environmental stress in a wide range of ecosystems.

Relationships between chlorophyll fluorescence parameters and photochemical reflectance index of tree species adapted to different temperature regimes

2006 ◽  
Vol 33 (3) ◽  
pp. 241 ◽  
Author(s):  
Jen-Hsien Weng ◽  
Yaw-Nan Chen ◽  
Tien-Szu Liao
Keyword(s):  
Chlorophyll Fluorescence ◽  
Mangifera Indica ◽  
Photochemical Quenching ◽  
Weak Correlation ◽  
Tropical Fruit ◽  
Chlorophyll Fluorescence Parameters ◽  
Psii Efficiency ◽  
Photochemical Reflectance Index ◽  
Fluorescence Parameters ◽  
Leaf Level

Chlorophyll fluorescence parameters and spectral reflectance at leaf level were measured at both predawn and noon, under different temperatures and natural light conditions from autumn to winter. Predawn Fv / Fm of both mango (Mangifera indica L.), a tropical fruit tree, and Podocarpus nagi Zoll. et Moritz., a subtropical conifer, decreased with decreasing temperature, with the former to a greater extent than the latter. Yet, predawn Fv / Fm of Taiwan alder (Alnus formosana Makino), a broadleaf tree widely distributed from the lowlands to 3000 m above sea level in Taiwan, was less influenced by temperature. Nevertheless, taking all three species into consideration, predawn Fv / Fm showed a strong correlation with predawn photochemical reflectance index [(PRIp), PRI = (R531 − R570) / (R531 + R570), where R = reflectance]. For the data obtained at noon, ΔF / Fm′ showed a significant but weak correlation with PRI (PRIn). However, stronger correlation between ΔF / Fm′ and ΔPRI (PRIp − PRIn) was found. In addition, while a non-significant or weak correlation between non-photochemical quenching (NPQ) and PRIn was observed in species sensitive to low temperature, their NPQ was significantly correlated with ΔPRI. We conclude that PRIp can serve as an indicator of the seasonal variation of potential PSII efficiency; and ΔPRI reflects the actual photodissipation as well as actual PSII efficiency during illumination. For the three species in this study, the PRI provides a more consistent measure of the variation in predawn fluorescence values than for steady-state values measured under normal seasonally varying daylight illumination.

scholarly journals On-line Fault Detection and Diagnosis for Greenhouse Environmental Control

2001 ◽  
Author(s):  
Ido Seginer ◽  
Louis D. Albright ◽  
Robert W. Langhans
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
Fault Detection ◽  
Early Detection ◽  
Data Base ◽  
Crop Rotation ◽  
Hyperspectral Remote Sensing ◽  
Sugar Beets ◽  
Detection And Identification ◽  
Wide Range

Background Early detection and identification of faulty greenhouse operation is essential, if losses are to be minimized by taking immediate corrective actions. Automatic detection and identification would also free the greenhouse manager to tend to his other business. Original objectives The general objective was to develop a method, or methods, for the detection, identification and accommodation of faults in the greenhouse. More specific objectives were as follows: 1. Develop accurate systems models, which will enable the detection of small deviations from normal behavior (of sensors, control, structure and crop). 2. Using these models, develop algorithms for an early detection of deviations from the normal. 3. Develop identifying procedures for the most important faults. 4. Develop accommodation procedures while awaiting a repair. The Technion team focused on the shoot environment and the Cornell University team focused on the root environment. Achievements Models: Accurate models were developed for both shoot and root environment in the greenhouse, utilizing neural networks, sometimes combined with robust physical models (hybrid models). Suitable adaptation methods were also successfully developed. The accuracy was sufficient to allow detection of frequently occurring sensor and equipment faults from common measurements. A large data base, covering a wide range of weather conditions, is required for best results. This data base can be created from in-situ routine measurements. Detection and isolation: A robust detection and isolation (formerly referred to as 'identification') method has been developed, which is capable of separating the effect of faults from model inaccuracies and disturbance effects. Sensor and equipment faults: Good detection capabilities have been demonstrated for sensor and equipment failures in both the shoot and root environment. Water stress detection: An excitation method of the shoot environment has been developed, which successfully detected water stress, as soon as the transpiration rate dropped from its normal level. Due to unavailability of suitable monitoring equipment for the root environment, crop faults could not be detected from measurements in the root zone. Dust: The effect of screen clogging by dust has been quantified. Implications Sensor and equipment fault detection and isolation is at a stage where it could be introduced into well equipped and maintained commercial greenhouses on a trial basis. Detection of crop problems requires further work. Dr. Peleg was primarily responsible for developing and implementing the innovative data analysis tools. The cooperation was particularly enhanced by Dr. Peleg's three summer sabbaticals at the ARS, Northem Plains Agricultural Research Laboratory, in Sidney, Montana. Switching from multi-band to hyperspectral remote sensing technology during the last 2 years of the project was advantageous by expanding the scope of detected plant growth attributes e.g. Yield, Leaf Nitrate, Biomass and Sugar Content of sugar beets. However, it disrupted the continuity of the project which was originally planned on a 2 year crop rotation cycle of sugar beets and multiple crops (com and wheat), as commonly planted in eastern Montana. Consequently, at the end of the second year we submitted a continuation BARD proposal which was turned down for funding. This severely hampered our ability to validate our findings as originally planned in a 4-year crop rotation cycle. Thankfully, BARD consented to our request for a one year extension of the project without additional funding. This enabled us to develop most of the methodology for implementing and running the hyperspectral remote sensing system and develop the new analytical tools for solving the non-repeatability problem and analyzing the huge hyperspectral image cube datasets. However, without validation of these tools over a ful14-year crop rotation cycle this project shall remain essentially unfinished. Should the findings of this report prompt the BARD management to encourage us to resubmit our continuation research proposal, we shall be happy to do so.  

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