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

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

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High Throughput Field Phenotyping of Wheat Plant Height and Growth Rate in Field Plot Trials Using UAV Based Remote Sensing

Remote Sensing ◽

10.3390/rs8121031 ◽

2016 ◽

Vol 8 (12) ◽

pp. 1031 ◽

Cited By ~ 139

Author(s):

Fenner Holman ◽

Andrew Riche ◽

Adam Michalski ◽

March Castle ◽

Martin Wooster ◽

...

Keyword(s):

Remote Sensing ◽

Growth Rate ◽

Plant Height ◽

High Throughput ◽

Wheat Plant ◽

Field Plot ◽

Field Phenotyping

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

Remote Sensing ◽

10.3390/rs9080794 ◽

2017 ◽

Vol 9 (8) ◽

pp. 794 ◽

Cited By ~ 9

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

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Applying remote sensing expertise to crop improvement: progress and challenges to scale up high throughput field phenotyping from research to industry

10.1117/12.2229389 ◽

2016 ◽

Author(s):

David Gouache ◽

Katia Beauchêne ◽

Agathe Mini ◽

Antoine Fournier ◽

Benoit de Solan ◽

...

Keyword(s):

Remote Sensing ◽

High Throughput ◽

Crop Improvement ◽

Scale Up ◽

Field Phenotyping

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Site Characteristics Mediate the Relationship Between Forest Productivity and Satellite Measured Solar Induced Fluorescence

Frontiers in Forests and Global Change ◽

10.3389/ffgc.2021.695269 ◽

2021 ◽

Vol 4 ◽

Author(s):

Theresia Yazbeck ◽

Gil Bohrer ◽

Pierre Gentine ◽

Luping Ye ◽

Nicola Arriga ◽

...

Keyword(s):

Remote Sensing ◽

Water Stress ◽

Gross Primary Production ◽

Canopy Structure ◽

Structural Response ◽

Local Effect ◽

High Temporal Resolution ◽

Canopy Conductance ◽

Area Index ◽

The Relationship

Solar-Induced Chlorophyll Fluorescence (SIF) can provide key information about the state of photosynthesis and offers the prospect of defining remote sensing-based estimation of Gross Primary Production (GPP). There is strong theoretical support for the link between SIF and GPP and this relationship has been empirically demonstrated using ground-based, airborne, and satellite-based SIF observations, as well as modeling. However, most evaluations have been based on monthly and annual scales, yet the GPP:SIF relations can be strongly influenced by both vegetation structure and physiology. At the monthly timescales, the structural response often dominates but short-term physiological variations can strongly impact the GPP:SIF relations. Here, we test how well SIF can predict the inter-daily variation of GPP during the growing season and under stress conditions, while taking into account the local effect of sites and abiotic conditions. We compare the accuracy of GPP predictions from SIF at different timescales (half-hourly, daily, and weekly), while evaluating effect of adding environmental variables to the relationship. We utilize observations for years 2018–2019 at 31 mid-latitudes, forested, eddy covariance (EC) flux sites in North America and Europe and use TROPOMI satellite data for SIF. Our results show that SIF is a good predictor of GPP, when accounting for inter-site variation, probably due to differences in canopy structure. Seasonally averaged leaf area index, fraction of absorbed photosynthetically active radiation (fPAR) and canopy conductance provide a predictor to the site-level effect. We show that fPAR is the main factor driving errors in the linear model at high temporal resolution. Adding water stress indicators, namely canopy conductance, to a multi-linear SIF-based GPP model provides the best improvement in the model precision at the three considered timescales, showing the importance of accounting for water stress in GPP predictions, independent of the SIF signal. SIF is a promising predictor for GPP among other remote sensing variables, but more focus should be placed on including canopy structure, and water stress effects in the relationship, especially when considering intra-seasonal, and inter- and intra-daily resolutions.

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High-throughput phenotyping of a large tomato collection under water deficit: Combining UAVs’ remote sensing with conventional leaf-level physiologic and agronomic measurements

Agricultural Water Management ◽

10.1016/j.agwat.2021.107283 ◽

2022 ◽

Vol 260 ◽

pp. 107283

Author(s):

Mateu Fullana-Pericàs ◽

Miquel À. Conesa ◽

Jorge Gago ◽

Miquel Ribas-Carbó ◽

Jeroni Galmés

Keyword(s):

Remote Sensing ◽

Water Deficit ◽

High Throughput ◽

Leaf Level ◽

High Throughput Phenotyping

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Water Stress Regime of Irrigated Crops Based on Remote Sensing and Ground-Based Data

Agronomy ◽

10.3390/agronomy11061117 ◽

2021 ◽

Vol 11 (6) ◽

pp. 1117

Author(s):

Anatoly Mikhailovich Zeyliger ◽

Olga Sergeevna Ermolaeva

Keyword(s):

Remote Sensing ◽

Water Stress ◽

Crop Yield ◽

Satellite Monitoring ◽

Agricultural Crop ◽

Crop Water ◽

Stress Regime ◽

Irrigation Water Use ◽

Crop Water Stress ◽

Crop Irrigation

In the past few decades, combinations of remote sensing technologies with ground-based methods have become available for use at the level of irrigated fields. These approaches allow an evaluation of crop water stress dynamics and irrigation water use efficiency. In this study, remotely sensed and ground-based data were used to develop a method of crop water stress assessment and analysis. Input datasets of this method were based on the results of ground-based and satellite monitoring in 2012. Required datasets were collected for 19 irrigated alfalfa crops in the second year of growth at three study sites located in Saratovskoe Zavolzhie (Saratov Oblast, Russia). Collected datasets were applied to calculate the dynamics of daily crop water stress coefficients for all studied crops, thereby characterizing the efficiency of crop irrigation. Accordingly, data on the crop yield of three harvests were used. An analysis of the results revealed a linear relationship between the crop yield of three cuts and the average value of the water stress coefficient. Further application of this method may be directed toward analyzing the effectiveness of irrigation practices and the operational management of agricultural crop irrigation.

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Plant ecophysiological processes in spectral profiles: perspective from a deciduous broadleaf forest

Journal of Plant Research ◽

10.1007/s10265-021-01302-7 ◽

2021 ◽

Author(s):

Hibiki M. Noda ◽

Hiroyuki Muraoka ◽

Kenlo Nishida Nasahara

Keyword(s):

Remote Sensing ◽

Optical Properties ◽

Spectral Reflectance ◽

Canopy Structure ◽

Terrestrial Ecosystems ◽

Optical Remote Sensing ◽

Canopy Reflectance ◽

Broadleaf Forest ◽

Leaf Optical Properties ◽

Deciduous Broadleaf Forest

AbstractThe need for progress in satellite remote sensing of terrestrial ecosystems is intensifying under climate change. Further progress in Earth observations of photosynthetic activity and primary production from local to global scales is fundamental to the analysis of the current status and changes in the photosynthetic productivity of terrestrial ecosystems. In this paper, we review plant ecophysiological processes affecting optical properties of the forest canopy which can be measured with optical remote sensing by Earth-observation satellites. Spectral reflectance measured by optical remote sensing is utilized to estimate the temporal and spatial variations in the canopy structure and primary productivity. Optical information reflects the physical characteristics of the targeted vegetation; to use this information efficiently, mechanistic understanding of the basic consequences of plant ecophysiological and optical properties is essential over broad scales, from single leaf to canopy and landscape. In theory, canopy spectral reflectance is regulated by leaf optical properties (reflectance and transmittance spectra) and canopy structure (geometrical distributions of leaf area and angle). In a deciduous broadleaf forest, our measurements and modeling analysis of leaf-level characteristics showed that seasonal changes in chlorophyll content and mesophyll structure of deciduous tree species lead to a seasonal change in leaf optical properties. The canopy reflectance spectrum of the deciduous forest also changes with season. In particular, canopy reflectance in the green region showed a unique pattern in the early growing season: green reflectance increased rapidly after leaf emergence and decreased rapidly after canopy closure. Our model simulation showed that the seasonal change in the leaf optical properties and leaf area index caused this pattern. Based on this understanding we discuss how we can gain ecophysiological information from satellite images at the landscape level. Finally, we discuss the challenges and opportunities of ecophysiological remote sensing by satellites.

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Applying RGB- and Thermal-Based Vegetation Indices from UAVs for High-Throughput Field Phenotyping of Drought Tolerance in Forage Grasses

Remote Sensing ◽

10.3390/rs13010147 ◽

2021 ◽

Vol 13 (1) ◽

pp. 147

Author(s):

Tom De Swaef ◽

Wouter H. Maes ◽

Jonas Aper ◽

Joost Baert ◽

Mathias Cougnon ◽

...

Keyword(s):

Drought Stress ◽

Drought Tolerance ◽

Lolium Perenne ◽

High Throughput ◽

Festuca Arundinacea ◽

Stress Index ◽

Forage Grasses ◽

Crop Water Stress Index ◽

Ploidy Levels ◽

Field Phenotyping

The persistence and productivity of forage grasses, important sources for feed production, are threatened by climate change-induced drought. Breeding programs are in search of new drought tolerant forage grass varieties, but those programs still rely on time-consuming and less consistent visual scoring by breeders. In this study, we evaluate whether Unmanned Aerial Vehicle (UAV) based remote sensing can complement or replace this visual breeder score. A field experiment was set up to test the drought tolerance of genotypes from three common forage types of two different species: Festuca arundinacea, diploid Lolium perenne and tetraploid Lolium perenne. Drought stress was imposed by using mobile rainout shelters. UAV flights with RGB and thermal sensors were conducted at five time points during the experiment. Visual-based indices from different colour spaces were selected that were closely correlated to the breeder score. Furthermore, several indices, in particular H and NDLab, from the HSV (Hue Saturation Value) and CIELab (Commission Internationale de l’éclairage) colour space, respectively, displayed a broad-sense heritability that was as high or higher than the visual breeder score, making these indices highly suited for high-throughput field phenotyping applications that can complement or even replace the breeder score. The thermal-based Crop Water Stress Index CWSI provided complementary information to visual-based indices, enabling the analysis of differences in ecophysiological mechanisms for coping with reduced water availability between species and ploidy levels. All species/types displayed variation in drought stress tolerance, which confirms that there is sufficient variation for selection within these groups of grasses. Our results confirmed the better drought tolerance potential of Festuca arundinacea, but also showed which Lolium perenne genotypes are more tolerant.

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