Application of thermal imaging and hyperspectral remote sensing for crop water deficit stress monitoring

The effect of soil modification on changing the availability of water and the onset of crop water deficit stress in wheat was assessed during 2 drying periods. The different methods of determining the onset of stress generally agreed with each other. Differences were either related to the different parts of the canopy measured or to different physiological processes measured. Because foliage temp. was continuously monitored, the dynamic development of stress in relation to increasing soil water deficit and root growth became evident. The allowable soil water deficit at the onset of stress varied widely between soil treatments and the stage of crop growth at which deficit stress occurred. Physically modifying the soil increased plant available water by 80%. This resulted from both changes in amount of soil water stored and through a more uniformly distributed root system. Wheat growing in undisturbed soil was unable to adapt to post-anthesis stress, as frequent irrigations prior to anthesis concentrated root distribution in the upper layers.

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Next generation phenotyping for developing climate resilient rice varieties

ORYZA- An International Journal on Rice ◽

10.35709/ory.2019.56.s.2 ◽

2019 ◽

Vol 56 (Special Issue) ◽

pp. 92-105

Author(s):

Rabi N Sahoo ◽

C Viswanathan ◽

Gopal Krishna ◽

Bappa Das ◽

Swati Goel ◽

...

Keyword(s):

Remote Sensing ◽

Water Deficit ◽

Multispectral Imaging ◽

Sugar Content ◽

Stress Index ◽

Water Deficit Stress ◽

Next Generation ◽

Crop Water Stress Index ◽

Non Invasive ◽

Rice Genotypes

Present paper deals with different components of next generation phenomics for characterizing rice genotypes for water deficit stress. Major sensors used in the study were non-imaging hyperspectal remote sensing, thermal imaging at ground platform and RGB and multispectral imaging sensors from drone platform. Different spectral indices were evaluated along with new proposed index and different multivariate models were studied for non-invasive estimation of relative water content (RWC) and sugar content in rice plant using spectral reflectance data collected in spectral range 350 to 2500 nm. Spectral data were further used for spectral discrimination of rice genotypes. Crop water stress index derived from thermal images acquired for rice genotypes could well characterize the drought resistant and sensitive genotypes. Initial study on field phenotyping through drone remote sensing using multispectral and RGB sensor was also explored to capture differential response of genotypes, trait and heat map mapping. All developed protocols as reliable alternative to conventional methods are fast, economic and non-invasive and in use in plant phenomics centre for high throughput plant phenotyhping for water deficit stress studies.

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Crop Water Productivity Estimation with Hyperspectral Remote Sensing

Advanced Applications in Remote Sensing of Agricultural Crops and Natural Vegetation ◽

10.1201/9780429431166-5 ◽

2018 ◽

pp. 79-96

Author(s):

Michael Marshall ◽

Itiya Aneece ◽

Daniel Foley ◽

Cai Xueliang ◽

Trent Biggs

Keyword(s):

Remote Sensing ◽

Water Productivity ◽

Hyperspectral Remote Sensing ◽

Crop Water ◽

Crop Water Productivity ◽

Productivity Estimation

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Mapping Agewise Discrimination of Arecanut Crop Water Requirement Using Hyperspectral Remote Sensing

Aquatic Procedia ◽

10.1016/j.aqpro.2015.02.186 ◽

2015 ◽

Vol 4 ◽

pp. 1437-1444 ◽

Cited By ~ 4

Author(s):

B.E. Bhojaraja ◽

Gaurav Hegde ◽

U. Pruthviraj ◽

Amba Shetty ◽

M.K. Nagaraj

Keyword(s):

Remote Sensing ◽

Hyperspectral Remote Sensing ◽

Water Requirement ◽

Crop Water Requirement ◽

Crop Water

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Agricultural Stress Monitoring using Remote Sensing Data

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.c4688.029320 ◽

2020 ◽

Vol 9 (3) ◽

pp. 335-337

Keyword(s):

Remote Sensing ◽

Stress Level ◽

Hyperspectral Image ◽

Atmospheric Correction ◽

Remote Sensing Data ◽

Hyperspectral Remote Sensing ◽

Hyperspectral Data ◽

Stress Monitoring ◽

Chlorophyll Contents ◽

Sensing Data

This study consist of experiments on Hyperspectral remote sensing data for monitoring field stress using remote sensing tools. We have segmented Hyperspectral image and then calculated stress level using ENVI tool. EO-I hyperspectral remote sensing data from hyperion space born sensor has been used as the key input. QUACK (Quick Atmospheric Correction) algorithm has been used for atmospheric correction of hyperspectral data. EO-1, hyperion sensors data It has been observed that stress level depends on chlorophyll contents of a leaf. It has been observed that green field is with less stress and rock where no chlorophyll contents have most stress. We have also shown stress level in the scale of 1 to 9.

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Evaluation of different water absorption bands, indices and multivariate models for water-deficit stress monitoring in rice using visible-near infrared spectroscopy

Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy ◽

10.1016/j.saa.2020.119104 ◽

2021 ◽

Vol 247 ◽

pp. 119104

Author(s):

Bappa Das ◽

Rabi N. Sahoo ◽

Sourabh Pargal ◽

Gopal Krishna ◽

Rakesh Verma ◽

...

Keyword(s):

Infrared Spectroscopy ◽

Water Absorption ◽

Water Deficit ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Water Deficit Stress ◽

Stress Monitoring ◽

Multivariate Models ◽

Absorption Bands

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

Remote Sensing ◽

10.3390/rs11101240 ◽

2019 ◽

Vol 11 (10) ◽

pp. 1240 ◽

Cited By ~ 26

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.

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