Leaf water content estimation using top-of-canopy airborne hyperspectral data

Raj, Rahul; Walker, Jeffrey P.; Vinod, Vishal; Pingale, Rohit; Naik, B. Balaji; Adinarayana, J.

doi:10.1016/j.jag.2021.102393

Cited by 15 publications

(13 citation statements)

References 59 publications

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“…ASWC, available soil water content; LAI, leaf area index; CWC, canopy water content; VDM, vegetation dry mass; VWC, vegetation water content in all leaves and stems; WI water index; NDWI, normalized difference water index; NDII, normalized difference infrared index NDVI, normalized difference vegetation index; and OSAVI, optimal soil-adjusted vegetation index χ 2 , chi-squared; df, degree of freedom; GFI, goodness-of-fit index; RMSEA, root mean square error of approximation. Instantaneous water stress and long-term water stress are two kinds of plant water stress at different phases [34,53]. The impact of instantaneous water stress can be reflected in the decrease in leaf water content, which influences stomatal activity and gaseous exchange between plants and the ambient atmosphere [35,54,55].…”

Section: Linking the Dynamic Variations In Svis With Abiotic And Biotic Factorsmentioning

confidence: 99%

“…Variations in plant water status can be captured by the differences in canopy reflectance information. Thus, the remotely sensed estimation of plant water status using optical data is feasible and useful for monitoring and assessing for agricultural drought [34]. Consequently, it is necessary to understand the underlying mechanism of spectral vegetation index responses to the impact of water stress on the dynamics of plant water content.…”

Section: Impact Mechanism Of the Soil Drying Process On The Svismentioning

confidence: 99%

“…There are two different concepts related to plant water stress: instantaneous water stress and long-term water stress [34]. Instantaneous water stress generally lowers leaf water content, while long-term stress significantly affects crop physical parameters, such as LAI, canopy height, and biomass [35][36][37][38].…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Characteristics of Canopy and Vegetation Water Content during an Entire Maize Growing Season in Relation to Spectral-Based Indices

Zhou

Song

et al. 2022

Remote Sensing

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A variety of spectral vegetation indices (SVIs) have been constructed to monitor crop water stress. However, their abilities to reflect dynamic canopy water content (CWC) and vegetation water content (VWC) during the growing season have not been concurrently examined, and the underlying mechanisms remain unclear, especially in relation to soil drying. In this study, a field experiment was conducted and designed with various irrigation regimes applied during two consecutive growing seasons of maize. The results showed that CWC, VWC, and the SVIs exhibited obvious trends of first increasing and then decreasing within a growing season. In addition, VWC was allometrically related to CWC across the two growing seasons. A linear relationship between the five SVIs and CWC occurred within a certain CWC range (0.01–0.41 kg m−2), while the relationship between these SVIs and VWC was nonlinear. Furthermore, the five SVIs indicated critical values for VWC, and these values were 1.12 and 1.15 kg m−2 for the water index (WI) and normalized difference water index (NDWI), respectively; however, the normalized difference infrared index (NDII), normalized difference vegetation index (NDVI), and optimal soil-adjusted vegetation index (OSAVI) had the same critical value of 0.55 kg m−2. Therefore, in comparison to the NDII, NDVI, and OSAVI, the WI and NDWI better reflected the crop water content based on their sensitives to CWC and VWC. Moreover, CWC was the most important direct biotic driver of the dynamics of SVIs, while leaf area index (LAI) was the most important indirect biotic driver. VWC was a critical indirect regulator of WI, NDWI, NDII, and OSAVI dynamics, whereas vegetation dry mass (VDM) was the critical indirect regulator of NDVI dynamics. These findings may provide additional information for estimating agricultural drought and insights on the impact mechanism of soil water deficits on SVIs.

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Section: Linking the Dynamic Variations In Svis With Abiotic And Biotic Factorsmentioning

confidence: 99%

Section: Impact Mechanism Of the Soil Drying Process On The Svismentioning

confidence: 99%

See 1 more Smart Citation

Dynamic Characteristics of Canopy and Vegetation Water Content during an Entire Maize Growing Season in Relation to Spectral-Based Indices

Zhou

Song

et al. 2022

Remote Sensing

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“…• 674 nm: A water absorption wavelength has previously been identified at 698 nm by Braun and Smirnov (1993) [40] and validated by Raj et al (2021) [41] on spectroradiometer and drone-based hyperspectral data. Thus, to create a reliable strawberry water content index, the 698 nm wavelength should be connected with any other wavelength in the 670-750 nm range.…”

Section: Strawberry Fruit Water Content Metricmentioning

confidence: 87%

Strawberry Water Content Estimation and Ripeness Classification Using Hyperspectral Sensing

2022

Self Cite

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We propose data-driven approaches to water content estimation and ripeness classification of the strawberry fruit. A narrowband hyperspectral spectroradiometer was used to collect reflectance signatures from 43 strawberry fruits at different ripeness levels. Then, the ground truth water content was obtained using the oven-dry method. To estimate the water content, 674 and 698 nm bands were selected to create a normalized difference strawberry water content index. The index was used as an input to a logarithmic model for estimating fruit water content. The model for water content estimation gave a correlation coefficient of 0.82 and Root Mean Squared Error (RMSE) of 0.0092 g/g. For ripeness classification, a Support Vector Machine (SVM) model using the full spectrum as input achieved over 98% accuracy. Our analysis further show that, in the absence of the full spectrum data, using our proposed water content index as input, which uses reflectance values from only two frequency bands, achieved 71% ripeness classification accuracy, which might be adequate for certain applications with limited sensing resources.

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“…VWC of wheat crops is also a significant growth indicator during different development stages [ 6 ]. Water availability not only affects wheat photosynthesis, but also the grain filling rate and, ultimately, yield [ 21 , 22 ]. Traditionally, wheat crop water content has been determined by manually sampling plants to obtain fresh weight and dry weight and then calculating crop water content [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Seasonal Mapping of Irrigated Winter Wheat Traits in Argentina with a Hybrid Retrieval Workflow Using Sentinel-2 Imagery

et al. 2022

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Earth observation offers an unprecedented opportunity to monitor intensively cultivated areas providing key support to assess fertilizer needs and crop water uptake. Routinely, vegetation traits mapping can help farmers to monitor plant development along the crop’s phenological cycle, which is particularly relevant for irrigated agricultural areas. The high spatial and temporal resolution of the Sentinel-2 (S2) multispectral instrument leverages the possibility to estimate leaf area index (LAI), canopy chlorophyll content (CCC), and vegetation water content (VWC) from space. Therefore, our study presents a hybrid retrieval workflow combining a physically-based strategy with a machine learning regression algorithm, i.e., Gaussian processes regression, and an active learning technique to estimate LAI, CCC and VWC of irrigated winter wheat. The established hybrid models of the three traits were validated against in-situ data of a wheat campaign in the Bonaerense valley, South of the Buenos Aires Province, Argentina, in the year 2020. We obtained good to highly accurate validation results with LAI: R2 = 0.92, RMSE = 0.43 m2 m−2, CCC: R2 = 0.80, RMSE = 0.27 g m−2 and VWC: R2 = 0.75, RMSE = 416 g m−2. The retrieval models were also applied to a series of S2 images, producing time series along the seasonal cycle, which reflected the effects of fertilizer and irrigation on crop growth. The associated uncertainties along with the obtained maps underlined the robustness of the hybrid retrieval workflow. We conclude that processing S2 imagery with optimised hybrid models allows accurate space-based crop traits mapping over large irrigated areas and thus can support agricultural management decisions.

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Leaf water content estimation using top-of-canopy airborne hyperspectral data

Cited by 15 publications

References 59 publications

Dynamic Characteristics of Canopy and Vegetation Water Content during an Entire Maize Growing Season in Relation to Spectral-Based Indices

Dynamic Characteristics of Canopy and Vegetation Water Content during an Entire Maize Growing Season in Relation to Spectral-Based Indices

Strawberry Water Content Estimation and Ripeness Classification Using Hyperspectral Sensing

Seasonal Mapping of Irrigated Winter Wheat Traits in Argentina with a Hybrid Retrieval Workflow Using Sentinel-2 Imagery

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