Winter Wheat Canopy Water Content Monitoring Based on Spectral Transforms and “Three-edge” Parameters

Peng, Zhigong; Lin, Shaozhe; Zhang, Baozhong; Zheng, Wei; Liu, Lu; Han, Ning; Cai, Jiabing; Chen, He

doi:10.1016/j.agwat.2020.106306

Cited by 21 publications

(8 citation statements)

References 21 publications

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“…As a major abiotic stress, drought has a direct impact on physiological and biochemical processes, and the morphological structure of crop plants, ultimately contributing to yield loss or poor Remote Sens. 2022, 14, 584 2 of 17 crop quality [5][6][7][8]. Therefore, accurate real-time monitoring of crop water content is useful for enhancing our agricultural water management ability and improving the resource use efficiency of crops [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, the relationship between the NDII and VWC was indirect and allometric, and the NDII was more related to CWC than to VWC [21]. Due to the limitations of destructive sampling methods or poor weather conditions during entire crop growing seasons, many studies are restricted to constructing spectral monitoring models for specific growth stages or a short growth period [5]. As the spectral characteristics of plants vary significantly across growth stages [33], the applicability of stage-specific or period-specific models to the entire growth period remains unknown.…”

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: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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“…In order to fully explore spectral features of ShB lesions on the leaf blade and the leaf sheath, two spectral analysis methods, "three-edge" parameters and narrow-band vegetation indexes [20][21][22], were used in this study.…”

Section: "Three-edge" Parameters and Narrow-band Vegetation Indicesmentioning

confidence: 99%

“…An increase in the amount of chlorophyll, for example, causes a red-edge shift towards longer wavelengths. A series of spectral characteristic parameters are constructed in these ranges, including position, amplitude, area, ratio and normalized ratio [20,[23][24][25]. These are termed "three-edge" parameters.…”

Section: "Three-edge" Parameters and Narrow-band Vegetation Indicesmentioning

confidence: 99%

Identification of Rice Sheath Blight through Spectral Responses Using Hyperspectral Images

Lin

Guo

Tan

et al. 2020

Sensors

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Sheath blight (ShB), caused by Rhizoctonia solani AG1-I, is one of the most important diseases in rice worldwide. The symptoms of ShB primarily develop on leaf sheaths and leaf blades. Hyperspectral remote sensing technology has the potential of rapid, efficient and accurate detection and monitoring of the occurrence and development of rice ShB and other crop diseases. This study evaluated the spectral responses of leaf blade fractions with different development stages of ShB symptoms to construct the spectral feature library of rice ShB based on “three-edge” parameters and narrow-band vegetation indices to identify the disease on the leaves. The spectral curves of leaf blade lesions have significant changes in the blue edge, green peak, yellow edge, red valley, red edge and near-infrared regions. The variables of the normalized index between green peak amplitude and red valley amplitude (Rg − Ro)/(Rg + Ro), the normalized index between the yellow edge area and blue edge area (SDy − SDb)/(SDy + SDb), the ratio index of green peak amplitude and red valley amplitude (Rg/Ro) and the nitrogen reflectance index (NRI) had high relevance to the disease. At the leaf scale, the importance weights of all attributes decreased with the effect of non-infected areas in a leaf by the ReliefF algorithm, with Rg/Ro being the indicator having the highest importance weight. Estimation rate of 95.5% was achieved in the decision tree classifier with the parameter of Rg/Ro. In addition, it was found that the variety degree of absorptive valley, reflection peak and reflecting steep slope was different in the blue edge, green and red edge regions, although there were similar spectral curve shapes between leaf sheath lesions and leaf blade lesions. The significant difference characteristic was the ratio index of the red edge area and green peak area (SDr/SDg) between them. These results can provide the basis for the development of a specific sensor or sensors system for detecting the ShB disease in rice.

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“…The fundamental of remote estimation is the interaction between plant chemical compositions, such as LWC, with electromagnetic radiation [11]. Numerous research efforts focus on developing approaches for LWC estimation based on spectral regions from red edge to SWIR [12,13]. There are two widespread methods: (1) the use of spectral indices calculated by the reflectance of wavebands relevant to leaf water status (e.g., water index and normalized difference water index) [14,15], and (2) radiative transfer modeling, that simulates the light penetration path through the canopy, based on physical laws [16].…”

Section: Introductionmentioning

confidence: 99%

Estimating Vertical Distribution of Leaf Water Content within Wheat Canopies after Head Emergence

Kong

Huang

et al. 2021

Remote Sensing

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Monitoring vertical profile of leaf water content (LWC) within wheat canopies after head emergence is vital significant for increasing crop yield. However, the estimation of vertical distribution of LWC from remote sensing data is still challenging due to the effects of wheat spikes and the efficacy of sensor measurement from the nadir direction. Using two-year field experiments with different growth stages after head emergence, N rates, wheat cultivars, we investigated the vertical distribution of LWC within canopies, the changes of canopy reflectance after spikes removal, the relationship between spectral indices and LWC in the upper-, middle- and bottom-layer. The interrelationship among vertical LWC were constructed, and four ratio of reflectance difference (RRD) type of indices were proposed based on the published WI and NDWSI indices to determine vertical distribution of LWC. The results indicated a bell shape distribution of LWC in wheat plants with the highest value appeared at the middle layer, and significant linear correlations between middle-LWC vs. upper-LWC and middle-LWC vs. bottom-LWC (r ≥ 0.92) were identified. The effects of wheat spikes on spectral reflectance mainly occurred in near infrared to shortwave infrared regions, which then decreased the accuracy of LWC estimation. Spectral indices at the middle layer outperformed the other two layers in LWC assessment and were less susceptible to wheat spikes effects, in particular, the newly proposed narrow-band WI-4 and NDWSI-4 indices exhibited great potential in tracking the changes of middle-LWC (R2 = 0.82 and 0.84, respectively). By taking into account the effects of wheat spikes and the interrelationship of vertical LWC within canopies, an indirect induction strategy was developed for modeling the upper-LWC and bottom-LWC. It was found that the indirect induction models based on the WI-4 and NDWSI-4 indices were more effective than the models obtained from conventional direct estimation method, with R2 of 0.78 and 0.81 for the upper-LWC estimation, and 0.75 and 0.74 for the bottom-LWC estimation, respectively.

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Winter Wheat Canopy Water Content Monitoring Based on Spectral Transforms and “Three-edge” Parameters

Cited by 21 publications

References 21 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

Identification of Rice Sheath Blight through Spectral Responses Using Hyperspectral Images

Estimating Vertical Distribution of Leaf Water Content within Wheat Canopies after Head Emergence

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