Retrieval of Vegetation Canopy Water Content Based on Spectral Index Method

Zhang, Lin Jing; Zhang, Hong; Zhu, Xiao Bo; Sun, Lin

doi:10.4028/www.scientific.net/amm.295-298.2446

Cited by 3 publications

(3 citation statements)

References 4 publications

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“…The 11 vegetation indices and their calculation formulas were shown in Table 2. These vegetation indices were composed of various combinations of water‐sensitive bands, used for retrieving VWC (Haldar et al., 2020; Wang and Qu, 2007; Zhang et al., 2018). As Sentinel‐2 has two short‐wave infrared bands, they can provide NDWI1, NDWI2, MSI1, and MSI2 (band 11 and band 12 of Sentinel‐2 are SWIR1 and SWIR2, respectively, NDWI1 and MSI1 correspond to band 11 of Sentinel‐2, and NDWI2 and MSI2 correspond to band 12 of Sentinel‐2).…”

Section: Methodsmentioning

confidence: 99%

Modeling and Assessment of Vegetation Water Content on Soil Moisture Retrieval via the Synergistic Use of Sentinel‐1 and Sentinel‐2

Wang

Jin

et al. 2022

Earth and Space Science

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As one of the important parameters of the fifty "basic climate variables", soil moisture plays an important role in ecology, hydrology, climatology, and meteorology (Attarzadeh et al., 2018;Laachrate et al., 2020). From an ecological point of view, soil moisture is an important water element absorbed by plant roots, which directly affects plant growth, soil respiration, and plant function (Blyth et al., 2011). For the hydrology, soil moisture controls surface infiltration and redistribution of surface runoff and is an important part of the global water cycle (Kornelsen & Coulibaly, 2013). From a climatological perspective, soil moisture regulates the distribution and circulation of water and energy on the land surface, which in turn affects the global climate pattern (Seneviratne et al., 2010).Studies on carbon and water cycles, climate change, and energy balance with large-scale and spatial-temporal characteristics often require the participation of global soil moisture products such as Soil Moisture Active and Passive (SMAP), Soil Moisture and Ocean Salinity (SMOS), etc (Carrera et al., 2017;Jones et al., 2017). However, these products with low spatial resolution cannot be used for research studies on crop yield evaluation, drought monitoring, and agricultural irrigation management (Carrao et al., 2016;Rivers et al., 2015). Ground measurements based on this point is often expensive and laborious. Therefore, it is extremely necessary and important to achieve high spatial and temporal resolutions of soil moisture retrieval. Optical and thermal infrared remote sensing are susceptible to surface coverings and severely affected by weather clouds and fog, and the accuracy of soil moisture retrieval is greatly reduced. High-resolution Synthetic Aperture Radar (SAR) data with microwave bands of band C, band X, and band L (e.g., COSMO-SkyMed, Sentinel-1, and ALOS), which have the advantage of being unaffected by weather, low frequency band, and being sensitive to soil moisture, and thus

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Section: Methodsmentioning

confidence: 99%

Modeling and Assessment of Vegetation Water Content on Soil Moisture Retrieval via the Synergistic Use of Sentinel‐1 and Sentinel‐2

Wang

Jin

et al. 2022

Earth and Space Science

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“…Spectral vegetation indices are related to crop growth information to some extent because they are susceptible to the geographical conditions under which crops are grown [1]. The NDVI can reflect indicators such as crop water content changes and leaf area index [26], and the near-infrared (NIR) and shortwave-infrared (SWIR) bands are closely related to water absorption bands; these bands can respond to changes in vegetation leaf water content within a certain range by combining reference bands and corresponding characteristic bands [3]. Therefore, ten spectral vegetation indices were selected, as shown in Table 1, and their Pearson correlation coefficients with water content were calculated separately.…”

Section: Spectral Vegetation Indicesmentioning

confidence: 99%

“…A lack of leaf water can affect leaf activity, leading to hindered material transport and reduced chlorophyll content, thereby inhibiting photosynthesis [2], which plays a key role in the growth and yield of citrus tangerines. In recent years, hyperspectral technology has been widely used for the quantitative estimation of various crop growth indicators, such as chlorophyll content, leaf area index, and water content [1,[3][4][5][6], due to its advantages of multiple bands and excellent accuracy [7]. Therefore, further exploration of the use of hyperspectral technology for predicting leaf water content information during citrus growth is highly important for assisting in monitoring citrus growth.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Citrus Leaf Water Content Estimations Based on the Continuous Wavelet Transform and Fractional Derivative Methods

Dou,

Zhang,

Deng

et al. 2024

Horticulturae

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Citrus tangerines are famous fruits worldwide, and monitoring the water content of citrus leaves is highly important for citrus production. However, there are still challenges in quantitatively estimating the water content of citrus leaves using hyperspectral technology, and the random noise generated during spectral acquisition and the overlapping peaks in the sensitive band of the citrus leaf water content will affect estimation accuracy. To solve these problems and further explore the roles of the continuous wavelet transform (CWT) and fractional-order derivative (FOD) in the estimation of citrus leaf water content, this study intends to use of CWT and FOD to decompose the original spectrum, and then compare the correlation between the original spectrum and leaf water content to explore whether the decomposition treatment has improved the correlation between spectrum and leaf moisture content. Then, the successive projections algorithm (SPA) was used to select feature bands and combine spectral vegetation indices. Partial least squares regression (PLSR) was used to construct water-content inversion models for citrus leaves, and the inversion accuracies of two commonly used spectral preprocessing methods were compared. The results indicate that (1) the CWT can improve the sensitivity of the spectrum to the citrus leaf water content to a certain extent, and the inversion accuracy of the CWT is approximately 5% greater than that of the FOD. (2) On the basis of the CWT and FOD methods, the inversion accuracy of the citrus leaf water content based on SPA screening increased by 9.61% and 9.29%, respectively, compared with the original spectrum. (3) Under CWT decomposition, Scale4 of the Gaus1 wavelet was screened by the SPA, and the inversion model of citrus leaf water content was constructed by combining the spectral vegetation index NDVI with the best results. The R-squared (R2) and root mean square error (RMSE) values were 0.7491 and 0.0284, respectively, which were both 0.0138 greater than those of the best inversion model for the FOD R2. In conclusion, the CWT-SPA combined with the spectral vegetation index can improve the sensitivity of the spectrum to the citrus leaf water content, eliminate a large amount of redundant data, and enhance the prediction ability and stability of the citrus leaf water content.

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Effect of different SRFs on time series of spectral indices, between sentinel-2 and other sensors for the purpose of vegetation land cover monitoring

Imanyfar

Hasanlou

2017

jgit

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Quality and quantity of vegetation land cover is considered as one of the important aspects of environment. Detection of trends in natural phenomena such as vegetation, requires long-term studies, more than lifetime of a satellite. On the other hand, combining data from different sensors could lead to formation of false changes. One of the main causes of false changes is different spectral sensitivity functions (SRFs), among sensors under study. In this regard, the impact of these factors should be eliminated or reduced as much as possible by a procedure named relative calibration which is the main goal of this research. There are similarities between Landsat satellites series and SPOT-5 with Sentinel-2 in many aspects, so MSI (the Sentinel-2's sensor) has capacity for data continuity. In this study, by incorporating polynomial equations, Landsat sensors (OLI, ETM +, ETM) and SPOT-5 were calibrated relative to MSI. The combination of radiative transfer models; PROSPECT-4 for leaf and 4SAIL for canopy, were used to simulate 50000 top of canopy synthetic spectral signatures and then soil effect was combined with them using linear spectral mixture model. After all, 150000 signatures were simulated. These spectral signatures were transformed to equivalent reflectance values (Blue, Red, NIR and SWIR) and spectral indices (NDVI, EVI and NDWI). 80% of spectral signatures were selected randomly for solving relative calibration models. Also, for validation purpose, remained simulated (20%) and 38 top of canopy measured spectral signatures were used. According to the results, linear equation can model the difference (caused by SRF) between MSI and others quite well and there is no need for more complicated equations. In general, results of this research show high and acceptable correlation for all reflectance bands and indices. It is more necessary to perform a relative calibration pre-processing step for EVI time series. Amongst reflectance bands, NIR has the highest continuity.

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Retrieval of Vegetation Canopy Water Content Based on Spectral Index Method

Cited by 3 publications

References 4 publications

Modeling and Assessment of Vegetation Water Content on Soil Moisture Retrieval via the Synergistic Use of Sentinel‐1 and Sentinel‐2

Modeling and Assessment of Vegetation Water Content on Soil Moisture Retrieval via the Synergistic Use of Sentinel‐1 and Sentinel‐2

Comparison of Citrus Leaf Water Content Estimations Based on the Continuous Wavelet Transform and Fractional Derivative Methods

Effect of different SRFs on time series of spectral indices, between sentinel-2 and other sensors for the purpose of vegetation land cover monitoring

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