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Why reflectance is low for vegetation in red band? 

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For all of these data the model has been found to provide an effective quantitative representation of the shape and position of the vegetation red edge reflectance in terms of four parameters of physical significance: 1R shoulder reflectance R s, chlorophyll-well minimum reflectance R 0, red edge inflection point wavelength λp and reflectance minimum wavelength λ0.
We conclude that both the liquid water and the dry materials contribute to the reflectance spectra of green vegetation in the 1.0–2.5 μm region.
The normalized difference vegetation index and ratio of green (520–600 nm) to red (630–690 nm) band reflectance factors, however, seemed to be more accurate in monitoring them.
While recent literature proposes the red-edge feature of vegetation near 0.7 μm as a signature for land plants, observations in near-IR bands can be equally or even better suited for this purpose.
In comparison to broad band reflectance, the results indicate that red edg...
We caution that some mineral reflectance edges are similar in slope and strength to vegetation's red edge (albeit at different wavelengths); if an extrasolar planet reflectance edge is detected care must be taken with its interpretation.
At the regional scale, the sensitivity of reflectance to variation in vegetation variables is highly influenced by the mixed pixels.

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Is NDVI capable to detect water deficiency in arable land?5 answersNormalized Difference Vegetation Index (NDVI) is indeed capable of detecting water deficiency in arable land. Studies have shown that NDVI can rapidly detect the effects of water stress on field crops. NDVI readings have been successful in differentiating plant responses to drought conditions. Additionally, NDVI has been utilized in monitoring droughts by establishing correlations with evapotranspiration and temperature. Furthermore, research has demonstrated that NDVI, along with other indices like TVDI and VTCI, can provide valuable spatial information on drought conditions, with MERSI data showing promising results in drought monitoring. Therefore, NDVI, in conjunction with other indices, proves to be a valuable tool for detecting water deficiency in arable land, offering insights for effective agricultural management.
Why is too much sunlight is bad for plants vegetation?4 answersToo much sunlight is bad for plants because it can lead to photodamage and inactivation of the photosynthetic machinery, specifically photosystem II (PSII). Excess sunlight can result in the production of reactive oxygen species (ROS), which cause photo-oxidative damage to plants. If leaves or fruit are exposed to harmful ultraviolet radiation or accumulate too much light radiation, it can lead to tissue damage and eventually cell death, a condition known as sunburn. Plants have evolved protective and response mechanisms to mitigate the negative effects of light stress, including utilizing ROS as a signaling molecule for response against light stress. Additionally, plants have developed a systemic messenger system in which signals produced in leaves exposed to high light intensities travel to distant parts of the plant and switch on expression of protective antioxidant genes.
What is the concept of vegetation spectral index?5 answersVegetation spectral indices are mathematical calculations that use reflectance values from different spectral bands to provide information about the state and characteristics of vegetation. These indices are used in remote sensing to monitor vegetation dynamics, such as phenology, stress levels, and biomass. They are derived from spectral reflectance data and can represent various vegetation parameters, including canopy structure, leaf pigment content, and photosynthetic potential. Different studies have proposed and evaluated various vegetation spectral indices, such as the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Hyperspectral Image-based Vegetation Index (HSVI). These indices aim to improve the accuracy and sensitivity of vegetation monitoring by considering specific spectral features and addressing issues like saturation in high biomass conditions.
What does research say about Vegetation mapping through remotely sensed images?5 answersVegetation mapping through remotely sensed images has been a topic of active research. Various methods and techniques have been proposed to improve the accuracy and efficiency of vegetation mapping. Machine learning algorithms, such as Random Forest and AdaBoost, have shown promising results in integrating vegetation indices and spectral bands for accurate classification of vegetation cover. Deep learning models, such as convolutional neural networks (CNN), have been found to be powerful in extracting phenological features from individual remote sensing images, enabling the mapping of vegetation growth days and the start of the growing season. Additionally, the use of correlation methods, such as Pearson's correlation coefficient and spectral angle mapper, has proven to be effective in comparing spectral signatures and classifying vegetation in hyperspectral images. Furthermore, the integration of texture features derived from Gray-Level Co-Occurrence Matrix (GLCM) with spectral bands and vegetation indices has shown to increase the accuracy of vegetation mapping, particularly in detecting the variability of forest species at the stand level.
What range of NDVI value indicate good vegetation?4 answersThe range of NDVI values that indicate good vegetation varies depending on the specific study and region. In the Weihe River Basin, the most suitable growth conditions for vegetation were found to be an NDVI range of 0.288 to 0.702. In the Tibetan Plateau, the yearly averaged NDVI was 0.064, with values steadily decreasing from southeast to northwest. In a cool-temperate deciduous broad-leaved forest, NDVI values of 0.2-0.3 and 0.6-0.7 in the spring were found to be indicative of leaf expansion. However, no specific NDVI values were identified for indicating defoliation. In sparsely vegetated arid regions, NDVI∗ and NDVIoffset were found to perform best, with an r2 value of 0.77. Overall, the specific range of NDVI values indicating good vegetation can vary depending on the location and specific vegetation types being studied.
What color represents vegetation?9 answers

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