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Le, T.S.; Harper, R.; Dell, B. Remote Sensing for Detection of Plant Water Stress. Encyclopedia. Available online: https://encyclopedia.pub/entry/46402 (accessed on 05 July 2024).
Le TS, Harper R, Dell B. Remote Sensing for Detection of Plant Water Stress. Encyclopedia. Available at: https://encyclopedia.pub/entry/46402. Accessed July 05, 2024.
Le, Thai Son, Richard Harper, Bernard Dell. "Remote Sensing for Detection of Plant Water Stress" Encyclopedia, https://encyclopedia.pub/entry/46402 (accessed July 05, 2024).
Le, T.S., Harper, R., & Dell, B. (2023, July 04). Remote Sensing for Detection of Plant Water Stress. In Encyclopedia. https://encyclopedia.pub/entry/46402
Le, Thai Son, et al. "Remote Sensing for Detection of Plant Water Stress." Encyclopedia. Web. 04 July, 2023.
Remote Sensing for Detection of Plant Water Stress
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This entry is adapted from the peer-reviewed paper 10.3390/rs15133360

In the context of climate change, the occurrence of water stress in forest ecosystems, which are solely dependent on precipitation, has exhibited a rising trend, even among species that are typically regarded as drought-tolerant. Remote sensing techniques offer an efficient, comprehensive, and timely approach for monitoring forests at local and regional scales. These techniques also enable the development of diverse indicators of plant water status, which can play a critical role in evaluating forest water stress.

drought forest management leaf and canopy spectral traits

1. General Principles

The principle of remote sensing in vegetation observation involves using sensors to measure the different wavelengths of electromagnetic radiation emitted or reflected by plants and their surrounding environment [1][2]. Healthy vegetation reflects and absorbs different wavelengths in a characteristic way, which can be detected by remote sensing instruments [1][3]. For example, healthy vegetation absorbs most of the visible light spectrum, but reflects a high proportion of near-infrared radiation. This means that healthy vegetation appears green in visible composite images but appears bright in near-infrared images. By analyzing the patterns of reflected or emitted electromagnetic radiation, remote sensing instruments can provide information about vegetation properties such as leaf area index (LAI), chlorophyll content, water content, and biomass [4][5][6][7]. This information can be used to monitor vegetation health, identify areas of vegetation stress, and estimate crop yields [1][2].
The typical spectral reflectance of vegetation exhibits high reflectance in the near-infrared (NIR) region (around 700–1300 nm) and lower reflectance in the visible region (around 400–700 nm) [8][9][10]. This is due to high chlorophyll absorption in the visible wavelengths and strong reflectance by the internal structures and water content of the plant cells in the NIR region [11][12]. Additionally, vegetation tends to have low reflectance in the shortwave infrared (SWIR) region (around 1300–2500 nm) due to the absorption by water and cellulose in plant tissues [13][14][15]. The interior leaf structure and biochemical components, such as greenness content (chlorophyll and carotenoid pigments), water, nitrogen, cellulose, and lignin, play a major role in the spectral characteristics of plants responding to radiation of different wavelengths [6][15][16]. The primary factors governing the spectral responses of leaves in the visible wavelengths are pigments, especially chlorophyll [3], which is closely related to photosynthetic capacity and overall primary productivity [3][17]. In addition, the cell structure of the leaf affects the spectral reflectance characteristics at NIR wavelengths, while the water content in the leaf governs the interaction with the wavelengths in the SWIR region.
Water stress occurs when plants experience a shortage of water, leading to changes in their physiological and biochemical processes that reduce vegetation health [18][19][20]. These changes can be detected through remote sensing in order to monitor the health of plants and identify areas of water stress [2][21][22][23]. One way to detect water stress in plants is by measuring changes in the reflectance of visible and near-infrared light [21][22][24]. In general, stressed plants will have a lower reflectance in the near-infrared region and a higher reflectance in the visible region [3]. Another approach to detecting water stress is by measuring changes in the thermal properties of plants using thermal infrared sensors [25][26][27]. As plants become water-stressed, they may have a higher leaf temperature due to reduced transpiration for cooling and heat accumulation [18][28].
Numerous studies have confirmed the significant connections between leaf chlorophyll and water content, canopy temperature, and plant water stress [28][29][30][31]. Inferring that any reductions in greenness and water content are a sign of plant stress, these indicators, along with canopy temperature, have therefore been utilised as “surrogates” of plant water stress. There are several unique spectral bands and vegetation spectral reflectance indices that can be used to evaluate the chlorophyll and water content of plants [4][6][32]. With the rapid development of remote sensing in terms of spectral resolution, precision, and accuracy, measurements of narrower reflectance bands have allowed researchers to develop more innovative methods to detect plant water stress, including measurements of photochemical reflectance and chlorophyll fluorescence [33].

2. Vegetation Indices

A variety of vegetation indices (VIs) have been developed to monitor changes in vegetation and related physiological processes by utilizing the spectral reflectance characteristics of plants captured through different imaging methods combining reflectance at particular spectral wavelengths.
Typical VIs are calculated on the basis of the reflectance in the red (600–700 nm) and part of the NIR (700–900 nm) spectral regions, whereas Water VIs use reflectance in the SWIR bands. In addition, Pigment VIs reflect the concentrations of leaf pigments, mainly chlorophyll, by using green and red-edge reflectance. The red-edge narrow band around 700 nm is unique due to its correspondence to the threshold between the spectral regions with high light absorption (<700 nm) and high light reflectance (>700 nm) by chlorophyll pigments [34]. On the other hand, Temperature VIs have been developed with the participation of thermal infrared signals which provide information concerning land surface and canopy temperature. This information is usually combined with fractional vegetation coverage and Typical VIs to form a high-potential trapezoid theory to express the decline in plant physiological processes as a symptom of stress [35].

3. Spectral Characteristics of Leaf Chlorophyll Content

Chlorophyll is a pigment that plays a crucial role in photosynthesis, by which plants convert light energy into chemical energy. Specifically, chlorophyll molecules within chloroplasts in plant cells absorb light energy and use it to drive the synthesis of organic compounds such as glucose, which the plant uses for growth and metabolism. Chlorophyll is also responsible for giving plants their green colour, as it absorbs light most efficiently in the blue and red bands of the visible light spectrum and reflects green light.
Reduced leaf chlorophyll concentration in stressed plants alters the ability of plants to absorb solar radiation, changing their typical spectrum reflectance patterns by a decrease in green reflection and increases in red and blue reflections [36][37]. Therefore, remote-sensed detection of water stress in plant requires a determination of typical spectral reflectance patterns of healthy plants as a basis for comparison.
It has been established that certain reflectance wavelengths in the red and near-infrared spectrum are responsive to changes in chlorophyll pigments. Maximum reflectance sensitivity to chlorophyll contents have been reported at the wavelengths 550 and 700 nm [9][33][38]. As a result, numerous spectral indices have been developed utilising the combination of spectral reflectance at these wavelengths by describing the relationships between the reflectance value and chlorophyll content of leaves, including the widely used normalised difference vegetation index (NDVI) [10] and various chlorophyll indices (CIs) [34][39][40]. However, these relationships are inconsistent because chlorophyll concentration can vary between plant species, with leaf age, or even among individuals of the same species in different habitat conditions. Coops et al. [6] recommend caution when using this type of index to estimate plant water stress across various plant species, crop types, or biomes.

4. Spectral Characteristics of Leaf Water Content

Water has a strong absorption feature in the mid-infrared (MIR) region (around 1300–2500 nm), and this absorption becomes more pronounced as the water content increases [41][42]. Therefore, plant tissues with higher water content tend to exhibit lower reflectance in the MIR wavelengths. Additionally, water content also affects the spectral reflectance in the visible and NIR regions. As water content decreases, the reflectance in the NIR region decreases while the reflectance in the visible region increases. Several studies have confirmed the significant correlation between NIR and MIR reflectance and water content in vegetation and soils [4][40][41][42][43][44].
Many water–vegetation indices have been derived from the reflectance of NIR, MIR, and SWIR regions of the electromagnetic spectrum. For assessing the water content in leaves utilizing remote sensing, Tucker [44] used a band within the range of 550 to 1750 nm. Furthermore, Musick and Pelletier [41] suggested using the ratio of spectral bands between 550–1750 nm and 2080–2350 nm. Nevertheless, in the laboratory study of Hunt and Rock [42], a strong correlation was observed between water content, leaf area, and the spectral index derived from the reflectance at 820 and 1600 nm. Especially in the SWIR region from 1400 to 2500 nm, strong relationships between specific spectral bands and many field measurements as indication of plant water stress, such as relative water content, leaf water potential, stomatal conductance, and cell wall elasticity, have been determined [4][14][45]. Faurtyot and Baret [14] also suggested that the spectral bands at 1530 and 1720 nm were optimal for assessing plant water content.
The normalised difference water index (NDWI) developed by Gao [43] is one of the most widely used indices for water content assessment as an indication of plant water stress. It is calculated using the NIR and SWIR wavelengths, which are sensitive to the presence of water in plant tissues. The formula for NDWI is (NIR − SWIR)/(NIR + SWIR), where NIR refers to the reflectance at a near-infrared wavelength of 860 nm and SWIR refers to the reflectance at a short-wave infrared wavelength of 1240 nm. NDWI has been widely used to estimate water content for various tree species [46], particularly in areas where water availability is limited or where drought stress is prevalent [22].

5. Spectral Characteristics of Canopy Temperature

The spectral characteristics of canopy temperature refer to the way that plants emit thermal radiation in different parts of the electromagnetic spectrum, depending on their temperature [23][27][47]. The temperature of a plant canopy is influenced by a number of factors, including solar radiation, air temperature, humidity, and plant water use. In the thermal infrared (TIR) region of the spectrum, plants emit radiation at wavelengths between 800 and 1400 nm, which can be used to estimate their temperature [48][49][50].
When plants experience water stress, they close their stomata to reduce water loss, which in turn reduces evaporative cooling. This causes the temperature of the plant canopy to equilibrate with ambient conditions. In contrast, well-hydrated plants can maintain transpiration and evaporative cooling, resulting in cooler canopy temperatures [31]. Therefore, canopy temperature can be used as a direct indication of plant water stress. By measuring canopy temperature remotely using thermal infrared imaging, it is possible to detect water stress in crops and natural vegetation. The spectral characteristics of canopy temperature can be used to assess plant stress and water use efficiency [51], and to monitor environmental conditions such as drought, heat stress, and wildfire risk [2][25][52]. Various spectral indices have been developed using TIR data to estimate canopy temperature and detect plant stress, such as the crop water stress index (CWSI) [53] and the temperature vegetation dryness index (TVDI) [35].
The crop water stress index (CWSI) is a spectral index used to assess plant water stress based on vegetation temperature. It was developed to quantify the degree of water stress in crops, and it is calculated as the difference between the canopy temperature and the air temperature, normalised by the difference between the canopy temperature and the temperature of a well-watered reference surface.
TVDI is a spectral index used to assess vegetation water stress based on canopy temperature and the amount of vegetation cover. The TVDI is calculated by taking the difference between the surface temperature (measured by thermal sensors) and the temperature of the surrounding environment, and dividing it by the difference between the surface temperature and a reference temperature that represents maximum transpiration under the same atmospheric conditions. A higher TVDI value indicates more severe water stress, while a lower value indicates adequate water supply.

6. Spectral Characteristics of Plant Photosynthetic Efficiency

The photosynthetic efficiency of plants can be assessed using a variety of spectral characteristics. In addition to the common measure of chlorophyll content, the photochemical reflectance index (PRI) reflects changes in the xanthophyll-cycle pigment pool that protects the plant from excess light energys [40][54][55]. Additionally, the spectral response in the red and far-red wavelengths, including the emission of chlorophyll fluorescence, can also indicate changes in photosynthetic efficiency, as plants often adjust their photosynthetic machinery in response to changes in light conditions [30][52][56].
The PRI is a vegetation index that uses the difference in reflectance between the 531 and 570 nm wavelengths to estimate changes in the xanthophyll-cycle pigment pool [54]. PRI has been shown to be a sensitive indicator of plant stress, particularly in response to changes in light and water availability. PRI can be measured using high-resolution spectrometers or hyperspectral sensors that are capable of capturing narrow spectral bands in the visible and near-infrared regions. The PRI signal can be quantified using the PRI ratio, which is the difference between the reflectance at 531 and 570 nm divided by the sum of the reflectance at 531 and 570 nm. Zhang et al. [57] suggested using reflectance at other wavelengths (i.e., 512, 515, 551, 555, 602, 645, 667, 668 nm) instead of 570 nm. Gamon et al. [58] also developed PRI using alternative wavelengths (515, 525, 535, 545 nm) for 531 nm.
Chlorophyll fluorescence has distinct spectral characteristics that can be detected using remote sensing [30][33][59][60]. When plants absorb light energy in the photosynthetic process, some of it is dissipated as heat, while the rest is used to power the conversion of carbon dioxide and water into organic compounds. However, if the amount of absorbed light energy exceeds the amount needed for photosynthesis, excess energy is dissipated as fluorescence. Chlorophyll fluorescence emits in the red and far-red regions of the spectrum mainly in the 650–750 nm spectral range [61], with peak emission occurring at around 685 nm [33][62].

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Thai Son Le
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