Retrieval of leaf water content from remotely sensed data using a vegetation index model constructed with shortwave infrared reflectances

2018 ◽  
Vol 40 (5-6) ◽  
pp. 2313-2323 ◽  
Author(s):  
Ziyang Zhang ◽  
Bo-Hui Tang ◽  
Zhao-Liang Li
Keyword(s):  
Water Content ◽  
Vegetation Index ◽  
Remotely Sensed ◽  
Leaf Water ◽  
Leaf Water Content ◽  
Remotely Sensed Data ◽  
Index Model ◽  
Shortwave Infrared

scholarly journals Relation of leaf water content with real evapotranspiration and biomass in Caatinga biome, using remote sensing data

2017 ◽  
Vol 10 (5) ◽  
pp. 1545
Author(s):  
Josiclêda Domiciano Galvíncio
Keyword(s):  
Energy Balance ◽  
Water Content ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Surface Energy Balance ◽  
Climatic Variability ◽  
Leaf Water ◽  
Leaf Water Content ◽  
Content Index ◽  
Caatinga Biome

R E S U M OA Caatinga é um biome que sofre com grande variabilidade climática anual e intraanual. Essa variabilidade climática faz com que o bioma em grande parte do ano sofra com grande estresse hídrico. Estudar as relações existentes entre o conteúdo de água na planta e outras variáveis do ecossistemas, tais como: biomassa e evapotranspiração pode auxiliar e prever impactos da escassez hídrica e seca climatológica sobre a produção de biomassa do bioma Caatinga. Assim, este estudo pretende analisar as relações existentes entre o conteúdo de água na folha com a biomassa e evapotranspiração em área do bioma caatinga localizado em São José do Sabugi, Paraiba, Brasil. Foi utilizado o algoritmo SEBAL-Surface Energy Balance para estimar a evapotranspiração e o foram calculados os índices de vegetação NDVI- Normalized Difference Vegetation Index, SAVI- Soil Adjusted Vegetation Index e o índice de conteúdo de água na folha LWCI- Leaf Water Content Index. Os resultados mostraram uma boa relação existente entre os índices de vegetação e o conteúdo de água na folha, sendo r=0.76 para o SAVI e 0.64 para o NDVI. Para a evapotranspiração a correlação foi de r =0.386. Conclui-se que a quantidade de água na folha está altamente correlacionada com a biomassa.Palavra chave: bioma, sazonalidade, seca, semiárido. A B S T R A C TThe Caatinga is a biome that suffers from high annual and intra-annual climatic variability. This climatic variability makes the biome in great part of the year suffer with high great water stress. To study the relationships between water content in the plant and other ecosystem variables, such as: biomass and evapotranspiration can help and predict impacts of water scarcity and climatological drought on the biomass production of the Caatinga biome. Thus, this study intends to analyze the relationship between water content in the leaf with biomass and evapotranspiration in the area of the caatinga biome located in São José do Sabugi, Paraiba, Brazil. The SEBAL-Surface Energy Balance algorithm was used to estimate the evapotranspiration and NDVI-Normalized Difference Vegetation Index, SAVI-Soil Adjusted Vegetation Index and the water content index in the LWCI- Leaf Water Content Index. were calculated. The results showed a good relationship between vegetation index and leaf water content, with r = 0.76 for SAVI and 0.64 for NDVI. For evapotranspiration the correlation was r = 0.386. It is concluded that the amount of water in the leaf is highly correlated with the biomass.Keywords: biome, seasonality, dry, semiarid

scholarly journals Unsupervised classification of saturated areas using a time series of remotely sensed images

2007 ◽  
Vol 4 (3) ◽  
pp. 1663-1696 ◽  
Author(s):  
D. A. DeAlwis ◽  
Z. M. Easton ◽  
H. E. Dahlke ◽  
W. D. Philpot ◽  
T. S. Steenhuis
Keyword(s):  
Water Content ◽  
Management Practices ◽  
Spatial Information ◽  
New York State ◽  
Soil Surface ◽  
Resource Planning ◽  
Remotely Sensed ◽  
Leaf Water Content ◽  
Remotely Sensed Data ◽  
Remotely Sensed Images

Abstract. The spatial distribution of saturated areas is an important consideration in numerous applications, such as water resource planning or sighting of management practices. However, in humid well vegetated climates where runoff is produced by saturation excess processes on hydrologically active areas (HAA) the delineation of these areas can be difficult and time consuming. Much of the non-point source pollution in these watersheds originates from these HAAs. Thus, a technique that can simply and reliably predict these areas would be a powerful tool for scientists and watershed managers tasked with implementing practices to improve water quality. Remotely sensed data is a source of spatial information and could be used to identify HAAs, should a proper technique be developed. The objective of this study is to develop a methodology to determine the spatial variability of saturated areas using a temporal sequence of remotely sensed images. The Normalized Difference Water Index (NDWI) was derived from medium resolution LANDSAT 7 ETM+ imagery collected over seven months in the Town Brook watershed in the Catskill Mountains of New York State and used to characterize the areas that were susceptible to saturation. We found that within a single landcover type, saturated areas were characterized by the soil surface water content when the vegetation was dormant and leaf water content of vegetation during the growing season. The resulting HAA map agreed well with both observed and spatially distributed computer simulated saturated areas. This methodology appears promising for delineating saturated areas in the landscape.

scholarly journals MEASURING LEAF WATER CONTENT USING MULTISPECTRAL TERRESTRIAL LASER SCANNING

2017 ◽  
Vol XLII-3/W3 ◽  
pp. 81-85
Author(s):  
S. Junttila ◽  
M. Vastaranta ◽  
R. Linnakoski ◽  
J. Sugano ◽  
H. Kaartinen ◽  
...  
Keyword(s):  
Water Content ◽  
Fungal Pathogens ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Leaf Water ◽  
Leaf Water Content ◽  
Stable Conditions ◽  
Tree Stress ◽  
Simple Ratio ◽  
Shortwave Infrared

Climate change is increasing the amount and intensity of disturbance events, i.e. drought, pest insect outbreaks and fungal pathogens, in forests worldwide. Leaf water content (LWC) is an early indicator of tree stress that can be measured remotely using multispectral terrestrial laser scanning (MS-TLS). LWC affects leaf reflectance in the shortwave infrared spectrum which can be used to predict LWC from spatially explicit MS-TLS intensity data. Here, we investigated the relationship between LWC and MS-TLS intensity features at 690&amp;thinsp;nm, 905&amp;thinsp;nm and 1550&amp;thinsp;nm wavelengths with Norway spruce seedlings in greenhouse conditions. We found that a simple ratio of 905&amp;thinsp;nm and 1550&amp;thinsp;nm wavelengths was able to explain 84&amp;thinsp;% of the variation (R2) in LWC with a respective prediction accuracy of 0.0041&amp;thinsp;g/cm<sup>2</sup>. Our results showed that MS-TLS can be used to estimate LWC with a reasonable accuracy in environmentally stable conditions.

scholarly journals Unsupervised classification of saturated areas using a time series of remotely sensed images

2007 ◽  
Vol 11 (5) ◽  
pp. 1609-1620 ◽  
Author(s):  
D. A. de Alwis ◽  
Z. M. Easton ◽  
H. E. Dahlke ◽  
W. D. Philpot ◽  
T. S. Steenhuis
Keyword(s):  
Surface Water ◽  
Water Content ◽  
Management Practices ◽  
Spatial Information ◽  
New York State ◽  
Resource Planning ◽  
Remotely Sensed ◽  
Leaf Water Content ◽  
Remotely Sensed Data ◽  
Remotely Sensed Images

Abstract. The spatial distribution of saturated areas is an important consideration in numerous applications, such as water resource planning or siting of management practices. However, in humid well vegetated climates where runoff is produced by saturation excess processes on hydrologically active areas (HAA) the delineation of these areas can be difficult and time consuming. A technique that can simply and reliably predict these areas would be a powerful tool for scientists and watershed managers tasked with implementing practices to improve water quality. Remotely sensed data is a source of spatial information and could be used to identify HAAs. This study describes a methodology to determine the spatial variability of saturated areas using a temporal sequence of remotely sensed images. The Normalized Difference Water Index (NDWI) was derived from medium resolution Landsat 7 ETM+ imagery collected over seven months in the Town Brook watershed in the Catskill Mountains of New York State and used to characterize the areas susceptible to saturation. We found that within a single land cover, saturated areas were characterized by the soil surface water content when the vegetation was dormant and leaf water content of the vegetation during the growing season. The resulting HAA map agreed well with both observed and spatially distributed computer simulated saturated areas (accuracies from 49 to 79%). This methodology shows that remote sensing can be used to capture temporal variations in vegetation phenology as well as spatial/temporal variation in surface water content, and appears promising for delineating saturated areas in the landscape.

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

2021 ◽  
Vol 102 ◽  
pp. 102393
Author(s):  
Rahul Raj ◽  
Jeffrey P. Walker ◽  
Vishal Vinod ◽  
Rohit Pingale ◽  
Balaji Naik ◽  
...  
Keyword(s):  
Water Content ◽  
Hyperspectral Data ◽  
Leaf Water ◽  
Leaf Water Content

scholarly journals Simulating Heat Stress of Coal Gangue Spontaneous Combustion on Vegetation Using Alfalfa Leaf Water Content Spectral Features as Indicators

2021 ◽  
Vol 13 (13) ◽  
pp. 2634
Author(s):  
Qiyuan Wang ◽  
Yanling Zhao ◽  
Feifei Yang ◽  
Tao Liu ◽  
Wu Xiao ◽  
...  
Keyword(s):  
Heat Stress ◽  
Water Content ◽  
Spontaneous Combustion ◽  
Leaf Water ◽  
Coal Gangue ◽  
Leaf Water Content ◽  
Spectral Features ◽  
Lasso Regression ◽  
Stress Assessment ◽  
First Derivative

Vegetation heat-stress assessment in the reclamation areas of coal gangue dumps is of great significance in controlling spontaneous combustion; through a temperature gradient experiment, we collected leaf spectra and water content data on alfalfa. We then obtained the optimal spectral features of appropriate leaf water content indicators through time series analysis, correlation analysis, and Lasso regression analysis. A spectral feature-based long short-term memory (SF-LSTM) model is proposed to estimate alfalfa’s heat stress level; the live fuel moisture content (LFMC) varies significantly with time and has high regularity. Correlation analysis of the raw spectrum, first-derivative spectrum, spectral reflectance indices, and leaf water content data shows that LFMC and spectral data were the most strongly correlated. Combined with Lasso regression analysis, the optimal spectral features were the first-derivative spectral value at 1661 nm (abbreviated as FDS (1661)), RVI (1525,1771), DVI (1412,740), and NDVI (1447,1803). When the classification strategies were divided into three categories and the time sequence length of the spectral features was set to five consecutive monitoring dates, the SF-LSTM model had the highest accuracy in estimating the heat stress level in alfalfa; the results provide an important theoretical basis and technical support for vegetation heat-stress assessment in coal gangue dump reclamation areas.

Spectral assessments of wheat plants grown in pots and containers under saline conditions

2013 ◽  
Vol 40 (4) ◽  
pp. 409 ◽  
Author(s):  
Harald Hackl ◽  
Bodo Mistele ◽  
Yuncai Hu ◽  
Urs Schmidhalter
Keyword(s):  
Water Potential ◽  
Water Content ◽  
Aboveground Biomass ◽  
Leaf Water ◽  
Leaf Water Content ◽  
Spectral Indices ◽  
Fresh Weight ◽  
Normalised Difference Vegetation Index ◽  
Spectral Sensing ◽  
Reflectance Ratio

Spectral measurements allow fast nondestructive assessment of plant traits under controlled greenhouse and close-to-field conditions. Field crop stands differ from pot-grown plants, which may affect the ability to assess stress-related traits by nondestructive high-throughput measurements. This study analysed the potential to detect salt stress-related traits of spring wheat (Triticum aestivum L.) cultivars grown in pots or in a close-to-field container platform. In two experiments, selected spectral indices assessed by active and passive spectral sensing were related to the fresh weight of the aboveground biomass, the water content of the aboveground biomass, the leaf water potential and the relative leaf water content of two cultivars with different salt tolerance. The traits were better ascertained by spectral sensing of container-grown plants compared with pot-grown plants. This may be due to a decreased match between the sensors’ footprint and the plant area of the pot-grown plants, which was further characterised by enhanced senescence of lower leaves. The reflectance ratio R760 : R670, the normalised difference vegetation index and the reflectance ratio R780 : R550 spectral indices were the best indices and were significantly related to the fresh weight, the water content of the aboveground biomass and the water potential of the youngest fully developed leaf. Passive sensors delivered similar relationships to active sensors. Across all treatments, both cultivars were successfully differentiated using either destructively or nondestructively assessed parameters. Although spectral sensors provide fast and qualitatively good assessments of the traits of salt-stressed plants, further research is required to describe the potential and limitations of spectral sensing.

Effect of dust deposition on spectrum-based estimation of leaf water content in urban plant

2019 ◽  
Vol 104 ◽  
pp. 41-47 ◽  
Author(s):  
Wenpeng Lin ◽  
Yuan Li ◽  
Shiqiang Du ◽  
Yuanfan Zheng ◽  
Jun Gao ◽  
...  
Keyword(s):  
Water Content ◽  
Leaf Water ◽  
Leaf Water Content ◽  
Dust Deposition
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