Estimating vegetation water content with hyperspectral data for different canopy scenarios: Relationships between AVIRIS and MODIS indexes

Cheng, Yi‐Feng; Zarco‐Tejada, Pablo J.; Riaño, David; Rueda, Carlos; Ustin, Susan L.

doi:10.1016/j.rse.2006.07.005

Cited by 150 publications

(103 citation statements)

References 44 publications

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“…It is necessary to investigate the effects of the proposed angular variables in further field observations, as well as in the physical modeling for the canopy in terms of light-plant-soil interactions (Jacquemoud et al, 2009). Although most existing models simplify actual vegetation canopy into homogeneous leaf layers, the parameters of planting row, for instance, have been introduced into some advanced studies (Cheng et al, 2006;Yao et al, 2008). Hence, our simple and low cost methodology may effectively be evaluated and/or improved by applying these computer models.…”

Section: Discussionmentioning

confidence: 99%

Estimating Paddy Rice Leaf Area Index with Fixed Point Continuous Observation of Near Infrared Reflectance Using a Calibrated Digital Camera

Shibayama

Sakamoto

Takada

et al. 2011

Plant Production Science

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Section: Discussionmentioning

confidence: 99%

Estimating Paddy Rice Leaf Area Index with Fixed Point Continuous Observation of Near Infrared Reflectance Using a Calibrated Digital Camera

Shibayama

Sakamoto

Takada

et al. 2011

Plant Production Science

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“…Plant reflectance properties in the red and near infrared regions are especially recruited for differentiating soil, water and vegetation with vegetation indices (Glenn et al, 2008). Vegetation indices can be used to estimate vegetation water content (Cheng et al, 2006, based on gravimetric or leaf water content (Cheng et al, 2011, Cheng et al, 2013, equivalent water thickness (EWT; water depth/per pixel; knowledge of pixel area provides the volume estimate), changes which are detectable in the short wave infrared region (1.1-2.5 μm), and evapotranspiration processes by including temperature from thermal infrared measurements (Glenn et al, 2010, Nagler et al, 2005a, Nagler et al, 2005b, thus together, identifying agricultural regions receiving excess or insufficient water supplies. Plant physiology and thus reflectance properties respond differently depending upon the time of year, crop type, and management strategies.…”

Section: Monitoring Crop Canopies and Water Stressmentioning

confidence: 99%

Food, water, and fault lines: Remote sensing opportunities for earthquake-response management of agricultural water

Rodriguez

Ustin

Sandoval-Solís

et al. 2016

Science of The Total Environment

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Earthquakes often cause destructive and unpredictable changes that can affect local hydrology (e.g. groundwater elevation or reduction) and thus disrupt land uses and human activities. Prolific agricultural regions overlie seismically active areas, emphasizing the importance to improve our understanding and monitoring of hydrologic and agricultural systems following a seismic event. A thorough data collection is necessary for adequate postearthquake crop management response; however, the large spatial extent of earthquake's impact makes challenging the collection of robust data sets for identifying locations and magnitude of these impacts. Observing hydrologic responses to earthquakes is not a novel concept, yet there is a lack of methods and tools for assessing earthquake's impacts upon the regional hydrology and agricultural systems. The objective of this paper is to describe how remote sensing imagery, methods and tools allow detecting crop responses and damage incurred after earthquakes because a change in the regional hydrology. Many remote sensing datasets are long archived with extensive coverage and with well-documented methods to assess plant-water relations. We thus connect remote sensing of plant water relations to its utility in agriculture using a post-earthquake agrohydrologic remote sensing (PEARS) framework; specifically in agro-hydrologic relationships associated with recent earthquake events that will lead to improved water management.© 2016 Elsevier B.V. All rights reserved. Keywords:Agro-hydrology Disaster management Monitoring Plant-water relations Post-earthquake agrohydrologic remote sensing (PEARS) Remote sensing Background, scope & needEarthquake events threaten food security, resource management and human life, and are observed to be steadily increasing (Ellsworth, 2013). As observed earthquake events continue to increase, it is important to explore and improve response and recovery strategies. Infrastructural damages, especially in urban environments, are at the forefront of research in remote sensing of earthquake-associated damage. Agricultural environments also face catastrophic impacts, often due to adverse hydrologic behavior following the event. The earthquake-water linkage poses particular threats to agricultural productivity, yet difficulty lies in predicting the location, destructiveness, and extent of damage. While effects of earthquake-induced hydrologic changes on crops remain largely unexplored, remote sensing of crop water relations provide a suite of tools to monitor responses and to mitigate crop loss. Remote sensing can address these challenges with archived imagery that has extensive spatial coverage. There are conceptual models that explicitly walk through remote sensing in disaster management (Joyce et al., 2009b) and remote sensing of post-earthquake urban damage (Eguchi et al., 2003) -leaving a gap at the interface of post-earthquake remote sensing of agricultural impacts. We therefore draw attention to current research in understanding earthquake impacts on agric...

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“…This compilation was based on several studies [11,17,18,20,21,23] that used these indices to characterize the water status of the vegetation or to obtain a fire risk index. Vegetation indices used here are constructed from MODIS bands 1 (620-670 nm), 2 (841-876 nm), 3 (459-479 nm), 4 (545-565 nm), 5 (1,230-1,250 nm) and 6 (1,628-16,52 nm).…”

Section: Obtaining the Spectral Indicesmentioning

confidence: 99%

“…Moreover, in [19] a sensitivity analysis has been done in order to analyze the relationship between spectral indices and different measurements of vegetation water content (FMC and EWT, Equivalent Water Thickness) and the conclusion is that generally the spectral indices are more sensitive to EWT than to FMC. Furthermore, some studies have shown that vegetation indices can be used directly to characterize the water status of the vegetation [20,21]. Moreover, vegetation indices have been shown to be useful as fire risk indicators [10,22].…”

Section: Introductionmentioning

confidence: 99%

“…These authors showed that indices based on red/NIR (EVI: Enhanced Vegetation Index, NDVI: Normalized Difference Vegetation Index, SAVI: Soil Adjusted Vegetation Index) provided better results in grassland areas, while those based on NIR and short-wavelength infrared (SWIR) (NDII: Normalized Difference Infrared Index, GVMI: Global Vegetation Moisture Index, NDWI: Normalized Difference Water Index) worked better in shrubland areas. In [21] a comparison between different spectral indices was performed in a crop and shrubs area, and in a conifer forest. Best results were obtained using EVI in the crop and shrubs sites, while NDWI lead to better results in the forested area than the other indices.…”

Section: Introductionmentioning

confidence: 99%

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Modeling Fire Danger in Galicia and Asturias (Spain) from MODIS Images

2014

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Forest fires are one of the most dangerous natural hazards, especially when they are recurrent. In areas such as Galicia (Spain), forest fires are frequent and devastating. The development of fire risk models becomes a very important prevention task for these regions. Vegetation and moisture indices can be used to monitor vegetation status; however, the different indices may perform differently depending on the vegetation species. Eight different spectral indices were selected to determine the most appropriate index in Galicia. This study was extended to the adjacent region of Asturias. Six years of MODIS (Moderate Resolution Imaging Spectroradiometer) images, together with ground fire data in a 10 × 10 km grid basis were used. The percentage of fire events met the variations suffered by some of the spectral indices, following a linear regression in both Galicia and Asturias. The Enhanced Vegetation Index (EVI) was the index leading to the best results. Based on these results, a simple fire danger model was established, using logistic regression, by combining the EVI variation with other variables, such as fire history in each cell and period of the year. A seventy percent overall concordance was obtained between estimated and observed fire frequency.

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Estimating vegetation water content with hyperspectral data for different canopy scenarios: Relationships between AVIRIS and MODIS indexes

Cited by 150 publications

References 44 publications

Estimating Paddy Rice Leaf Area Index with Fixed Point Continuous Observation of Near Infrared Reflectance Using a Calibrated Digital Camera

Estimating Paddy Rice Leaf Area Index with Fixed Point Continuous Observation of Near Infrared Reflectance Using a Calibrated Digital Camera

Food, water, and fault lines: Remote sensing opportunities for earthquake-response management of agricultural water

Modeling Fire Danger in Galicia and Asturias (Spain) from MODIS Images

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