Hyperspectral Remote Sensing of Vegetation and Agricultural Crops

Thenkabail, Prasad S.; Gumma, M K; Teluguntla, Pardhasaradhi; Mohammed, Irshad A.

doi:10.1201/b11222-41

Cited by 290 publications

(313 citation statements)

References 25 publications

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“…The permissible bandwidths for DSIR760-R739 determined by the optimal1.05 criterion were found to be 5 nm at both the 760 nm and 739 nm center wavelengths. The result of this study is consistent with previous studies showing that narrow-band SIs from hyperspectral remote sensing are suitable for monitoring crop growth (e.g., [33,60]). However, even though the bandwidths of DSIR760-R739 are wider (e.g., 15 nm for both wavelengths), the predictive accuracy of the model was still higher than existing SIs ( Figure 5).…”

Section: Discussionsupporting

confidence: 92%

“…Previous research demonstrated the suitability of hyperspectral remote sensing for monitoring crop growth [31][32][33]. Particularly for LAI predictions, the saturation problem of NDVI under moderate to high LAI conditions has been extensively investigated by developing new SIs using hyperspectral data [34,35].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, investigating the impact of new SI bandwidths on predictive accuracy is important-from an economic standpoint-for designing sensors [36]. In addition, the large numbers of hyperspectral bands are redundant; selection of important bands for crop monitoring is key to maximizing the efficiency of spectral data use [33].…”

Section: Introductionmentioning

confidence: 99%

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Spectral Index for Quantifying Leaf Area Index of Winter Wheat by Field Hyperspectral Measurements: A Case Study in Gifu Prefecture, Central Japan

et al. 2015

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Timely and nondestructive monitoring of leaf area index (LAI) using remote sensing techniques is crucial for precise and efficient management of crops. In this paper, a new spectral index (SI) for estimating LAI of winter wheat (Triticum aestivum L.) is proposed on the basis of field hyperspectral measurements. A simple index based on the empirical relationships between LAIs and SIs of all available two-waveband combinations from hyperspectral data is developed by considering the difference between reflectance values at 760 and 739 nm (DSIR760-R739 = R760 -R739). Among published and newly developed SIs, DSIR760-R739 exhibited a significant and strong linear relationship with LAI OPEN ACCESSRemote Sens. 2015, 7 5330 and showed outstanding performance in LAI assessments. The permissible bandwidths for broad-band DSIR760-R739 investigated using simulated reflectance were 5 nm for both 760 and 739 nm center wavelengths. The results indicate that the linear regression model based on the narrow-band and broad-band DSIR760-R739 is a simple but accurate method for timely and nondestructive monitoring of LAI.

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Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

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Spectral Index for Quantifying Leaf Area Index of Winter Wheat by Field Hyperspectral Measurements: A Case Study in Gifu Prefecture, Central Japan

et al. 2015

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“…where ρ is the density of pure water (1 g/cm 3 ), and A is the leaf area (cm 2 ). During the ripening stage, the leaves at the bottom two layers of winter wheat were drying down, with minimal water content, thus we removed samples with extremely low water content from the analysis, resulting in 165 samples used in the analysis.…”

Section: Leaf Sampling and Water Content Measurementsmentioning

confidence: 99%

“…Reflectance in the near-infrared (NIR) region (750-1300 nm) and shortwave-infrared (SWIR) region (1300-2500 nm) is largely influenced by water and dry matter in the leaves, although it is also affected by leaf structure, canopy structure, and leaf area index (LAI) [3][4][5]. Strong water absorption bands can be found in the SWIR region, centered on 1450, 1940, and 2500 nm, and weak water absorption bands are located in the NIR region near 970 and 1200 nm.…”

Section: Introductionmentioning

confidence: 99%

Remote Estimation of Leaf and Canopy Water Content in Winter Wheat with Different Vertical Distribution of Water-Related Properties

Liu

et al. 2015

Remote Sensing

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This study analyzed the vertical distribution of gravimetric water content (GWC), relative water content (RWC), and equivalent water thickness (EWT) in winter wheat during heading and early ripening stages, and evaluated the position of leaf number at which Vegetation Indexes (VIs) can best retrieve canopy water-related properties of winter wheat. Results demonstrated that the vertical distribution of these properties followed a near-bell-shaped curve with the highest values at the intermediate leaf position. GWC of the top three or four leaves during the heading stage and the top two or three leaves during the early ripening stage can represent the GWC of the whole canopy, but the RWC and EWT of the whole canopy should be calculated based on the top four leaves. At leaf level, the analysis demonstrated strong relationships between EWT and VIs for the top leaf layer, but for GWCD, GWCF, and RWC, the strongest relationships with VIs were found in the intermediate leaf layers. At canopy level, VIs provided the most accurate estimation of GWCfor the top three or four leaves. Water absorption-based VIs could estimate canopy EWT of winter wheat for the top four leaves, but the suitable bands sensitive to water absorptions should be carefully selected for the studied species.

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Estimation of Woody and Herbaceous Leaf Area Index in Sub‐Saharan Africa Using MODIS Data

Kahiu

Hanan

2018

JGR Biogeosciences

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Savannas are widespread global biomes covering ~20% of terrestrial ecosystems on all continents except Antarctica. These ecosystems play a critical role in regulating terrestrial carbon cycle, ecosystem productivity, and the hydrological cycle and contribute to human livelihoods and biodiversity conservation. Despite the importance of savannas in ecosystem processes and human well‐being, the presence of mixed woody and herbaceous components at scales much finer than most medium‐ and coarse‐resolution satellite imagery poses significant challenges to their effective representation in remote sensing and modeling of vegetation dynamics. Although previous studies have attempted to separate woody and herbaceous components, the focus on greenness indices and fractional cover provides little insight into spatiotemporal variability in woody and herbaceous vegetation structure, in particular, leaf area index (LAI). This paper presents a method to partition 1 km spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) aggregate green leaf area index (LAIA) from 2003 to 2015, into separate woody (LAIW) and herbaceous (LAIH) constituents in both drought seasonal savannas and moist tropical forests of Sub‐Saharan Africa (SSA). In our analysis, we use an allometric relationship describing the variation in peak within‐canopy woody LAI of dominant tree species (LAIWpinc) across gradients in mean annual precipitation, coupled with independent estimates of woody canopy cover (τw), to constrain seasonally changing LAIW. We present the LAI partitioning approach and highlight the broad spatial and temporal patterns of woody and herbaceous LAI across SSA. The long‐term average 8 day phenologies of woody and herbaceous LAI (averaged across 2003–2015) are available for evaluation, research, and application purposes.

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Hyperspectral Remote Sensing of Vegetation and Agricultural Crops

Cited by 290 publications

References 25 publications

Spectral Index for Quantifying Leaf Area Index of Winter Wheat by Field Hyperspectral Measurements: A Case Study in Gifu Prefecture, Central Japan

Spectral Index for Quantifying Leaf Area Index of Winter Wheat by Field Hyperspectral Measurements: A Case Study in Gifu Prefecture, Central Japan

Remote Estimation of Leaf and Canopy Water Content in Winter Wheat with Different Vertical Distribution of Water-Related Properties

Estimation of Woody and Herbaceous Leaf Area Index in Sub‐Saharan Africa Using MODIS Data

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