Leaf Area Index Estimation Using Vegetation Indices Derived From Airborne Hyperspectral Images in Winter Wheat

Xie, Qiaoyun; Huang, Wenjiang; Liang, Dong; Chen, Pengfei; Wu, Chaoyang; Yang, Guijun; Zhang, Jingcheng; Huang, Liusheng; Zhang, Dongyan

doi:10.1109/jstars.2014.2342291

Cited by 58 publications

(32 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many previous studies have successfully estimated crop biomass using airborne hyperspectral data [1,17]. However, our study found that VIs derived from CASI data had limited capability in estimating the biomass of maize.…”

Section: Discussionmentioning

confidence: 65%

“…Many previous studies have investigated a list of VIs derived from hyperspectral data for crop biomass estimation [1,7,17,43]. Based on the findings of these studies, six VIs were selected to estimate biomass of maize in this study (Table 2): Simple Ratio Vegetation Index (SR) [44], Normalized Difference Vegetation Index (NDVI) [45], Modified Simple Ratio Index (MSR) [24], Modified Soil-Adjusted Vegetation Index (MSAVI) [46], Red Edge NDVI (ReNDVI) [47] and Red Edge Modified Simple Ratio Index (ReMSR) [15].…”

Section: Deriving Metrics From Hyperspectral Datamentioning

confidence: 99%

“…In this study, the relationship between field-observed biomass with each metric was first examined using both the linear and exponential regression analyses according to the previous studies [17,48]. These metrics included VIs derived from hyperspectral data and LiDAR-derived metrics.…”

Section: Regression Analysismentioning

confidence: 99%

“…The physical models are complex, thus, only a few previous studies conducted crop biomass estimation using the inversion of physical models [11,12]. In contrast, spectral vegetation indices (VIs) were commonly used to estimate crop biophysical variables due to the simplicity and practicality [13][14][15][16][17]. However, the estimation accuracy of crop biomass is usually affected by background soil reflectance, atmospheric and water absorption using broadband VIs.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Estimating the Biomass of Maize with Hyperspectral and LiDAR Data

Wang

Nie

et al. 2016

Remote Sensing

View full text Add to dashboard Cite

Abstract:The accurate estimation of crop biomass during the growing season is very important for crop growth monitoring and yield estimation. The objective of this paper was to explore the potential of hyperspectral and light detection and ranging (LiDAR) data for better estimation of the biomass of maize. First, we investigated the relationship between field-observed biomass with each metric, including vegetation indices (VIs) derived from hyperspectral data and LiDAR-derived metrics. Second, the partial least squares (PLS) regression was used to estimate the biomass of maize using VIs (only) and LiDAR-derived metrics (only), respectively. Third, the fusion of hyperspectral and LiDAR data was evaluated in estimating the biomass of maize. Finally, the biomass estimates were validated by a leave-one-out cross-validation (LOOCV) method. Results indicated that all VIs showed weak correlation with field-observed biomass and the highest correlation occurred when using the red edge-modified simple ratio index (ReMSR). Among all LiDAR-derived metrics, the strongest relationship was observed between coefficient of variation (H CV ) of digital terrain model (DTM) normalized point elevations with field-observed biomass. The combination of VIs through PLS regression could not improve the biomass estimation accuracy of maize due to the high correlation between VIs. In contrast, the H CV combined with H mean performed better than one LiDAR-derived metric alone in biomass estimation (R 2 = 0.835, RMSE = 374.655 g/m 2 , RMSE CV = 393.573 g/m 2 ). Additionally, our findings indicated that the fusion of hyperspectral and LiDAR data can provide better biomass estimates of maize (R 2 = 0.883, RMSE = 321.092 g/m 2 , RMSE CV = 337.653 g/m 2 ) compared with LiDAR or hyperspectral data alone.

show abstract

Section: Discussionmentioning

confidence: 65%

Section: Deriving Metrics From Hyperspectral Datamentioning

confidence: 99%

Section: Regression Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Estimating the Biomass of Maize with Hyperspectral and LiDAR Data

Wang

Nie

et al. 2016

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…In addition, the PHI images were denoised with the Savitzky-Golay method to further improve the image quality (Xie et al, 2014). The denoised spectrum acquired with PHI sensor on April 18, May 17 and May 31 were presented in Fig.…”

Section: Airborne Campaignsmentioning

confidence: 99%

Remote estimation of canopy nitrogen content in winter wheat using airborne hyperspectral reflectance measurements

Zhou

Huang

Kong

et al. 2016

Advances in Space Research

Self Cite

View full text Add to dashboard Cite

Timely and accurate assessment of canopy nitrogen content (CNC) provides valuable insight into rapid and real-time nitrogen status monitoring in crops. A semi-empirical approach based on spectral index was extensively used for nitrogen content estimation. However, in many cases, due to specific vegetation types or local conditions, the applicability and robustness of established spectral indices for nitrogen retrieval were limited. The objective of this study was to investigate the optimal spectral index for winter wheat (Triticum aestivum L.) CNC estimation using Pushbroom Hyperspectral Imager (PHI) airborne hyperspectral data. Data collected from two different field experiments that were conducted during the major growth stages of winter wheat in 2002 and 2003 were used. Our results showed that a significant linear relationship existed between nitrogen and chlorophyll content at the canopy level, and it was not affected by cultivars, growing conditions and nutritional status of winter wheat. Nevertheless, it varied with growth stages. Periods around heading stage mainly worsened the relationship and CNC estimation, and CNC assessment for growth stages before and after heading could improve CNC retrieval accuracy to some extent. CNC assessment with PHI airborne hyperspectra suggested that spectral indices based on red-edge band including narrowband and broadband CI red-edge , NDVI-like and ND 705 showed convincing results in CNC retrieval. NDVI-like and ND 705 were sensitive to detect CNC changes less than 5 g/m 2 , narrowband and broadband CI red-edge were sensitive to a wide range of CNC variations. Further evaluation of CNC retrieval using field measured hyperspectra indicated that NDVI-like was robust and exhibited the highest accuracy in CNC assessment, and spectral indices (CI red-edge and CI green ) that established on narrow or broad bands showed no obvious difference in CNC assessment. Overall, our study suggested that NDVI-like was the optimal indicator for winter wheat CNC retrieval.

show abstract

Historic declines in growth portend trembling aspen death during a contemporary leaf miner outbreak in Alaska

et al. 2021

View full text Add to dashboard Cite

Climate change-driven droughts and insect outbreaks are becoming more frequent and widespread, increasing forest vulnerability to mortality. By addressing the impacts of climate and insects on tree growth preceding death, we can better understand tree mortality risk under a changing climate. Here, we used tree stature and interannual growth (basal area increment; BAI) to assess processes leading to trembling aspen (Populus tremuloides) survival or mortality during an unprecedented leaf miner (Phyllocnistis populiella) outbreak in boreal North America. We identified eight sites (22 plots) in the longest running forest monitoring network in Alaska, spanning~350 km of latitude, that experienced ≥0.25 MgÁha À1 Áyr À1 aspen mortality during the outbreak. We compared the size and canopy position, growth patterns, and sensitivity to climate and leaf mining of aspen that survived (living; n = 84) vs. died (dying; n = 76) and linked the normalized difference vegetation index (NDVI) to plot-level aspen growth and stand biomass recruitment, growth, and mortality. Dying aspen were in the subcanopy, smaller in diameter, and after a drought in 1957 had lower growth than living aspen until death. Before the outbreak, growth of all trees was positively influenced by moisture and negatively by temperature, but only living trees maintained this climate response during the outbreak. Leaf mining reduced growth of both groups, exerting at least a twofold greater impact than climate. The NDVI captured plot-level tree growth and stand biomass growth and mortality, yet it was nearly two times more strongly associated with living than dying tree growth and 12 times more strongly associated with biomass growth than mortality. These differences suggest that NDVI may inadequately detect insect-driven dieback and dispersed mortality of aspen across the boreal biome. Our findings reveal that a historic drought triggered a multi-decadal growth decline that predisposed aspen to mortality during the leaf miner outbreak and that while aspen growth is influenced by moisture and temperature, it is more strongly affected by P. populiella. We conclude that as the climate warms and insect outbreaks increase in frequency and magnitude at high latitudes, we should expect to see persistent and greater declines in aspen growth and increases in mortality.

show abstract

Leaf Area Index Estimation Using Vegetation Indices Derived From Airborne Hyperspectral Images in Winter Wheat

Cited by 58 publications

References 22 publications

Estimating the Biomass of Maize with Hyperspectral and LiDAR Data

Estimating the Biomass of Maize with Hyperspectral and LiDAR Data

Remote estimation of canopy nitrogen content in winter wheat using airborne hyperspectral reflectance measurements

Historic declines in growth portend trembling aspen death during a contemporary leaf miner outbreak in Alaska

Contact Info

Product

Resources

About