- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
Use of Hyperspectral Image Data Outperforms Vegetation Indices in Prediction of Maize Yield
摘要： Hyperspectral cameras can provide reflectance data at hundreds of wavelengths. This information can be used to derive vegetation indices (VIs) that are correlated with agronomic and physiological traits. However, the data generated by hyperspectral cameras are richer than what can be summarized in a VI. Therefore, in this study, we examined whether prediction equations using hyperspectral image data can lead to better predictive performance for grain yield than what can be achieved using VIs. For hyperspectral prediction equations, we considered three estimation methods: ordinary least squares, partial least squares (a dimension reduction method), and a Bayesian shrinkage and variable selection procedure. We also examined the benefits of combining reflectance data collected at different time points. Data were generated by CIMMYT in 11 maize (Zea mays L.) yield trials conducted in 2014 under heat and drought stress. Our results indicate that using data from 62 bands leads to higher prediction accuracy than what can be achieved using individual VIs. Overall, the shrinkage and variable selection method was the best-performing one. Among the models using data from a single time point, the one using reflectance collected at 28 d after flowering gave the highest prediction accuracy. Combining image data collected at multiple time points led to an increase in prediction accuracy compared with using single-time-point data.
关键词： maize yield,hyperspectral imaging,prediction accuracy,vegetation indices,Bayesian methods
更新于2025-09-23 15:23:52
-
Retrieval of Maize Leaf Area Index Using Hyperspectral and Multispectral Data
摘要： Field spectra acquired from a handheld spectroradiometer and Sentinel-2 images spectra were used to investigate the applicability of hyperspectral and multispectral data in retrieving the maize leaf area index in low-input crop systems, with high spatial and intra-annual variability, and low yield, in southern Mozambique, during three years. Seventeen vegetation indices, comprising two and three band indices, and nine machine learning regression algorithms (MLRA) were tested for the statistical approach while five cost functions were tested in the look-up-table (LUT) inversion approach. The three band vegetation indices were selected, specifically the modified difference index (mDId: 725; 715; 565) for the hyperspectral dataset and the modified simple ratio (mSRc: 740; 705; 865) for the multispectral dataset of field spectra and the three band spectral index (TBSIb: 665; 865; 783) for the Sentinel-2 dataset. The relevant vector machine was the selected MLRA for the two datasets of field spectra (multispectral and hyperspectral) while the support vector machine was selected for the Sentinel-2 data. When using the LUT inversion technique, the minimum contrast estimation and the Bhattacharyya divergence cost functions were the best performing. The vegetation indices outperformed the other two approaches, with the TBSIb as the most accurate index (RMSE = 0.35). At the field scale, spectral data from Sentinel-2 can accurately retrieve the maize leaf area index in the study area.
关键词： hyperspectral,multispectral,vegetation indices,Sentinel-2,machine learning regression algorithms,PROSAIL,field-spectroradiometer,LUT inversion,leaf area index
更新于2025-09-19 17:15:36
-
Soil Salinity Mapping and Hydrological Drought Indices Assessment in Arid Environments Based on Remote Sensing Techniques
摘要： Vegetation indices are mostly described as crop water derivatives. Normalized Difference Vegetation Index (NDVI) is one of the oldest remote sensing applications that widely used to evaluate crop vigor directly and crop water relationships indirectly. Recently, several NDVI derivatives are exclusively used to assess crop water relationships. Four hydrological drought indices are examined in the current research study. Water Supply Vegetation Index (WSVI), Soil Adjusted Vegetation Index (SAVI), Moisture Stress Index (MSI) and Normalized Difference Infrared Index (NDII) are implemented in the current study as an indirect tool to map the effect of different soil salinity level on crop water stress in arid environments. In arid environments; such as Saudi Arabia, water resources are under pressure especially groundwater levels. Groundwater wells are rapidly depleted due to the heavy abstraction of the reserved water. Heavy abstractions of groundwater; which exceed crop water requirements in most of the cases are powered by high evaporation rates in the designated study area because of the long days of extremely hot summer. Landsat OLI-8 data were extensively used in the current research to obtain several vegetation indices in response to soil salinity in Wadi Ad-Waser. Principal Component Analysis and Artificial Neural Network Analysis are complementary tools to understand the regression pattern of the hydrological drought indices in the designated study area.
关键词： Soil Salinity Mapping,Arid Environment,Vegetation Indices,Remote Sensing techniques
更新于2025-09-19 17:15:36
-
Spectral data source effect on crop state estimation by vegetation indices
摘要： Spectral vegetation indices (VIs) are a well-known and widely used method for crop state estimation. The ability to monitor crop state by such indices is an important tool for agricultural management. Even though differences in imagery and point-based spectroscopy are obvious, their impact on crop state estimation by VIs is not well-studied. The aim of this study was to assess the performance level of the selected VIs calculated from spaceborne multispectral imagery and point-based field spectroscopy in application to crop state estimation. For this purpose, irrigated chickpea field was monitored by RapidEye satellite mission and additional measurements by field spectrometer were obtained. Estimated VIs average and coefficient of variation from each observation were compared with physical crop measurements: leaf water content, LAI and chlorophyll level. The results indicate that indices calculated from spaceborne spectral images regardless of the claimed response commonly react on phenology of the irrigated chickpea. This feature makes spaceborne spectral imagery an appropriate data source for monitoring crop development, crop water needs and yield prediction. VIs calculated from field spectrometer were sensitive for estimating pigment concentration and photosynthesis rate. Yet, a hypersensitivity of field spectral measures might lead to a very high variability (up to 69%) of the calculated values. Consequently, the high spatial variability of field spectral measurements depreciates the estimation agricultural field state by average mean only. Nevertheless, the spatial variability might have certain behavior trend, e.g., a significant increase in the active growth or stress and can be an independent feature for field state assessment.
关键词： Vegetation indices,Spatial variability,Agriculture management,Field spectroscopy,Spaceborne spectral imagery
更新于2025-09-11 14:15:04
-
[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Potential of Landsat-Oli for Seagrass and Algae Species Detection and Discrimination in Bahrain National Water Using Spectral Reflectance
摘要： Seagrass (Halodule uninervis and Halophila stipulacea) and algae (green and brown) species are widely distributed along the coastal zones of the Bahrain national water. In this study the potential of Landsat-OLI VNIR spectral bands was investigated for distinction and discrimination among these species using spectral reflectances. The measured spectra’s of each species considering different coverage rate (0, 10, 30, 75 and 100%) were transformed using continuum-removed (CR) approach, resampled and convolved in the solar-reflective spectral bands of OLI using a radiative transfer code, then converted to water vegetation indices (WVI). Regression analysis were performed between the transformed WVI and the coverage rates of each species individually (seagrass and algae) and mixed; as well between WVI and NIR reflectances. Spectral and CR analyses showed that the blue and the green bands perform better than the coastal and the red bands for seagrass and algae classes’ discrimination. This result was further corroborated by the WVI. Regression results between the coverage rates and WVI calculated with green and NIR bands showed that the TDAVI and WAVI discriminate significantly among the mixed species (R2 of 0.70), and between individual species (R2 of 0.80 for algae and for seagrass). Accomplished between WVI and NIR reflectances, regression correlations were more significant when all mixed samples (R2 of 0.95) have been considered, likewise when we consider individually the two seagrass (R2 of 0.95) and the two algae species (R2 of 0.82).
关键词： Bahrain,Algae,Seagrass,Spectral signature,Landsat-OLI,Water vegetation indices
更新于2025-09-11 14:15:04
-
Detection of <i>Firmiana danxiaensis</i> Canopies by a Customized Imaging System Mounted on an UAV Platform
摘要： The objective of this study was to test the effectiveness of mapping the canopies of Firmiana danxiaensis (FD), a rare and endangered plant species in China, from remotely sensed images acquired by a customized imaging system mounted on an unmanned aerial vehicle (UAV). The work was conducted in an experiment site (approximately 10 km2) at the foot of Danxia Mountain in Guangdong Province, China. The study was conducted as an experimental task for a to-be-launched large-scale FD surveying on Danxia Mountain (about 200 km2 in area) by remote sensing on UAV platforms. First, field-based spectra were collected through hand-held hyperspectral spectroradiometer and then analyzed to help design a classification schema which was capable of differentiating the targeted plant species in the study site. Second, remote-sensed images for the experiment site were acquired and calibrated through a variety of preprocessing steps. Orthoimages and a digital surface model (DSM) were generated as input data from the calibrated UAV images. The spectra and geometry features were used to segment the preprocessed UAV imagery into homogeneous patches. Lastly, a hierarchical classification, combined with a support vector machine (SVM), was proposed to identify FD canopies from the segmented patches. The effectiveness of the classification was evaluated by on-site GPS recordings. The result illustrated that the proposed hierarchical classification schema with a SVM classifier on the remote sensing imagery collected by the imaging system on UAV provided a promising method for mapping of the spatial distribution of the FD canopies, which serves as a replacement for field surveys in the attempt to realize a wide-scale plant survey by the local governments.
关键词： UAV,SVM,hierarchical classification,Firmiana danxiaensis,spectral analysis,remote sensing,image segmentation,vegetation indices
更新于2025-09-10 09:29:36