scholarly journals Estimating growth and photosynthetic properties of wheat grown in simulated saline field conditions using hyperspectral reflectance sensing and multivariate analysis

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Salah El-Hendawy ◽  
Nasser Al-Suhaibani ◽  
Majed Alotaibi ◽  
Wael Hassan ◽  
Salah Elsayed ◽  
...  
Keyword(s):  
Near Infrared ◽  
Agricultural Sector ◽  
Hyperspectral Data ◽  
Field Conditions ◽  
Least Squares Regression ◽  
Hyperspectral Reflectance ◽  
Full Spectrum ◽  
Salinity Levels ◽  
Shortwave Infrared ◽  
Related Trait

Abstract The timely estimation of growth and photosynthetic-related traits in an easy and nondestructive manner using hyperspectral data will become imperative for addressing the challenges of environmental stresses inherent to the agricultural sector in arid conditions. However, the handling and analysis of these data by exploiting the full spectrum remains the determining factor for refining the estimation of crop variables. The main objective of this study was to estimate growth and traits underpinning photosynthetic efficiency of two wheat cultivars grown under simulated saline field conditions and exposed to three salinity levels using hyperspectral reflectance information from 350–2500 nm obtained at two years. Partial least squares regression (PLSR) based on the full spectrum was applied to develop predictive models for estimating the measured parameters in different conditions (salinity levels, cultivars, and years). Variable importance in projection (VIP) of PLSR in combination with multiple linear regression (MLR) was implemented to identify important waveband regions and influential wavelengths related to the measured parameters. The results showed that the PLSR models exhibited moderate to high coefficients of determination (R2) in both the calibration and validation datasets (0.30–0.95), but that this range of R2 values depended on parameters and conditions. The PLSR models based on the full spectrum accurately and robustly predicted three of four parameters across all conditions. Based on the combination of PLSR-VIP and MLR analysis, the wavelengths selected within the visible (VIS), red-edge, and middle near-infrared (NIR) wavebands were the most sensitive to all parameters in all conditions, whereas those selected within the shortwave infrared (SWIR) waveband were effective for some parameters in particular conditions. Overall, these results indicated that the PLSR analysis and band selection techniques can offer a rapid and nondestructive alternative approach to accurately estimate growth- and photosynthetic-related trait responses to salinity stress.

scholarly journals Mapping Canopy Chlorophyll Content in a Temperate Forest Using Airborne Hyperspectral Data

2020 ◽  
Vol 12 (21) ◽  
pp. 3573
Author(s):  
J. Malin Hoeppner ◽  
Andrew K. Skidmore ◽  
Roshanak Darvishzadeh ◽  
Marco Heurich ◽  
Hsing-Chung Chang ◽  
...  
Keyword(s):  
Chlorophyll Content ◽  
Spectral Resolution ◽  
Near Infrared ◽  
Deciduous Forest ◽  
Mixed Forest ◽  
Hyperspectral Data ◽  
Least Squares Regression ◽  
Area Index ◽  
Management Interventions ◽  
Shortwave Infrared

Chlorophyll content, as the primary pigment driving photosynthesis, is directly affected by many natural and anthropogenic disturbances and stressors. Accurate and timely estimation of canopy chlorophyll content (CCC) is essential for effective ecosystem monitoring to allow for successful management interventions to occur. Hyperspectral remote sensing offers the possibility to accurately estimate and map canopy chlorophyll content. In the past, research has predominantly focused on the use of hyperspectral data on canopy chlorophyll content retrieval of crops and grassland ecosystems. Therefore, in this study, a temperate mixed forest, the Bavarian Forest National Park in Germany, was chosen as the study site. We compared different statistical models (narrowband vegetation indices (VIs), partial least squares regression (PLSR) and random forest (RF)) in their accuracy to predict CCC using airborne hyperspectral data. The airborne hyperspectral imagery was acquired by the AisaFenix sensor (623 bands; 3.5 nm spectral resolution in the visible near-infrared (VNIR) region, and 12 nm spectral resolution in the shortwave infrared (SWIR) region; 3 m spatial resolution) on July 6, 2017. In situ leaf chlorophyll content and leaf area index measurements were sampled from the upper canopy of coniferous, mixed, and deciduous forest stands in July and August 2017. The study yielded the highest retrieval accuracies with PLSR (root mean square error (RMSE) = 0.25 g/m2, R2 = 0.66). It further indicated specific spectral regions within the visible (390–400 nm and 470–540 nm), red edge (680–780 nm), near-infrared (1050–1100 nm) and shortwave infrared regions (2000–2270 nm) that were important for CCC retrieval. The results showed that forest CCC can be mapped with relatively high accuracies using image spectroscopy.

scholarly journals Deriving seasonal dynamics in ecosystem properties of semi-arid savanna grasslands from in situ-based hyperspectral reflectance

2015 ◽  
Vol 12 (15) ◽  
pp. 4621-4635 ◽  
Author(s):  
T. Tagesson ◽  
R. Fensholt ◽  
S. Huber ◽  
S. Horion ◽  
I. Guiro ◽  
...  
Keyword(s):  
Seasonal Dynamics ◽  
Near Infrared ◽  
Spectral Characteristics ◽  
Arid Ecosystems ◽  
Hyperspectral Reflectance ◽  
Data Set ◽  
Ecosystem Properties ◽  
Shortwave Infrared ◽  
Semi Arid

Abstract. This paper investigates how hyperspectral reflectance (between 350 and 1800 nm) can be used to infer ecosystem properties for a semi-arid savanna grassland in West Africa using a unique in situ-based multi-angular data set of hemispherical conical reflectance factor (HCRF) measurements. Relationships between seasonal dynamics in hyperspectral HCRF and ecosystem properties (biomass, gross primary productivity (GPP), light use efficiency (LUE), and fraction of photosynthetically active radiation absorbed by vegetation (FAPAR)) were analysed. HCRF data (ρ) were used to study the relationship between normalised difference spectral indices (NDSIs) and the measured ecosystem properties. Finally, the effects of variable sun sensor viewing geometry on different NDSI wavelength combinations were analysed. The wavelengths with the strongest correlation to seasonal dynamics in ecosystem properties were shortwave infrared (biomass), the peak absorption band for chlorophyll a and b (at 682 nm) (GPP), the oxygen A band at 761 nm used for estimating chlorophyll fluorescence (GPP and LUE), and blue wavelengths (ρ412) (FAPAR). The NDSI with the strongest correlation to (i) biomass combined red-edge HCRF (ρ705) with green HCRF (ρ587), (ii) GPP combined wavelengths at the peak of green reflection (ρ518, ρ556), (iii) LUE combined red (ρ688) with blue HCRF (ρ436), and (iv) FAPAR combined blue (ρ399) and near-infrared (ρ1295) wavelengths. NDSIs combining near infrared and shortwave infrared were strongly affected by solar zenith angles and sensor viewing geometry, as were many combinations of visible wavelengths. This study provides analyses based upon novel multi-angular hyperspectral data for validation of Earth-observation-based properties of semi-arid ecosystems, as well as insights for designing spectral characteristics of future sensors for ecosystem monitoring.

scholarly journals Mapping Soil Alkalinity and Salinity in Northern Songnen Plain, China with the HJ-1 Hyperspectral Imager Data and Partial Least Squares Regression

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3855 ◽  
Author(s):  
Lin Bai ◽  
Cuizhen Wang ◽  
Shuying Zang ◽  
Changshan Wu ◽  
Jinming Luo ◽  
...  
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Soil Ph ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Hyperspectral Data ◽  
Validation Dataset ◽  
Least Squares Regression ◽  
Songnen Plain ◽  
Mean Square Errors

In arid and semi-arid regions, identifying and monitoring of soil alkalinity and salinity are in urgently need for preventing land degradation and maintaining ecological balances. In this study, physicochemical, statistical, and spectral analysis revealed that potential of hydrogen (pH) and electrical conductivity (EC) characterized the saline-alkali soils and were sensitive to the visible and near infrared (VIS-NIR) wavelengths. On the basis of soil pH, EC, and spectral data, the partial least squares regression (PLSR) models for estimating soil alkalinity and salinity were constructed. The R2 values for soil pH and EC models were 0.77 and 0.48, and the root mean square errors (RMSEs) were 0.95 and 17.92 dS/m, respectively. The ratios of performance to inter-quartile distance (RPIQ) for the soil pH and EC models were 3.84 and 0.14, respectively, indicating that the soil pH model performed well but the soil EC model was not considerably reliable. With the validation dataset, the RMSEs of the two models were 1.06 and 18.92 dS/m. With the PLSR models applied to hyperspectral data acquired from the hyperspectral imager (HSI) onboard the HJ-1A satellite (launched in 2008 by China), the soil alkalinity and salinity distributions were mapped in the study area, and were validated with RMSEs of 1.09 and 17.30 dS/m, respectively. These findings revealed that the hyperspectral images in the VIS-NIR wavelengths had the potential to map soil alkalinity and salinity in the Songnen Plain, China.

scholarly journals Estimating Soil Arsenic Content with Visible and Near-Infrared Hyperspectral Reflectance

2020 ◽  
Vol 12 (4) ◽  
pp. 1476 ◽  
Author(s):  
Lei Han ◽  
Rui Chen ◽  
Huili Zhu ◽  
Yonghua Zhao ◽  
Zhao Liu ◽  
...  
Keyword(s):  
Near Infrared ◽  
Back Propagation ◽  
Hyperspectral Data ◽  
Pollution Monitoring ◽  
World Health ◽  
Support Vector ◽  
Inversion Method ◽  
Arsenic Content ◽  
Least Squares Regression ◽  
Soil Arsenic

Soil arsenic (AS) contamination has attracted a great deal of attention because of its detrimental effects on environments and humans. AS and inorganic AS compounds have been classified as a class of carcinogens by the World Health Organization. In order to select a high-precision method for predicting the soil AS content using hyperspectral techniques, we collected 90 soil samples from six different land use types to obtain the soil AS content by chemical analysis and hyperspectral data based on an indoor hyperspectral experiment. A partial least squares regression (PLSR), a support vector regression (SVR), and a back propagation neural network (BPNN) were used to establish a relationship between the hyperspectral and the soil AS content to predict the soil AS content. In addition, the feasibility and modeling accuracy of different interval spectral resampling, different spectral pretreatment methods, feature bands, and full-band were compared and discussed to explore the best inversion method for estimating soil AS content by hyperspectral. The results show that 10 nm + second derivative (SD) + BPNN is the optimum method to predict soil AS content estimation; R v 2 is 0.846 and residual predictive deviation (RPD) is 2.536. These results can expand the representativeness and practicability of the model to a certain extent and provide a scientific basis and technical reference for soil pollution monitoring.

scholarly journals Moving-Window-Improved Monte Carlo Uninformative Variable Elimination Combining Successive Projections Algorithm for Near-Infrared Spectroscopy (NIRS)

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Weiwei Jiang ◽  
Changhua Lu ◽  
Yujun Zhang ◽  
Wei Ju ◽  
Jizhou Wang ◽  
...  
Keyword(s):  
Variable Selection ◽  
Diesel Fuel ◽  
Prediction Accuracy ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Moving Window ◽  
Least Squares Regression ◽  
Wavelength Selection ◽  
Full Spectrum ◽  
Ethylene Concentration

The MC-UVE-SPA method is commonly proposed as a variable selection approach for multivariate calibration. However, the SPA tends to select wavelength variables that are sparsely distributed over the wavelength ranges of the variables selected by the MC-UVE algorithm, and the MC-UVE-SPA cascade cannot improve the problem of wavelength point discontinuity. It is addressed in this paper by proposing a moving-window- (MW-) improved MC-UVE-SPA wavelength selection algorithm. The proposed algorithm improves the continuity of the selected wavelength variables and thereby better exploits the advantages of the MC-UVE algorithm and the SPA to obtain regression models with high prediction accuracy. The MC-UVE, MC-UVE-SPA, and MC-UVE-SPA-MW algorithms are applied for conducting wavelength variable selection for the NIR spectral absorbance data of corn, diesel fuel, and ethylene. Here, partial least squares regression (PLSR) models reflecting the oil content of corn, the boiling point of diesel fuel, and the ethylene concentration are established after conducting wavelength selection using the MC-UVE algorithm, and corresponding multiple linear regression (MLR) models are established after conducting wavelength selection using the MC-UVE-SPA and MC-UVE-SPA-MW algorithms. Experimental results demonstrate that the progressive elimination of uncorrelated and collinear variables generates increasingly simplified partial-spectrum models with greater prediction accuracy than the full-spectrum model. Among the three wavelength selection algorithms, the MC-UVE-SPA selected the least number of wavelength variables, while the proposed MC-UVE-SPA-MW algorithm provided models with the greatest prediction accuracy.

scholarly journals Prediction of Soil Nutrient Contents Using Visible and Near-Infrared Reflectance Spectroscopy

2019 ◽  
Vol 8 (10) ◽  
pp. 437 ◽  
Author(s):  
Yiping Peng ◽  
Li Zhao ◽  
Yueming Hu ◽  
Guangxing Wang ◽  
Lu Wang ◽  
...  
Keyword(s):  
Near Infrared ◽  
Regional Scale ◽  
Soil Nutrient ◽  
Hyperspectral Data ◽  
Estimation Accuracy ◽  
Soil Productivity ◽  
Least Squares Regression ◽  
Near Infrared Reflectance ◽  
Nutrient Contents ◽  
Soil Nutrient Contents

Quickly and efficiently monitoring soil nutrient contents using remote sensing technology is of great significance for farmland soil productivity, food security and sustainable agricultural development. Current research has been conducted to estimate and map soil nutrient contents in large areas using hyper-spectral techniques, however, it is difficult to obtain accurate estimates. In order to improve the estimation accuracy of soil nutrient contents, we introduced a GA-BPNN method, which combined a back propagation neural network (BPNN) with the genetic algorithm optimization (GA). This study was conducted in Guangdong, China, based on soil nutrient contents and hyperspectral data. The prediction accuracies from a partial least squares regression (PLSR), BPNN and GA-BPNN were compared using field observations. The results showed that (1) Among three methods, the GA-BPNN provided the most accurate estimates of soil total nitrogen (TN), total phosphorus (TP) and total potassium (TK) contents; (2) Compared with the BPNN models, the GA-BPNN models significantly improved the estimation accuracies of the soil nutrient contents by decreasing the relative root mean square error (RRMSE) values by 15.9%, 5.6% and 20.2% at the sample point level, and 20.1%, 16.5% and 47.1% at the regional scale for TN, TP and TK, respectively. This indicated that by optimizing the parameters of BPNN, the GA-BPNN provided greater potential to improving the estimation; and (3) Soil TK content could be more accurately mapped by the GA-BPNN method using HuanJing-1A Hyperspectral Imager (HJ-1A HSI) (manufacturer: China Aerospace Science and Technology Corporation; Beijing, China) data with a RRMSE value of 20.37% than the soil TN and TP with the RRMSE values of 40.41% and 34.71%, respectively. This implied that the GA-BPNN model provided the potential to map the soil TK content for the large area. The research results provided an important reference for high-accuracy prediction of soil nutrient contents.

scholarly journals Mapping Water Infiltration Rate Using Ground and UAV Hyperspectral Data: A Case Study of Alento, Italy

2021 ◽  
Vol 13 (13) ◽  
pp. 2606
Author(s):  
Nicolas Francos ◽  
Nunzio Romano ◽  
Paolo Nasta ◽  
Yijian Zeng ◽  
Brigitta Szabó ◽  
...  
Keyword(s):  
Near Infrared ◽  
Soil Surface ◽  
Infiltration Rate ◽  
Hyperspectral Data ◽  
Spatial Uncertainty ◽  
Water Infiltration ◽  
Least Squares Regression ◽  
Spectral Measurements ◽  
Field Samples ◽  
Hyperspectral Sensor

Water infiltration rate (WIR) into the soil profile was investigated through a comprehensive study harnessing spectral information of the soil surface. As soil spectroscopy provides invaluable information on soil attributes, and as WIR is a soil surface-dependent property, field spectroscopy may model WIR better than traditional laboratory spectral measurements. This is because sampling for the latter disrupts the soil-surface status. A field soil spectral library (FSSL), consisting of 114 samples with different textures from six different sites over the Mediterranean basin, combined with traditional laboratory spectral measurements, was created. Next, partial least squares regression analysis was conducted on the spectral and WIR data in different soil texture groups, showing better performance of the field spectral observations compared to traditional laboratory spectroscopy. Moreover, several quantitative spectral properties were lost due to the sampling procedure, and separating the samples according to texture gave higher accuracies. Although the visible near-infrared–shortwave infrared (VNIR–SWIR) spectral region provided better accuracy, we resampled the spectral data to the resolution of a Cubert hyperspectral sensor (VNIR). This hyperspectral sensor was then assembled on an unmanned aerial vehicle (UAV) to apply one selected spectral-based model to the UAV data and map the WIR in a semi-vegetated area within the Alento catchment, Italy. Comprehensive spectral and WIR ground-truth measurements were carried out simultaneously with the UAV–Cubert sensor flight. The results were satisfactorily validated on the ground using field samples, followed by a spatial uncertainty analysis, concluding that the UAV with hyperspectral remote sensing can be used to map soil surface-related soil properties.

scholarly journals Evaluation of Point Hyperspectral Reflectance and Multivariate Regression Models for Grapevine Water Status Estimation

2021 ◽  
Vol 13 (16) ◽  
pp. 3198
Author(s):  
Hsiang-En Wei ◽  
Miles Grafton ◽  
Michael Bretherton ◽  
Matthew Irwin ◽  
Eduardo Sandoval
Keyword(s):  
Variable Selection ◽  
Regression Models ◽  
Critical Period ◽  
Partial Least Squares Regression ◽  
Water Status ◽  
Hyperspectral Data ◽  
Least Squares Regression ◽  
Hyperspectral Reflectance ◽  
Selection Methods ◽  
Simple Ratio

Monitoring and management of plant water status over the critical period between flowering and veraison, plays a significant role in producing grapes of premium quality. Hyperspectral spectroscopy has been widely studied in precision farming, including for the prediction of grapevine water status. However, these studies were presented based on various combinations of transformed spectral data, feature selection methods, and regression models. To evaluate the performance of different modeling pipelines for estimating grapevine water status, a study spanning the critical period was carried out in two commercial vineyards at Martinborough, New Zealand. The modeling used six hyperspectral data groups (raw reflectance, first derivative reflectance, second derivative reflectance, continuum removal variables, simple ratio indices, and vegetation indices), two variable selection methods (Spearman correlation and recursive feature elimination based on cross-validation), an ensemble of selected variables, and three regression models (partial least squares regression, random forest regression, and support vector regression). Stem water potential (used as a proxy for vine water status) was measured by a pressure bomb. Hyperspectral reflectance was undertaken by a handheld spectroradiometer. The results show that the best predictive performance was achieved by applying partial least squares regression to simple ratio indices (R2 = 0.85; RMSE = 110 kPa). Models trained with an ensemble of selected variables comprising multicombination of transformed data and variable selection approaches outperformed those fitted using single combinations. Although larger data sizes are needed for further testing, this study compares 38 modeling pipelines and presents the best combination of procedures for estimating vine water status. This may lead to the provision of rapid estimation of vine water status in a nondestructive manner and highlights the possibility of applying hyperspectral data to precision irrigation in vineyards.

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