Water Turbidity Retrieval Based on UAV Hyperspectral Remote Sensing

The water components affecting turbidity are complex and changeable, and the spectral response mechanism of each water quality parameter is different. Therefore, this study mainly aimed at the turbidity monitoring by unmanned aerial vehicle (UAV) hyperspectral technology, and establishes a set of turbidity retrieval models through the artificial control experiment, and verifies the model’s accuracy through UAV flight and water sample data in the same period. The results of this experiment can also be extended to different inland waters for turbidity retrieval. Retrieval of turbidity values of small inland water bodies can provide support for the study of the degree of water pollution. We collected the images and data of aquaculture ponds and irrigation ditches in Dawa District, Panjin City, Liaoning Province. Twenty-nine standard turbidity solutions with different concentration gradients (concentration from 0 to 360 NTU—the abbreviation of Nephelometric Turbidity Unit, which stands for scattered turbidity.) were established through manual control and we simultaneously collected hyperspectral data from the spectral values of standard solutions. The sensitive band to turbidity was obtained after analyzing the spectral information. We established four kinds of retrieval, including the single band, band ratio, normalized ratio, and the partial least squares (PLS) models. We selected the two models with the highest R2 for accuracy verification. The band ratio model and PLS model had the highest accuracy, and R2 was, respectively, 0.65 and 0.72. The hyperspectral image data obtained by UAV were combined with the PLS model, which had the highest R2 to estimate the spatial distribution of water turbidity. The turbidity of the water areas in the study area was 5–300 NTU, and most of which are 5–80 NTU. It shows that the PLS models can retrieve the turbidity with high accuracy of aquaculture ponds, irrigation canals, and reservoirs in Dawa District of Panjin City, Liaoning Province. The experimental results are consistent with the conclusions of the field investigation.

Quantifying Nutrient Content in the Leaves of Cowpea Using Remote Sensing

Although hyperspectral remote sensing techniques have increasingly been used in the nutritional quantification of plants, it is important to understand whether the method shows a satisfactory response during the various phenological stages of the crop. The aim of this study was to quantify the levels of phosphorus (P), potassium (K), calcium (Ca) and zinc (Zn) in the leaves of Vigna Unguiculata (L.) Walp using spectral data obtained by a spectroradiometer. A randomised block design was used, with three treatments and twenty-five replications. The crop was evaluated at three growth stages: V4, R6 and R9. Single-band models were fitted using simple correlations. For the band ratio models, the wavelengths were selected by 2D correlation. For the models using partial least squares regression (PLSR), the stepwise method was used. The model showing the best fit was used to estimate the phosphorus content in the single-band (R² = 0.62; RMSE = 0.54 and RPD = 1.61), band ratio (R² = 0.66; RMSE = 0.65 and RPD = 1.52) and PLSR models, using data from each of the phenological stages (R² = 0.80; RMSE = 0.47 and RPD = 1.66). Accuracy in modelling leaf nutrients depends on the phenological stage, as well as the amount of data used, and is more accurate with a larger number of samples.

Estimation Of Water Quality Using Geomatics Techniques for The Garraf River Basin

This study was conducted for the purpose of estimating the soil and water quality of the Gharraf River Basin in the north of Dhi Qar Governorate using geomatics techniques represented by geographic information systems (GIS), remote sensing (RS) and positioning systems (GNSS). Which is about (90 km) from the city center. The chemical analyzes of the water samples showed that the degree of interaction was between (7.84-7.7) and the electrical conductivity (dS.m¯1.1-1.05), and the total dissolved substances were between (1106-1051ppm), and the mathematical statistical relationships were weakly correlated with the ratios of the visible space bands. pH, electrical conductivity and total dissolved materials. Calcium ratios in the study area ranged between (ppm 47.4-107) and there was a significant correlation with the range (B/R + B) with a value of (R2 = 0.51), and the results showed the ratios of magnesium in the study area between (ppm 9.67 - 26.61.) Between it and the band ratio (B/R + B)), a correlation relationship with a value of (R2=0.525), potassium recorded an average between (3.1-ppm 5.5), and there was a significant correlation between it and the band ratio (B/R +R) and it reached (R2=0.665). ), found a statistical relationship between sodium and the ratio of the band (B/R + R)) and a significant correlation was recorded with a value of (R2 = 0.527). - 102.52) And there was a correlation between the presence of chloride and the ratio of the range (B/NIR + G) as it was recorded (R2 = 0.593), the bicarbonate recorded ratios between (ppm 1.8-2.7), and there was a statistical relationship between the bicarbonate and the ratio of the range (C / R). ) amounted to (R2 = 0.573), nitrate values were recorded in the study area between (4 - 3.45 ppm) and there was a significant correlation between them and the range (B5) as it reached (R2 = 0.581), sulfate values were recorded between (207.25 - 277.5 ppm) and through Statistical analysis found that there is a correlation between The presence of sulfate with the ratio (C + B + G + R + NIR) which amounted to (R2 = 0.596), the sodium adsorption ratio (SAR) was calculated, as its values ranged between (3.192 - 0.147) and most of the statistical relationships were weakly related to the spatial ratios and were gradually The hardness values in the study area are between (99.7 - 198.1)(

Application of ASTER Data for Differentiating Carbonate Minerals and Evaluating MgO Content of Magnesite in the Jiao-Liao-Ji Belt, North China Craton

Numerous reports have successfully detected or differentiated carbonate minerals such as calcite and dolomite by using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). However, there is a need to determine whether existing methods can differentiate magnesite from other carbonate minerals. This study proposes optimal band ratio combinations and new thresholds to distinguish magnesite, dolomite, and calcite using ASTER shortwave-infrared (SWIR) data. These were determined based on the spectral and chemical analysis of rock samples collected from Liaoning, China and Danchon, North Korea and the reflectance values from ASTER images. The results demonstrated that the simultaneous use of thresholds 2.13 and 2.015 for relative absorption band depths (RBDs) of (6 + 8)/7 and (7 + 9)/8, respectively, was the most effective for magnesite differentiation. The use of RBDs and band ratios to discriminate between dolomite and calcite was sufficiently effective. However, talc, tremolite, clay, and their mixtures with dolomite and calcite, which are commonly found in the study area, hampered the classification. The assessment of the ASTER band ratios for magnesite grade according to magnesium oxide content indicated that a band ratio of 5/6 was the most effective for this purpose. Therefore, this study proved that ASTER SWIR data can be effectively utilized for the identification and grade assessment of magnesite on a regional scale.

Lithological mapping using remote sensing techniques: A case study of Alagbayan area, Dornogobi province, Mongolia

This study investigated the multispectral remote sensing techniques including ASTER, Landsat 8 OLI, and Sentinel 2A data in order to distinguish different lithological units in the Alagbayan area of Dornogobi province, Mongolia. Therefore, Principal component analysis (PCA), Band ratio (BR), and Support Vector Machine (SVM), which are widely used image enhancement methods, have been applied to the satellite images for lithological mapping. The result of supervised classification shows that Landsat data gives a better classification with an overall accuracy of 93.43% and a kappa coefficient of 0.92 when the former geologic map and thin section analysis were chosen as a reference for training samples. Moreover, band ratios of ((band 7 + band 9)/band 8) obtained from ASTER corresponds well with carbonate rocks. According to PCs, PC4, PC3 and PC2 in the RGB of Landsat, PC3, PC2, PC6 for ASTER data are chosen as a good indicator for different lithological units where Silurian, Carboniferous, Jurassic, and Cretaceous formations are easily distinguished. In terms of Landsat images, the most efficient BR was a ratio where BRs of 5/4 for alluvium, 4/7 for schist and 7/6 to discriminate granite. In addition, as a result of BR as well as PCA, Precambrian Khutag-Uul metamorphic complex and Norovzeeg formation can be identified but granite-gneiss and schist have not given satisfactory results.

Online Detection of Watercore Apples by Vis/NIR Full-Transmittance Spectroscopy Coupled with ANOVA Method

Watercore is an internal physiological disorder affecting the quality and price of apples. Rapid and non-destructive detection of watercore is of great significance to improve the commercial value of apples. In this study, the visible and near infrared (Vis/NIR) full-transmittance spectroscopy combined with analysis of variance (ANOVA) method was used for online detection of watercore apples. At the speed of 0.5 m/s, the effects of three different orientations (O1, O2, and O3) on the discrimination results of watercore apples were evaluated, respectively. It was found that O3 orientation was the most suitable for detecting watercore apples. One-way ANOVA was used to select the characteristic wavelengths. The least squares-support vector machine (LS-SVM) model with two characteristic wavelengths obtained good performance with the success rates of 96.87% and 100% for watercore and healthy apples, respectively. In addition, full-spectrum data was also utilized to determine the optimal two-band ratio for the discrimination of watercore apples by ANOVA method. Study showed that the threshold discrimination model established based on O3 orientation had the same detection accuracy as the optimal LS-SVM model for samples in the prediction set. Overall, full-transmittance spectroscopy combined with the ANOVA method was feasible to online detect watercore apples, and the threshold discrimination model based on two-band ratio showed great potential for detection of watercore apples.

Comparison of Different Multispectral Images to Map Hydrothermal Alteration Zones in Kokap, Kulon Progo

Using remote sensing data for hydrothermal alteration mapping beside saving time and reducing cost leads to increased accuracy. In this study, the result of multispectral remote sensing tehcniques has been compare for manifesting hydrothermal alteration in Kokap, Kulon Progo. Three multispectral images, including ASTER, Landsat 8, and Sentinel-2, were compared in order to find the highest overall accuracy using principle component analysis (PCA) and directed component analysis (DPC). Several subsets band combinations were used as PCA and DPC input to targeting the key mineral of alteration. Multispectral classification with the maximum likelihood algorithm was performed to map the alteration types based on training and testing data and followed by accuracy evaluation. Two alteration zones were succeeded to be mapped: argillic zone and propylitic zone. Results of these image classification techniques were compared with known alteration zones from previous study. DPC combination of band ratio images of 5:2 and 6:7 of Landsat 8 imagery yielded a classification accuracy of 56.4%, which was 5.05% and 10.13% higher than those of the ASTER and Sentinel-2 imagery. The used of DEM together with multispectral images was increase the accuracy of hydrothermal alteration mapping in the study area.

A Methodology for CO2 Retrieval Applied to Hyperspectral PRISMA Data

The aim of this work is to develop and test a simple methodology for CO2 emission retrieval applied to hyperspectral PRISMA data. Model simulations are used to infer the best SWIR channels for CO2 retrieval purposes, the weight coefficients for a Continuum Interpolated Band Ratio (CIBR) index calculation, and the factor for converting the CIBR values to XCO2 (ppm) estimations above the background. This method has been applied to two test cases relating to the LUSI volcanic area (Indonesia) and the Solfatara area in the caldera of Campi Flegrei (Italy). The results show the capability of the method to detect and estimate CO2 emissions at a local spatial scale and the potential of PRISMA acquisitions for gas retrieval. The limits of the method are also evaluated and discussed, indicating a satisfactory application for medium/strong emissions and over soils with a reflectance greater than 0.1.