Wide Field View Research Articles

Spatial resolution enhancement in holographic imaging via angular spectrum expansion

Digital holography numerically restores three-dimensional image information using optically captured diffractive waves. The required bandwidth is larger than that of hologram pixel at a closer distance in the Fresnel diffraction regime, which results in the formation of aliased replica patterns in digital hologram. From the analysis of sampling phenomenon, the replica functions are revealed to be the components of higher angular spectra of hologram. A undersampled hologram consists of the moire patterns formed by the modulation of original function by complex exponential function. There is a one-to-one correspondence between the replicas in both real and Fourier spaces. The acquisition methods of high-resolution images over a wide field view are proposed on the basis of the expansion process of angular spectrum by using replicas. Only the captured hologram with low numerical aperature or low resolution restores a high-resolution image when using an optimization algorithm. Numerical simulations and optical experiments are performed to investigate the proposed scheme.

Generation of a 16 m/10-day fractional vegetation cover product over China based on Chinese GaoFen-1 observations: method and validation

ABSTRACTAs China has recently launched the GaoFen-1 satellite (GF-1) carrying on the wide-field view (WFV) sensor, it is a challenging task to make full use of its observations to produce the fractional vegetation cover (FVC). In light of this, our study presents a comprehensive algorithm to generate a 16 m/10-day FVC product by considering the vegetation types characteristics. For forests, considering the foliage clumping effect, FVC was estimated from the gap probability theory using GF-1 leaf area index (LAI) and clumping index (CI) as a priori knowledge; for non-forests, FVC was estimated from the dimidiate pixel model using GF-1 normalized difference vegetation index (NDVI). The performance of GF-1 FVC from 2018 to 2020 was evaluated using FVC ground measurements obtained from 7 sites for crops, grasslands, and forests in China. The direct validation indicated that the performance of the FVC product was satisfactory, as evidenced by R2 = 0.55, RMSE = 0.15 and BIAS = 0.01 for all vegetation types. Furthermore, the GF-1 FVC exhibited better performance compared to the GEOV3 FVC at a spatial resolution of 300 meters. Moreover, the 10-day temporal interval of GF-1 FVC product successfully facilitated the extraction of regional phenological information at a spatial resolution of 16 meters.

Does the Rational Function Model’s Accuracy for GF1 and GF6 WFV Images Satisfy Practical Requirements?

The Gaofen-1 (GF-1) and Gaofen-6 (GF-6) satellites have acquired many GF-1 and GF-6 wide-field-view (WFV) images. These images have been made available for free use globally. The GF-1 WFV (GF-1) and GF-6 WFV (GF-6) images have rational polynomial coefficients (RPCs). In practical applications, RPC corrections of GF-1 and GF-6 images need to be completed using the rational function model (RFM). However, can the accuracy of the rational function model satisfy practical application requirements? To address this issue, a geometric accuracy method was proposed in this paper to evaluate the accuracy of the RFM of GF-1 and GF-6 images. First, RPC corrections were completed using the RFM and refined RFM, respectively. The RFM was constructed using the RPCs and Shuttle Radar Topography Mission (SRTM) 90 m DEM. The RFM was refined via affine transformation based on control points (CPs), which resulted in a refined RFM. Then, an automatic matching method was proposed to complete the automatic matching of GF-1/GF-6 images and reference images, which enabled us to obtain many uniformly distributed CPs. Finally, these CPs were used to evaluate the geometric accuracy of the RFM and refined RFM. The 14th-layer Google images of the corresponding area were used as reference images. In the experiments, the advantages and disadvantages of BRIEF, SIFT, and the proposed method were first compared. Then, the values of the root mean square error (RSME) of 10,561 Chinese, French, and Brazilian GF-1 and GF-6 images were calculated and statistically analyzed, and the local geometric distortions of the GF-1 and GF-6 images were evaluated; these were used to evaluate the accuracy of the RFM. Last, the accuracy of the refined RFM was evaluated using the eight GF-1 and GF-6 images. The experimental results indicate that the accuracy of the RFM for most GF-1 and GF-6 images cannot meet the actual use requirement of being better than 1.0 pixel, the accuracy of the refined RFM for GF-1 images cannot meet practical requirement of being better than 1.0 pixel, and the accuracy of the refined RFM for most GF-6 images meets the practical requirement of being better than 1.0 pixel. However, the RMSE values that meet the requirements are between 0.9 and 1.0, and the geometric accuracy can be further improved.

Investigating mechanisms of laser pulse-induced reflectivity modulations in photoacoustic remote sensing with a 10 million frames-per-second camera

Photoacoustic remote sensing has been recently developed as an all-optical imaging modality capable of imaging a variety of endogenous contrast agents label-free. Initially predicted laser pulse-induced refractive index perturbation-based interrogation beam reflectivity modulations have been found to be orders of magnitude smaller than those typically observed experimentally. In this report we utilize a 10 million frames-per-second camera to further investigate these predicted reflectivity modulations, while also exploring other potential mechanisms of laser pulse-induced reflectivity modulations. Laser-induced motion is demonstrated both laterally for gold wires suspended and submerged in air and water, respectively, and carbon fibers submerged in water, and axial motion is observed in gold wires submerged in a depth gradient of intralipid solution. This laser-induced sample motion is anticipated to cause reflectivity modulations local to the interrogation beam profile in microscopy set-ups. Non-motion-based maximum intensity modulations of 3% are also observed in gold wires submerged in water, indicating the presence of the originally predicted reflectivity modulations. Overall, these observations are important as they provide a widefield view of laser-pulse interactions unavailable in previous point scanning-based photoacoustic remote sensing microscopy configurations, where observed mechanisms occur on time-scales orders of magnitude faster than equivalent field of view point scanning capabilities.

A wide-field view on multiple stellar populations in 28 Milky Way globular clusters

ABSTRACT The majority of Galactic globular clusters (GCs) contain multiple stellar populations displaying specific chemical abundance variations. In particular, GCs generally contain a ‘primordial’ population with abundances similar to field stars, along with an ‘enriched’ population exhibiting light element anomalies. In this paper, we present a homogeneous and wide-view analysis of multiple stellar populations in 28 Galactic GCs. By using a combination of HST photometry together with wide-field, ground-based photometry we are able to analyse between 84 per cent and 99 per cent of all stars in each cluster. For each GC, we classify stars into separate sub-populations using the well-established CUBI colour index, and investigate the spatial distributions of these populations. Our results show that dynamically young GCs can contain either centrally concentrated enriched or primordial populations, or no centrally concentrated population. Dynamically old GCs show fully mixed populations as expected. The existence of clusters born with centrally concentrated primordial (and homogeneously mixed) populations exacerbates the mass-budget problem facing many cluster formation scenarios. The diversity in these results also highlights the need for additional theories that can account for the wide variety of initial conditions that we find. We finally investigate the enriched star fraction as a function of different global parameters in our GC sample, using also data for young and low-mass clusters from the Small- and Large Magellanic Clouds and confirm earlier results that the enriched star fraction strongly correlates with the initial mass of a cluster.

Angular Effect Correction for Improved LAI and FVC Retrieval Using GF-1 Wide Field View Data

Angular Effect Correction for Improved LAI and FVC Retrieval Using GF-1 Wide Field View Data

Extraction of rocky desertification information in karst area by using different multispectral sensor data and multiple endmember spectral mixture analysis method

As a typical form of land degradation, karst rocky desertification seriously restricts the development of the regional social economy and seriously threatens the living environment of residents. Fractional vegetation cover (fVC) and bare rock fraction (fBR) are important indicators to identify and evaluate rocky desertification. However, it is a great challenge to obtain fVC and fBR due to the complex terrain and fragmentation of karst rocky desertification areas. In this study, comparisons between Sentinel-2A Multispectral Instrument (Sentinel-2), Landsat-8 Operational Land Imager (Landsat-8), and GF-6 Wide Field View (GF-6) sensors for retrieving fVC and fBR are presented. The multiple endmember spectral mixture analysis (MESMA) and measured spectral dates were used to overcome the limitations of Spectral mixture analysis (SMA). Subsequently, fVC and fBR were validated using root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The results showed that: 1) Sentinel-2 performed best in estimating fVC and obtained the highest accuracy (R2 = 0.6259, root mean square error = 0.1568, mean absolute error = 0.1215), followed by GF-6 and Landsat 8; in the extraction of the fBR, the performance was relatively the same, and the implementation of Sentinel-2 was also the best (R2 = 0.4911, root mean square error = 0.0714, mean absolute error = 0.0539), followed by GF-6 and Landsat 8. 2) Sentinel-2 images have higher resolution, the narrowest band range, and the most significant number of bands, which can better extract information about fVC and fBR in rocky desertification areas. 3) For the three optical sensors, the spatial resolution of the images is more important to extract the information of fVC and fBR in the rocky desertification areas. 4) In general, the extraction accuracy of fBR is not as good as that of fVC. The complicated ecological and geological environment of decertified areas has more influence on the effect of extraction of the fBR. 5) The Sentinel-2 achieves high accuracy for both fVC and fBR under different-level application scenarios. It thus has great potential for application in rocky desertification information extraction.

DIC measurement for large-scale structures based on adaptive warping image stitching.

As a representative method of optical non-interference measurement, digital image correlation (DIC) technology is a non-contact optical mechanics method that can measure the displacement and deformation of the whole field. However, when the measurement range of the field is too large, the existing DIC method cannot measure the full-field strain accurately, which limits the application of the DIC measurement in the case of a large size and wide-field view. To address this issue, a DIC measurement method for large-scale structures based on adaptive warping image stitching is proposed in this paper. First, multiple adjacent high-resolution images are collected at different locations of large-scale structures. Secondly, the collected images are stitched by applying the adaptive warping image stitching algorithm to obtain a panoramic image. Finally, the DIC algorithm is applied to solve the whole deformation field. In the experiments, we first verify the feasibility of the proposed method for image matching and fusion through the numerical simulation of a rigid body translation experiment. Then the accuracy and robustness of the proposed method in practical application are verified by rigid body translation and a three-point bending experiment. The experimental results demonstrate that the measurement range of DIC is improved significantly with the adaptive warping image stitching algorithm.

Satellite Observations of Karman Vortex Streets Induced by Islands from the Aspects of SSC in Hangzhou Bay, China

We explored the details of Karman vortex streets (KVS) induced by islands in the Hangzhou Bay using high resolution Chinese Gaofen-1 data and Landsat-8 data. Thirty images, including 15 images obtained by Wide-Field-View (WFV) sensor from Chinese Gaofen-1 and 15 images obtained by Operational Land Imager (OLI) aboard Landsat-8, were applied to retrieve Suspended Sediment Concentration (SSC) based on a prior model. The results show that: 1) The Karman vortex streets induced by small islands can be detected clearly by high resolution satellite data from the change of SSC. 2) The Karman vortex streets contain 3-5 subunits, and the further away from the island, the larger the scale of vortex streets. Generally, the length of the vortex street is between 1500 m and 9800 m.

Land Surface Albedo Estimation With Chinese GF-1 WFV Data in Northwest China

Land surface albedo (LSA) is one of the driving factors in the energy balance of surface radiation and the interaction between the earth and atmosphere. LSA is an important parameter that is widely used in surface energy balance, medium- and long-term weather forecasting, and global change studies. GF-1 wide field view (WFV) data provide a spatial resolution of 16 m and temporally intensive land surface observations, but efficient algorithm was still lacking for quantitatively land surface parameters estimation. It is essential to improve the data use ability by generating efficient land surface parameter retrieval algorithms. This study proposed an LSA retrieval algorithm by using GF-1 WFV data. Land surface bidirectional reflectance distribution function characteristic parameters were used to represent the non-Lambertian characteristic of land surface. The top of atmosphere (TOA) reflectance is simulated by the 6S radiative transfer model by considering non-Lambertian land surfaces. Linear regression is applied in the TOA reflectance, and LSA is simulated with the surface bidirectional reflectance characteristic parameters to build a lookup table. The proposed algorithm can estimate LSA with high accuracy according to the TOA reflectance without the complex multistep inversion process. The validation results of ground measurements in Northwest China for different land cover types show that the algorithm is effective, and the overall root mean square error was 0.036 when compared with field observation. The algorithm also shows great consistency with Landsat albedo data. The proposed algorithm is of great significance for improving GF data utilization.

Land Cover and Crop Classification Based on Red Edge Indices Features of GF-6 WFV Time Series Data

Time series of vegetation indices can be utilized to capture crop phenology information, and have been widely used in land cover and crop classification, phenological feature extraction, and planting structure monitoring. This is of great significance for guiding agricultural production and formulating agricultural policies. According to the characteristics of the GF-6 satellite’s newly-added red edge bands, wide field view and high-frequency imaging, the time series of vegetation indices about multi-temporal GF-6 WFV data are used for the study of land cover and crop classification. In this study, eight time steps of GF-6 WFV data were selected from March to October 2019 in Hengshui City. The normalized difference vegetation index (NDVI) time series and 10 different red edge spectral indices time series were constructed. Then, based on principal component analysis (PCA), using two feature selection and evaluation methods, stepwise discriminant analysis (SDA) and random forest (RF), the red edge vegetation index of normalized difference red edge (NDRE) was selected. Seven different lengths of NDVI, NDRE and NDVI&NDRE time series were reconstructed by the Savizky-Golay (S-G) smoothing algorithm. Finally, an RF classification algorithm was used to analyze the influence of time series length and red edge indices features on land cover and crop classification, and the planting structure and distribution of crops in the study area were obtained. The results show that: (1) Compared with the NDRE red edge time series, the NDVI time series is more conducive to the improvement of the overall classification accuracy of crops, and NDRE can assist NDVI in improving the crop classification accuracy; (2) With the shortening of NDVI and NDRE time series, the accuracy of crop classification is gradually decreased, and the decline is gradually accelerated; and (3) Through the combination of the NDVI and NDRE time series, the accuracy of crop classification with different time series lengths can be improved compared with the single NDVI time series, which is conducive to improving the classification accuracy and timeliness of crops. This study has fully tapped the application potential of the new red edge bands of GF-6 WFV time series data, which can provide references for crop identification and classification of time series data such as NDVI and red edge vegetation index of different lengths. At the same time, it promotes the application of optical satellite data with red edge bands in the field of agricultural remote sensing.

Identifying Spatial and Temporal Characteristics of Land Surface Albedo Using GF-1 WFV Data

Land surface albedo (LSA) is an important parameter that affects surface–air interactions and controls the surface radiation energy budget. The spatial and temporal variation characteristics of LSA reflect land surface changes and further influence the local climate. Ganzhou District, which belongs to the middle of the Hexi Corridor, is a typical irrigated agricultural and desert area in Northwest China. The study of the interaction of LSA and the land surface is of great significance for understanding the land surface energy budget and for ground measurements. In this study, high spatial and temporal resolution GF-1 wide field view (WFV) data were used to explore the spatial and temporal variation characteristics of LSA in Ganzhou District. First, the surface albedo of Ganzhou District was estimated by the GF-1 WFV. Then, the estimated results were verified by the surface measured data, and the temporal and spatial variation characteristics of surface albedo from 2014 to 2018 were analyzed. The interaction between albedo and precipitation or temperature was analyzed based on precipitation and temperature data. The results show that the estimation of surface albedo based on GF-1 WFV data was of high accuracy, which can meet the accuracy requirements of spatial and temporal variation characteristic analysis of albedo. There are obvious geographic differences in the spatial distribution of surface albedo in Ganzhou, with the overall distribution characteristics being high in the north and low in the middle. The interannual variation in annual average surface albedo in Ganzhou shows a trend of slow fluctuations and gradual increases. The variation in annual albedo is characterized by “double peaks and a single valley”, with the peaks occurring from December to February at the end and beginning of the year, and the valley occurring from June to August. Surface albedo was negatively correlated with precipitation and temperature in most areas of Ganzhou.

Forest classification using synthetic GF-1/WFV time series and phenological parameters

High-accuracy forest type mapping is crucial for forest resource inventory to support forest management, conservation biology, and ecological restoration. The Chinese GF-1 satellite can provide high-spatial resolution and low-cost data for forestry monitoring. However, it is difficult to obtain time-series GF-1 images with high spatial resolution and high temporal frequency because of technical constraints and cloud contamination, which affects the accuracy of forest type extraction. We proposed a method for mapping forest type using Chinese GF-1/widefield view (WFV) multi-spectral data, the fused GF-1/WFV normalized difference vegetation index (NDVI) time series, and the phenological parameters. We used the enhanced spatial and temporal adaptive reflectance fusion model algorithm to fuse Chinese GF-1/WFV and Moderate Resolution Imaging Spectroradiometer NDVI to generate high-spatiotemporal resolution NDVI time-series image data and extract phenological parameters in the northeastern part of Hubei Province, China. We then designed four different combinations of GF-1/WFV spectral features, the fused NDVI time-series features, and the phenological parameters. Finally, a random forest algorithm is used to classify forest types in the study area. The results showed that the proposed method can obtain satisfactory results. The overall accuracy based on the combination of GF-1/WFV spectral features, high-resolution time-series NDVI, and phenological features was 89.68%. Compared with the classification using only GF-1/WFV spectral data, the overall accuracy was improved by 9.60% points. These results demonstrate that the proposed method can support forest type survey and mapping at the regional or global scale.

Using Eco-Geographical Zoning Data and Crowdsourcing to Improve the Detection of Spurious Land Cover Changes

To address problems in remote sensing image change detection, this study proposes a method for identifying spurious changes based on an eco-geographical zoning knowledge base and crowdsourced data mining. After preliminary change detection using the super pixel cosegmentation method, eco-geographical zoning is introduced, and the rules of spurious change are collected based on the knowledge of expert interpreters, and from statistics on existing land cover products according to each eco-geographical zone. Uncertain changed patches with a high possibility of spurious change according to the eco-geographical zoning rule were published in the form of a map service on an online platform, and then crowd tagging information on spurious changed patches was collected. The Hyperlink-Induced Topic Search (HITS) algorithm was used to calculate the spurious change degree of changed patches. We selected the northern part of Laos as the experimental area and the Chinese GF-1 Wide Field View (WFV) images for change detection to verify the effectiveness of the method. The results show that the accuracy of change detection improves by 23% after removing the spurious changes. Spurious changes caused by clouds, river water turbidity, spectral differences in cultivated land before and after harvest, and changes in shrubs, grassland, and forest density, can be removed using an eco-geographical zoning knowledge base and crowdsourced data mining methods.

Super-resolution method using generative adversarial network for Gaofen wide-field-view images

Accurate information on the spatial distribution of crops is of great significance for scientific research and production practices. Such accurate information can be extracted from high-spatial-resolution optical remote sensing images. However, acquiring these images with a wide coverage is difficult. We established a model named multispectral super-resolution generative adversarial network (MS_SRGAN) for generating high-resolution 4-m images using Gaofen 1 wide-field-view (WFV) 16-m images. The MS_SRGAN model contains a generator and a discriminator. The generator network is composed of feature extraction units and feature fusion units with a symmetric structure, and the attention mechanism is introduced to constrain the spectral value of the feature map during feature extraction. The generator loss introduces feature loss to describe the feature difference of the image. This is realized using pre-trained discriminator parameters and a partial discriminator network. In addition to realizing feature loss, the discriminator network, which is a simple convolutional neural network, also realizes adversarial loss. Adversarial loss can provide some fake high frequency details to the generator to get a more sharpened image. In the Gaofen 1 WFV image test, the performance of MS_SRGAN was compared with that of Bicubic, EDSR, SRGAN, and ESRGAN. The results show that the spectral angle mapper (3.387) and structural similarity index measure (0.998) of MS_SRGAN are higher than those of the other models. In addition, the image obtained by MS_SRGAN is more realistic; its texture details and color distribution are closer to the reference image to a greater extent.

Extraction of fractional vegetation cover in arid desert area based on Chinese GF-6 satellite

Abstract The red edge band is considered as one of the diagnosable characteristics of green plants, but the large-scale remote sensing retrieval of fractional vegetation coverage (FVC) based on the red edge band is still rare. To explore the application of the red edge band in the remote sensing estimation of FVC, this study proposed a new vegetation index (normalized difference red edge index, RENDVI) based on the two red edge bands of Chinese GaoFen-6 satellite (GF-6). The FVC estimated by using three vegetation indices (NDVI, RENDVI1, and RENDVI2) were evaluated based on the field survey FVC obtained in Minqin Basin of Gansu Province. The results showed that there was a good linear correlation between the FVC estimated by GF-6 WFV data and the FVC investigated in the field, and the most reasonable estimation of FVC was obtained based on RENDVI2 model (R 2 = 0.97611 and RMSE = 0.07075). Meanwhile, the impact of three confidence levels (1, 2, and 5%) on FVC was also analyzed in this study. FVC obtained from NDVI and RENDVI2 has the highest accuracy at 2% confidence, while FVC based on RENDVI1 achieved the best accuracy at 5% confidence. It could be concluded that it is feasible and reliable to estimate FVC based on red edge bands, and the GF-6 Wide Field View (WFV) data with high temporal and spatial resolution provide a new data source for remote sensing estimation of FVC.

Analysis of Anti-interference Ability of Hyperspectral Sensitive Features to Wheat Powdery Mildew

The development of ground-based, airborne and spaceborne remote sensing has greatly facilitated the identification and diagnosis of various objects. Corresponding algorithms and methods of removing interference from remotely sensed imagery have been proposed. Nevertheless, the studies on anti-interference ability of selected features have not been fully considered. In our study, the hyperspectral reflectance of leaf-scale powdery mildew (Erysiphe graminis) on winter wheat were collected as the testing dataset. A total of seven representative spectral features of Landsat-8 Operational Land Imager (OLI) and GaoFen-1 Wide-Field-View (WFV) was selected, namely, original blue, green, red, near-infrared (NIR) bands and normalized difference vegetation index (NDVI), normalized difference greenness index (NDGI), structure insensitive pigment index (SIPI). Four hyperspectral vegetation indices including red edge (MSR) simple ratio index, NDVI, green band and SIPI were also selected. Three primary background noises including soil, cloud and white poplar (Populus alba L.) were added into the spectral signal. The correlation coefficient (R) between disease severities (0, 1, 2, 3 and 4) and spectral features was used to estimate the anti-interference ability. The results show that there is a generally similar spectral performance for the two sensors, but Landsat-8 OLI is superior to GF-1 WVF in terms of spectral response. The green band was greatly affected with the R values decreasing from 0.77 to 0.35. The MSR and NDVI showed a gradual decrease with the increase of three background noises. The study shows that background noises must be removed when acquiring spectral data and stable spectral features should be also selected by evaluating the anti-interference ability.

An observational study of endoscopic versus microscopic type I tympanoplasty in tubotympanic chronic otitis media in a tertiary health care centre

<p class="abstract"><strong>Background:</strong> Chronic otitis media (COM) is a widespread disease in the developing countries. It is treated by tympanoplasty, traditionally performed with the microscope which limits the visual field in the deep recesses of the middle ear, especially in narrow external auditory canal. Using endoscope has minimised these difficulties by providing a wide field view with angled magnification, reducing the duration of surgery. The advantages and disadvantages of both techniques is evaluated in our study.</p><p class="abstract"><strong>Methods:</strong> In this observational study, 60 patients of tubotympanic COM between 12-60 years of age with small to medium sized central perforation and air-bone gap of less than 45 decibel were included. They were divided into two groups (group A and group B) of 30 patients each. Group A underwent microscopic type 1 tympanoplasty and group B underwent endoscopic type 1 tympanoplasty. The duration of the surgery, the post-operative improvement in hearing and the graft uptake was analysed after 3 months of surgery. The post-operative hospital stay duration was noted. </p><p class="abstract"><strong>Results:</strong> The post-operative hearing improvement and the graft uptake after 3 months of the surgery was statistically not significant between the two groups. But the mean duration of the operation and the post-operative hospital stay was significantly lower in the endoscopic group with a p value <0.05.</p><p class="abstract"><strong>Conclusions:</strong> Endoscopes are better in otological procedures as it reduces the surgical duration, provides better visibility, lessens the post-operative hospital stay, reducing the economic burden of the patient.</p>

Consistency analysis of GF-1 and GF-6 satellite wide field view multi-spectral band reflectance

Chinese GF-1 and GF-6 satellites are currently operational for routine Earth observation, which can acquire multi-spectral data with moderate spatial resolution, high temporal resolution and wide coverage. There are slight differences between the wide field view (WFV) sensors onboard both satellites. Therefore, consistency analysis is very necessary for the networking of GF-1 and GF-6 WFV data to enhance the observation frequency. In this study, spectral band reflectance and vegetation indices in three regions dominated by urban, cropland and grassland, and forest were used to analyze the consistency of GF-1 and GF-6 WFV data. Consistency analysis results showed that the average coefficients of determination (R2) between GF-1 and GF-6 WFV data were 0.86, 0.88, 0.88 and 0.93 for blue, green, red and NIR bands, as well as 0.86, 0.82 and 0.6 for normalized difference vegetation index (NDVI), ratio vegetation index (RVI) and soil adjusted vegetation index (SAVI), respectively. Therefore, the subtle difference and high correlation of spectral band reflectance and vegetation indices indicated that the GF-1 and GF-6 WFV data had a degree of good consistency and had the potential of networking to monitor the rapid change of the Earth surface.

An Effective Method for Canopy Chlorophyll Content Estimation of Marsh Vegetation Based on Multiscale Remote Sensing Data

High-precision canopy chlorophyll content (CCC) inversion for marsh vegetation is of great significance for marsh protection and restoration. However, it is difficult to collect the CCC measured data for marsh vegetation that matches the pixel scale of remote sensing image. This article proposes a new method based on unmanned aerial vehicle (UAV) multispectral images to obtain multiscale marsh vegetation CCC sample data. A random forest (RF) regression algorithm was used to evaluate the application performance of GF-1 wide field view (WFV), Landsat-8 Operational Land Imager (OLI), and Sentinel-2 multispectral instrument (MSI) satellite remote sensing data in marsh vegetation CCC inversion. In addition, parameter optimization of the RF regression model was used to construct an optimization algorithm suitable for marsh vegetation, and the importance of input variables was quantitatively evaluated. The results showed that the UAV multispectral images assisted in the acquisition of marsh vegetation CCC sample data, as the method expanded the number of CCC samples while quantifying the CCC sample data collection accuracy [ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ≥ 0.86, root mean square error (RMSE) ≤ 6.98 SPAD], which improved the CCC inversion accuracy compared with traditional sampling methods. Extracting pure vegetation pixels through binary classification reduces the uncertainty of the UAV-scale CCC inversion results. Parameter optimization of the RF regression model further improves the CCC inversion accuracy at GF-1 WFV, Landsat-8 OLI, and Sentinel-2 MSI scales. Among the three satellite remote sensing data, Sentinel-2 MSI achieved the highest CCC inversion accuracy for marsh vegetation ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.79, RMSE = 10.96 SPAD) due to the inclusion of red-edge bands that are more sensitive to vegetation properties. Red-edge Chlorophyll Index (Cl <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">red-edge</sub> ) and Green Chlorophyll Index (Cl <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">green</sub> ) have the highest influence on the CCC inversion accuracy among input variables.