Remote Sensing

18 pages, 8898 KiB

Open AccessArticle

Evaluating Machine Learning and Geostatistical Methods for Spatial Gap-Filling of Monthly ESA CCI Soil Moisture in China

by Hao Sun and Qian Xu

Remote Sens. 2021, 13(14), 2848; https://doi.org/10.3390/rs13142848 - 20 Jul 2021

Cited by 11 | Viewed by 3210

Abstract

Obtaining large-scale, long-term, and spatial continuous soil moisture (SM) data is crucial for climate change, hydrology, and water resource management, etc. ESA CCI SM is such a large-scale and long-term SM (longer than 40 years until now). However, there exist data gaps, especially [...] Read more.

Obtaining large-scale, long-term, and spatial continuous soil moisture (SM) data is crucial for climate change, hydrology, and water resource management, etc. ESA CCI SM is such a large-scale and long-term SM (longer than 40 years until now). However, there exist data gaps, especially for the area of China, due to the limitations in remote sensing of SM such as complex topography, human-induced radio frequency interference (RFI), and vegetation disturbances, etc. The data gaps make the CCI SM data cannot achieve spatial continuity, which entails the study of gap-filling methods. In order to develop suitable methods to fill the gaps of CCI SM in the whole area of China, we compared typical Machine Learning (ML) methods, including Random Forest method (RF), Feedforward Neural Network method (FNN), and Generalized Linear Model (GLM) with a geostatistical method, i.e., Ordinary Kriging (OK) in this study. More than 30 years of passive–active combined CCI SM from 1982 to 2018 and other biophysical variables such as Normalized Difference Vegetation Index (NDVI), precipitation, air temperature, Digital Elevation Model (DEM), soil type, and in situ SM from International Soil Moisture Network (ISMN) were utilized in this study. Results indicated that: (1) the data gap of CCI SM is frequent in China, which is found not only in cold seasons and areas but also in warm seasons and areas. The ratio of gap pixel numbers to the whole pixel numbers can be greater than 80%, and its average is around 40%. (2) ML methods can fill the gaps of CCI SM all up. Among the ML methods, RF had the best performance in fitting the relationship between CCI SM and biophysical variables. (3) Over simulated gap areas, RF had a comparable performance with OK, and they outperformed the FNN and GLM methods greatly. (4) Over in situ SM networks, RF achieved better performance than the OK method. (5) We also explored various strategies for gap-filling CCI SM. Results demonstrated that the strategy of constructing a monthly model with one RF for simulating monthly average SM and another RF for simulating monthly SM disturbance achieved the best performance. Such strategy combining with the ML method such as the RF is suggested in this study for filling the gaps of CCI SM in China. Full article

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26 pages, 6939 KiB

Open AccessProject Report

Results of the Dragon 4 Project on New Ocean Remote Sensing Data for Operational Applications

by Ferran Gibert, Jacqueline Boutin, Wolfgang Dierking, Alba Granados, Yan Li, Eduard Makhoul, Junmin Meng, Alexandre Supply, Ester Vendrell, Jean-Luc Vergely, Jin Wang, Jungang Yang, Kunsheng Xiang, Xiaobin Yin and Xi Zhang

Remote Sens. 2021, 13(14), 2847; https://doi.org/10.3390/rs13142847 - 20 Jul 2021

Cited by 2 | Viewed by 3379

Abstract

This paper provides an overview of the Dragon 4 project dealing with operational monitoring of sea ice and sea surface salinity (SSS) and new product developments for altimetry data. To improve sea ice thickness retrieval, a new method was developed to match the [...] Read more.

This paper provides an overview of the Dragon 4 project dealing with operational monitoring of sea ice and sea surface salinity (SSS) and new product developments for altimetry data. To improve sea ice thickness retrieval, a new method was developed to match the Cryosat-2 radar waveform. Additionally, an automated sea ice drift detection scheme was developed and tested on Sentinel-1 data, and the sea ice drifty capability of Gaofen-4 geostationary optical data was evaluated. A second topic included implementation and validation of a prototype of a Fully-Focussed SAR processor adapted for Sentinel-3 and Sentinel-6 altimeters and evaluation of its performance with Sentinel-3 data over the Yellow Sea; the assessment of sea surface height (SSH), significant wave height (SWH), and wind speed measurements using different altimeters and CFOSAT SWIM; and the fusion of SSH measurements in mapping sea level anomaly (SLA) data to detect mesoscale eddies. Thirdly, the investigations on the retrieval of SSS include simulations to analyse the performances of the Chinese payload configurations of the Interferometric Microwave Radiometer and the Microwave Imager Combined Active and Passive, SSS retrieval under rain conditions, and the combination of active and passive microwave to study extreme winds. Full article

(This article belongs to the Special Issue ESA - NRSCC Cooperation Dragon 4 Final Results)

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17 pages, 5223 KiB

Open AccessArticle

Retrieving Doppler Frequency via Local Correlation Method of Segmented Modeling

by Lue Chen, Jinsong Ping, Jianfeng Cao, Xiang Liu, Na Wang, Zhen Wang, Ping Zhu, Mei Wang, Haijun Man, Fei Fan, Weitao Lu, Jing Sun and Songtao Han

Remote Sens. 2021, 13(14), 2846; https://doi.org/10.3390/rs13142846 - 20 Jul 2021

Cited by 7 | Viewed by 2369

Abstract

The high accuracy radio Doppler frequency is critical for navigating a deep space probe and for planetary radio science experiments. In this paper, we propose a novel method based on the local correlation of segmented modeling to retrieve Doppler frequency by processing an [...] Read more.

The high accuracy radio Doppler frequency is critical for navigating a deep space probe and for planetary radio science experiments. In this paper, we propose a novel method based on the local correlation of segmented modeling to retrieve Doppler frequency by processing an open-loop radio link signal from one single ground station. Simulations are implemented, which prove the validity of this method. Mars Express (MEX) and Tianwen-1 observation experiments were carried out by Chinese Deep Space Stations (CDSS). X-band Doppler frequency observables were retrieved by the proposed method to participate in orbit determination. The results show that the accuracy of velocity residuals of orbit determination in open-loop mode is from 0.043 mm/s to 0.061 mm/s in 1 s integration; the average accuracy of Doppler frequency is about 3.3 mHz in 1 s integration and about 0.73 mHz in 60 s integration. The Doppler accuracy here is better than that of the digital baseband receiver at CDSS. The algorithm is efficient and flexible when the deep space probe is in a high dynamic mode and in low signal to noise ratio (SNR). This will benefit Chinese deep space exploration missions and planetary radio science experiments. Full article

(This article belongs to the Special Issue Methods of Precise Orbit Determination and Autonomous Navigation for Interplanetary Space Probes)

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11 pages, 9958 KiB

Open AccessArticle

Shallow Shear-Wave Velocity Structure beneath the West Lake Area in Hangzhou, China, from Ambient-Noise Tomography

by Zhongen Chen, Xuewei Bao and Wencai Yang

Remote Sens. 2021, 13(14), 2845; https://doi.org/10.3390/rs13142845 - 20 Jul 2021

Cited by 3 | Viewed by 2666

Abstract

Urban geophysical exploration plays an important role in the sustainable development of and the mitigation of geological hazards in metropolitan areas. However, it is not suitable to implement active seismic methods in densely populated urban areas. The rapidly developing ambient-noise tomography (ANT) method [...] Read more.

Urban geophysical exploration plays an important role in the sustainable development of and the mitigation of geological hazards in metropolitan areas. However, it is not suitable to implement active seismic methods in densely populated urban areas. The rapidly developing ambient-noise tomography (ANT) method is a promising technique for imaging the near-surface seismic velocity structure. We selected the West Lake area of the city of Hangzhou as a case study to probe the shallow subsurface shear-wave velocity (Vs) structure using ANT. We conducted seismic interferometry on the ambient-noise data recorded by 28 seismograph stations during a time period of 17 days. Fundamental-mode Rayleigh-wave group- and phase-velocity dispersion data were measured from cross-correlation functions and then inverted for a 3D Vs model of the uppermost 1 km that covers an area of about 7 km × 8 km. The tomographic results reveal two prominent anomalies, with high velocities in the southwest and low velocities in the northeast. The fast anomaly corresponds to the presence of limestone and sandstone, whereas the slow anomaly is due to the relatively low-velocity rhyolite and volcanic tuff in the area. The boundary between the two anomalies lies to the NE of an NW–SE trending fault, indicating that the fault dips toward the NE. In addition, the pronounced low-velocity anomalies appear under the Baoshi mountain, likely due to the thick rhyolite and volcanic tuff beneath the extinct volcano. Our results correlate well with regional geological features and suggest that ANT could be a promising technique for facilitating the exploration of urban underground space. Full article

(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)

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15 pages, 6141 KiB

Open AccessArticle

Improving CyGNSS-Based Land Remote Sensing: Track-Wise Data Calibration Schemes

by Qingyun Yan, Ting Hu, Shuanggen Jin, Weimin Huang, Yan Jia, Tiexi Chen and Jian Wang

Remote Sens. 2021, 13(14), 2844; https://doi.org/10.3390/rs13142844 - 20 Jul 2021

Cited by 4 | Viewed by 2807

Abstract

Cyclone Global Navigation Satellite System (CyGNSS) data have been used for generating several intermediate products, such as surface reflectivity (

Γ

), to facilitate a wide variety of land remote sensing applications. The accuracy of

Γ

relies on precise knowledge of the effective [...] Read more.

Cyclone Global Navigation Satellite System (CyGNSS) data have been used for generating several intermediate products, such as surface reflectivity (

Γ

), to facilitate a wide variety of land remote sensing applications. The accuracy of

Γ

relies on precise knowledge of the effective instantaneous radiative power (EIRP) of the transmitted GNSS signals in the direction of the specular reflection point, the precise knowledge of zenith antenna patterns which in turn affects estimates of EIRP, the good knowledge of receive antenna patterns etc. However, obtaining accurate estimates on these parameters completely is still a challenge. To solve this problem, in this paper, an effective method is proposed for calibrating the CyGNSS

Γ

product in a track-wise manner. Here, two different criteria for selecting data to calibrate and three reference options as targets of the calibrating data are examined. Accordingly, six calibration schemes corresponding to six different combinations are implemented and the resulting

Γ

products are assessed by (1) visual inspection and (2) evaluation of their associated soil moisture retrieval results. Both visual inspection and retrieval validation demonstrate the effectiveness of the proposed schemes, which are respectively demonstrated by the immediate removal/fix of track-wisely noisy data and obvious enhancement of retrieval accuracy with the calibrated

Γ

. Moreover, the schemes are tested using all the available CyGNSS level 1 version 3.0 data and the good results obtained from such a large volume of data further illustrate their robustness. This work provides an effective and robust way to calibrate the CyGNSS

Γ

result, which will further improve relevant remote sensing applications in the future. Full article

(This article belongs to the Special Issue GNSS-Reflectometry and Remote Sensing of Soil Moisture)

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14 pages, 9432 KiB

Open AccessArticle

Pre-Launch Radiometric Characterization of EMI-2 on the GaoFen-5 Series of Satellites

by Minjie Zhao, Fuqi Si, Haijin Zhou, Yu Jiang, Chunyan Ji, Shimei Wang, Kai Zhan and Wenqing Liu

Remote Sens. 2021, 13(14), 2843; https://doi.org/10.3390/rs13142843 - 20 Jul 2021

Cited by 10 | Viewed by 2795

Abstract

The environmental trace gas monitoring instrument (EMI) is a space-borne imaging spectrometer onboard GaoFen-5, which was launched in May 2018, covering wavelengths in the range of 240–710 nm to measure NO₂, O₃, HCHO, and SO₂. An advanced [...] Read more.

The environmental trace gas monitoring instrument (EMI) is a space-borne imaging spectrometer onboard GaoFen-5, which was launched in May 2018, covering wavelengths in the range of 240–710 nm to measure NO₂, O₃, HCHO, and SO₂. An advanced EMI-2 instrument with a higher spatial resolution and sufficient signal-to-noise is currently planned for launch on the GaoFen-5(02) satellite in 2021. The EMI-2 instrument bidirectional scattering distribution function (BSDF) is obtained from the absolute irradiance and radiance calibration on-ground. Based on EMI-2 earth and sun optical paths, the key factors of BSDF parameters are introduced. An NIST-calibrated 1000 W FEL quartz tungsten halogen lamp and a 2D turntable are adopted for the absolute irradiance calibration. A large aperture integrating sphere system is used for the absolute radiance calibration. Based on absolute irradiance and radiance calibration functions, the BSDF parameters are obtained, with accuracy of 4.9% for UV1, 4.3% for UV2, 4.1% for VIS1, and 4.2% for VIS2. The on-ground measurement results show that the reflectance spectrum can be calculated from BSDF parameters. On-orbit application of the EMI-2 instrument BSDF are also discussed. Full article

(This article belongs to the Section Atmospheric Remote Sensing)

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30 pages, 7884 KiB

Open AccessArticle

Identification of Aerosol Pollution Hotspots in Jiangsu Province of China

by Yu Wang, Md. Arfan Ali, Muhammad Bilal, Zhongfeng Qiu, Song Ke, Mansour Almazroui, Md. Monirul Islam and Yuanzhi Zhang

Remote Sens. 2021, 13(14), 2842; https://doi.org/10.3390/rs13142842 - 20 Jul 2021

Cited by 16 | Viewed by 4214

Abstract

Aerosol optical depth (AOD) is an important atmospheric parameter for climate change assessment, human health, and for total ecological situation studies both regionally and globally. This study used 21-year (2000–2020) high-resolution (1 km) Multiangle Implementation of Atmospheric Correction (MAIAC) algorithm-based AOD from the [...] Read more.

Aerosol optical depth (AOD) is an important atmospheric parameter for climate change assessment, human health, and for total ecological situation studies both regionally and globally. This study used 21-year (2000–2020) high-resolution (1 km) Multiangle Implementation of Atmospheric Correction (MAIAC) algorithm-based AOD from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the Terra and Aqua satellites. MAIAC AOD was evaluated against Aerosol Robotic Network (AERONET) data across three sites (Xuzhou-CUMT, NUIST, and Taihu) located in Jiangsu Province. The study also investigated the spatiotemporal distributions and variations in AOD, with associated trends, and measured the impact of meteorology on AOD in the 13 cities of Jiangsu Province. The evaluation results demonstrated a high correlation (r = 0.867~0.929) between MAIAC AOD and AERONET data, with lower root mean squared error (RMSE = 0.130~0.287) and mean absolute error (MAE = 0.091~0.198). In addition, the spatial distribution of AOD was higher (>0.60) in most cities except the southeast of Nantong City (AOD < 0.4). Seasonally, higher AOD was seen in summer (>0.70) than in spring, autumn, and winter, whereas monthly AOD peaked in June (>0.9) and had a minimum in December (<0.4) for all the cities. Frequencies of 0.3 ≤ AOD < 0.4 and 0.4 ≤ AOD < 0.5 were relatively common, indicating a turbid atmosphere, which may be associated with anthropogenic activities, increased emissions, and changes in meteorological circumstances. Trend analysis showed significant increases in AOD during 2000–2009 for all the cities, perhaps reflecting a booming economy and industrial development, with significant emissions of sulfur dioxide (SO₂), and primary aerosols. China’s strict air pollution control policies and control of vehicular emissions helped to decrease AOD from 2010 to 2019, enhancing air quality throughout the study area. A notably similar pattern was observed for AOD and meteorological parameters (LST: land surface temperature, WV: water vapor, and P: precipitation), signifying that meteorology plays a role in terms of increasing and decreasing AOD. Full article

(This article belongs to the Special Issue High Resolution Active Optical Remote Sensing Observations of Aerosols, Clouds and Aerosol-Cloud Interactions and Their Implication to Climate II)

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25 pages, 5443 KiB

Open AccessArticle

Non-Parametric Statistical Approaches for Leaf Area Index Estimation from Sentinel-2 Data: A Multi-Crop Assessment

by Margherita De Peppo, Andrea Taramelli, Mirco Boschetti, Alberto Mantino, Iride Volpi, Federico Filipponi, Antonella Tornato, Emiliana Valentini and Giorgio Ragaglini

Remote Sens. 2021, 13(14), 2841; https://doi.org/10.3390/rs13142841 - 20 Jul 2021

Cited by 15 | Viewed by 3173

Abstract

The leaf area index (LAI) is a key biophysical variable for agroecosystem monitoring, as well as a relevant state variable in crop modelling. For this reason, temporal and spatial determination of LAI are required to improve the understanding of several land surface processes [...] Read more.

The leaf area index (LAI) is a key biophysical variable for agroecosystem monitoring, as well as a relevant state variable in crop modelling. For this reason, temporal and spatial determination of LAI are required to improve the understanding of several land surface processes related to vegetation dynamics and crop growth. Despite the large number of retrieved LAI products and the efforts to develop new and updated algorithms for LAI estimation, the available products are not yet capable of capturing site-specific variability, as requested in many agricultural applications. The objective of this study was to evaluate the potential of non-parametric approaches for multi-temporal LAI retrieval by Sentinel-2 multispectral data, in comparison with a VI-based parametric approach. For this purpose, we built a large database combining a multispectral satellite data set and ground LAI measurements collected over two growing seasons (2018 and 2019), including three crops (i.e., winter wheat, maize, and alfalfa) characterized by different growing cycles and canopy structures, and considering different agronomic conditions (i.e., at three farms in three different sites). The accuracy of parametric and non-parametric methods for LAI estimation was assessed by cross-validation (CV) at both the pixel and field levels over mixed-crop (MC) and crop-specific (CS) data sets. Overall, the non-parametric approach showed a higher accuracy of prediction at pixel level than parametric methods, and it was also observed that Gaussian Process Regression (GPR) did not provide any significant difference (p-value > 0.05) between the predicted values of LAI in the MC and CS data sets, regardless of the crop. Indeed, GPR at the field level showed a cross-validated coefficient of determination (R²_CV) higher than 0.80 for all three crops. Full article

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20 pages, 103083 KiB

Open AccessArticle

Exploring the Impacts and Temporal Variations of Different Building Roof Types on Surface Urban Heat Island

by Yingbin Deng, Renrong Chen, Yichun Xie, Jianhui Xu, Ji Yang and Wenyue Liao

Remote Sens. 2021, 13(14), 2840; https://doi.org/10.3390/rs13142840 - 20 Jul 2021

Cited by 9 | Viewed by 3987

Abstract

This study examined the impact of different types of building roofs on urban heat islands. This was carried out using building roof data from remotely sensed Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) imagery. The roofs captured included white [...] Read more.

This study examined the impact of different types of building roofs on urban heat islands. This was carried out using building roof data from remotely sensed Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) imagery. The roofs captured included white surface, blue steel, dark metal, other dark material, and residential roofs; these roofs were compared alongside three natural land covers (i.e., forest trees, grassland, and water). We also collected ancillary data including building height, building density, and distance to the city center. The impacts of various building roofs on land surface temperature (LST) were examined by analyzing their correlation and temporal variations. First, we examined the LST characteristics of five building roof types and three natural land covers using boxplots and variance analysis with post hoc tests. Then, multivariate regression analysis was used to explore the impact of building roofs on LST. There were three key findings in the results. First, the mean LSTs for five different building roofs statistically differed from each other; these differences were more significant during the hot season than the cool season. Second, the impact of the five types of roofs on LSTs varied considerably from each other. Lastly, the contribution of the five roof types to LST variance was more substantial during the cool season. These findings unveil specific urban heat retention drivers, in which different types of building roofs are one such driver. The outcomes from this research may help policymakers develop more effective strategies to address the surface urban heat island phenomenon and its related health concerns. Full article

(This article belongs to the Special Issue Remote Sensing of Urban Impervious Surfaces: Mapping, Monitoring, and Modeling the Dynamics of Urban Impervious Surfaces with Multisource Remote Sensing Data)

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13 pages, 1202 KiB

Open AccessTechnical Note

On the Geomagnetic Field Line Resonance Eigenfrequency Variations during Seismic Event

by Mirko Piersanti, William Jerome Burger, Vincenzo Carbone, Roberto Battiston, Roberto Iuppa and Pietro Ubertini

Remote Sens. 2021, 13(14), 2839; https://doi.org/10.3390/rs13142839 - 19 Jul 2021

Cited by 2 | Viewed by 2651

Abstract

In this paper, we report high statistical evidence for a seismo–ionosphere effects occurring in conjunction with an earthquake. This finding supports a lithosphere-magnetosphere coupling mechanism producing a plasma density variation along the magnetic field lines, mechanically produced by atmospheric acoustic gravity waves (AGWs) [...] Read more.

In this paper, we report high statistical evidence for a seismo–ionosphere effects occurring in conjunction with an earthquake. This finding supports a lithosphere-magnetosphere coupling mechanism producing a plasma density variation along the magnetic field lines, mechanically produced by atmospheric acoustic gravity waves (AGWs) impinging the ionosphere. We have analysed a large sample of earthquakes (EQ) using ground magnetometers data: in 28 of 42 analysed case events, we detect a temporary stepwise decrease (

Δ f

) of the magnetospheric field line resonance (FLR) eigenfrequency (

f^{*}

).

Δ f

decreases of ∼5–25 mHz during ∼20–35 min following the time of the EQ. We present an analytical model for

f^{*}

, able to reproduce the behaviour observed during the EQ. Our work is in agreement with recent results confirming co-seismic direct coupling between lithosphere, ionosphere and magnetosphere opening the way to new remote sensing methods, from space/ground, of the earth seismic activity. Full article

(This article belongs to the Special Issue Remote Sensing for Natural Hazards Assessment and Control)

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21 pages, 707 KiB

Open AccessReview

A Review of Reconstructing Remotely Sensed Land Surface Temperature under Cloudy Conditions

by Yaping Mo, Yongming Xu, Huijuan Chen and Shanyou Zhu

Remote Sens. 2021, 13(14), 2838; https://doi.org/10.3390/rs13142838 - 19 Jul 2021

Cited by 44 | Viewed by 5610

Abstract

Land surface temperature (LST) is an important environmental parameter in climate change, urban heat islands, drought, public health, and other fields. Thermal infrared (TIR) remote sensing is the main method used to obtain LST information over large spatial scales. However, cloud cover results [...] Read more.

Land surface temperature (LST) is an important environmental parameter in climate change, urban heat islands, drought, public health, and other fields. Thermal infrared (TIR) remote sensing is the main method used to obtain LST information over large spatial scales. However, cloud cover results in many data gaps in remotely sensed LST datasets, greatly limiting their practical applications. Many studies have sought to fill these data gaps and reconstruct cloud-free LST datasets over the last few decades. This paper reviews the progress of LST reconstruction research. A bibliometric analysis is conducted to provide a brief overview of the papers published in this field. The existing reconstruction algorithms can be grouped into five categories: spatial gap-filling methods, temporal gap-filling methods, spatiotemporal gap-filling methods, multi-source fusion-based gap-filling methods, and surface energy balance-based gap-filling methods. The principles, advantages, and limitations of these methods are described and discussed. The applications of these methods are also outlined. In addition, the validation of filled LST values’ cloudy pixels is an important concern in LST reconstruction. The different validation methods applied for reconstructed LST datasets are also reviewed herein. Finally, prospects for future developments in LST reconstruction are provided. Full article

(This article belongs to the Special Issue Land Surface Temperature Estimation Using Remote Sensing)

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43 pages, 20227 KiB

Open AccessFeature PaperReview

Deep Learning in Forestry Using UAV-Acquired RGB Data: A Practical Review

by Yago Diez, Sarah Kentsch, Motohisa Fukuda, Maximo Larry Lopez Caceres, Koma Moritake and Mariano Cabezas

Remote Sens. 2021, 13(14), 2837; https://doi.org/10.3390/rs13142837 - 19 Jul 2021

Cited by 83 | Viewed by 12325

Abstract

Forests are the planet’s main CO

_{2}

filtering agent as well as important economical, environmental and social assets. Climate change is exerting an increased stress, resulting in a need for improved research methodologies to study their health, composition or evolution. Traditionally, information about [...] Read more.

Forests are the planet’s main CO

_{2}

filtering agent as well as important economical, environmental and social assets. Climate change is exerting an increased stress, resulting in a need for improved research methodologies to study their health, composition or evolution. Traditionally, information about forests has been collected using expensive and work-intensive field inventories, but in recent years unoccupied autonomous vehicles (UAVs) have become very popular as they represent a simple and inexpensive way to gather high resolution data of large forested areas. In addition to this trend, deep learning (DL) has also been gaining much attention in the field of forestry as a way to include the knowledge of forestry experts into automatic software pipelines tackling problems such as tree detection or tree health/species classification. Among the many sensors that UAVs can carry, RGB cameras are fast, cost-effective and allow for straightforward data interpretation. This has resulted in a large increase in the amount of UAV-acquired RGB data available for forest studies. In this review, we focus on studies that use DL and RGB images gathered by UAVs to solve practical forestry research problems. We summarize the existing studies, provide a detailed analysis of their strengths paired with a critical assessment on common methodological problems and include other information, such as available public data and code resources that we believe can be useful for researchers that want to start working in this area. We structure our discussion using three main families of forestry problems: (1) individual Tree Detection, (2) tree Species Classification, and (3) forest Anomaly Detection (forest fires and insect Infestation). Full article

(This article belongs to the Special Issue Feature Paper Special Issue on Forest Remote Sensing)

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20 pages, 10194 KiB

Open AccessArticle

Remote Sensing of Aerated Flows at Large Dams: Proof of Concept

by Matthias Kramer and Stefan Felder

Remote Sens. 2021, 13(14), 2836; https://doi.org/10.3390/rs13142836 - 19 Jul 2021

Cited by 10 | Viewed by 3059

Abstract

Dams are important for flood mitigation, water supply, and hydroelectricity. Every dam has a water conveyance structure, such as a spillway, to safely release extreme floods when needed. The flows down spillways are often self-aerated and spillway design has typically been investigated in [...] Read more.

Dams are important for flood mitigation, water supply, and hydroelectricity. Every dam has a water conveyance structure, such as a spillway, to safely release extreme floods when needed. The flows down spillways are often self-aerated and spillway design has typically been investigated in laboratory experiments, which is due to limitations in suitable full scale flow measurement instrumentation and safety considerations. Prototype measurements of aerated flows are urgently needed to quantify potential scale effects and to provide missing validation data for design guidelines and numerical simulations. Herein, an image-based analysis of free-surface flows on a stepped spillway was conducted from a top-view perspective at laboratory scale (fixed camera installation) and prototype scale (drone footage). The drone videos were obtained from citizen science data. Analyses allowed to remotely estimate the location of the inception point of free-surface aeration, air–water surface velocities, and their fluctuations, as well as the residual energy at the downstream end of the chute. The laboratory results were successfully validated against intrusive phase-detection probe data, while the prototype observations provided proof of concept at full scale. This study highlights the feasibility of image-based measurements at prototype spillways. It demonstrates how citizen science data can be used to advance our understanding of real world air–water flow processes and lays the foundations for the remote collection of long-missing prototype data. Full article

(This article belongs to the Special Issue Advances in Remote Sensing in Coastal and Hydraulic Engineering)

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34 pages, 33923 KiB

Open AccessEditor’s ChoiceArticle

Improvement of a Dasymetric Method for Implementing Sustainable Development Goal 11 Indicators at an Intra-Urban Scale

by Mariella Aquilino, Maria Adamo, Palma Blonda, Angela Barbanente and Cristina Tarantino

Remote Sens. 2021, 13(14), 2835; https://doi.org/10.3390/rs13142835 - 19 Jul 2021

Cited by 12 | Viewed by 4044

Abstract

Local and Regional Authorities require indicators at the intra-urban scale to design adequate policies to foster the achievement of the objectives of Sustainable Development Goal (SDG) 11. Updated high-resolution population density and settlement maps are the basic input products for such indicators and [...] Read more.

Local and Regional Authorities require indicators at the intra-urban scale to design adequate policies to foster the achievement of the objectives of Sustainable Development Goal (SDG) 11. Updated high-resolution population density and settlement maps are the basic input products for such indicators and their sub-indicators. When provided at the intra-urban scale, these essential variables can facilitate the extraction of population flows, including both local and regular migrant components. This paper discusses a modification of the dasymetric method implemented in our previous work, aimed at improving the population density estimation. The novelties of our paper include the introduction of building height information and site-specific weight values for population density correction. Based on the proposed improvements, selected indicators/sub-indicators of four SDG 11 targets were updated or newly implemented. The output density map error values are provided in terms of the mean absolute error, root mean square error and mean absolute percentage indicators. The values obtained (i.e., 2.3 and 4.1 people, and 8.6%, respectively) were lower than those of the previous dasymetric method. The findings suggest that the new methodology can provide updated information about population fluxes and processes occurring over the period 2011–2020 in the study site—Bari city in southern Italy. Full article

(This article belongs to the Special Issue Earth Observations for Sustainable Development Goals)

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17 pages, 10448 KiB

Open AccessArticle

Rigidity Strengthening of Landslide Materials Measured by Seismic Interferometry

by Keng-Hao Kang, Wei-An Chao, Che-Ming Yang, Ming-Chien Chung, Yu-Ting Kuo, Chih-Hsiang Yeh, Hsin-Chang Liu, Chun-Hung Lin, Chih-Pin Lin, Jyh-Jong Liao, Jui-Ming Chang, Yin-Jeh Ngui, Chien-Hsin Chen and Tung-Lin Tai

Remote Sens. 2021, 13(14), 2834; https://doi.org/10.3390/rs13142834 - 19 Jul 2021

Cited by 4 | Viewed by 2984

Abstract

Landslides have caused extensive infrastructure damage and caused human fatalities for centuries. Intense precipitation and large earthquakes are considered to be two major landslide triggers, particularly in the case of catastrophic landslides. The most widely accepted mechanistic explanation for landslides is the effective-stress [...] Read more.

Landslides have caused extensive infrastructure damage and caused human fatalities for centuries. Intense precipitation and large earthquakes are considered to be two major landslide triggers, particularly in the case of catastrophic landslides. The most widely accepted mechanistic explanation for landslides is the effective-stress dependent shear strength reduction due to increases in pore water pressure. The Chashan landslide site, selected for the present study, has been intensively studied from geological, geophysical, geodetic, geotechnical, hydrological, and seismological perspectives. Our seismic monitoring of daily relative velocity changes (dv/v) indicated that landslide material decreases coincided with the first half of the rainy period and increased during the latter half of the rainy period. The geodetic surveys before and after the rainy period identified vertical subsidence without horizontal movement. The results from the multidisciplinary investigation enabled us to draw a conceptual model of the landslide recovery process induced by water loading. Where all sliding materials were stable (safety factor > 1.0), unconsolidated landslide colluvium and impermeable sliding surfaces trapped the seepage water to form a water tank, provided that compact forces were acting on the materials below the sliding boundary. The vertical force of compaction facilitates an increase in the cohesion and strength of landslide materials, thereby increasing the landslide materials’ stability. We demonstrated that the recovery process periodically occurs only under the combined conditions of prolonged and intense precipitation and the related stability conditions. Full article

(This article belongs to the Special Issue Advances in Seismic Interferometry)

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18 pages, 2246 KiB

Open AccessArticle

Identifying Optimal Wavelengths as Disease Signatures Using Hyperspectral Sensor and Machine Learning

by Xing Wei, Marcela A. Johnson, David B. Langston, Jr., Hillary L. Mehl and Song Li

Remote Sens. 2021, 13(14), 2833; https://doi.org/10.3390/rs13142833 - 19 Jul 2021

Cited by 26 | Viewed by 5964

Abstract

Hyperspectral sensors combined with machine learning are increasingly utilized in agricultural crop systems for diverse applications, including plant disease detection. This study was designed to identify the most important wavelengths to discriminate between healthy and diseased peanut (Arachis hypogaea L.) plants infected [...] Read more.

Hyperspectral sensors combined with machine learning are increasingly utilized in agricultural crop systems for diverse applications, including plant disease detection. This study was designed to identify the most important wavelengths to discriminate between healthy and diseased peanut (Arachis hypogaea L.) plants infected with Athelia rolfsii, the causal agent of peanut stem rot, using in-situ spectroscopy and machine learning. In greenhouse experiments, daily measurements were conducted to inspect disease symptoms visually and to collect spectral reflectance of peanut leaves on lateral stems of plants mock-inoculated and inoculated with A. rolfsii. Spectrum files were categorized into five classes based on foliar wilting symptoms. Five feature selection methods were compared to select the top 10 ranked wavelengths with and without a custom minimum distance of 20 nm. Recursive feature elimination methods outperformed the chi-square and SelectFromModel methods. Adding the minimum distance of 20 nm into the top selected wavelengths improved classification performance. Wavelengths of 501–505, 690–694, 763 and 884 nm were repeatedly selected by two or more feature selection methods. These selected wavelengths can be applied in designing optical sensors for automated stem rot detection in peanut fields. The machine-learning-based methodology can be adapted to identify spectral signatures of disease in other plant-pathogen systems. Full article

(This article belongs to the Special Issue Plant Phenotyping for Disease Detection)

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22 pages, 5727 KiB

Open AccessArticle

A Spliced Satellite Optical Camera Geometric Calibration Method Based on Inter-Chip Geometry Constraints

by Tao Wang, Yan Zhang, Yongsheng Zhang, Zhenchao Zhang, Xiongwu Xiao, Ying Yu and Longhui Wang

Remote Sens. 2021, 13(14), 2832; https://doi.org/10.3390/rs13142832 - 19 Jul 2021

Cited by 10 | Viewed by 2801

Abstract

When in orbit, spliced satellite optical cameras are affected by various factors that degrade the actual image stitching precision and the accuracy of their data products. This is a major bottleneck in the current remote sensing technology. Previous geometric calibration research has mostly [...] Read more.

When in orbit, spliced satellite optical cameras are affected by various factors that degrade the actual image stitching precision and the accuracy of their data products. This is a major bottleneck in the current remote sensing technology. Previous geometric calibration research has mostly focused on stitched satellite images and has largely ignored the inter-chip relationship among original multi-chip images, resulting in accuracy loss in geometric calibration and subsequent image products. Therefore, in this paper, a novel geometric calibration method is proposed for spliced satellite optical cameras. The integral geometric calibration model was developed on inter-chip geometry constraints among multi-chip images, including the corresponding external and internal calibration models. The proposed approach improves uncontrolled geopositioning accuracy and enhances mosaic precision at the same time. For evaluation, images from the optical butting satellite ZiYuan-3 (ZY-3) and mechanical interleaving satellite Tianhui-1 (TH-1) were used for the experiments. Multiple sets of satellite data of the Songshan Calibration field and other regions were used to evaluate the reliability, stability, and applicability of the calibration parameters. The experiment results found that the proposed method obtains reliable camera alignment angles and interior calibration parameters and generates high-precision seamless mosaic images. The calibration scheme is not only suitable for mechanical interleaving cameras with large geometric displacement among multi-chip images but is also effective for optical butting cameras with minor chip offset. It also significantly improves uncontrolled geopositioning accuracy for both types of spliced satellite images. Moreover, the proposed calibration procedure results in multi-chip satellite images being seamlessly stitched together and mosaic errors within one pixel. Full article

(This article belongs to the Special Issue Feature Papers for Remote Sensing Image Processing Section)

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20 pages, 8973 KiB

Open AccessArticle

Evaluation of Eight Global Precipitation Datasets in Hydrological Modeling

by Yiheng Xiang, Jie Chen, Lu Li, Tao Peng and Zhiyuan Yin

Remote Sens. 2021, 13(14), 2831; https://doi.org/10.3390/rs13142831 - 19 Jul 2021

Cited by 23 | Viewed by 3866

Abstract

The number of global precipitation datasets (PPs) is on the rise and they are commonly used for hydrological applications. A comprehensive evaluation on their performance in hydrological modeling is required to improve their performance. This study comprehensively evaluates the performance of eight widely [...] Read more.

The number of global precipitation datasets (PPs) is on the rise and they are commonly used for hydrological applications. A comprehensive evaluation on their performance in hydrological modeling is required to improve their performance. This study comprehensively evaluates the performance of eight widely used PPs in hydrological modeling by comparing with gauge-observed precipitation for a large number of catchments. These PPs include the Global Precipitation Climatology Centre (GPCC), Climate Hazards Group Infrared Precipitation with Station dataset (CHIRPS) V2.0, Climate Prediction Center Morphing Gauge Blended dataset (CMORPH BLD), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Climate Data Record (PERSIANN CDR), Tropical Rainfall Measuring Mission multi-satellite Precipitation Analysis 3B42RT (TMPA 3B42RT), Multi-Source Weighted-Ensemble Precipitation (MSWEP V2.0), European Center for Medium-range Weather Forecast Reanalysis 5 (ERA5) and WATCH Forcing Data methodology applied to ERA-Interim Data (WFDEI). Specifically, the evaluation is conducted over 1382 catchments in China, Europe and North America for the 1998-2015 period at a daily temporal scale. The reliabilities of PPs in hydrological modeling are evaluated with a calibrated hydrological model using rain gauge observations. The effectiveness of PPs-specific calibration and bias correction in hydrological modeling performances are also investigated for all PPs. The results show that: (1) compared with the rain gauge observations, GPCC provides the best performance overall, followed by MSWEP V2.0; (2) among the eight PPs, the ones incorporating daily gauge data (MSWEP V2.0 and CMORPH BLD) provide superior hydrological performance, followed by those incorporating 5-day (CHIRPS V2.0) and monthly (TMPA 3B42RT, WFDEI, and PERSIANN CDR) gauge data. MSWEP V2.0 and CMORPH BLD perform better than GPCC, underscoring the effectiveness of merging multiple satellite and reanalysis datasets; (3) regionally, all PPs exhibit better performances in temperate regions than in arid or topographically complex mountainous regions; and (4) PPs-specific calibration and bias correction both can improve the streamflow simulations for all eight PPs in terms of the Nash and Sutcliffe efficiency and the absolute bias. This study provides insights on the reliabilities of PPs in hydrological modeling and the approaches to improve their performance, which is expected to provide a reference for the applications of global precipitation datasets. Full article

(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)

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20 pages, 5222 KiB

Open AccessArticle

Monitoring and Mapping Vineyard Water Status Using Non-Invasive Technologies by a Ground Robot

by Juan Fernández-Novales, Verónica Saiz-Rubio, Ignacio Barrio, Francisco Rovira-Más, Andrés Cuenca-Cuenca, Fernando Santos Alves, Joana Valente, Javier Tardaguila and María Paz Diago

Remote Sens. 2021, 13(14), 2830; https://doi.org/10.3390/rs13142830 - 19 Jul 2021

Cited by 26 | Viewed by 4150

Abstract

There is a growing need to provide support and applicable tools to farmers and the agro-industry in order to move from their traditional water status monitoring and high-water-demand cropping and irrigation practices to modern, more precise, reduced-demand systems and technologies. In precision viticulture, [...] Read more.

There is a growing need to provide support and applicable tools to farmers and the agro-industry in order to move from their traditional water status monitoring and high-water-demand cropping and irrigation practices to modern, more precise, reduced-demand systems and technologies. In precision viticulture, very few approaches with ground robots have served as moving platforms for carrying non-invasive sensors to deliver field maps that help growers in decision making. The goal of this work is to demonstrate the capability of the VineScout (developed in the context of a H2020 EU project), a ground robot designed to assess and map vineyard water status using thermal infrared radiometry in commercial vineyards. The trials were carried out in Douro Superior (Portugal) under different irrigation treatments during seasons 2019 and 2020. Grapevines of Vitis vinifera L. Touriga Nacional were monitored at different timings of the day using leaf water potential (Ψ_l) as reference indicators of plant water status. Grapevines’ canopy temperature (T_c) values, recorded with an infrared radiometer, as well as data acquired with an environmental sensor (T_air, RH, and AP) and NDVI measurements collected with a multispectral sensor were automatically saved in the computer of the autonomous robot to assess and map the spatial variability of a commercial vineyard water status. Calibration and prediction models were performed using Partial Least Squares (PLS) regression. The best prediction models for grapevine water status yielded a determination coefficient of cross-validation (r²_cv) of 0.57 in the morning time and a r²_cv of 0.42 in the midday. The root mean square error of cross-validation (RMSE_cv) was 0.191 MPa and 0.139 MPa at morning and midday, respectively. Spatial–temporal variation maps were developed at two different times of the day to illustrate the capability to monitor the grapevine water status in order to reduce the consumption of water, implementing appropriate irrigation strategies and increase the efficiency in the real time vineyard management. The promising outcomes gathered with the VineScout using different sensors based on thermography, multispectral imaging and environmental data disclose the need for further studies considering new variables related with the plant water status, and more grapevine cultivars, seasons and locations to improve the accuracy, robustness and reliability of the predictive models, in the context of precision and sustainable viticulture. Full article

(This article belongs to the Special Issue Digital Agriculture with Remote Sensing)

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18 pages, 3442 KiB

Open AccessArticle

Characterizing the Relationship between the Sediment Grain Size and the Shoreline Variability Defined from Sentinel-2 Derived Shorelines

by Carlos Cabezas-Rabadán, Josep E. Pardo-Pascual and Jesus Palomar-Vázquez

Remote Sens. 2021, 13(14), 2829; https://doi.org/10.3390/rs13142829 - 19 Jul 2021

Cited by 16 | Viewed by 4823

Abstract

Sediment grain size is a fundamental parameter conditioning beach-face morphology and shoreline changes. From remote sensing data, an efficient definition of the shoreline position as the water–land interface may allow studying the geomorphological characteristics of the beaches. In this work, shoreline variability is [...] Read more.

Sediment grain size is a fundamental parameter conditioning beach-face morphology and shoreline changes. From remote sensing data, an efficient definition of the shoreline position as the water–land interface may allow studying the geomorphological characteristics of the beaches. In this work, shoreline variability is defined by extracting a set of Satellite Derived Shorelines (SDS) covering about three and a half years. SDS are defined from Sentinel 2 imagery with high accuracy (about 3 m RMSE) using SHOREX. The variability is related to a large dataset of grain-size samples from the micro-tidal beaches at the Gulf of Valencia (Western Mediterranean). Both parameters present an inverse and non-linear relationship probably controlled by the beach-face slope. High shoreline variability appears associated with fine sands, followed by a rapid decrease (shifting point about medium/coarse sand) and subsequent small depletions as grain sizes increases. The relationship between both parameters is accurately described by a numerical function (R² about 0.70) when considering samples at 137 open beaches. The definition of the variability is addressed employing different proxies, coastal segment lengths, and quantity of SDS under diverse oceanographic conditions, allowing to examine the effect they have on the relation with the sediment size. The relationship explored in this work improves the understanding of the mutual connection between sediment size, beach-face slope, and shoreline variability, and it may set up the basis for a rough estimation of sediment grain size from satellite optical imagery. Full article

(This article belongs to the Special Issue Satellite Mapping and Monitoring of the Coastal Zone)

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15 pages, 3731 KiB

Open AccessCommunication

Gap-Free LST Generation for MODIS/Terra LST Product Using a Random Forest-Based Reconstruction Method

by Yao Xiao, Wei Zhao, Mingguo Ma and Kunlong He

Remote Sens. 2021, 13(14), 2828; https://doi.org/10.3390/rs13142828 - 19 Jul 2021

Cited by 22 | Viewed by 3479

Abstract

Land surface temperature (LST) is a crucial input parameter in the study of land surface water and energy budgets at local and global scales. Because of cloud obstruction, there are many gaps in thermal infrared remote sensing LST products. To fill these gaps, [...] Read more.

Land surface temperature (LST) is a crucial input parameter in the study of land surface water and energy budgets at local and global scales. Because of cloud obstruction, there are many gaps in thermal infrared remote sensing LST products. To fill these gaps, an improved LST reconstruction method for cloud-covered pixels was proposed by building a linking model for the moderate resolution imaging spectroradiometer (MODIS) LST with other surface variables with a random forest regression method. The accumulated solar radiation from sunrise to satellite overpass collected from the surface solar irradiance product of the Feng Yun-4A geostationary satellite was used to represent the impact of cloud cover on LST. With the proposed method, time-series gap-free LST products were generated for Chongqing City as an example. The visual assessment indicated that the reconstructed gap-free LST images can sufficiently capture the LST spatial pattern associated with surface topography and land cover conditions. Additionally, the validation with in situ observations revealed that the reconstructed cloud-covered LSTs have similar performance as the LSTs on clear-sky days, with the correlation coefficients of 0.92 and 0.89, respectively. The unbiased root mean squared error was 2.63 K. In general, the validation work confirmed the good performance of this approach and its good potential for regional application. Full article

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19 pages, 29895 KiB

Open AccessArticle

Comparison of Modelling Strategies to Estimate Phenotypic Values from an Unmanned Aerial Vehicle with Spectral and Temporal Vegetation Indexes

by Pengcheng Hu, Scott C. Chapman, Huidong Jin, Yan Guo and Bangyou Zheng

Remote Sens. 2021, 13(14), 2827; https://doi.org/10.3390/rs13142827 - 19 Jul 2021

Cited by 10 | Viewed by 2515

Abstract

Aboveground dry weight (AGDW) and leaf area index (LAI) are indicators of crop growth status and grain yield as affected by interactions of genotype, environment, and management. Unmanned aerial vehicle (UAV) based remote sensing provides cost-effective and non-destructive methods for the high-throughput phenotyping [...] Read more.

Aboveground dry weight (AGDW) and leaf area index (LAI) are indicators of crop growth status and grain yield as affected by interactions of genotype, environment, and management. Unmanned aerial vehicle (UAV) based remote sensing provides cost-effective and non-destructive methods for the high-throughput phenotyping of crop traits (e.g., AGDW and LAI) through the integration of UAV-derived vegetation indexes (VIs) with statistical models. However, the effects of different modelling strategies that use different dataset compositions of explanatory variables (i.e., combinations of sources and temporal combinations of the VI datasets) on estimates of AGDW and LAI have rarely been evaluated. In this study, we evaluated the effects of three sources of VIs (visible, spectral, and combined) and three types of temporal combinations of the VI datasets (mono-, multi-, and full-temporal) on estimates of AGDW and LAI. The VIs were derived from visible (RGB) and multi-spectral imageries, which were acquired by a UAV-based platform over a wheat trial at five sampling dates before flowering. Partial least squares regression models were built with different modelling strategies to estimate AGDW and LAI at each prediction date. The results showed that models built with the three sources of mono-temporal VIs obtained similar performances for estimating AGDW (RRMSE = 11.86% to 15.80% for visible, 10.25% to 16.70% for spectral, and 10.25% to 16.70% for combined VIs) and LAI (RRMSE = 13.30% to 22.56% for visible, 12.04% to 22.85% for spectral, and 13.45% to 22.85% for combined VIs) across prediction dates. Mono-temporal models built with visible VIs outperformed the other two sources of VIs in general. Models built with mono-temporal VIs generally obtained better estimates than models with multi- and full-temporal VIs. The results suggested that the use of UAV-derived visible VIs can be an alternative to multi-spectral VIs for high-throughput and in-season estimates of AGDW and LAI. The combination of modelling strategies that used mono-temporal datasets and a self-calibration method demonstrated the potential for in-season estimates of AGDW and LAI (RRMSE normally less than 15%) in breeding or agronomy trials. Full article

(This article belongs to the Special Issue Unmanned Aerial Vehicles for Photogrammetry)

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24 pages, 9759 KiB

Open AccessArticle

Vessel Target Echo Characteristics and Motion Compensation for Shipborne HFSWR under Non-Uniform Linear Motion

by Yonggang Ji, Yiming Wang, Weimin Huang, Weifeng Sun, Jie Zhang, Ming Li and Xiaoyu Cheng

Remote Sens. 2021, 13(14), 2826; https://doi.org/10.3390/rs13142826 - 19 Jul 2021

Cited by 12 | Viewed by 2507

Abstract

For shipborne high-frequency surface wave radar (HFSWR), the movement of the ship has a great impact on the radar echo, thus affecting target detection performance. In this paper, the characteristics of the target echo spectrum and the motion compensation methods for shipborne HFSWR [...] Read more.

For shipborne high-frequency surface wave radar (HFSWR), the movement of the ship has a great impact on the radar echo, thus affecting target detection performance. In this paper, the characteristics of the target echo spectrum and the motion compensation methods for shipborne HFSWR are investigated. Firstly, simulation analysis of echo from a moving target under different ship motion conditions was conducted with a focus on the frequency shift and broadening characteristics of the target echo spectrum. The simulation results show that the non-uniform linear motion and yaw of the ship will shift and broaden the target echoes, resulting in signal-to-noise ratio (SNR) reduction. When the ship velocity and yaw angle change periodically, false target echo peaks will appear in the echo spectrum, which will reduce the accuracy of target detection. To tackle this problem, a motion compensation scheme for the target echo is proposed, including the heading compensation for the effect of yaw and the velocity compensation for non-uniform movement. The influence of the velocity and yaw angle measurement accuracy on the compensation results is also analyzed. Finally, the target echo characteristics and motion compensation method of shipborne HFSWR are verified with experimental data. Full article

(This article belongs to the Section Ocean Remote Sensing)

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15 pages, 3347 KiB

Open AccessArticle

Modulation of Wind-Wave Breaking by Long Surface Waves

by Vladimir A. Dulov, Aleksandr E. Korinenko, Vladimir N. Kudryavtsev and Vladimir V. Malinovsky

Remote Sens. 2021, 13(14), 2825; https://doi.org/10.3390/rs13142825 - 18 Jul 2021

Cited by 9 | Viewed by 2600

Abstract

This paper reports the results of field measurements of wave breaking modulations by dominant surface waves, taken from the Black Sea research platform at wind speeds ranging from 10 to 20 m/s. Wave breaking events were detected by video recordings of the sea [...] Read more.

This paper reports the results of field measurements of wave breaking modulations by dominant surface waves, taken from the Black Sea research platform at wind speeds ranging from 10 to 20 m/s. Wave breaking events were detected by video recordings of the sea surface synchronized and collocated with the wave gauge measurements. As observed, the main contribution to the fraction of the sea surface covered by whitecaps comes from the breaking of short gravity waves, with phase velocities exceeding 1.25 m/s. Averaging of the wave breaking over the same phases of the dominant long surface waves (LWs, with wavelengths in the range from 32 to 69 m) revealed strong modulation of whitecaps. Wave breaking occurs mainly on the crests of LWs and disappears in their troughs. Data analysis in terms of the modulation transfer function (MTF) shows that the magnitude of the MTF is about 20, it is weakly wind-dependent, and the maximum of whitecapping is windward-shifted from the LW-crest by 15 deg. A simple model of whitecaps modulations by the long waves is suggested. This model is in quantitative agreement with the measurements and correctly reproduces the modulations’ magnitude, phase, and non-sinusoidal shape. Full article

(This article belongs to the Special Issue Passive Remote Sensing of Oceanic Whitecaps)

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19 pages, 5826 KiB

Open AccessArticle

Global Analysis of the Relationship between Reconstructed Solar-Induced Chlorophyll Fluorescence (SIF) and Gross Primary Production (GPP)

by Haiqiang Gao, Shuguang Liu, Weizhi Lu, Andrew R. Smith, Rubén Valbuena, Wende Yan, Zhao Wang, Li Xiao, Xi Peng, Qinyuan Li, Yujun Feng, Morag McDonald, Tim Pagella, Juyang Liao, Zhenming Wu and Gui Zhang

Remote Sens. 2021, 13(14), 2824; https://doi.org/10.3390/rs13142824 - 18 Jul 2021

Cited by 20 | Viewed by 4199

Abstract

Solar-induced chlorophyll fluorescence (SIF) is increasingly known as an effective proxy for plant photosynthesis, and therefore, has great potential in monitoring gross primary production (GPP). However, the relationship between SIF and GPP remains highly uncertain across space and time. Here, we analyzed the [...] Read more.

Solar-induced chlorophyll fluorescence (SIF) is increasingly known as an effective proxy for plant photosynthesis, and therefore, has great potential in monitoring gross primary production (GPP). However, the relationship between SIF and GPP remains highly uncertain across space and time. Here, we analyzed the SIF (reconstructed, SIFc)–GPP relationships and their spatiotemporal variability, using GPP estimates from FLUXNET2015 and two spatiotemporally contiguous SIFc datasets (CSIF and GOSIF). The results showed that SIFc had significant positive correlations with GPP at the spatiotemporal scales investigated (p < 0.001). The generally linear SIFc–GPP relationships were substantially affected by spatial and temporal scales and SIFc datasets. The GPP/SIFc slope of the evergreen needleleaf forest (ENF) biome was significantly higher than the slopes of several other biomes (p < 0.05), while the other 11 biomes showed no significant differences in the GPP/SIFc slope between each other (p > 0.05). Therefore, we propose a two-slope scheme to differentiate ENF from non-ENF biome and synopsize spatiotemporal variability of the GPP/SIFc slope. The relative biases were 7.14% and 11.06% in the estimated cumulative GPP across all EC towers, respectively, for GOSIF and CSIF using a two-slope scheme. The significantly higher GPP/SIFc slopes of the ENF biome in the two-slope scheme are intriguing and deserve further study. In addition, there was still considerable dispersion in the comparisons of CSIF/GOSIF and GPP at both site and biome levels, calling for discriminatory analysis backed by higher spatial resolution to systematically address issues related to landscape heterogeneity and mismatch between SIFc pixel and the footprints of flux towers and their impacts on the SIF–GPP relationship. Full article

(This article belongs to the Section Earth Observation Data)

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16 pages, 11099 KiB

Open AccessTechnical Note

Seasonal M₂ Internal Tides in the Arabian Sea

by Jingyi Ma, Daquan Guo, Peng Zhan and Ibrahim Hoteit

Remote Sens. 2021, 13(14), 2823; https://doi.org/10.3390/rs13142823 - 18 Jul 2021

Cited by 6 | Viewed by 3213

Abstract

Internal tides play a crucial role in ocean mixing. To explore the seasonal features of mode-1

M_{2}

internal tides in the Arabian Sea, we analyzed their propagation and energy distribution using along-track sea-level anomaly data collected by satellite altimeters. We identified four [...] Read more.

Internal tides play a crucial role in ocean mixing. To explore the seasonal features of mode-1

M_{2}

internal tides in the Arabian Sea, we analyzed their propagation and energy distribution using along-track sea-level anomaly data collected by satellite altimeters. We identified four primary source regions of internal tides: Abd al Kuri Island, the Carlsberg Ridge, the northeastern Arabian Sea, and the Maldive Islands. The baroclinic signals that originate from Abd al Kuri Island propagate meridionally, whereas those originating from the west coast of India propagate southwestward. The strength and energy flux of the internal tides in the Arabian Sea exhibit significant seasonal and spatial variability. The internal tides generated during winter are more energetic and can propagate further than those generated in summer. Doppler shifting and horizontal variations in stratification can explain the differences in the internal tides’ seasonal distributions. Full article

(This article belongs to the Section Ocean Remote Sensing)

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16 pages, 3244 KiB

Open AccessArticle

Cotton Stand Counting from Unmanned Aerial System Imagery Using MobileNet and CenterNet Deep Learning Models

by Zhe Lin and Wenxuan Guo

Remote Sens. 2021, 13(14), 2822; https://doi.org/10.3390/rs13142822 - 18 Jul 2021

Cited by 31 | Viewed by 5046 | Correction

Abstract

An accurate stand count is a prerequisite to determining the emergence rate, assessing seedling vigor, and facilitating site-specific management for optimal crop production. Traditional manual counting methods in stand assessment are labor intensive and time consuming for large-scale breeding programs or production field [...] Read more.

An accurate stand count is a prerequisite to determining the emergence rate, assessing seedling vigor, and facilitating site-specific management for optimal crop production. Traditional manual counting methods in stand assessment are labor intensive and time consuming for large-scale breeding programs or production field operations. This study aimed to apply two deep learning models, the MobileNet and CenterNet, to detect and count cotton plants at the seedling stage with unmanned aerial system (UAS) images. These models were trained with two datasets containing 400 and 900 images with variations in plant size and soil background brightness. The performance of these models was assessed with two testing datasets of different dimensions, testing dataset 1 with 300 by 400 pixels and testing dataset 2 with 250 by 1200 pixels. The model validation results showed that the mean average precision (mAP) and average recall (AR) were 79% and 73% for the CenterNet model, and 86% and 72% for the MobileNet model with 900 training images. The accuracy of cotton plant detection and counting was higher with testing dataset 1 for both CenterNet and MobileNet models. The results showed that the CenterNet model had a better overall performance for cotton plant detection and counting with 900 training images. The results also indicated that more training images are required when applying object detection models on images with different dimensions from training datasets. The mean absolute percentage error (MAPE), coefficient of determination (R²), and the root mean squared error (RMSE) values of the cotton plant counting were 0.07%, 0.98 and 0.37, respectively, with testing dataset 1 for the CenterNet model with 900 training images. Both MobileNet and CenterNet models have the potential to accurately and timely detect and count cotton plants based on high-resolution UAS images at the seedling stage. This study provides valuable information for selecting the right deep learning tools and the appropriate number of training images for object detection projects in agricultural applications. Full article

(This article belongs to the Special Issue Deep Learning Methods for Crop Monitoring and Crop Yield Prediction)

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24 pages, 5611 KiB

Open AccessArticle

Simulation and Assessment of the Capabilities of Orbita Hyperspectral (OHS) Imagery for Remotely Monitoring Chlorophyll-a in Eutrophic Plateau Lakes

by Runfei Zhang, Zhubin Zheng, Ge Liu, Chenggong Du, Chao Du, Shaohua Lei, Yifan Xu, Jie Xu, Meng Mu, Shun Bi and Jianzhong Li

Remote Sens. 2021, 13(14), 2821; https://doi.org/10.3390/rs13142821 - 18 Jul 2021

Cited by 15 | Viewed by 2959

Abstract

The chlorophyll-a (Chl-a) concentration of eutrophic lakes fluctuates significantly due to the disturbance of wind and anthropogenic activities on the water body. Consequently, estimation of the Chl-a concentration has become an immense challenge. Due to urgent demand and rapid development in high-resolution earth [...] Read more.

The chlorophyll-a (Chl-a) concentration of eutrophic lakes fluctuates significantly due to the disturbance of wind and anthropogenic activities on the water body. Consequently, estimation of the Chl-a concentration has become an immense challenge. Due to urgent demand and rapid development in high-resolution earth observation systems, it has become crucial to assess hyperspectral satellite imagery capabilities on inland water monitoring. The Orbita hyperspectral (OHS) satellite is the latest hyperspectral sensor with both high spectral and spatial resolution (2.5 nm and 10 m, respectively), which could provide great potential for remotely estimating the concentration of Chl-a for inland waters. However, there are still some deficiencies that are mainly manifested in the Chl-a concentration remote sensing retrieval model assessment and accuracy validation, as well as signal-to-noise ratio (SNR) estimation of OHS imagery for inland waters. Therefore, the radiometric performance of OHS imagery for water quality monitoring is evaluated in this study by comparing different atmospheric correction models and the SNR with several remote sensing images. Several crucial findings can be drawn: (1) the three-band model ((1/B15-1/B17)B19) developed by OHS imagery is most suitable for estimating the Chl-a concentration in Dianchi Lake, with the root-mean-square error (RMSE) and the mean absolute percentage error (MAPE) of 15.55 µg/L and 16.31%, respectively; (2) the applicability of the FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes) atmospheric correction model for OHS imagery in a eutrophic plateau lake (Dianchi Lake) was better than the 6S (Second Simulation of Satellite Signal in the Solar Spectrum) model, and QUAC (Quick Atmospheric Correction) model, as well as the dark pixel method; (3) the SNR of the OHS imagery was similar to that of Hyperion imagery and was significantly higher than SNR of the HSI imagery; (4) the spatial resolution showed slight influence on the SNR of the OHS imagery. The results show that OHS imagery could be applied to remote sensing retrieval of Chl-a in eutrophic plateau lakes and presents a new tool for dynamic hyperspectral monitoring of water quality. Full article

(This article belongs to the Special Issue Remote Sensing of Coastal Waters, Land Use/Cover, Lakes, Rivers and Watersheds)

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22 pages, 14587 KiB

Open AccessArticle

Mapping Plastic Greenhouses with Two-Temporal Sentinel-2 Images and 1D-CNN Deep Learning

by Haoran Sun, Lei Wang, Rencai Lin, Zhen Zhang and Baozhong Zhang

Remote Sens. 2021, 13(14), 2820; https://doi.org/10.3390/rs13142820 - 18 Jul 2021

Cited by 29 | Viewed by 4350

Abstract

Plastic greenhouses (PGs) are widely built near cities in China to produce vegetables and fruits. In order to promote sustainable agriculture, rural landscape construction, and better manage water resources, numerous remote sensing methods have been developed to identify and monitor the distribution of [...] Read more.

Plastic greenhouses (PGs) are widely built near cities in China to produce vegetables and fruits. In order to promote sustainable agriculture, rural landscape construction, and better manage water resources, numerous remote sensing methods have been developed to identify and monitor the distribution of PGs, of which many map PGs based on spectral responses and geometric shapes. In this study, we proposed a new fine- and coarse-scale mapping approach using two-temporal Sentinel-2 images with various seasonal characteristics and a one-dimensional convolutional neural network (1D-CNN). Having applied this approach in a pilot area study, the results were summarized as follows: (1) A time-series analysis of Sentinel-2 images showed that the reflectance of greenhouses changes during crop growth and development. In particular, the red-edge and near-infrared bands undergo a significant increase and then decrease during the whole crop growth period. Thus, two critical period images, containing a substantial difference in greenhouse reflectance, were sufficient to carry out an accurate and efficient mapping result. (2) The 1D-CNN classifier was used to map greenhouses by capturing subtle details and the overall trend of the spectrum curve. Overall, our approach showed higher classification accuracy than other approaches using support vector machines (SVM) or random forests (RF). In addition, the greenhouse area identified was highly consistent with the existing surfaces observed in very high-resolution images, with a kappa co-efficient of 0.81. (3) The narrow band feature differences (red-edge and near infrared narrow bands) in two-temporal Sentinel-2 images played a significant role in high-precision greenhouse mapping. The classification accuracy with narrow band features was much better than the maps produced without narrow band features. This scheme provided a method to digitize greenhouse precisely and publish its statistics for free, which enable advanced decision support for agriculture management. Full article

(This article belongs to the Special Issue Remote Sensing of Agricultural Greenhouses and Plastic-Mulched Farmland)

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21 pages, 17504 KiB

Open AccessArticle

Semi-Supervised Deep Learning for Lunar Crater Detection Using CE-2 DOM

by Sudong Zang, Lingli Mu, Lina Xian and Wei Zhang

Remote Sens. 2021, 13(14), 2819; https://doi.org/10.3390/rs13142819 - 18 Jul 2021

Cited by 12 | Viewed by 4043

Abstract

Lunar craters are very important for estimating the geological age of the Moon, studying the evolution of the Moon, and for landing site selection. Due to a lack of labeled samples, processing times due to high-resolution imagery, the small number of suitable detection [...] Read more.

Lunar craters are very important for estimating the geological age of the Moon, studying the evolution of the Moon, and for landing site selection. Due to a lack of labeled samples, processing times due to high-resolution imagery, the small number of suitable detection models, and the influence of solar illumination, Crater Detection Algorithms (CDAs) based on Digital Orthophoto Maps (DOMs) have not yet been well-developed. In this paper, a large number of training data are labeled manually in the Highland and Maria regions, using the Chang’E-2 (CE-2) DOM; however, the labeled data cannot cover all kinds of crater types. To solve the problem of small crater detection, a new crater detection model (Crater R-CNN) is proposed, which can effectively extract the spatial and semantic information of craters from DOM data. As incomplete labeled samples are not conducive for model training, the Two-Teachers Self-training with Noise (TTSN) method is used to train the Crater R-CNN model, thus constructing a new model—called Crater R-CNN with TTSN—which can achieve state-of-the-art performance. To evaluate the accuracy of the model, three other detection models (Mask R-CNN, no-Mask R-CNN, and Crater R-CNN) based on semi-supervised deep learning were used to detect craters in the Highland and Maria regions. The results indicate that Crater R-CNN with TTSN achieved the highest precision (of 91.4% and 88.5%, respectively) in the Highland and Maria regions, even obtaining the highest recall and F

_{1}

score. Compared with Mask R-CNN, no-Mask R-CNN, and Crater R-CNN, Crater R-CNN with TTSN had strong robustness and better generalization ability for crater detection within 1 km in different terrains, making it possible to detect small craters with high accuracy when using DOM data. Full article

(This article belongs to the Special Issue Cartography of the Solar System: Remote Sensing beyond Earth)

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Remote Sens., Volume 13, Issue 14 (July-2 2021) – 204 articles

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