Remote Sens., Volume 12, Issue 9 (May-1 2020) – 180 articles

Turbulent mixing is critical in affecting urban climate and air pollution. Nevertheless, our understanding of it, especially in a cloud-topped boundary layer (CTBL), remains limited. High-temporal resolution observations provide sufficient information of vertical velocity profiles, which is essential for turbulence studies in the atmospheric boundary layer (ABL). We conducted Doppler Light Detection and Ranging (LiDAR) measurements in 2019 using the 3-Dimensional Real-time Atmospheric Monitoring System (3DREAMS) to reveal the characteristics of typical daytime turbulent mixing processes in CTBL over Hong Kong. We assessed the contribution of cloud radiative cooling on turbulent mixing and determined the altitudinal dependence of the contribution of surface heating and vertical wind shear to turbulent mixing. Our results show that more downdrafts and updrafts in spring and autumn were observed and positively associated with seasonal cloud fraction. These results reveal that cloud radiative cooling was the main source of downdraft, which was also confirmed by our detailed case study of vertical velocity. Compared to winter and autumn, cloud base heights were lower in spring and summer. Cloud radiative cooling contributed ~32% to turbulent mixing even near the surface, although the contribution was relatively weaker compared to surface heating and vertical wind shear. Surface heating and vertical wind shear together contributed to ~45% of turbulent mixing near the surface, but wind shear can affect up to ~1100 m while surface heating can only reach ~450 m. Despite the fact that more research is still needed to further understand the processes, our findings provide useful references for local weather forecast and air quality studies. Full article

A flood-producing heavy rainfall event occurred at the mountainous coastal region in the northeast of South Korea on 5–6 August 2018, subsequent to extreme heat waves, through a quasi-stationary mesoscale convective system (MCS). We analyzed the storm environment via a multi-data approach using high-resolution (1-km) simulations from the Weather Research and Forecasting (WRF) and in situ/satellite/radar observations. The brightness temperature, from the Advanced Himawari Imager water vapor band, and the composite radar reflectivity were used to identify characteristics of the MCS and associated precipitations. The following factors affected this back-building MCS: low-level convergence by the Korea easterlies (Kor’easterlies), carrying moist air into the coast; strong vertical wind shear, making the updraft tilted and sustained; coastal fronts and back-building convection bands, formed through interactions among the Kor’easterlies, cold pool outflows, and orography; mid-level advection of cold air and positive relative vorticity, enhancing vertical convection and potential instability; and vigorous updraft releasing potential instability. The pre-storm synoptic environment provided favorable conditions for storm development such as high moisture and temperature over the coastal area and adjacent sea, and enhancement of the Kor’easterlies by expansion of a surface high pressure system. Upper-level north-northwesterly winds prompted the MCS to propagate south-southeastward along the coastline. Full article

Computational imaging using coded apertures offers all-electronic operation with a substantially reduced hardware complexity for data acquisition. At the core of this technique is the single-pixel coded aperture modality, which produces spatio-temporarily varying, quasi-random bases to encode the back-scattered radar data replacing the conventional pixel-by-pixel raster scanning requirement of conventional imaging techniques. For a frequency-diverse computational imaging radar, the coded aperture is of significant importance, governing key imaging metrics such as the orthogonality of the information encoded from the scene as the frequency is swept, and hence the conditioning of the imaging problem, directly impacting the fidelity of the reconstructed images. In this paper, we present dielectric lens loading of coded apertures as an effective way to increase the information coding capacity of frequency-diverse antennas for computational imaging problems. We show that by lens loading the coded aperture for the presented imaging problem, the number of effective measurement modes can be increased by 32% while the conditioning of the imaging problem is improved by a factor of greater than two times. Full article

Geolocation information is an important feature of remote sensing image data that is captured through a variety of passive or active observation sensors, such as push-broom electro-optical sensor, synthetic aperture radar (SAR), light detection and ranging (LIDAR) and sound navigation and ranging (SONAR). As a fundamental processing step to locate an image, geo-positioning is used to determine the ground coordinates of an object from image coordinates. A variety of sensor models have been created to describe geo-positioning process. In particular, Open Geospatial Consortium (OGC) has defined the Sensor Model Language (SensorML) specification in its Sensor Web Enablement (SWE) initiative to describe sensors including the geo-positioning process. It has been realized using syntax from the extensible markup language (XML). Besides, two standards defined by the International Organization for Standardization (ISO), ISO 19130-1 and ISO 19130-2, introduced a physical sensor model, a true replacement model, and a correspondence model for the geo-positioning process. However, a standardized encoding for geo-positioning sensor models is still missing for the remote sensing community. Thus, the interoperability of remote sensing data between application systems cannot be ensured. In this paper, a standardized encoding of remote sensing geo-positioning sensor models is introduced. It is semantically based on ISO 19130-1 and ISO 19130-2, and syntactically based on OGC SensorML. It defines a cross mapping of the sensor models defined in ISO 19130-1 and ISO 19130-2 to the SensorML, and then proposes a detailed encoding method to finalize the XML schema (an XML schema here is the structure to define an XML document), which will become a profile of OGC SensorML. It seamlessly unifies the sensor models defined in ISO 19130-1, ISO 19130-2, and OGC SensorML. By enabling a standardized description of sensor models used to produce remote sensing data, this standard is very promising in promoting data interoperability, mobility, and integration in the remote sensing domain. Full article

This paper presents an approach for 3D reconstruction of heritage scenes using a videogrammetric-based device. The system, based on two video cameras with different characteristics, uses a combination of visual simultaneous localization and mapping (SLAM) and photogrammetry technologies. VSLAM, together with a series of filtering algorithms, is used for the optimal selection of images and to guarantee that the user does not lose tracking during data acquisition in real time. The different photogrammetrically adapted tools in this device and for this type of handheld capture are explained. An evaluation of the device is carried out, including comparisons with the Faro Focus X 330 laser scanner, through three case studies in which multiple aspects are analyzed. We demonstrate that the proposed videogrammetric system is 17 times faster in capturing data than the laser scanner and that the post-processing of the system is fully automatic, but takes more time than the laser scanner in post-processing. It can also be seen that the accuracies of both systems and the generated textures are very similar. Our evaluation demonstrates the possibilities of considering the proposed system as a new professional-quality measurement instrument. Full article

Hyperspectral image (HSI) classification plays an important role in the automatic interpretation of the remotely sensed data. However, it is a non-trivial task to classify HSI accurately and rapidly due to its characteristics of having a large amount of data and massive noise points. To address this problem, in this work, a novel, semi-supervised, superpixel-level classification method for an HSI was proposed based on a graph and discrete potential (SSC-GDP). The key idea of the proposed scheme is the construction of the weighted connectivity graph and the division of the weighted graph. Based on the superpixel segmentation, a weighted connectivity graph is constructed usingthe weighted connection between a superpixel and its spatial neighbors. The generated graph is then divided into different communities/sub-graphs by using a discrete potential and the improved semi-supervised Wu–Huberman (ISWH) algorithm. Each community in the weighted connectivity graph represents a class in the HSI. The local connection strategy, together with the linear complexity of the ISWH algorithm, ensures the fast implementation of the suggested SSC-GDP method. To prove the effectiveness of the proposed spectral–spatial method, two public benchmarks, Indian Pines and Salinas, were utilized to test the performance of our proposal. The comparative test results confirmed that the proposed method was superior to several other state-of-the-art methods. Full article

Imperviousness is an important indicator for monitoring urbanization and environmental changes, and is evaluated widely in urban areas, but not in rural areas. An accurate impervious surface area (ISA) map in rural areas is essential to achieve environmental conservation and sustainable rural development. Global land-cover products such as MODIS MCD12Q1, ESA CCI-LC, and Global Urban Land are common resources for environmental practitioners to collect land-cover information including ISAs. However, global products tend to focus on large ISA agglomerations and may not identify fragmented ISA extents in less populated regions. Land-use planners and practitioners have to map ISAs if it is difficult to obtain such spatially explicit information from local governments. A common and consistent approach for rural ISA mapping is yet to be established. A case study of the Liping County, a typical rural region in southwest China, was undertaken with the objectives of assessing the global land-cover products in the context of rural ISA mapping and proposing a simple and feasible approach for the mapping. This approach was developed using Landsat 8 imagery and by applying a random forests classifier. An appropriate number of training samples were distributed to towns or villages across all townships in the study area for classification. The results demonstrate that the global land-cover products identified major ISA agglomerations, specifically at the county seat; however, other fragmented ISAs over the study area were overlooked. In contrast, the map created using the developed approach inferred ISAs across all townships with an overall accuracy of 91%. A large amount of training samples together with geographic information of towns or villages is the key suggestion to identify and map ISAs in rural areas. Full article

We examine satellite-derived aerosol optical depth (AOD) data during the period 2000–2018 over the Middle East to evaluate the contribution of anthropogenic pollution. We focus on Iraq, where US troops were present for nearly nine years. We begin with a plausibility argument linking anthropogenic influence and AOD signature. We then calculate the percent change in AOD every two years. To pinpoint the causes for changes in AOD on a spatial basis, we distinguish between synoptically “calm” periods and those with vigorous synoptic activity. This was done on high-resolution 10 km AOD retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor (Terra satellite). We found spatiotemporal variability in the intensity of the AOD and its standard deviation along the dust-storm corridor during three studied periods: before Operation Iraqi Freedom (OIF) (1 March 2000–19 March 2003), during OIF (20 March 2003–1 September 2010), and Operation New Dawn (OND; 1 September 2010–18 December 2011), and after the US troops’ withdrawal (19 December 2011–31 December 2018). Pixels of military camps and bases, major roads and areas of conflict, and their corresponding AOD values, were selected to study possible effects. We found that winter, with its higher frequency of days with synoptically “calm” conditions compared to spring and summer, was the best season to quantitatively estimate the impact of these ground-based sources. Surprisingly, an anthropogenic impact on the AOD signature was also visible during vigorous synoptic activity. Meteorological conditions that favor detection of these effects using space imagery are discussed, where the effects are more salient than in surrounding regions with similar meteorological conditions. This exceeds expectations when considering synoptic variations alone. Full article

Clouds are significant barriers to the application of optical remote sensing images. Accurate cloud detection can help to remove contaminated pixels and improve image quality. Many cloud detection methods have been developed. However, traditional methods either rely heavily on thermal infrared bands or clear-sky images. When traditional cloud detection methods are used with Gaofen 4 (GF-4) imagery, it is very difficult to separate objects with similar spectra, such as ice, snow, and bright sand, from clouds. In this paper, we propose a new method, named Real-Time-Difference (RTD), to detect clouds using a pair of images obtained by the GF-4 satellite. The RTD method has four main steps: (1) data preprocessing, including transforming digital value (DN) to Top of Atmosphere (TOA) reflectance, and orthographic and geometric correction; (2) the computation of a series of cloud indexes for a single image to highlight clouds; (3) the calculation of the difference between a pair of real-time images in order to obtain moved clouds; and (4) confirming the clouds and background by analyzing their physical and dynamic features. The RTD method was validated in three sites located in the Hainan, Liaoning, and Xinjiang areas of China. The results were compared with those of a popular classifier, Support Vector Machine (SVM). The results showed that RTD outperformed SVM; for the Hainan, Liaoning, and Xinjiang areas, respectively, the overall accuracy of RTD reached 95.9%, 94.1%, and 93.9%, and its Kappa coefficient reached 0.92, 0.88, and 0.88. In the future, we expect RTD to be developed into an important means for the rapid detection of clouds that can be used on images from geostationary orbit satellites. Full article

When retrieving Aerosol Optical Depth (AOD) from passive satellite sensors, the vertical distribution of aerosols usually needs to be assumed, potentially causing uncertainties in the retrievals. In this study, we use the Moderate Resolution Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) sensors as examples to investigate the impact of aerosol vertical distribution on AOD retrievals. A series of sensitivity experiments was conducted using radiative transfer models with different aerosol profiles and surface conditions. Assuming a 0.2 AOD, we found that the AOD retrieval error is the most sensitive to the vertical distribution of absorbing aerosols; a −1 km error in aerosol scale height can lead to a ~30% AOD retrieval error. Moreover, for this aerosol type, ignoring the existence of the boundary layer can further result in a ~10% AOD retrieval error. The differences in the vertical distribution of scattering and absorbing aerosols within the same column may also cause −15% (scattering aerosols above absorbing aerosols) to 15% (scattering aerosols below absorbing aerosols) errors. Surface reflectance also plays an important role in affecting the AOD retrieval error, with higher errors over brighter surfaces in general. The physical mechanism associated with the AOD retrieval errors is also discussed. Finally, by replacing the default exponential profile with the observed aerosol vertical profile by a micro-pulse lidar at the Beijing-PKU site in the VIIRS retrieval algorithm, the retrieved AOD shows a much better agreement with surface observations, with the correlation coefficient increased from 0.63 to 0.83 and bias decreased from 0.15 to 0.03. Our study highlights the importance of aerosol vertical profile assumption in satellite AOD retrievals, and indicates that considering more realistic profiles can help reduce the uncertainties. Full article

Recognition of the spatial variation in tree species composition is a necessary precondition for wise management and conservation of forests. In the Peruvian Amazonia, this goal is not yet achieved mostly because adequate species inventory data has been lacking. The recently started Peruvian national forest inventory (INFFS) is expected to change the situation. Here, we analyzed genus-level variation, summarized through non-metric multidimensional scaling (NMDS), in a set of 157 INFFS inventory plots in lowland to low mountain rain forests (<2000 m above sea level) using Landsat satellite imagery and climatic, edaphic, and elevation data as predictor variables. Genus-level floristic patterns have earlier been found to be indicative of species-level patterns. In correlation tests, the floristic variation of tree genera was most strongly related to Landsat variables and secondly to climatic variables. We used random forest regression, under varying criteria of feature selection and cross-validation, to predict the floristic composition on the basis of Landsat and environmental data. The best model explained >60% of the variation along NMDS axes 1 and 2 and 40% of the variation along NMDS axis 3. We used this model to predict the three NMDS dimensions at a 450-m resolution over all of the Peruvian Amazonia and classified the pixels into 10 floristic classes using k-means classification. An indicator analysis identified statistically significant indicator genera for 8 out of the 10 classes. The results are congruent with earlier studies, suggesting that the approach is robust and can be applied to other tropical regions, which is useful for reducing research gaps and for identifying suitable areas for conservation. Full article

Obtaining detailed data on the spatio-temporal variation in crop phenology is critical to increasing our understanding of agro-ecosystem function, such as their response to weather variation and climate change. It is challenging to collect such data over large areas through field observations. The use of satellite remote sensing data has made phenology data collection easier, although the quality and the utility of such data to understand agro-ecosystem function have not been widely studied. Here, we evaluated satellite data-based estimates of rice phenological stages in California, USA by comparing them with survey data and with predictions by a temperature-driven phenology model. We then used the satellite data-based estimates to quantify the crop phenological response to changes in weather. We used time-series of MODIS satellite data and PhenoRice, a rule-based rice phenology detection algorithm, to determine annual planting, heading and harvest dates of paddy rice in California between 2002 and 2017. At the state level, our satellite-based estimates of rice phenology were very similar to the official survey data, particularly for planting and harvest dates (RMSE = 3.8–4.0 days). Satellite based observations were also similar to predictions by the DD10 temperature-driven phenology model. We analyzed how the timing of these phenological stages varied with concurrent temperature and precipitation over this 16-year time period. We found that planting was earlier in warm springs (−1.4 days °C⁻¹ for mean temperature between mid-April and mid-May) and later in wet years (5.3 days 100 mm^-1 for total precipitation from March to April). Higher mean temperature during the pre-heading period of the growing season advanced heading by 2.9 days °C⁻¹ and shortened duration from planting to heading by 1.9 days °C⁻¹. The entire growing season was reduced by 3.2 days °C⁻¹ because of the increased temperature during the rice season. Our findings confirm that satellite data can be an effective way to estimate variations in rice phenology and can provide critical information that can be used to improve understanding of agricultural responses to weather variation. Full article

Many previous studies have attempted to distinguish fog from clouds using low-orbit and geostationary satellite observations from visible (VIS) to longwave infrared (LWIR) bands. However, clouds and fog have often been misidentified because of their similar spectral features. Recently, advanced meteorological geostationary satellites with improved spectral, spatial, and temporal resolutions, including Himawari-8/9, GOES-16/17, and GeoKompsat-2A, have become operational. Accordingly, this study presents an improved algorithm for detecting daytime sea fog using one VIS and one near-infrared (NIR) band of the Advanced Himawari Imager (AHI) of the Himawari-8 satellite. We propose a regression-based relationship for sea fog detection using a combination of the Normalized Difference Snow Index (NDSI) and reflectance at the green band of the AHI. Several case studies, including various foggy and cloudy weather conditions in the Yellow Sea for three years (2017–2019), have been performed. The results of our algorithm showed a successful detection of sea fog without any cloud mask information. The pixel-level comparison results with the sea fog detection based on the shortwave infrared (SWIR) band (3.9 μm) and the brightness temperature difference between SWIR and LWIR bands of the AHI showed high statistical scores for probability of detection (POD), post agreement (PAG), critical success index (CSI), and Heidke skill score (HSS). Consequently, the proposed algorithms for daytime sea fog detection can be effective in daytime, particularly twilight, conditions, for many satellites equipped with VIS and NIR bands. Full article

Small target detection is a crucial technique that restricts the performance of many infrared imaging systems. In this paper, a novel detection model of infrared small target via non-convex tensor rank surrogate joint local contrast energy (NTRS) is proposed. To improve the latest infrared patch-tensor (IPT) model, a non-convex tensor rank surrogate merging tensor nuclear norm (TNN) and the Laplace function, is utilized for low rank background patch-tensor constraint, which has a useful property of adaptively allocating weight for every singular value and can better approximate ${\mathcal{l}}_0$ -norm. Considering that the local prior map can be equivalent to the saliency map, we introduce a local contrast energy feature into IPT detection framework to weight target tensor, which can efficiently suppress the background and preserve the target simultaneously. Besides, to remove the structured edges more thoroughly, we suggest an additional structured sparse regularization term using the ${\mathcal{l}}_{1,1,2}$ -norm of third-order tensor. To solve the proposed model, a high-efficiency optimization way based on alternating direction method of multipliers with the fast computing of tensor singular value decomposition is designed. Finally, an adaptive threshold is utilized to extract real targets of the reconstructed target image. A series of experimental results show that the proposed method has robust detection performance and outperforms the other advanced methods. Full article

Canopy height serves as a good indicator of forest carbon content. Remote sensing-based direct estimations of canopy height are usually based on Light Detection and Ranging (LiDAR) or Synthetic Aperture Radar (SAR) interferometric data. LiDAR data is scarcely available for the Indian tropics, while Interferometric SAR data from commercial satellites are costly. High temporal decorrelation makes freely available Sentinel-1 interferometric data mostly unsuitable for tropical forests. Alternatively, other remote sensing and biophysical parameters have shown good correlation with forest canopy height. The study objective was to establish and validate a methodology by which forest canopy height can be estimated from SAR and optical remote sensing data using machine learning models i.e., Random Forest (RF) and Symbolic Regression (SR). Here, we analysed the potential of Sentinel-1 interferometric coherence and Sentinel-2 biophysical parameters to propose a new method for estimating canopy height in the study site of the Bhitarkanika wildlife sanctuary, which has mangrove forests. The results showed that interferometric coherence, and biophysical variables (Leaf Area Index (LAI) and Fraction of Vegetation Cover (FVC)) have reasonable correlation with canopy height. The RF model showed a Root Mean Squared Error (RMSE) of 1.57 m and R² value of 0.60 between observed and predicted canopy heights; whereas, the SR model through genetic programming demonstrated better RMSE and R² values of 1.48 and 0.62 m, respectively. The SR also established an interpretable model, which is not possible via any other machine learning algorithms. The FVC was found to be an essential variable for predicting forest canopy height. The canopy height maps correlated with ICESat-2 estimated canopy height, albeit modestly. The study demonstrated the effectiveness of Sentinel series data and the machine learning models in predicting canopy height. Therefore, in the absence of commercial and rare data sources, the methodology demonstrated here offers a plausible alternative for forest canopy height estimation. Full article

It has been demonstrated that solar-induced chlorophyll fluorescence (SIF) is linearly related to the primary production of photosynthesis (GPP) in various ecosystems. However, it is unknown whether such linear relationships have been established in senescent crops. SIF and GPP can be expressed as the products of absorbed photosynthetically active radiation (APAR) with the SIF yield and photosystem II (PSII) operating efficiency, respectively. Thus, the relationship between SIF and GPP can be represented by the relationship between the SIF yield and PSII operating efficiency when the APAR has the same value. Therefore, we analyzed the relationship between the SIF yield and the PSII operating efficiency to address the abovementioned question. Here, diurnal measurements of the canopy SIF (760 nm, F760) of soybean and sweet potato were manually measured and used to calculate the SIF yield. The PSII operating efficiency was calculated from measurements of the chlorophyll fluorescence at the leaf level using the FluorImager chlorophyll fluorescence imaging system. Meanwhile, field measurements of the gas exchange and other physiological parameters were also performed using commercial-grade devices. The results showed that the SIF yield was not linearly related to the PSII operating efficiency at the diurnal scale, reflecting the nonlinear relationship between SIF and GPP. This nonlinear relationship mainly resulted from the heterogeneity and diurnal dynamics of the PSII operating efficiency and from the intrinsic diurnal changes in the maximum efficiency of the PSII photochemistry and the proportion of opened PSII centers. Intensifying respiration was another factor that complicated the response of photosynthesis to the variation in environmental conditions and negatively impacted the relationship between the SIF yield and the PSII operating efficiency. The nonlinear relationship between the SIF yield and PSII efficiency might yield errors in the estimation of GPP using the SIF measurements of senescent crops. Full article

Understanding how delta islands grow and change at contemporary, interannual timescales remains a key scientific goal and societal need, but the high-resolution, high frequency morphodynamic data that would be most useful for this are as yet logistically prohibitive. The recorded water levels needed for relative elevation analysis are also often lacking. This paper presents a new approach for hindcasting intertidal marsh-top elevations (HIME) to resolve ecogeomorphic change, even in a young, rapidly changing fluvial delta setting, at sub-decadal temporal resolution and at the spatial resolution of widely available optical remote sensing imagery (e.g., 30 m Landsat). The HIME method first calculates: (i) the probability of land exposure in a set of historical imagery from a user-defined discrete timespan (e.g., months or years); (ii) the probability of water level non-exceedance from water level records, which need not be complete nor coincident with the imagery; and (iii) the systematic variation in local mean water level with distance along the primary hydraulic gradient. The HIME method then combines these inputs to estimate a marsh-top elevation map for each historical timespan of interest. The method was developed, validated, applied, and results analyzed to investigate time-lapse evolution of the Wax Lake Delta in Louisiana, USA, every three years, over two decades (1993–2013). The hindcast maps of delta island extents and elevations evidenced ecogeomorphic system self-organization around four stable attractors, or elevation platforms, at about −0.3 m (subtidal), 0.2 m, 0.4 m, and 0.9 m (supratidal) NAVD88. The HIME results also yielded a time series of net subaerial sediment accumulation, and specific locations and magnitudes of gains and losses, at scales from 30 m to delta-wide (~100 km³) and 6 to 21 years. Average subaerial net sediment accumulation at the Wax Lake Delta (WLD) was estimated as 0.6 cm/yr during the study period. Finally, multiple linear regression models were successfully trained on the HIME elevation maps to model evolving delta island morphologies based on simple geometric factors, such as distance down-delta and position on a delta island; the models also successfully reproduced an average delta topset slope of 1.4 cm. Overall, this study’s development and application of the HIME method added detailed insights to recent, transient ecogeomorphological change at the WLD, and demonstrated the potential of the new approach for accurately reconstructing past intertidal topographies and dynamic change. Full article

The accurate remote estimation of the Secchi disk depth (Z_SD) in turbid waters is essential in the monitoring the ecological environment of lakes. Using the field measured Z_SD and the remote sensing reflectance (Rrs(λ)) data, a new semi-analytical algorithm (denoted as Z_SDZ) for retrieving Z_SD was developed from Rrs(λ), and it was applied to Geostationary Ocean Color Imager (GOCI) images in extremely turbid waters. Our results are as follows: (1) the Z_SDZ performs well in estimating Z_SD in turbid water bodies (0.15 m < Z_SD < 2.5 m). By validating with the field measured data that were collected in four turbid inland lakes, the determination coefficient (R²) is determined to be 0.89, with a mean absolute square percentage error (MAPE) of 22.39%, and root mean square error (RMSE) of 0.24 m. (2) The Z_SDZ improved the retrieval accuracy of Z_SD in turbid waters and outperformed the existing semi-analytical schemes. (3) The developed algorithm and GOCI data are in order to map the hourly variation of Z_SD in turbid inland waters, the GOCI-derived results reveal a significant spatiotemporal variation in our study region, which are significantly driven by wind forcing. This study can provide a new approach for estimating water transparency in turbid waters, offering important support for the management of inland waters. Full article

Although plastic pollution is one of the most noteworthy environmental issues nowadays, there is still a knowledge gap in terms of monitoring the spatial distribution of plastics, which is needed to prevent its negative effects and to plan mitigation actions. Unmanned Aerial Vehicles (UAVs) can provide suitable data for mapping floating plastic, but most of the methods require visual interpretation and manual labeling. The main goals of this paper are to determine the suitability of deep learning algorithms for automatic floating plastic extraction from UAV orthophotos, testing the possibility of differentiating plastic types, and exploring the relationship between spatial resolution and detectable plastic size, in order to define a methodology for UAV surveys to map floating plastic. Two study areas and three datasets were used to train and validate the models. An end-to-end semantic segmentation algorithm based on U-Net architecture using the ResUNet50 provided the highest accuracy to map different plastic materials (F1-score: Oriented Polystyrene (OPS): 0.86; Nylon: 0.88; Polyethylene terephthalate (PET): 0.92; plastic (in general): 0.78), showing its ability to identify plastic types. The classification accuracy decreased with the decrease in spatial resolution, performing best on 4 mm resolution images for all kinds of plastic. The model provided reliable estimates of the area and volume of the plastics, which is crucial information for a cleaning campaign. Full article

A multi-sensor and multi-scale monitoring tool for the spatially explicit and periodic monitoring of eutrophication in a small drinking water reservoir is presented. The tool was built with freely available satellite and in situ data combined with Unmanned Aerial Vehicle (UAV)-based technology. The goal is to evaluate the performance of a multi-platform approach for the trophic state monitoring with images obtained with MultiSpectral Sensors on board satellites Sentinel 2 (S2A and S2B), Landsat 8 (L8) and UAV. We assessed the performance of three different sensors (MultiSpectral Instrument (MSI), Operational Land Imager (OLI) and Rededge Micasense) for retrieving the pigment chlorophyll-a (chl-a), as a quantitative descriptor of phytoplankton biomass and trophic level. The study was conducted in a waterbody affected by cyanobacterial blooms, one of the most important eutrophication-derived risks for human health. Different empirical models and band indices were evaluated. Spectral band combinations using red and near-infrared (NIR) bands were the most suitable for retrieving chl-a concentration (especially 2 band algorithm (2BDA), the Surface Algal Bloom Index (SABI) and 3 band algorithm (3BDA)) even though blue and green bands were useful to classify UAV images into two chl-a ranges. The results show a moderately good agreement among the three sensors at different spatial resolutions (10 m., 30 m. and 8 cm.), indicating a high potential for the development of a multi-platform and multi-sensor approach for the eutrophication monitoring of small reservoirs. Full article

Forest plantations are globally important for the economy and are significant for carbon sequestration. Properly managing plantations requires accurate information about stand timber stocks. In this study, we used the area (ABA) and individual tree (ITD) based approaches for estimating stem volume in fast-growing Eucalyptus spp forest plantations. Herein, we propose a new method to improve individual tree detection (ITD) in dense canopy homogeneous forests and assess the effects of stand age, slope and scan angle on ITD accuracy. Field and Light Detection and Ranging (LiDAR) data were collected in Eucalyptus urophylla x Eucalyptus grandis even-aged forest stands located in the mountainous region of the Rio Doce Valley, southeastern Brazil. We tested five methods to estimate volume from LiDAR-derived metrics using ABA: Artificial Neural Network (ANN), Random Forest (RF), Support Vector Machine (SVM), and linear and Gompertz models. LiDAR-derived canopy metrics were selected using the Recursive Feature Elimination algorithm and Spearman’s correlation, for nonparametric and parametric methods, respectively. For the ITD, we tested three ITD methods: two local maxima filters and the watershed method. All methods were tested adding our proposed procedure of Tree Buffer Exclusion (TBE), resulting in 35 possibilities for treetop detection. Stem volume for this approach was estimated using the Schumacher and Hall model. Estimated volumes in both ABA and ITD approaches were compared to the field observed values using the F-test. Overall, the ABA with ANN was found to be better for stand volume estimation ( $r_{y\widehat{y}}$ = 0.95 and RMSE = 14.4%). Although the ITD results showed similar precision ( $r_{y\widehat{y}}$ = 0.94 and RMSE = 16.4%) to the ABA, the results underestimated stem volume in younger stands and in gently sloping terrain (<25%). Stem volume maps also differed between the approaches; ITD represented the stand variability better. In addition, we discuss the importance of LiDAR metrics as input variables for stem volume estimation methods and the possible issues related to the ABA and ITD performance. Full article

Wise soil management requires detailed soil information, but conventional soil class mapping in a rather coarse spatial resolution cannot meet the demand for precision agriculture. With the advantages of non-destructiveness, rapid cost-efficiency, and labor savings, the spectroscopic technique has proved its high potential for success in soil classification. Previous studies mainly focused on predicting soil classes using a single sensor. In this study, we attempted to compare the predictive ability of visible near infrared (vis-NIR) spectra, mid-infrared (MIR) spectra, and their fused spectra for soil classification. A total of 146 soil profiles were collected from Zhejiang, China, and the soil properties and spectra were measured by their genetic horizons. Along with easy-to-measure auxiliary soil information (soil organic matter, soil texture, color and pH), four spectral data, including vis-NIR, MIR, their simple combination (vis-NIR-MIR), and outer product analysis (OPA) fused spectra, were used for soil classification using a multiple objectives mixed support vector machine model. The independent validation results showed that the classification model using MIR (accuracy of 64.5%) was slightly better than that using vis-NIR (accuracy of 64.2%). The predictive model built on vis-NIR-MIR did not improve the classification accuracy, having the lowest accuracy of 61.1%, which likely resulted from an over-fitting problem. The model based on OPA fused spectra performed best with an accuracy of 68.4%. Our results prove the potential of fusing vis-NIR and MIR using OPA for improving prediction ability for soil classification. Full article

We studied the influence of the statistical properties of soil moisture changes on the Interferometric Synthetic Aperture Radar (InSAR) coherence and closure phase to determine whether the InSAR coherence and closure phase can be used to estimate soil moisture changes. We generated semi-synthetic multi-looked interferograms by pairing n real single-looked pixels of an observed SAR image with n synthetic single-looked pixels. The synthetic SAR data are generated from the real SAR data by applying soil moisture changes with a pre-defined mean and standard deviation of changes. Our results show that the diversity of soil moisture changes within the multi-look window gives rise to decorrelation, a multi-looked phase artifact, and a non-zero phase triplet. The decorrelation and closure phase increase by enlarging the diversity of soil moisture changes. We also showed that non-soil moisture changes can lead to larger decorrelations and closure phases. Furthermore, the diversity of phase changes, decorrelation, and closure phases are correlated with land cover type. We concluded that the closure phase and coherence are independent of the magnitude of soil moisture changes and are inappropriate tools to estimate soil moisture changes. Coherence, however, can be used as a proxy for soil moisture changes if the diversity and magnitude of soil moisture changes within a multi-looked pixel are strongly correlated. Full article

Satellites provide a temporally discontinuous record of hydrological conditions along Earth’s rivers (e.g., river width, height, water quality). The degree to which archived satellite data effectively capture the overall population of river flow frequency is unknown. Here, we use the entire archives of Landsat 5, 7, and 8 to determine when a cloud-free image is available over the United States Geological Survey (USGS) river gauges located on Landsat-observable rivers. We compare the flow frequency distribution derived from the daily gauge record to the flow frequency distribution derived from ideally sampling gauged discharge based on the timing of cloud-free Landsat overpasses. Examining the patterns of flow frequency across multiple gauges, we find that there is not a statistically significant difference between the flow frequency distribution associated with observations contained within the Landsat archive and the flow frequency distribution derived from the daily gauge data (α = 0.05), except for hydrological extremes like maximum and minimum flow. At individual gauges, we find that Landsat observations span a wide range of hydrological conditions (97% of total flow variability observed in 90% of the study gauges) but the degree to which the Landsat sample can represent flow frequency distribution varies from location to location and depends on sample size. The results of this study indicate that the Landsat archive is, on average, representative of the temporal frequencies of hydrological conditions present along Earth’s large rivers with broad utility for hydrological, ecologic and biogeochemical evaluations of river systems. Full article

The use of new and modern sensors in forest inventory has become increasingly efficient. Nevertheless, the majority of forest inventory data are still collected manually, as part of field surveys. The reason for this is the sometimes time-consuming and incomplete data acquisition with static terrestrial laser scanning (TLS). The use of personal laser scanning (PLS) can reduce these disadvantages. In this study, we assess a new personal laser scanner and compare it with a TLS approach for the estimation of tree position and diameter in a wide range of forest types and structures. Traditionally collected forest inventory data are used as reference. A new density-based algorithm for position finding and diameter estimation is developed. In addition, several methods for diameter fitting are compared. For circular sample plots with a maximum radius of 20 m and lower diameter at breast height (dbh) threshold of 5 cm, tree mapping showed a detection of 96% for PLS and 78.5% for TLS. Using plot radii of 20 m, 15 m, and 10 m, as well as a lower dbh threshold of 10 cm, the respective detection rates for PLS were 98.76%, 98.95%, and 99.48%, while those for TLS were considerably lower (86.32%, 93.81%, and 98.35%, respectively), especially for larger sample plots. The root mean square error (RMSE) of the best dbh measurement was 2.32 cm (12.01%) for PLS and 2.55 cm (13.19%) for TLS. The highest precision of PLS and TLS, in terms of bias, were 0.21 cm (1.09%) and −0.74 cm (−3.83%), respectively. The data acquisition time for PLS took approximately 10.96 min per sample plot, 4.7 times faster than that for TLS. We conclude that the proposed PLS method is capable of efficient data capture and can detect the largest number of trees with a sufficient dbh accuracy. Full article

Total precipitable water (TPW) is an important key factor in the global water cycle and climate change. The knowledge of TPW characteristics at spatial and temporal scales could help us to better understand our changing environment. Currently, many algorithms are available to retrieve TPW from optical and microwave sensors. There are still no available TPW data over land from FY-3D MWRI, which was launched by China in 2017. However, the TPW product over land is a key element for the retrieval of many ecological environment parameters. In this paper, an improved algorithm was developed to retrieve TPW over land from the brightness temperature of FY-3D MWRI. The major improvement is that surface emissivity, which is a key parameter in the retrieval of TPW in all-weather conditions, was developed and based on an improved algorithm according to the characteristics of FY-3D MWRI. The improvement includes two aspects, one is selection of appropriate ancillary data in estimating surface emissivity parameter Δε_18.7/Δε_23.8 in clear sky conditions, and the other is an improvement of the Δε_18.7/Δε_23.8 estimation function in cloudy conditions according to the band configuration of FY-3D MWRI. Finally, TPW retrieved was validated using TPW observation from the SuomiNet GPS and global distributed Radiosonde Observations (RAOB) networks. According to the validation, TPW retrieved using observations from FY-3D MWRI and ancillary data from Aqua MODIS had the best quality. The root mean square error (RMSE) and correlation coefficient between the retrieved TPW and observed TPW from RAOB were 5.47 and 0.94 mm, respectively. Full article

The traveling public judges the quality of a road mostly by its roughness and/or ride quality. Hence, mapping, monitoring, and maintaining adequate pavement smoothness is of high importance to State Departments of Transportation in the US. Current methods rely mostly on in situ measurements and are, therefore, time consuming and costly when applied at the network scale. This paper studies the applicability of satellite radar remote sensing data, specifically, high-resolution Synthetic Aperture Radar (SAR) data acquired at X-band, to the network-wide mapping of pavement roughness of roads in the US. Based on a comparison of high-resolution X-band Cosmo-SkyMed images with road roughness data in the form of International Roughness Index (IRI) measurements, we found that X-band radar brightness generally increases when pavement roughness worsens. Based on these findings, we developed and inverted a model to distinguish well maintained road segments from segments in need of repair. Over test sites in Augusta County, VA, we found that our classification scheme reaches an overall accuracy of 92.6%. This study illustrates the capacity of X-band SAR for pavement roughness mapping and suggests that incorporating SAR into DOT operations could be beneficial. Full article

Soil moisture is an important factor affecting the global climate and environment, which can be monitored by microwave remote sensing all day and under all weather conditions. However, existing monostatic radars and microwave radiometers have their own limitations in monitoring soil moisture with shallower depths. The emerging remote sensing of signal of opportunity (SoOp) provides a new method for soil moisture monitoring, but only an experimental perspective was proposed at present, and its mechanism is not clear. In this paper, based on the traditional surface scattering models, we employed the polarization synthesis method, the coordinate transformation, and the Mueller matrix, to develop bistatic radar circular polarization models that are suitable for SoOP remote sensing. Using these models as a tool, the bistatic scattering versus the observation frequency, soil moisture, scattering zenith angle, and scattering azimuth at five different circular polarizations (LR, HR, VR, + 45° R, and −45° R) are simulated and analyzed. The results show that the developed models can determine the optimal observation combination of polarizations and observation angle. The systematic analysis of the scattering characteristics of random rough surfaces provides an important guiding significance for the design of space-borne payloads, the analysis of experimental data, and the development of backward inversion algorithms for more effective SoOP remote sensing. Full article

This study presents a methodology for developing a high-resolution (2 m) urban tree canopy leaf area inventory in different tree phenological seasons and a subsequent application of the methodology to a 625 km² urban area in Tokyo. Satellite remote sensing has the advantage of imaging large areas simultaneously. However, mapping the tree canopy cover and leaf area accurately is still difficult in a highly heterogeneous urban landscape. The WorldView-2/3 satellite imagery at the individual tree level (2 m resolution) was used to map urban trees based on a simple pixel-based classification method. The comparison of our mapping results with the tree canopy cover derived from aerial photography shows that the error margin is within an acceptable range of 5.5% at the 3.0 km² small district level, 5.0% at the 60.9 km² municipality level, and 1.2% at the 625 km² city level. Furthermore, we investigated the relationship between the satellite data (vegetation index) and in situ tree-measurement data (leaf area index) to develop a simple model to directly map the tree leaf area from the WorldView-2/3 imagery. The estimated total leaf area in Tokyo urban area in the leaf-on season (633 km²) was twice that of the leaf-off season (319 km²). Our results also showed that the estimated total leaf area in Tokyo urban area was 1.9–6.2 times higher than the results of the moderate-resolution (30 m) satellite imagery. Full article

The raphidophyte Chattonella spp. and diatom Skeletonema spp. are the dominant harmful algal species of summer blooms in Ariake Sea, Japan. A new bio-optical algorithm based on backscattering features has been developed to differentiate harmful raphidophyte blooms from diatom blooms using MODIS imagery. Bloom waters were first discriminated from other water types based on the distinct spectral shape of the remote sensing reflectance $R_{rs}$ (λ) data derived from MODIS. Specifically, bloom waters were discriminated by the positive value of Spectral Shape, SS (645), which arises from the $R_{rs}$ (λ) shoulder at 645 nm in bloom waters. Next, the higher cellular-specific backscattering coefficient, estimated from MODIS data and quasi-analytical algorithm (QAA) of raphidophyte, Chattonella spp., was utilized to discriminate it from blooms of the diatom, Skeletonema spp. A new index $b_{bp-index}\left(555\right)$ was calculated based on a semi-analytical bio-optical model to discriminate the two algal groups. This index, combined with a supplemental Red Band Ratio (RBR) index, effectively differentiated the two bloom types. Validation of the method was undertaken using MODIS satellite data coincident with confirmed bloom observations from local field surveys, which showed that the newly developed method, based on backscattering features, could successfully discriminate the raphidophyte Chattonella spp. from the diatom Skeletonema spp. and thus provide reliable information on the spatial distribution of harmful blooms in Ariake Sea. Full article