Remote Sensing — Open Access Journal

To extend the scope of high frequency (HF) radio oceanography, a new HF radar model, named shore-to-air bistatic HF radar, has been proposed for ocean observations. To explore this model, the first-order scattering coefficient and the second-order electromagnetic scattering coefficient for shore-to-air bistatic HF radar are derived using the perturbation method. In conjunction with the contribution of the hydrodynamic component, the second-order scattering coefficient is derived. Based on the derived scattering coefficients, we analyzed the simulated echo Doppler spectra for various scattering angles and azimuthal angles, operation frequencies, wind speeds, and directions of wind, which may provide the guideline on the extraction of sea state information for shore-to-air bistatic HF radar. The singularities in the simulated echo Doppler spectra are discussed using the normalized constant Doppler frequency contours. In addition, the scattering coefficients of shore-to-air bistatic HF radar are compared with that of monostatic HF radar and land-based bistatic HF radar. The results verify the correctness of the proposed scattering coefficients. The model of shore-to-air bistatic HF radar is effective for ocean observations. Full article

Precipitation plays an important role in the food production of Southern Africa. Understanding the spatial and temporal variations of precipitation is helpful for improving agricultural management and flood and drought risk assessment. However, a comprehensive precipitation pattern analysis is challenging in sparsely gauged and underdeveloped regions. To solve this problem, Version 7 Tropical Rainfall Measuring Mission (TRMM) precipitation products and Google Earth Engine (GEE) were adopted in this study for the analysis of spatiotemporal patterns of precipitation in the Zambezi River Basin. The Kendall’s correlation and sen’s Slop reducers in GEE were used to examine precipitation trends and magnitude, respectively, at annual, seasonal and monthly scales from 1998 to 2017. The results reveal that 10% of the Zambezi River basin showed a significant decreasing trend of annual precipitation, while only 1% showed a significant increasing trend. The rainy-season precipitation appeared to have a dominant impact on the annual precipitation pattern. The rainy-season precipitation was found to have larger spatial, temporal and magnitude variation than the dry-season precipitation. In terms of monthly precipitation, June to September during the dry season were dominated by a significant decreasing trend. However, areas presenting a significant decreasing trend were rare (<12% of study area) and scattered during the rainy-season months (November to April of the subsequent year). Spatially, the highest and lowest rainfall regions were shifted by year, with extreme precipitation events (highest and lowest rainfall) occurring preferentially over the northwest side rather than the northeast area of the Zambezi River Basin. A “dry gets dryer, wet gets wetter” (DGDWGW) pattern was also observed over the study area, and a suggestion on agriculture management according to precipitation patterns is provided in this study for the region. This is the first study to use long-term remote sensing data and GEE for precipitation analysis at various temporal scales in the Zambezi River Basin. The methodology proposed in this study is helpful for the spatiotemporal analysis of precipitation in developing countries with scarce gauge stations, limited analytic skills and insufficient computation resources. The approaches of this study can also be operationally applied to the analysis of other climate variables, such as temperature and solar radiation. Full article

Identification of complete drivers for phenology changes is crucial for developing
prediction models of plant phenology. In addition to climatic factors, the interaction among
phenological events has recently been reported as an important driver for the phenology changes
of forests, savannas, and grasslands. However, open questions remain as to whether the
phenological interaction exists in agricultural ecosystems, among which winter wheat plays a vital
role in feeding human beings. In this study, we investigated the interaction among the phenological
events of winter wheat in the North China Plain (NCP) using both field and satellite data.
Considering the large discrepancies between the existing satellite estimation and field
measurements of winter wheat phenology, we first improved the MODIS-based estimation of
green-up date (GUD), heading date (HD), and maturity date (MD) through a re-calibrated relative
threshold method (RTM) in the NCP. The GUD, HD, and MD were accurately estimated with the
mean absolute errors (MAE) and root mean squared errors (RMSE) lower than 7.5 days, compared
with the RMSEs ranging from 12.0 to 36.1 days in previous studies. Then, the relationships among
the GUD, HD, and MD were analyzed using the field data collected at agricultural meteorological
stations. The GUD (HD) showed a significantly positive correlation with the HD (MD).
Quantitatively, a one-day earlier GUD (HD) would result in an earlier HD (MD) of 0.57 days (0.60
days). Furthermore, we applied the partial correlation analysis to the improved MODIS estimation
of GUD, HD, and MD to investigate their interactions by considering the simultaneous influences
from climatic factors. The results showed that the HD (MD) with 85.2% (94.5%) of all winter wheat
pixels presented a significantly positive correlation with the GUD (HD). Meanwhile, the GUD (HD)
with 84.2% (33.3%) of the entire winter wheat area presented a significantly negative correlation
with pre-season temperature. These results suggest that both the climatic factors and phenological
interactions should be included in the future development of winter wheat phenology models to
improve the prediction accuracies. Full article

The northern and western isles of Scotland have proved fertile ground for archaeological investigation over the last 100 years. However, the nature of the landscape with its rugged coastlines and irregular topography, together with rapid peat growth rates, make for challenging surveying. Commonly, an archaeological monument or series of monuments is identified but little is known about the surrounding areas and, in particular, the palaeo-landscapes within which the monuments are located. This situation is exemplified by the standing stones of Calanais in Lewis. Here, surrounding peat bogs have buried a significant portion of the landscape around which the stones were first erected. This project identifies remote sensing geophysical techniques that are effective in mapping the buried (lost) landscape and thus aid better contextualisation of the stone monuments within it. Further, the project demonstrates the most appropriate techniques for prospecting across these buried landscapes for as yet unidentified stone features associated with the lives of the people who constructed the monuments. Full article

Random forest (RF) has obtained great success in hyperspectral image (HSI) classification. However, RF cannot leverage its full potential in the case of limited labeled samples. To address this issue, we propose a unified framework that embeds active learning (AL) and semi-supervised learning (SSL) into RF (ASSRF). Our aim is to utilize AL and SSL simultaneously to improve the performance of RF. The objective of the proposed method is to use a small number of manually labeled samples to train classifiers with relative high classification accuracy. To achieve this goal, a new query function is designed to query the most informative samples for manual labeling, and a new pseudolabeling strategy is introduced to select some samples for pseudolabeling. Compared with other AL- and SSL-based methods, the proposed method has several advantages. First, ASSRF utilizes the spatial information to construct a query function for AL, which can select more informative samples. Second, in addition to providing more labeled samples for SSL, the proposed pseudolabeling method avoids bias caused by AL-labeled samples. Finally, the proposed model retains the advantages of RF. To demonstrate the effectiveness of ASSRF, we conducted experiments on three real hyperspectral data sets. The experimental results have shown that our proposed method outperforms other state-of-the-art methods. Full article

Wet path delay (WPD) for satellite altimetry has been provided from external sources, raising the need of converting this value between different altitudes. The only expression available for this purpose considers the same altitude reduction, irrespective of geographic location and time. The focus of this study is the modelling of the WPD altitude dependence, aiming at developing improved expressions. Using ERA5 pressure level fields (2010–2013), WPD vertical profiles were computed globally. At each location and for each vertical profile, an exponential function was fitted using least squares, determining the corresponding decay coefficient. The time evolution of these coefficients reveals regions where they are highly variable, making this modelling more difficult, and regions where an annual signal exists. The output of this modelling consists of a set of so-called University of Porto (UP) coefficients, dependent on geographic location and time. An assessment with ERA5 data (2014) shows that for the location where the Kouba coefficient results in a maximum Root Mean Square (RMS) error of 3.2 cm, using UP coefficients this value is 1.2 cm. Independent comparisons with WPD derived from Global Navigation Satellite Systems and radiosondes show that the use of UP coefficients instead of Kouba’s leads to a decrease in the RMS error larger than 1 cm. Full article

Phytophthora root rot (PRR) disease is a major threat in avocado orchards, causing extensive production loss and tree death if left unmanaged. Regular assessment of tree health is required to enable implementation of the best agronomic management practices. Visual canopy appraisal methods such as the scoring of defoliation are subjective and subject to human error and inconsistency. Quantifying canopy porosity using red, green and blue (RGB) colour imagery offers an objective alternative. However, canopy defoliation, and porosity is considered a ‘lag indicator’ of PRR disease, which, through root damage, incurs water stress. Restricted transpiration is considered a ‘lead indicator’, and this study sought to compare measured canopy porosity with the restricted transpiration resulting from PRR disease, as indicated by canopy temperature. Canopy porosity was calculated from RGB imagery acquired by a smartphone and the restricted transpiration was estimated using thermal imagery acquired by a FLIR B250 hand-held thermal camera. A sample of 85 randomly selected trees were used to obtain RGB imagery from the shaded side of the canopy and thermal imagery from both shaded and sunlit segments of the canopy; the latter were used to derive the differential values of mean canopy temperature (Δ T_mean), crop water stress index (Δ CWSI), and stomatal conductance index (Δ I_g). Canopy porosity was observed to be exponentially, inversely correlated with Δ CWSI and Δ I_g (R² > 90%). The nature of the relationship also points to the use of canopy porosity at early stages of canopy decline, where defoliation has only just commenced and detection is often beyond the capability of subjective human assessment. Full article

The National Land Cover Database (NLCD) 2016 provides a suite of data products, including land cover and land cover change of the conterminous United States from 2001 to 2016, at two- to three-year intervals. The development of this product is part of an effort to meet the growing demand for longer temporal duration and more frequent, accurate, and consistent land cover and change information. To accomplish this, we designed a new land cover strategy and developed comprehensive methods, models, and procedures for NLCD 2016 implementation. Major steps in the new procedures consist of data preparation, land cover change detection and classification, theme-based postprocessing, and final integration. Data preparation includes Landsat imagery selection, cloud detection, and cloud filling, as well as compilation and creation of more than 30 national-scale ancillary datasets. Land cover change detection includes single-date water and snow/ice detection algorithms and models, two-date multi-index integrated change detection models, and long-term multi-date change algorithms and models. The land cover classification includes seven-date training data creation and 14-run classifications. Pools of training data for change and no-change areas were created before classification based on integrated information from ancillary data, change-detection results, Landsat spectral and temporal information, and knowledge-based trajectory analysis. In postprocessing, comprehensive models for each land cover theme were developed in a hierarchical order to ensure the spatial and temporal coherence of land cover and land cover changes over 15 years. An initial accuracy assessment on four selected Landsat path/rows classified with this method indicates an overall accuracy of 82.0% at an Anderson Level II classification and 86.6% at the Anderson Level I classification after combining the primary and alternate reference labels. This methodology was used for the operational production of NLCD 2016 for the Conterminous United States, with final produced products available for free download. Full article

Automated methods to extract buildings from very high resolution (VHR) remote sensing data have many applications in a wide range of fields. Many convolutional neural network (CNN) based methods have been proposed and have achieved significant advances in the building extraction task. In order to refine predictions, a lot of recent approaches fuse features from earlier layers of CNNs to introduce abundant spatial information, which is known as skip connection. However, this strategy of reusing earlier features directly without processing could reduce the performance of the network. To address this problem, we propose a novel fully convolutional network (FCN) that adopts attention based re-weighting to extract buildings from aerial imagery. Specifically, we consider the semantic gap between features from different stages and leverage the attention mechanism to bridge the gap prior to the fusion of features. The inferred attention weights along spatial and channel-wise dimensions make the low level feature maps adaptive to high level feature maps in a target-oriented manner. Experimental results on three publicly available aerial imagery datasets show that the proposed model (RFA-UNet) achieves comparable and improved performance compared to other state-of-the-art models for building extraction. Full article

This paper introduces a new algorithm (BUNTUS—Built-up, Nighttime Light, and Travel time for Urban Size) using remote sensing techniques to delineate urban boundaries. The paper is part of a larger study of the role of urbanisation in changing fossil fuel emissions. The method combines estimates of land cover, nighttime lights, and travel times to classify contiguous urban areas. The method is automatic, global and uses data sets with enough duration to establish trends. Validation using ground truth from Landsat-8 OLI images revealed an overall accuracy ranging from 60% to 95%. Thus, this approach is capable of describing spatial distributions and giving detailed information of urban extents. We demonstrate the method with examples from Brisbane, Australia, Melbourne, Australia, and Beijing, China. The new method meets the criteria for studying overall trends in urban emissions. Full article

This study focuses on the evaluation of global Haiyang-2A satellite scatterometer (HSCAT) operational wind products from 2012 to 2018. In order to evaluate HSCAT winds, HSCAT operational wind products were collocated with buoy measurements and rainfall data. Error varieties under different atmospheric stratification and rainfall conditions were taken into consideration. After data quality control, the average bias and root mean square error (RMSE) between buoys and HSCAT data were 0.1 m/s and 1.3 m/s for wind speed, and 1° and 27° for wind direction, respectively. Especially, the varieties of the wind direction difference change a lot under non-neutral atmospheric conditions. HSCAT wind speeds are overestimated with an increasing rainfall rate while wind directions tend to be perpendicular to buoys’. In brief, the HSCAT wind product qualities are not stable during 2012 to 2018, especially for the data in 2015 and 2016. Atmospheric stratification and rain effects should be considered in wind retrieval and marine application. Full article

This article presents an approach to identify Green Infrastructure (GI), its benefits and condition. This information enables environmental agencies to prioritise conservation, management and restoration strategies accordingly. The study focuses on riparian areas due to their potential to supply Ecosystem Services (ES), such as water quality, biodiversity, soil protection and flood or drought risk reduction. Natural Water Retention Measures (NWRM) related to agriculture and forestry are the type of GI considered specifically within these riparian areas. The approach is based on ES condition indicators, defined by the European Environment Agency (EEA) to support the policy targets of the 2020 Biodiversity Strategy. Indicators that can be assessed through remote sensing techniques are used, namely: capacity to provide ecosystem services, proximity to protected areas, greening response and water stress. Specifically, the approach uses and evaluates the potential of freely available products from the Copernicus Land Monitoring Service (CLMS) to monitor GI. Moreover, vegetation and water indices are calculated using data from the Sentinel-2 MSI Level-2A scenes and integrated in the analysis. The approach has been tested in the Italian Po river basin in 2018. Firstly, agriculture and forest NWRM were identified in the riparian areas of the river network. Secondly, the Riparian Zones products from the CLMS local component and the satellite-based indices were linked to the aforementioned ES condition indicators. This led to the development of a pixel-based model that evaluates the identified GI according to: (i) its disposition to provide riparian regulative ES and (ii) its condition in the analysed year. Finally, the model was used to prioritise GI for conservation or restoration initiatives, based on its potential to deliver ES and current condition. Full article

Synthetic aperture radar (SAR) satellite data provide a valuable means for the large-scale and long-term monitoring of structural components of forest stands. The potential of TanDEM-X interferometric SAR (InSAR) for the assessment of forest structural properties has been widely verified. However, present studies are mostly restricted to homogeneous forests and do not account for stratification in assessing model performance. A systematic sensitivity analysis of the TanDEM-X SAR signal to forest structural parameters was carried out with emphasis on different strata of forest stands (location of the study site, forest type, and development stage). Forest structure was parameterized by forest height metrics and stem volume. Results show that X-band volume coherence is highly sensitive to the forest canopy. Volume scattering within the canopy is dependent on the vertical heterogeneity of the forest stand. In general, TanDEM-X coherence is more sensitive to forest vertical structure compared to backscatter. The relations between TanDEM-X volume coherence and forest structural properties were significant at the level of a single test site as well as across sites in temperate forests in Germany. Forest type does not affect the overall relationship between the SAR signal and the forests’ vertical structure. The prediction of forest structural parameters based on the outcome of the sensitivity analysis yielded model accuracies between 15% (relative root mean square error) for Lorey’s height and 32% for stem volume. The global database of single-polarized bistatic TanDEM-X data provides an important source for mapping structural parameters in temperate forests at large scale, irrespective of forest type. Full article

The land use structure is a key component to understand the complexity of urban systems because it provides a snapshot of urban dynamics and how people use space. This paper integrates socially sensed activity data with a remotely sensed land cover product in order to infer urban land use and its changes over time. We conducted a case study in the Washington D.C.–Baltimore metropolitan area to identify the pattern of land use change from undeveloped to developed land, including residential and non-residential uses for a period covering 1986–2008. The proposed approach modeled physical and behavioral features of land parcels from a satellite-based impervious surface cover change product and georeferenced Tweets, respectively. A model assessment with random forests classifiers showed that the proposed classification workflow could classify residential and non-residential land uses at an accuracy of 81%, 4% better than modeling the same land uses from physical features alone. Using the timestamps of the impervious surface cover change product, the study also reconstructed the timeline of the identified land uses. The results indicated that the proposed approach was capable of mapping detailed land use and change in an urban region, and represents a new and viable way forward for urban land use surveying that could be especially useful for surveying and tracking changes in cities where traditional approaches and mapping products (i.e., from remote sensing products) may have a limited capacity to capture change. Full article

Peas (Pisum sativum L.) belong among the world’s oldest domesticated crops, serving as a source of proteins, complex carbohydrates, vitamins and minerals. Autumn sowing allows a higher biomass production as well as the avoidance of the drought and heat stresses of late spring. However, the character of European continental winters limits plant growth and development through cold stress. This work sought parameters that reflect the cold tolerance of pea plants and consequently to suggest an afila-type pea cultivar with resilience to European continental winters. For this purpose, we employed indoor remote sensing technology and compared the 22-day-long acclimation to 5 °C of four pea cultivars: Arkta, with normal leaves and the known highest cold resistance to European continental winters, and Enduro, Terno and CDC Le Roy, all of the afila type. Besides evaluation of shoot growth rate and quenching analysis of chlorophyll fluorescence (ChlF) by imaging methods, we measured the chlorophyll content and ChlF induction with a nonimaging fluorometer. Here we show that the acclimation to cold of the Arkta exhibits a different pattern than the other cultivars. Arkta showed the fastest retardation of photosynthesis and shoot growth, which might be part of its winter survival strategy. Terno, on the other hand, showed sustained photosynthetic performance and growth, which might be an advantageous strategy for spring. Surprisingly, Enduro showed sustained photosynthesis in the stipules, which transferred and acclimated to 5 °C (cold-acclimated). However, of all the cultivars, Enduro had the strongest inhibition of photosynthesis in new stipules that developed after the transition to cold (cold-developed). We conclude that the parameters of ChlF spatial imaging calculated as averages from whole plants are suboptimal for the characterization of various cold acclimation strategies. The most marked changes were obtained when the new cold-developed leaves were analyzed separately from the rest of the plant. Full article

Recovery trajectories derived from remote sensing data are widely used to monitor ecosystem recovery after disturbance events, but these trajectories are often retrieved without a precise understanding of the land cover within a scene. As a result, the sources of variability in post-disturbance recovery trajectories are poorly understood. In this study, we monitored the recovery of chaparral and conifer species following the 2007 Zaca Fire, which burned 97,270 ha in Santa Barbara County, California. We combined field survey data with two time series remote sensing products: the relative delta normalized burn ratio (RdNBR) and green vegetation (GV) fractions derived from spectral mixture analysis. Recovery trajectories were retrieved for stands dominated by six different chaparral species. We also retrieved recovery trajectories for stands of mixed conifer forest. We found that the two remote sensing products were equally effective at mapping vegetation cover across the burn scar. The GV fractions (r(78) = 0.552, p < 0.001) and normalized burn ratio (r(78) = 0.555, p < 0.001) had nearly identical correlations with ground reference data of green vegetation cover. Recovery of the chaparral species was substantially affected by the 2011–2017 California drought. GV fractions for the chaparral species generally declined between 2011 and 2016. Physiological responses to fire and drought were important sources of variability between the species. The conifer stands did not exhibit a drought signal that was directly correlated with annual precipitation, but the drought likely delayed the return to pre-fire conditions. As of 2018, 545 of the 756 conifer stands had not recovered to their pre-fire GV fractions. Spatial and temporal variation in species composition were important sources of spectral variability in the chaparral and conifer stands. The chaparral stands in particular had highly heterogeneous species composition. Dominant species accounted for between 30% and 53% of the land cover in the surveyed chaparral patches, so non-dominant land cover types strongly influenced remote sensing signals. Our study reveals that prolonged drought can delay or alter the post-fire recovery of Mediterranean ecosystems. It is also the first study to critically examine how fine-scale variability in land cover affects time series remote sensing analyses. Full article

Accurate and spatially-distributed precipitation information is vital to the study of the regional hydrological cycle and water resources, as well as for environmental management. To provide high spatio-temporal resolution precipitation estimates over insufficient rain-gauge areas, great efforts have been taken in using the Normalized Difference Vegetation Index (NDVI) and other land surface variables to improve the spatial resolution of satellite precipitation datasets. However, the strong spatio-temporal heterogeneity of precipitation and the “hysteresis phenomenon” of the relationship between precipitation and vegetation has limited the application of these downscaling methods to high temporal resolutions. To overcome this limitation, a new temporal downscaling method was proposed in this study by introducing daily soil moisture data to explore the relationship between precipitation and the soil moisture increment index. The performance of this proposed temporal downscaling was assessed by downscaling the Tropical Rainfall Measuring Mission (TRMM) precipitation data from a monthly scale to a daily scale over the Hekouzhen to Tongguan of the Yellow River in 2013, and the downscaled daily precipitation datasets were validated with in-situ measurement from 23 rainfall observation stations. The validation results indicate that the downscaled daily precipitation agrees with the rain gauge observations, with a correlation coefficient of 0.59, a mean error range of 1.70 mm, and a root mean square error of 5.93 mm. In general, the monthly precipitation decomposition method proposed in this paper has combined the advantage of both microwave remote sensing products. It has acceptable precision and can generate precipitation on a diurnal scale. It is an important development in the field of using auxiliary data to perform temporal downscaling. Furthermore, this method also provides a reference example for the temporal downscaling of other low temporal resolution datasets. Full article

3D semantic segmentation of point cloud aims at assigning semantic labels to each point by utilizing and respecting the 3D representation of the data. Detailed 3D semantic segmentation of urban areas can assist policymakers, insurance companies, governmental agencies for applications such as urban growth assessment, disaster management, and traffic supervision. The recent proliferation of remote sensing techniques has led to producing high resolution multimodal geospatial data. Nonetheless, currently, only limited technologies are available to fuse the multimodal dataset effectively. Therefore, this paper proposes a novel deep learning-based end-to-end Point-wise LiDAR and Image Multimodal Fusion Network (PMNet) for 3D segmentation of aerial point cloud by fusing aerial image features. PMNet respects basic characteristics of point cloud such as unordered, irregular format and permutation invariance. Notably, multi-view 3D scanned data can also be trained using PMNet since it considers aerial point cloud as a fully 3D representation. The proposed method was applied on two datasets (1) collected from the urban area of Osaka, Japan and (2) from the University of Houston campus, USA and its neighborhood. The quantitative and qualitative evaluation shows that PMNet outperforms other models which use non-fusion and multimodal fusion (observational-level fusion and feature-level fusion) strategies. In addition, the paper demonstrates the improved performance of the proposed model (PMNet) by over-sampling/augmenting the medium and minor classes in order to address the class-imbalance issues. Full article

This study investigates synthetic aperture radar (SAR) time series of the Sentinel-1 mission acquired over the Atacama Desert, Chile, between March 2015 and December 2018. The contribution analyzes temporal and spatial variations of Sentinel-1 interferometric SAR (InSAR) coherence and exemplarily illustrates factors that are responsible for observed signal differences. The analyses are based on long temporal baselines (365–1090 days) and temporally dense time series constructed with short temporal baselines (12–24 days). Results are compared to multispectral data of Sentinel-2, morphometric features of the digital elevation model (DEM) TanDEM-X WorldDEM™, and to a detailed governmental geographic information system (GIS) dataset of the local hydrography. Sentinel-1 datasets are suited for generating extensive, nearly seamless InSAR coherence mosaics covering the entire Atacama Desert (>450 × 1100 km) at a spatial resolution of 20 × 20 meter per pixel. Temporal baselines over several years lead only to very minor decorrelation, indicating a very high signal stability of C-Band in this region, especially in the hyperarid uplands between the Coastal Cordillera and the Central Depression. Signal decorrelation was associated with certain types of surface cover (e.g., water or aeolian deposits) or with actual surface dynamics (e.g., anthropogenic disturbance (mining) or fluvial activity and overland flow). Strong rainfall events and fluvial activity in the periods 2015 to 2016 and 2017 to 2018 caused spatial patterns with significant signal decorrelation; observed linear coherence anomalies matched the reference channel network and indicated actual episodic and sporadic discharge events. In the period 2015–2016, area-wide loss of coherence appeared as strip-like patterns of more than 80 km length that matched the prevailing wind direction. These anomalies, and others observed in that period and in the period 2017–2018, were interpreted to be caused by overland flow of high magnitude, as their spatial location matched well with documented heavy rainfall events that showed cumulative precipitation amounts of more than 20 mm. Full article