This guest editorial introduces the JARS Special Section on Recent Advances in Geophysical Sensing of the Ocean: Remote and In Situ Methods.

Photosynthetically available radiation (PAR) and its attenuation with the depth represent a forcing (source) term in the governing equation for the temperature in the oceanic dynamical models. PAR usually comes from the atmospheric model predictions, whereas PAR’s attenuation schemes are internally prescribed (estimated) inside the oceanic dynamical model. We perform sensitivity analyses to investigate the impact that errors in model surface PAR and vertical attenuation of PAR have on the upper ocean model heat content. In the Monterey Bay area, we show that with a decrease in water clarity, the relative error in surface PAR introduces a larger error in the modeled upper 25 m ocean heat content than the same magnitude relative error in the attenuation coefficient. For Jerlov’s type “IA” water (attenuation coefficient is 0.049  m−1), the relative error in surface PAR introduces an error twice as large into the model heat content as the same magnitude relative error in the attenuation coefficient. For the more turbid water Jerlov’s type “III” (attenuation coefficient is 0.127  m−1), the relative error in surface PAR introduces error seven times as large into the model heat content than the same magnitude relative error in the attenuation coefficient. We present how the upper ocean heat content sensitivities to errors in PAR and its attenuation change in space and time. While the sensitivities to the errors in surface PAR are all positive, sensitivities to the errors in attenuation coefficient have positive and negative values, depending on location. They are positive in shallower water for locations on the shelf in the northern part of the bay and negative in deeper offshore waters. Sensitivities derived provide a capability to understand and control the impact of errors in PAR and its attenuation on the upper ocean model heat content predictions.

Comparison of Jason-1 altimetry retracked sea levels and high frequency (HF) radar velocity is examined within the region of the Great Barrier Reef, Australia. The comparison between both datasets is not direct because the altimetry derives only the geostrophic component, while the HF radar velocity includes information on both geostrophic and ageostrophic components, such as tides and winds. The comparison of altimetry and HF radar data is performed based on the parameter of surface velocity inferred from both datasets. The results show that 48% (10 out of 21 cases) of data have high (≥0.5) spatial correlation. The mean of spatial correlation for all 21 cases is 0.43. This value is within the range (0.42 to 0.5) observed by other studies. Low correlation is observed due to disagreement in the trend of velocity signals in which sometimes they have contradictions in the signal direction and the position of the peak is shifted. In terms of standard deviation of difference and root mean square error, both datasets show reasonable agreement with ≤2.5  cm s−1.

Oil spills on the ocean surface cause serious environmental, political, and economic problems. Therefore, these catastrophic threats to marine ecosystems require detection and monitoring. Hyperspectral sensors are powerful optical sensors used for oil spill detection with the help of detailed spectral information of materials. However, huge amounts of data in hyperspectral imaging (HSI) require fast and accurate computation methods for detection problems. Support vector data description (SVDD) is one of the most suitable methods for detection, especially for large data sets. Nevertheless, the selection of kernel parameters is one of the main problems in SVDD. This paper presents a method, inspired by ensemble learning, for improving performance of SVDD without tuning its kernel parameters. Additionally, a classifier selection technique is proposed to get more gain. The proposed approach also aims to solve the small sample size problem, which is very important for processing high-dimensional data in HSI. The algorithm is applied to two HSI data sets for detection problems. In the first HSI data set, various targets are detected; in the second HSI data set, oil spill detection in situ is realized. The experimental results demonstrate the feasibility and performance improvement of the proposed algorithm for oil spill detection problems.

Following the launch of the Suomi National Polar-orbiting Partnership satellite in October 2011 with the Visible Infrared Imager Radiometer Suite (VIIRS) sensor onboard, National Oceanic and Atmospheric Administration (NOAA) started generating a global level 2 preprocessed (L2P) sea surface temperature (SST) product. The NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) L2P data are organized into 144 10-min granules per day, with a total volume of ∼27  GB. The L2P product has been successfully assimilated in several level 4 (L4) analyses. At the same time, some other users requested a gridded level 3 (L3) product with a reduced data volume. An L3U “uncollated” product (in which multiple passes over the same grid are independently saved) was produced by mapping the L2P product into equal 0.02° grids. Similar to the L2P, the L3U data are also reported in 10-min granules, but with a daily volume <1  GB. Currently, the NOAA VIIRS L3U SST product is operationally used or tested in several major international numerical weather prediction centers. The L3U shows comparable performance with L2P, suggesting that both products can be used interchangeably as input into L4 analyses. The original L2P pixel-level swath data continue to be produced and available to interested users from NOAA (NCEI) and JPL (Physical Oceanography) data archives.

Measurements of diurnal changes in ocean color in turbid coastal regions in the Gulf of Mexico were characterized using above water spectral radiometry from a National Aeronautics and Space Administration (aerosol robotic network-WaveCIS CSI-06) site that can provide 8 to 10 observations per day. Satellite capability to detect diurnal changes in ocean color was characterized using hourly overlapping afternoon orbits of the visual infrared imaging radiometer suite (VIIRS) Suomi National Polar-orbiting Partnership ocean color sensor and validated with in situ observations. The monthly cycle of diurnal changes was investigated for different water masses using VIIRS overlaps. Results showed the capability of satellite observations to monitor hourly color changes in coastal regions that can be impacted by vertical movement of optical layers, in response to tides, resuspension, and river plume dispersion. The spatial variability of VIIRS diurnal changes showed the occurrence and displacement of phytoplankton blooming and decaying processes. The diurnal change in ocean color was above 20%, which represents a 30% change in chlorophyll-a. Seasonal changes in diurnal ocean color for different water masses suggest differences in summer and winter responses to surface processes. The diurnal changes observed using satellite ocean color can be used to define the following: surface processes associated with biological activity, vertical changes in optical depth, and advection of water masses.

The compressive line sensing imaging system adopts distributed compressive sensing (CS) to acquire data and reconstruct images. Dynamic CS uses Bayesian inference to capture the correlated nature of the adjacent lines. An image reconstruction technique that incorporates dynamic CS in the distributed CS framework was developed to improve the quality of reconstructed images. The effectiveness of the technique was validated using experimental data acquired in an underwater imaging test facility. Results that demonstrate contrast and resolution improvements will be presented. The improved efficiency is desirable for unmanned aerial vehicles conducting long-duration missions.

We apply a neural network (NN) technique to detect/track Karenia brevis harmful algal blooms (KB HABs) plaguing West Florida shelf (WFS) coasts from Visible-Infrared Imaging Radiometer Suite (VIIRS) satellite observations. Previously KB HABs detection primarily relied on the Moderate Resolution Imaging Spectroradiometer Aqua (MODIS-A) satellite, depending on its remote sensing reflectance signal at the 678-nm chlorophyll fluorescence band (Rrs678) needed for normalized fluorescence height and related red band difference retrieval algorithms. VIIRS, MODIS-A’s successor, does not have a 678-nm channel. Instead, our NN uses Rrs at 486-, 551-, and 671-nm VIIRS channels to retrieve phytoplankton absorption at 443 nm (aph443). The retrieved aph443 images are next filtered by applying limits, defined by (i) low Rrs551-nm backscatter and (ii) a minimum aph443 value associated with KB HABs. The filtered residual images are then converted to show chlorophyll-a concentrations [Chla] and KB cell counts. VIIRS retrievals using our NN and five other retrieval algorithms were compared and evaluated against numerous in situ measurements made over the four-year 2012 to 2016 period, for which VIIRS data are available. These comparisons confirm the viability and higher retrieval accuracies of the NN technique, when combined with the filtering constraints, for effective detection of KB HABs. Analysis of these results as well as sequential satellite observations and recent field measurements underline the importance of short-term temporal variabilities on retrieval accuracies.

We introduced a laser-based noncontact shallow water depth measurement technique from a flying unmanned aerial vehicle (UAV). The water depth is measured by imaging two laser beam spots scattered from the surface and bottom of the water. The effect of water surface waves and UAV tilt angles to the depth measurement has been studied for practical applications. We have further developed this laser-based detection system consisting of a green laser, a global positioning system, a camera with a narrow field of view lens, a laser range finder, and a single-board computer. The measurement system onboard of a UAV flying over a small lake has demonstrated satisfactory water depth measurement capability. The low-cost light weight UAV-based water depth measurement should benefit water depth monitoring, mapping, and reporting in a hazardous environment offering flexibility, mobility, and remote control safe operation.

The exchange of coastal waters between the Mississippi Sound (MSS), Mobile Bay, and Mississippi Bight is an important pathway for oil and pollutants into coastal ecosystems. This study investigated an event of strong and persistent inflow of shelf waters into MSS and Mobile Bay during October 2015 by combining in situ measurements, satellite ocean color data, and ocean model predictions. Navy Coastal Ocean Model predicted high-salinity shelf waters continuously flowing into the estuarine system and forecasted low-salinity waters trapped inside the estuaries which did not flush out until the passage of tropical cyclone Patricia’s remnants in late October. The October 2015 chlorophyll-a anomaly was significantly low inside and outside the MSS for the 2003 to 2015 time series. Similar low-chlorophyll-a anomalies were only seen in 2003. The October 2015 mean in situ salinities were up to 8 psu higher than mean from 2007 to 2015, and some estuarine stations showed persistent salinities above 30 psu for almost a month in agreement with model predictions. October 2015 was associated with low fall seasonal discharge, typical of fall season, and wind which was persistently out of the east to southeast [45–180]°. These persistent wind conditions were linked to the observed anomalous conditions.

The problem of waveform optimization design for cognitive radar (CR) in the presence of extended target with unknown target impulse response (TIR) is investigated. On the premise of ensuring the TIR estimation precision, a flexible waveform-constrained optimization design method taking both target detection and range resolution into account is proposed. In this method, both the estimate of TIR and transmitted waveform can be updated according to the environment information fed back by the receiver. Moreover, rather than optimizing waveforms for a single design criterion, the framework can synthesize waveforms that provide a trade-off between competing design criteria. The trade-off is determined by the parameter settings, which can be adjusted according to the requirement of radar performance in each cycle of CR. Simulation results demonstrate that CR with the proposed waveform performs better than a traditional radar system with a fixed waveform and offers more flexibility and practicability.

Local binary patterns (LBPs) have been extensively used to yield spatial features for the classification of general imagery, and a few recent works have applied these patterns to the classification of hyperspectral imagery. Although the conventional LBP formulation employs only the signs of differences between a central pixel and its surrounding neighbors, it has been recently demonstrated that the difference magnitudes also possess discriminative information. Consequently, a sign-and-magnitude LBP is proposed to provide a spatial–spectral class-conditional probability for a Bayesian maximum a posteriori formulation of hyperspectral classification wherein the prior probability is provided by a Markov random field. Experimental results demonstrate that the performance of the proposed approach is superior to that of other state-of-the-art algorithms, tending to result in smoother classification maps with fewer erroneous outliers even in the presence of noise.

The configuration of multiple uniformly spaced channels in azimuth can achieve high-resolution and wide-swath images for synthetic aperture radar (SAR) systems. The unambiguous Doppler spectrum can be reconstructed via digital beamforming (DBF) techniques in the azimuth multichannel SAR system. However, the performance of DBF deteriorates significantly because of the inevitable channel errors in practical applications. Since the SAR antennas are generally unweighted in azimuth, the power of the SAR signal is symmetrically distributed around the Doppler centroid in the azimuth frequency domain. Based on this observation, the covariance matrix of multichannel output in the range-Doppler domain can be regarded as a real matrix when there is no existence of channel errors and the correction of the baseband Doppler centroid is applied. Consequently, we propose a fast calibration algorithm to calculate channel phase errors. The proposed method can acquire the estimation of phase errors directly from the covariance matrix. Thus, the proposed method has the properties of high robustness and low computation load. Theoretical analysis and experiments validate the performance and the efficiency of the proposed algorithm.

By exploiting the prior information of azimuth-elevation (AE), a sparse recovery (SR)-based space-time adaptive processing (STAP) method called AESR-STAP is proposed. First, the supercomplete properties matrix in the SR is built based on flight configuration prior information such as elevation and azimuth to estimate the azimuth-elevation spectrum of the ground clutter in different range cells. Then, the range ambiguity STAP clutter can be eliminated according to the difference of the elevation of the clutter in different range cells. Finally, the AESR-STAP utilizes the spatial and temporal coupling relation of the ground clutter, namely, the nonlinear relation among the azimuth, the elevation, Doppler frequency, and spatial frequency, to estimate the clutter distribution in time and space dimensions and to calculate the clutter covariance matrix. Theoretical analysis and simulation experiments have shown that the proposed method can estimate the temporal and spatial distribution more accurately, eliminate the range ambiguity STAP clutter to improve the clutter suppression of the airborne radar, and effectively detect ground low-speed targets.

We present a framework of fast machine learning algorithms in the context of large-sized hyperspectral images classification from the theoretical to a practical viewpoint. In particular, we assess the performance of random forest (RF), rotation forest (RoF), and extreme learning machine (ELM) and the ensembles of RF and ELM. These classifiers are applied to two large-sized hyperspectral images and compared to the support vector machines. To give the quantitative analysis, we pay attention to comparing these methods when working with high input dimensions and a limited/sufficient training set. Moreover, other important issues such as the computational cost and robustness against the noise are also discussed.

With the development of earth observation technology, the spatial and temporal resolution of remote sensing images have improved dramatically, providing abundant data for detecting land use change in detail. However, extracting spatio-temporal and typological changes in land use from remote sensing image time series is still challenging. Landsat image time series and land survey data are combined to develop a method to detect in detail the time and types of land use change. From the Landsat images, the time series of normalized difference vegetation index variation slope (TSNVS) on each pixel was constructed. The average TSNVS of sample pixels was used as the reference series. Based on TSNVS, the change in land use at six time points (S1 to S6) was detected by measuring the similarity of the time series on undetermined pixels and reference series. Land survey data were used to identify detailed types of land use change. The 16 different types of land use change were identified. The results indicate that the proposed method is effective at detecting spatio-temporal and typological changes in land use from Landsat image time series. After verification, the overall accuracy was 92%, and the kappa coefficient was 0.9.

The classification of hyperspectral images benefits greatly from integration of spectral information and spatial context. There have been many means to incorporate spatial information into the classification, such as the Markov random field, extended morphological profiles, and segmentation-based methods. Recently, spatial filtering was introduced to improve the classification accuracy of hyperspectral images. Compared with other spectral-spatial algorithms, spatial filtering is simple and easy to implement. This advantage makes it suitable for practical applications. However, spatial filtering has not been given enough attention. A comprehensive comparative study of spatial filtering is conducted. Specifically, 10 kinds of filters are used to smooth the hyperspectral images and the classified maps, respectively. The experimental results show that most filtering-based classification methods perform well with high efficiency.

High-resolution satellite imagery permits verification of human rights land clearance violations across international borders as a result of unstable regimes or socio-economic upheaval. Without direct access to these areas to validate allegations of human rights abuse, the use of remote sensing tools, techniques, and data is extremely important. Humanitarian assessment can benefit from software-based solutions, involving radiometrically calibrated normalized difference vegetation index and temporal change imagery. We discuss the introduction of a matrix filter approach for change detection studies to help assist rapid building detection over large search areas against a bright background to evaluate internally displaced people in the 2005 Porta Farm Zimbabwe clearances. Future wide-scale near real-time space-based monitoring with a range of digital filters would be of great benefit to international human rights observers and human rights networks.

This study introduces a practical approach to implement real-time signal processing algorithms for general surveillance radar based on NVIDIA graphical processing units (GPUs). The pulse compression algorithms are implemented using compute unified device architecture (CUDA) libraries such as CUDA basic linear algebra subroutines and CUDA fast Fourier transform library, which are adopted from open source libraries and optimized for the NVIDIA GPUs. For more advanced, adaptive processing algorithms such as adaptive pulse compression, customized kernel optimization is needed and investigated. A statistical optimization approach is developed for this purpose without needing much knowledge of the physical configurations of the kernels. It was found that the kernel optimization approach can significantly improve the performance. Benchmark performance is compared with the CPU performance in terms of processing accelerations. The proposed implementation framework can be used in various radar systems including ground-based phased array radar, airborne sense and avoid radar, and aerospace surveillance radar.

This paper presents a practical approach to target detection for hyperspectral images. In target detection, it is normally assumed that the ground truth target signatures collected in a laboratory are available and one then uses them to search for targets in a given image. However, directly applying the laboratory signatures to the real data is not appropriate due to environmental differences between the ground truth data and real data. Conventional atmospheric compensation schemes such as the use of MODTRAN can help to improve the target detection performance. However, the computational load is huge and thus real-time applications may prohibit this compensation approach. We present results of an alternative compensation technique known as in-scene compensation, which is appealing as no complicated techniques such as MODTRAN are needed. Two in-scene methods for visible near-infrared/short-wave infrared range have been developed in the literature: empirical line method (ELM) and vegetation normalization (VN). Both approaches have advantages and disadvantages. We propose a hybrid in-scene compensation method that can be considered as a combination of ELM and VN and we call our method ELM augmented VN (EAVN). One key advantage of EAVN is that it combines the advantages of ELM and VN and eliminates their disadvantages. Compared to ELM, there is no need for two or more known target pixels in the test scene. Compared to VN, there is no need for dark pixels. Extensive experimental results using ground-based sensor data showed that the EAVN algorithm provides excellent compensation to environmental changes. After compensation, the receiver operating characteristics performance of target detection has been significantly improved by orders of magnitude in a number of cases, as compared to two standard compensation methods: quick atmospheric correction and internal average relative reflectance correction.

The inverse synthetic aperture radar (ISAR) imaging of high-speed targets is affected significantly by the phase modulation induced by the high-speed motion. To improve the imaging quality and efficiently suppress the influence of high-speed motion, a method of ISAR imaging via parametric sparse representation is proposed for high-speed targets. First, the echo is dynamically represented as a sparse signal via a flexible parametric sensing matrix according to the target high-speed motion. Subsequently, the sensing matrix is optimized through adaptive computation, during which the target velocity estimation is also achieved. Finally, the ISAR image of high-speed targets can be reconstructed with sparse sampling data. Compared to the existing method based on compressed sensing, the proposed method produces comparative imaging quality with less computational complexity and better robustness. Simulations are performed to validate the effectiveness of the method.

Archival aerial photographs are often the only reliable source of information about the area. However, these data are single-band data that do not allow unambiguous detection of particular forms of land cover. Thus, the authors of this article seek to develop a method of coloring panchromatic aerial photographs, which enable increasing the spectral information of such images. The study used data integration algorithms based on pansharpening, implemented in commonly used remote sensing programs: ERDAS, ENVI, and PCI. Aerial photos and Landsat multispectral data recorded in 1987 and 2016 were chosen. This study proposes the use of modified intensity-hue-saturation and Brovey methods. The use of these methods enabled the addition of red-green-blue (RGB) components to monochrome images, thus enhancing their interpretability and spectral quality. The limitations of the proposed method relate to the availability of RGB satellite imagery, the accuracy of mutual orientation of the aerial and the satellite data, and the imperfection of archival aerial photographs. Therefore, it should be expected that the results of coloring will not be perfect compared to the results of the fusion of recent data with a similar ground sampling resolution, but still, they will allow a more accurate and efficient classification of land cover registered on archival aerial photographs.

The performance of the weather research and forecasting (WRF) model relies on the accuracy of the initial field provided by data assimilation. The initial field usually contains large uncertainties, especially for regions where observations are sparse or lacking. Assimilating additional observations is an efficient way to reduce this uncertainty. The moderate resolution imaging spectroradiometer (MODIS) is one of the most critical data sources of various indirect assimilating applications due to its wide swath and remarkable quality. Therefore, assimilating MODIS data into WRF data assimilation (WRFDA) system is meaningful to improve the accuracy of weather forecasts. This study developed a module to directly assimilate the MODIS radiances into the WRFDA based on the 3-D variational data assimilation method and community radiative transfer model. An assimilation experiment was carried out on August 2014, from which the background field has been relatively improved. Specifically, the improvement of the temperature, humidity, and wind speed at the near surface layer is about 0.2°C, 1.2%, and 0.2  m/s, respectively. Additional capabilities and increased potential of MODIS data assimilation based on WRFDA need to be further investigated and tested under various conditions and applications.

This study evaluated the effect of flight altitude and canopy separation of container-grown Fire Chief™ arborvitae (Thuja occidentalis L.) on counting accuracy. Images were taken at 6, 12, and 22 m above the ground using unmanned aircraft systems. Plants were spaced to achieve three canopy separation treatments: 5 cm between canopy edges, canopy edges touching, and 5 cm of canopy edge overlap. Plants were placed on two different ground covers: black fabric and gravel. A counting algorithm was trained using Feature Analyst®. Total counting error, false positives, and unidentified plants were reported for images analyzed. In general, total counting error was smaller when plants were fully separated. The effect of ground cover on counting accuracy varied with the counting algorithm. Total counting error for plants placed on gravel (−8) was larger than for those on a black fabric (−2), however, false positive counts were similar for black fabric (6) and gravel (6). Nevertheless, output images of plants placed on gravel did not show a negative effect due to the ground cover but was impacted by differences in image spatial resolution.

Accurate mapping and monitoring of savanna and semiarid woodland biomes are needed to support the selection of areas of conservation, to provide sustainable land use, and to improve the understanding of vegetation. The potential of geostatistical features, derived from medium spatial resolution satellite imagery, to characterize contrasted landscape vegetation cover and improve object-based image classification is studied. The study site in Brazil includes cerrado sensu stricto, deciduous forest, and palm swamp vegetation cover. Sentinel 2 and Landsat 8 images were acquired and divided into objects, for each of which a semivariogram was calculated using near-infrared (NIR) and normalized difference vegetation index (NDVI) to extract the set of geostatistical features. The features selected by principal component analysis were used as input data to train a random forest algorithm. Tests were conducted, combining spectral and geostatistical features. Change detection evaluation was performed using a confusion matrix and its accuracies. The semivariogram curves were efficient to characterize spatial heterogeneity, with similar results using NIR and NDVI from Sentinel 2 and Landsat 8. Accuracy was significantly greater when combining geostatistical features with spectral data, suggesting that this method can improve image classification results.

The concentration of forage fiber content is critical in explaining the palatability of forage quality for livestock grazers in tropical grasslands. Traditional methods of determining forage fiber content are usually time consuming, costly, and require specialized laboratory analysis. With the potential of remote sensing technologies, determination of key fiber attributes can be made more accurately. This study aims to determine the effectiveness of known absorption wavelengths for detecting forage fiber biochemicals, neutral detergent fiber, acid detergent fiber, and lignin using hyperspectral data. Hyperspectral reflectance spectral measurements (350 to 2500 nm) of grass were collected and implemented within the random forest (RF) ensemble. Results show successful correlations between the known absorption features and the biochemicals with coefficients of determination (R2) ranging from 0.57 to 0.81 and root mean square errors ranging from 6.97 to 3.03  g/kg. In comparison, using the entire dataset, the study identified additional wavelengths for detecting fiber biochemicals, which contributes to the accurate determination of forage quality in a grassland environment. Overall, the results showed that hyperspectral remote sensing in conjunction with the competent RF ensemble could discriminate each key biochemical evaluated. This study shows the potential to upscale the methodology to a space-borne multispectral platform with similar spectral configurations for an accurate and cost effective mapping analysis of forage quality.

This paper presents two decomposition schemes for polarimetric synthetic aperture radar data. The proposed schemes intend to overcome the problem of scattering ambiguity and reduce the volume scattering power in oriented urban areas. The first proposed scheme uses an empirical volume model based on the correlation coefficients of the Pauli component in the horizontal–vertical basis, whereas the second one employs a volume model defined on correlation coefficients of the Pauli components expressed in the circular basis. The correlation coefficients are calculated from polarimetric interferometric synthetic aperture radar (PolInSAR) data. The characteristics adopted from these volume models are used to enhance the results of the decomposition schemes. The scattering powers estimated from the proposed methods give promising results compared to existing methods in the literature, particularly in urban areas since all the oriented built-up areas are well discriminated as double or odd bounce scattering. The methods are evaluated using the experimental airborne SAR sensor (E-SAR) PolInSAR L band data acquired on the Oberpfaffenhofen test site in Germany.

Colored dissolved organic matter (CDOM) and chlorophyll-a (Chla) are important water quality parameters and play crucial roles in aquatic environment. Remote sensing of CDOM and Chla concentrations for inland lakes is often limited by low spatial resolution. The newly launched Sentinel-2 satellite is equipped with high spatial resolution (10, 20, and 60 m). Empirical band ratio models were developed to derive CDOM and Chla concentrations in Lake Huron. The leave-one-out cross-validation method was used for model calibration and validation. The best CDOM retrieval algorithm is a B3/B5 model with accuracy coefficient of determination (R2)=0.884, root-mean-squared error (RMSE)=0.731  m−1, relative root-mean-squared error (RRMSE)=28.02%, and bias=−0.1  m−1. The best Chla retrieval algorithm is a B5/B4 model with accuracy R2=0.49, RMSE=9.972  mg/m3, RRMSE=48.47%, and bias=−0.116  mg/m3. Neural network models were further implemented to improve inversion accuracy. The applications of the two best band ratio models to Sentinel-2 imagery with 10  m×10  m pixel size presented the high potential of the sensor for monitoring water quality of inland lakes.

An investigation of the sensitivity of a gas-detecting, airborne differential absorption lidar to the wavelength-based reflectivity variations of the ground was made using the Jet Propulsion Laboratory’s (JPL) reflectance library. The JPL library contains 2287 data sets of reflective materials covering a wide range from manmade to lunar regolith. The study covered an online wavelength range of 400 to 4000 nm. Two assumptions were made to provide a path to analysis. The first was that an instrument developer could tolerate no more than 5% error on the overall answer due to reflectivity differences from wavelength separation. The second was that, regardless of atmospheric conditions, molecular cross section, starting power levels, or myriad other effects, the offline received signal is 10% higher than the online received signal. From this foundation, wavelength separation limits were determined when 99%, 95%, and 90% of the materials in the database met the error criteria. It was found that most applications need wavelength separations within about 0.5 nm for low error while some applications could use wavelengths separated by 10 nm or more. Example case studies are provided to demonstrate the applicability and use of the computed plots intended for informing early-stage instrument design.

The migration of aerofauna is a seasonal phenomenon of global scale, engaging billions of individuals in long-distance movements every year. Multiband lidar systems are commonly employed for the monitoring of aerosols and atmospheric gases, and a number of systems are operated regularly across Europe in the framework of the European Aerosol Lidar Network (EARLINET). This work examines the feasibility of utilizing EARLINET for the monitoring and classification of migratory fauna based on their pigmentation. An EARLINET Raman lidar system in Athens transmits laser pulses in three bands. By installing a four-channel digital oscilloscope on the system, the backscattered light from single-laser shots is measured. Roughly 100 h of data were gathered in the summer of 2013. The data were examined for aerofauna observations, and a total of 1735 observations interpreted as airborne organisms intercepting the laser beam were found during the study period in July to August 2013. The properties of the observations were analyzed spectrally and intercompared. A spectral multimodality that could be related to different observed species is shown. The system used in this pilot study is located in Athens, Greece. It is concluded that monitoring aerial migration using it and other similar systems is feasible with minor modifications, and that in-flight species classification could be possible.

Soil salinity is a complex problem that affects groundwater aquifers and agricultural lands in the semiarid regions. Remote sensing and spectroscopy database systems provide accuracy for salinity autodetection and dynamical delineation. Salinity detection techniques using polychromatic wavebands by field geocomputation and experimental data are time consuming and expensive. This paper presents an automated spectral detection and identification of salt minerals using a monochromatic waveband concept from multispectral bands—Landsat 8 Operational Land Imager (OLI) and Thermal InfraRed Sensor (TIRS) and spectroscopy United States Geological Survey database. For detecting mineral salts related to electrolytes, such as electronical and vibrational transitions, an integrated approach of salinity detection related to the optical monochromatic concept has been addressed. The purpose of this paper is to discriminate waveband intrinsic spectral similarity using the Beer–Lambert and Van 't Hoff laws for spectral curve extraction such as transmittance, reflectance, absorbance, land surface temperature, molar concentration, and osmotic pressure. These parameters are primordial for hydrodynamic salinity modeling and continuity identification using chemical and physical approaches. The established regression fitted models have been addressed for salt spectroscopy validation for suitable calibration and validation. Furthermore, our analytical tool is conducted for better decision interface using spectral salinity detection and identification in the Oran watershed, Algeria.

Encouraged by the EU INSPIRE directive requirements and recommendations, the Walloon authorities, similar to other EU regional or national authorities, want to develop operational land-cover (LC) and land-use (LU) mapping methods using existing geodata. Urban planners and environmental monitoring stakeholders of Wallonia have to rely on outdated, mixed, and incomplete LC and LU information. The current reference map is 10-years old. The two object-based classification methods, i.e., a rule- and a classifier-based method, for detailed regional urban LC mapping are compared. The added value of using the different existing geospatial datasets in the process is assessed. This includes the comparison between satellite and aerial optical data in terms of mapping accuracies, visual quality of the map, costs, processing, data availability, and property rights. The combination of spectral, tridimensional, and vector data provides accuracy values close to 0.90 for mapping the LC into nine categories with a minimum mapping unit of 15  m2. Such a detailed LC map offers opportunities for fine-scale environmental and spatial planning activities. Still, the regional application poses challenges regarding automation, big data handling, and processing time, which are discussed.

The number of rice-plant stems (S), directly affecting the competition among rice plants and contributing to rice yield, is estimated from laser data. The laser data were normalized to eliminate the increasing plant height effect. Relative spatial volume (rVslaser) was derived and scaled as an exponential function of S. A relationship between rVslaser and S is confirmed in two growing seasons (2014 and 2016, separately obtained by two different laser scanners). The scaling and exponent factors (β and α) depended on the planting geometry, planting density, and bottom position of the rice plants (Dbottom) but were almost independent of the number of divided layers in the rVslaser computation. From the estimated stem number using Dbottom at the 80th percentile (D80), the maximum S was obtained at ∼50 days after transplanting. In both years, the relative error in the estimate was below 0.10, and the bias was small. In the models with D80 in 2014 (MD201480) and D95 in 2016 (MD201695), the β and α values were very similar. Using the rVslaser measure, we can disregard the footprint characteristics and voxel size. The presented results support the proposed approach as a useful future method for estimating rice-plant stems.

Detailed spatial and temporal data on plant growth are critical to guide crop management. Conventional methods to determine field plant traits are intensive, time-consuming, expensive, and limited to small areas. The objective of this study was to examine the integration of data collected via unmanned aerial systems (UAS) at critical corn (Zea mays L.) developmental stages for plant height and its relation to plant biomass. The main steps followed in this research were (1) workflow development for an ultrahigh resolution crop surface model (CSM) with the goal of determining plant height (CSM-estimated plant height) using data gathered from the UAS missions; (2) validation of CSM-estimated plant height with ground-truthing plant height (measured plant height); and (3) final estimation of plant biomass via integration of CSM-estimated plant height with ground-truthing stem diameter data. Results indicated a correlation between CSM-estimated plant height and ground-truthing plant height data at two weeks prior to flowering and at flowering stage, but high predictability at the later growth stage. Log–log analysis on the temporal data confirmed that these relationships are stable, presenting equal slopes for both crop stages evaluated. Concluding, data collected from low-altitude and with a low-cost sensor could be useful in estimating plant height.