Biogeophysical Parameters Research Articles

Biomass Estimation with GNSS Reflectometry Using a Deep Learning Retrieval Model

GNSS Reflectometry (GNSS-R) is an emerging technique for the remote sensing of the environment. Traditional GNSS-R bio-geophysical parameter retrieval algorithms and deep learning models utilize observables derived from only the peak power of the delay-Doppler maps (DDMs), discarding the rest. This reduces the data available, which potentially hinders estimation accuracy. In addition, reflections from water bodies dominate the signal amplitude, and using only the peak power in those areas is challenging. Motivated by all the above, we propose a novel deep learning retrieval model for biomass estimation that uses the full DDM of surface reflectivity. Experiments using CYGNSS data have illustrated the improvements achieved when using the full DDM of surface reflectivity. Our proposed model was able to estimate biomass, trained using the ESA Climate Change Initiative (CCI) biomass map, outperforming the model that used peak reflectivity. Global and regional analysis is provided along with an illustration of how biomass estimation is achieved when using the full DDM around water bodies. GNSS-R could become an efficient method for biomass monitoring with fast revisit times. However, an elaborate calibration is necessary for the retrieval models, to associate GNSS-R data with bio-geophysical parameters on the ground. To achieve this, further developments with improved training data are required, as well as work using in situ validation data. Nevertheless, using GNSS-R and deep learning retrieval models has the potential to enable fast and persistent biomass monitoring and help us better understand our changing climate.

Negative Air Ion (NAI) Dynamics over Zhejiang Province, China, Based on Multivariate Remote Sensing Products

Negative air ions (NAIs), which are known as the “air vitamin”, have been widely used as a measure of air cleanness. Field observation provides an alternative way to record site-level NAIs. However, these observations fail to capture the regional distribution of NAIs due to the limited number of sites. In this study, satellite-based bio-geophysical parameters from the climate, topography, air quality, vegetation, and anthropogenic intensity were used to estimate the daily NAIs with the Random Forest model (RF). In situ NAI observations over Zhejiang Province, China were incorporated into the model. Daily NAIs were averaged to capture the spatio-temporal distribution. The results showed that (1) the RF algorithm performed better than traditional regression analysis and the common BP neural network to generate regional NAIs at a spatial scale of 500 m over the larger scale, with an RMSE of 258.62, R2 of 0.878 for model training, and R2 of 0.732 for model testing; (2) in the variable importance measures (VIM) analysis, 87.96% of the NAI variance was caused by the elevation, aspect, slope, surface temperature, solar-induced chlorophyll fluorescence (SIF), relative humidity (RH), and the concentration of carbon monoxide (CO), while path analysis indicated that SIF was one of the most important factors affecting NAI concentration across the whole region; (3) NAI concentrations in 87.16% of the region were classified above grade III (>500 ions cm−3), which was able to meet the needs of human health maintenance; (4) the highest NAI concentration was distributed over the southwest of the Zhejiang Province, where forest land dominates. The lowest NAI concentration was mostly found in the northeast regions, where urban areas are well-developed; and (5) among different land types, the NAI concentrations were ranked as forest land > water bodies > barren > grassland > croplands > urban and built-up. Among different seasons, summer and winter have the highest and lowest NAIs, respectively. Our study provided a substantial reference for ecosystem services assessment in Zhejiang Province.

Mapping Potential Locations of Reservoir Development Planning Based on Biogeophysical Conditions in Bulok Watershed of Lampung Province

Floods and droughts are problems that threaten the success of agricultural crops. The reservoir can be used as a water management system that can anticipate these problems. This study aims to examine the potential location of the reservoir development planning based on biogeophysical conditions in the Bulok Watershed of Lampung Province, which uses the overlay method and weighting and scoring techniques developed by Geographic Information System (GIS). The population covers the entire biogeophysical area of the Bulok watershed, with samples of 4 biogeophysical parameters, namely land cover, slope, soil conditions and geological conditions. The results showed that the distribution of land cover types that dominate in the Bulok watershed is mixed dry land agriculture. The most common slopes are flat to wavy slopes. The soil types that dominates are soils with andesite lithology, basalt, diorite, fine-grained tefra, and coarse-grained tefra. The geological formation that dominates is the Hulu Simpang Formation. Based on the 4 biogeophysical parameters selected in the study, there are 69 locations with great potential for planning the construction of reservoirs in the Bulok watershed. 20 points are spread over the administrative area of Tanggamus Regency, 23 points in Pringsewu Regency, and 26 points in Pesawaran Regency. Of the 87.670 ha of Bulok watershed area, 14.192 ha is very potential location area. Keywords: Biogeophysical Conditions, Potential Locations, Reservoir

Utilising Sentinel-1’s orbital stability for efficient pre-processing of sigma nought backscatter

For already more than seven years, the Sentinel-1 C-band Synthetic Aperture Radar (SAR) mission has been providing indispensable information for monitoring bio-geophysical parameters at fine temporal and spatial scales. As many applications require backscatter datacubes as input, enormous amounts of data have to be radiometrically and geometrically corrected to be in a common, Earth-fixed reference system. Pre-processing workflows accomplishing this task have already been established and are implemented in several software suites. However, typically, these workflows are computationally expensive which may lead to prohibitively large costs when generating multi-year Sentinel-1 datacubes for whole continents or the world. In this paper, we discuss existing approaches for generating sigma nought and projected local incidence angle (PLIA) data and present simplifications of the overall workflow relying on the unprecedented orbital stability of Sentinel-1. Propagating orbital deviations through the complete Sentinel-1 pre-processing pipeline helped us to simulate and identify PLIA as a static layer per relative orbit. The outcome of these simulations also provided the necessary information to replace iterative root-finding algorithms for determining the time of closest approach (TCA), i.o.w. the azimuth index, with a linear one — at no expense of radiometric accuracy. All experiments were performed using an in-house developed toolbox named wizsard, which made it possible to speed up Sentinel-1 pre-processing by approximately 4–5 times with respect to the Sentinel Application Platform (SNAP). This could pave the way for producing quality-curated, large-scale backscatter datacubes at continental and global scales in acceptable time frames.

Coastal vulnerability assessment based on multi-hazards and bio-geophysical parameters. case study - northwestern coastline of Guinea-Bissau

Coastal vulnerability assessment based on multi-hazards and bio-geophysical parameters. case study - northwestern coastline of Guinea-Bissau

Agricultural SandboxNL: A national-scale database of parcel-level processed Sentinel-1 SAR data

Synthetic Aperture Radar (SAR) data handling, processing, and interpretation are barriers preventing a rapid uptake of SAR data by application specialists and non-expert domain users in the field of agricultural monitoring. To improve the accessibility of Sentinel-1 data, we have generated a reduced-volume, multi-year Sentinel-1 SAR database. It includes mean and standard deviation of VV, VH and VH/VV backscatter, pixel counts, geometry, crop type, local incidence angle and azimuth angle at parcel-level. The database uses around 3100 Sentinel-1 images (5 TB) to produce a 12 GB time series database for approximately 770,000 crop parcels over the Netherlands for a period of three years. The database can be queried by Sentinel-1 system parameters (e.g. relative orbit) or user application-specific parameters (e.g. crop type, spatial extent, time period) for parcel level assessment. The database can be used to accelerate the development of new tools, applications and methodologies for agricultural and water related applications, such as parcel-level crop bio-geophysical parameter estimation, inter-annual variability analysis, drought monitoring, grassland monitoring and agricultural management decision-support.

Fine-Scale Mapping of Natural Ecological Communities Using Machine Learning Approaches

Remote sensing technology has been used widely in mapping forest and wetland communities, primarily with moderate spatial resolution imagery and traditional classification techniques. The success of these mapping efforts varies widely. The natural communities of the Laurentian Mixed Forest are an important component of Upper Great Lakes ecosystems. Mapping and monitoring these communities using high spatial resolution imagery benefits resource management, conservation and restoration efforts. This study developed a robust classification approach to delineate natural habitat communities utilizing multispectral high-resolution (60 cm) National Agriculture Imagery Program (NAIP) imagery data. For accurate training set delineation, NAIP imagery, soils data and spectral enhancement techniques such as principal component analysis (PCA) and independent component analysis (ICA) were integrated. The study evaluated the importance of biogeophysical parameters such as topography, soil characteristics and gray level co-occurrence matrix (GLCM) textures, together with the normalized difference vegetation index (NDVI) and NAIP water index (WINAIP) spectral indices, using the joint mutual information maximization (JMIM) feature selection method and various machine learning algorithms (MLAs) to accurately map the natural habitat communities. Individual habitat community classification user’s accuracies (UA) ranged from 60 to 100%. An overall accuracy (OA) of 79.45% (kappa coefficient (k): 0.75) with random forest (RF) and an OA of 75.85% (k: 0.70) with support vector machine (SVM) were achieved. The analysis showed that the use of the biogeophysical ancillary data layers was critical to improve interclass separation and classification accuracy. Utilizing widely available free high-resolution NAIP imagery coupled with an integrated classification approach using MLAs, fine-scale natural habitat communities were successfully delineated in a spatially and spectrally complex Laurentian Mixed Forest environment.

Retrieval of Biogeophysical Parameters From Bistatic Observations of Land at L-Band: A Theoretical Study

Bistatic and multistatic radar observations are mostly conceived to exploit the signal phase, e.g., for interferometry, tomography, and ocean current applications. In these situations, the distance between the transmitting and receiving antennas (bistatic baseline) is generally small in order to, among other reasons, keep high the coherence between the backscattered (monostatic) and bistatic radar echoes. Less evidence can be found in the literature on the exploitation of the signal amplitude (i.e., the scattering coefficient) of monostatic observations combined with bistatic ones, thus implementing a multistatic observation. In this case, to increase the information content in the inverse problem (i.e., the retrieval of biogeophysical parameters), the observations to be combined must be sufficiently independent in order to improve the accuracy of the retrieval. This article aims at identifying suitable geometrical configurations of a passive satellite radar flying in convoy with an active spaceborne SAR at L-band. Few bistatic datasets exist to verify this concept experimentally, so that electromagnetic models can help understanding its potential in different fields. The applications foreseen in this article are the retrieval of soil moisture and vegetation biomass. A model-based investigation shows that retrieval performances can be improved by combining monostatic and bistatic measurements in geometric configurations requiring very large along track and across track baselines. In this way, the scattering mechanisms involved in the monostatic and bistatic geometry are sufficiently different so that their combination increases the retrieval performances with respect to a monostatic acquisition.

Decorrelation of the Near-Specular Land Scattering in Bistatic Radar Systems

Signal fluctuations at the receiving antenna have been studied from decades by the radar community, especially to understand the decorrelation of the scattering in radar interferometry. This was done assuming uncorrelated point-like scatterers, leading to a simple model for the geometric decorrelation. In this case, the scattering is certainly incoherent. The quasi-specular reflections gathered under the illumination of signals of opportunity can exhibit significant temporal fluctuations. They are related to the statistical features of the surface roughness and can be observed even in almost flat regions, where a predominant coherent reflection could be expected. The presence of gentle undulations, however, i.e., those showed by surfaces having variations of the profiles comparable with the wavelength over the vertical scale, but much longer over the horizontal one, can determine transition regions where the scattering is neither coherent nor completely incoherent. In these conditions, the nature of the fluctuations of the scattering is not well understood and it requires additional studies. A discussion about the dominance of coherent or incoherent reflection in the Global Navigation Satellite System Reflectometry (GNSS-R) community is presently ongoing. To describe the nature of the scattering, and to understand the decorrelation of the near-specular components in GNSS-R, we propose a numerical study of the field collected by a moving airborne receiver based on the Kirchhoff approximation. Our study demonstrates that the near-specular scattering collected over representative natural landscapes by a GNSS-R receiver is partially coherent and essentially incoherent in most cases. Its correlation time is a function of the roughness parameters, namely standard deviation and correlation length, as well as of the system parameters (i.e., spatial resolution and height). The analysis can provide useful information for the interpretation of GNSS data, which present intrinsic variability that can significantly affect the retrieval of the relevant bio-geophysical parameters.

STUDY ON CYCLONE INDUCED PHYTOPLANKTON BLOOM IN THE ARABIAN SEA THROUGH GAP-FREE RECONSTRUCTED CHLOROPHYLL-A DATA

Abstract. Ocean surface phytoplankton responses to the tropical cyclone (TC)/storms have been extensively studied using satellite observations by aggregating the data into a weekly or bi-weekly composite. The reason behind is the significant limitations found in the satellite-based observation is the missing of valid data due to cloud cover, especially at the time of cyclone track passage. The data loss during the cyclone is found to be a significant barrier to efficiently investigate the response of chl-a and SST during cyclone track passage. Therefore it is necessary to rectify the above limitation to effectively study the impact of TC on the chlorophyll-a concentration (chl-a) and the sea surface temperature (SST) to achieve a complete understanding of their response to the TC prevailed in the Arabian Sea. Intending to resolve the limitation mentioned above, this study aims to reconstruct the MODIS-Aqua chl-a, and SST data using Data Interpolating Empirical Orthogonal Function (DINEOF) for all the 31 cyclonic events occurred in the Arabian Sea during 2003-2018 (16 years). Reconstructed satellite retrieved data covering all the cyclonic events were further used to investigate the chl-a and SST dynamics during TC. From the results, the exciting fact has been identified that only two TC over the eastern-AS were able to induce phytoplankton bloom. On investigating this scenario using sea surface temperature, it was disclosed that the availability of nutrients decides the suitable condition for the phytoplankton to proliferate in the surface ocean. Relevant to the precedent criterion, the results witnessed that the 2 TC (Phyan and Ockhi cyclone) prevailed in the eastern AS invoked a suitable condition for phytoplankton bloom. Other TC found to be less provocative either due to less intensity, origination region or the unsuitable condition. Thereby, gap-free reconstructed daily satellite-derived data efficiently investigates the response of bio-geophysical parameters during cyclonic events. Moreover, this study sensitised that though several TC strikes the AS, only two could impact phytoplankton productivity and SST found to highly consistent with the chl-a variability during the cyclone passage.

Warming Effort and Energy Budget Difference of Various Human Land Use Intensity: Case Study of Beijing, China

Human land use intensity affects the surface energy balance by changing the biogeophysical parameters. This study used Moderate Resolution Imaging Spectroradiometer remote sensing data and surface energy balance algorithms to quantify changes in surface energy budgets corresponding to changes in land use in Beijing from 2000 to 2015. Land use was reclassified by considering land use intensity. The difference in the latent heat flux (LE) and net radiation (Rn) (LE−Rn) expressed the warming or cooling effect. The results showed that: (i) The increasing trend of net longwave radiation in Beijing offset the decreasing trend of net shortwave radiation. The Rn changed slightly, while the LE and LE−Rn showed a significant increase of 0.55 and 0.56 W/(m²∙year), respectively. The findings indicated that considering only radiative forcing, or even Rn, was not enough to measure the impacts of land use change on the energy budget. (ii) The order of Rn, LE, and LE−Rn values from high to low were natural and seminatural areas, cropland, mixed pixel areas, urban expansion areas, and old urban areas. Compared with natural and seminatural areas, the changing LE−Rn trend in the other four land use types decreased with the increase in human impact intensity, indicating that human activities weakened the positive change trend of LE−Rn and increased the warming effect. (iii) Although the temporal trend of LE increased in Beijing from 2000 to 2015, the effect of Rn on LE−Rn was greater than that of LE, especially in the four land use types affected by human activities. The results for surface temperature in various land use types confirmed this point. This study highlights the energy budget differences of various land use types affected by human activities. It makes an important contribution to understanding the urban heat island effect from a biogeophysical perspective.

Application of Sentinel-2 MSI in Arctic Research: Evaluating the Performance of Atmospheric Correction Approaches Over Arctic Sea Ice

Multispectral remote sensing may be a powerful tool for areal retrieval of biogeophysical parameters in the Arctic sea ice. The MultiSpectral Instrument on board the Sentinel-2 (S-2) satellites of the European Space Agency offers new possibilities for Arctic research; S-2A and S-2B provide 13 spectral bands between 443 and 2202 nm and spatial resolutions between 10 and 60 m, which may enable the monitoring of large areas of Arctic sea ice. For an accurate retrieval of parameters such as surface albedo, the elimination of atmospheric influences in the data is essential. We therefore provide an evaluation of five currently available atmospheric correction processors for S-2 (ACOLITE, ATCOR, iCOR, Polymer, and Sen2Cor). We evaluate the results of the different processors using in situ spectral measurements of ice and snow and open water gathered north of Svalbard during RV Polarstern cruise PS106.1 in summer 2017. We used spectral shapes to assess performance for ice and snow surfaces. For open water, we additionally evaluated intensities. ACOLITE, ATCOR and iCOR performed well over sea ice and Polymer generated the best results over open water. ATCOR, iCOR and Sen2Cor failed in the image-based retrieval of atmospheric parameters (aerosol optical thickness, water vapor). ACOLITE estimated AOT within the uncertainty range of AERONET measurements. Parameterization based on external data, therefore, was necessary to obtain reliable results. To illustrate consequences of processor selection on secondary products we computed average surface reflectance of six bands and normalized difference melt index (NDMI) on an image subset. Medians of average reflectance and NDMI range from 0.80 to 0.97 and 0.12 to 0.18 while medians for TOA are 0.75 and 0.06, respectively.

Assessment of Approximations in Aerosol Optical Properties and Vertical Distribution into FLEX Atmospherically-Corrected Surface Reflectance and Retrieved Sun-Induced Fluorescence

Physically-based atmospheric correction of optical Earth Observation satellite data is used to accurately derive surface biogeophysical parameters free from the atmospheric influence. While water vapor or surface pressure can be univocally characterized, the compensation of aerosol radiometric effects relies on assumptions and parametric approximations of their properties. To determine the validity of these assumptions and approximations in the atmospheric correction of ESA’s FLEX/Sentinel-3 tandem mission, a systematic error analysis of simulated FLEX data within the O 2 absorption bands was conducted. This paper presents the impact of key aerosol parameters in atmospherically-corrected FLEX surface reflectance and the subsequent Sun-Induced Fluorescence retrieval (SIF). We observed that: (1) a parametric characterization of aerosol scattering effects increases the accuracy of the atmospheric correction with respect to the commonly implemented discretization of aerosol optical properties by aerosol types and (2) the Ångström exponent and the aerosol vertical distribution have a residual influence in the atmospherically-corrected surface reflectance. In conclusion, a multi-parametric aerosol characterization is sufficient for the atmospheric correction of FLEX data (and SIF retrieval) within the mission requirements in nearly 85% (70%) of the cases with average aerosol load conditions. The future development of the FLEX atmospheric correction algorithm would therefore gain from a multi-parametric aerosol characterization based on the synergy of FLEX and Sentinel-3 data.

Randomized kernels for large scale Earth observation applications

Current remote sensing applications of bio-geophysical parameter estimation and image classification have to deal with an unprecedented big amount of heterogeneous and complex data sources. New satellite sensors involving a high number of improved time, space and wavelength resolutions give rise to challenging computational problems. Standard physical inversion techniques cannot cope efficiently with this new scenario. Dealing with land cover classification of the new image sources has also turned to be a complex problem requiring large amount of memory and processing time. In order to cope with these problems, statistical learning has greatly helped in the last years to develop statistical retrieval and classification models that can ingest large amounts of Earth observation data. Kernel methods constitute a family of powerful machine learning algorithms, which have found wide use in remote sensing and geosciences. However, kernel methods are still not widely adopted because of the high computational cost when dealing with large scale problems, such as the inversion of radiative transfer models or the classification of high spatial-spectral-temporal resolution data. This paper introduces to the remote sensing community an efficient kernel method for fast statistical retrieval of atmospheric and biophysical parameters and image classification problems. We rely on a recently presented approximation to shift-invariant kernels using projections on random Fourier features. The method proposes an explicit mapping function defined through a set of projections randomly sampled from the Fourier domain. It is proved to approximate the implicit mapping of a kernel function. This allows to deal with large-scale data but taking advantage of kernel methods. The method is simple, computationally very efficient in both memory and processing costs, and easily parallelizable. We show that kernel regression and classification is now possible for datasets with millions of samples. Examples on atmospheric parameter retrieval from hyperspectral infrared sounders like IASI/Metop; large scale emulation and inversion of the familiar PROSAIL radiative transfer model on Sentinel-2 data; and the identification of clouds over landmarks in time series of MSG/Seviri images show the efficiency and effectiveness of the proposed technique.

Assessing the role of Mediterranean evergreen oaks canopy cover in land surface albedo and temperature using a remote sensing-based approach

Modifications in vegetation cover can have an impact on the climate through changes in biogeochemical and biogeophysical processes. In this paper, the tree canopy cover percentage of a savannah-like ecosystem (montado/dehesa) was estimated at Landsat pixel level for 2011, and the role of different canopy cover percentages on land surface albedo (LSA) and land surface temperature (LST) were analysed. A modelling procedure using a SGB machine-learning algorithm and Landsat 5-TM spectral bands and derived vegetation indices as explanatory variables, showed that the estimation of montado canopy cover was obtained with good agreement (R2 = 78.4%). Overall, montado canopy cover estimations showed that low canopy cover class (MT_1) is the most representative with 50.63% of total montado area. MODIS LSA and LST products were used to investigate the magnitude of differences in mean annual LSA and LST values between contrasting montado canopy cover percentages. As a result, it was found a significant statistical relationship between montado canopy cover percentage and mean annual surface albedo (R2 = 0.866, p < 0.001) and surface temperature (R2 = 0.942, p < 0.001). The comparisons between the four contrasting montado canopy cover classes showed marked differences in LSA (χ2 = 192.17, df = 3, p < 0.001) and LST (χ2 = 318.18, df = 3, p < 0.001). The highest montado canopy cover percentage (MT_4) generally had lower albedo than lowest canopy cover class, presenting a difference of −11.2% in mean annual albedo values. It was also showed that MT_4 and MT_3 are the cooler canopy cover classes, and MT_2 and MT_1 the warmer, where MT_1 class had a difference of 3.42 °C compared with MT_4 class. Overall, this research highlighted the role that potential changes in montado canopy cover may play in local land surface albedo and temperature variations, as an increase in these two biogeophysical parameters may potentially bring about, in the long term, local/regional climatic changes moving towards greater aridity.

A Survey on Gaussian Processes for Earth-Observation Data Analysis: A Comprehensive Investigation

Gaussian processes (GPs) have experienced tremendous success in biogeophysical parameter retrieval in the last few years. GPs constitute a solid Bayesian framework to consistently formulate many function approximation problems. This article reviews the main theoretical GP developments in the field, considering new algorithms that respect signal and noise characteristics, extract knowledge via automatic relevance kernels to yield feature rankings automatically, and allow applicability of associated uncertainty intervals to transport GP models in space and time that can be used to uncover causal relations between variables and can encode physically meaningful prior knowledge via radiative transfer model (RTM) emulation. The important issue of computational efficiency will also be addressed. These developments are illustrated in the field of geosciences and remote sensing at local and global scales through a set of illustrative examples. In particular, important problems for land, ocean, and atmosphere monitoring are considered, from accurately estimating oceanic chlorophyll content and pigments to retrieving vegetation properties from multi- and hyperspectral sensors as well as estimating atmospheric parameters (e.g., temperature, moisture, and ozone) from infrared sounders.

Uncertainty Evaluation of an In-Flight Absolute Radiometric Calibration Using a Statistical Monte Carlo Method

The absolute radiometric calibration of remote sensing sensors is crucial to the accurate retrieval of biogeophysical parameters through remote sensing. The radiometric calibration uncertainty is the index that describes the reliability of a calibration result and is usually empirically determined by assuming that all of the factors involved are independent of each other. Through a field campaign carried out in Inner Mongolia, China, which aimed to accurately calibrate remote sensing sensors, we developed a Monte Carlo method that statistically evaluates the radiometric calibration uncertainty. From Monte Carlo simulations, it was revealed that the overall uncertainty is much smaller than the root sum of squares of each factor, suggesting that there is some negative correlation among some of the factors. For a surface with a low reflectance (∼5%), the radiometric calibration uncertainty was ∼7.0%, whereas for a surface with a reflectance larger than 20%, the uncertainty was stable at ∼3.0%. This result suggests that the quality of remote sensing data should be carefully examined for surfaces with a low reflectance.

Prediction of Daily Global Solar Irradiation Using Temporal Gaussian Processes

Solar irradiation prediction is an important problem in geosciences with direct applications in renewable energy. Recently, a high number of machine learning techniques have been introduced to tackle this problem, mostly based on neural networks and support vector machines. Gaussian process regression (GPR) is an alternative nonparametric method that provided excellent results in other biogeophysical parameter estimation. In this letter, we evaluate GPR for the estimation of solar irradiation. Noting the nonstationary temporal behavior of the signal, we develop a particular time-based composite covariance to account for the relevant seasonal signal variations. We use a unique meteorological data set acquired at a radiometric station that includes both measurements and radiosondes, as well as numerical weather prediction models. We show that the so-called temporal GPR outperforms ten state-of-the-art statistical regression algorithms (even when including time information) in terms of accuracy and bias, and it is more robust to the number of predictions used.

Characterizing the Absorption Properties for Remote Sensing of Three Small Optically-Diverse South African Reservoirs

Characterizing the specific inherent optical properties (SIOPs) of water constituents is fundamental to remote sensing applications. Therefore, this paper presents the absorption properties of phytoplankton, gelbstoff and tripton for three small, optically-diverse South African inland waters. The three reservoirs, Hartbeespoort, Loskop and Theewaterskloof, are challenging for remote sensing, due to differences in phytoplankton assemblage and the considerable range of constituent concentrations. Relationships between the absorption properties and biogeophysical parameters, chlorophyll-a (chl-a), TChl (chl-a plus phaeopigments), seston, minerals and tripton, are established. The value determined for the mass-specific tripton absorption coefficient at 442 nm, a∗ (442), ranges from 0.024 to 0.263 m2·g−1. The value of the TChl-specific phytoplankton absorption coefficient (a∗ ) was strongly influenced by phytoplankton species, size, accessory pigmentation and biomass. a∗ (440) ranged from 0.056 to 0.018 m2·mg−1 in oligotrophic to hypertrophic waters. The positive relationship between cell size and trophic state observed in open ocean waters was violated by significant small cyanobacterial populations. The phycocyanin-specific phytoplankton absorption at 620 nm, a∗ (620), was determined as 0.007 m2·g−1 in a M. aeruginosa bloom. Chl-a was a better indicator of phytoplankton biomass than phycocyanin (PC) in surface scums, due to reduced accessory pigment production. Absorption budgets demonstrate that monospecific blooms of M. aeruginosa and C. hirundinella may be treated as “cultures”, removing some complexities for remote sensing applications. These results contribute toward a better understanding of IOPs and remote sensing applications in hypertrophic inland waters. However, the majority of the water is optically complex, requiring the usage of all the SIOPs derived here for remote sensing applications. The SIOPs may be used for developing remote sensing algorithms for the detection of biogeophysical parameters, including chl-a, suspended matter, tripton and gelbstoff, and in advanced remote sensing studies for phytoplankton type detection.

Multi-Temporal Airborne LiDAR-Survey and Field Measurements of Tropical Peat Swamp Forest to Monitor Changes

Natural tropical peat swamp forests are important for their rich biodiversity and serve as a huge carbon pool. However, peat swamp forests are decreasing due to deforestation, conversion into farm land, excessive draining, the use of shifting cultivation on a large scale, illegal logging, forest fire and palm oil plantation. Airborne laser scanning (ALS) also termed airborne Light Detection and Ranging (LiDAR) data is nowadays a good single sensor to investigate bio-geophysical parameters in remote tropical rain forest areas (e.g. tree canopy height which is strongly correlated with above ground biomass). Bi-temporal airborne LiDAR data acquired in August 2007 and August 2011 were used to characterize peat swamp forest changes located in Central Kalimantan, Indonesia. We measured the tree height and Canopy Height Model (CHM) with LiDAR, segmented the canopies and then compared the tree height with the field measurements. Additionally, we collected ground field measurements at Sabangau forest transect in order to characterize some biophysical properties of different peat swamp forest physiognomies such as diameter at breast height (DBH), tree-height, leaf area index (LAI), crown coverage and above ground biomass (AGB). From the bi-temporal LiDAR Data we analyzed the forest regrowth and the peat subsidence. This work can be promising in the REDD+ (Reducing Emissions from Deforestation and forest Degradation) framework of knowledge of tropical PSF. The LiDAR technology supports the MRV (Monitoring, Reporting, and Verification) aspect of REDD+.