A generalization of spatial and temporal fusion methods for remotely sensed surface parameters

Remotely sensed surface parameters, such as vegetation index, leaf area index, surface temperature, and evapotranspiration, show diverse spatial scales and temporal dynamics. Generally the spatial and temporal resolutions of remote-sensing data should match the characteristics of surface parameters under observation. These requirements sometimes cannot be provided by a single sensor due to the trade-off between spatial and temporal resolutions. Many spatial and temporal fusion (STF) methods have been proposed to derive the required data. However, the methodology suffers from disorderly development. To better inform future research, this study generalizes the existing methods from around 100 studies as spatial or temporal categories based on their physical assumptions related to spatial scales and temporal dynamics. To be specific, the assumptions are related to the scale invariance of the temporal information and temporal constancy of the spatial information. The spatial information can be contexture or spatial details. Experiments are conducted using Landsat data acquired on 13 dates in two study areas and simulated Moderate Resolution Imaging Spectroradiometer (MODIS) data. The results are presented to demonstrate the typical methods from each category. This study concludes the following. (1) Contexture methods depend heavily on how components maps (contexture) are defined. They are not recommended except when components maps can be estimated properly from observed images. (2) The spatial and temporal adaptive reflectance fusion model (STARFM) and enhanced STARFM (ESTARFM) methods belong to the temporal and spatial categories, respectively. Thus, STARFM and ESTARFM should be better applied to temporal variance – dominated and spatial variance – -dominated areas, respectively. (3) Non-linear methods, such as the sparse representation-based spatio-temporal reflectance fusion model, can successfully address land-cover changes in addition to phonological changes, thereby providing a promising option for STF problems in the future.     

Remote sensing of phycocyanin pigment in highly turbid inland waters in Lake Taihu,China

Cyanobacterial blooms are an environmental issue that can cause health hazards by toxins and malodorous compounds. The pigment phycocyanin is indicative of cyanobacterial presence. In eutrophic inland waters in which nitrogen is not a limiting nutrient, the phycocyanin concentration (PC) is closely related to cyanobacterial biomass. This study proposes a simple semi-analytical four-band algorithm for PC estimation to overcome the deficiency of existing algorithms. This algorithm was calibrated using a data set collected from Lake Taihu in 2007. Optimal reference wavelengths for the algorithm were located through model tuning and accuracy optimization. The algorithm was evaluated for its accuracy against an independent data set collected in 2008. The performance of the algorithm was also compared with that of the nested band-ratio algorithm, which was developed for PC estimation in turbid waters.

Although both algorithms enabled the establishment of a linear relationship between measured and predicted PC, the nested band-ratio algorithm did not have a satisfactory performance with either data set, having a high level of uncertainty. Its mean relative error stands at 51.07% and 51% for the 2007 and 2008 data sets, respectively. It accounted for 68% and 74% of the variation in PC in the 2007 and 2008 data sets, respectively. The four-band algorithm worked well in PC estimation. It accounted for 87% of the variation in PC for the 2007 data set and 86% of the variation in the 2008 data set. Furthermore, it decreased estimation uncertainty, compared with the nested band-ratio algorithm, by more than 20%. The values of mean relative error for the correspondence data sets are 29.1% and 30%. Therefore, the proposed four-band algorithm holds great potential in estimating PC in highly turbid waters.     

The Pythagoras number of real sum of squares polynomials and sum of square magnitudes of polynomials

In this paper, we conjecture a formula for the value of the Pythagoras number for real multivariate sum of squares polynomials as a function of the (total or coordinate) degree and the number of variables. The conjecture is based on the comparison between the number of parameters and the number of conditions for a corresponding low-rank representation. This is then numerically verified for a number of examples. Additionally, we discuss the Pythagoras number of (complex) multivariate Laurent polynomials that are sum of square magnitudes of polynomials on the $n$ -torus. For both types of polynomials, we also propose an algorithm to numerically compute the Pythagoras number and give some numerical illustrations.     

New criteria for exponential stability of nonlinear difference systems with time-varying delay

General nonlinear time-varying difference systems with time-varying delay are considered. Some new explicit criteria for global exponential stability are given. Two examples are given to illustrate the obtained results.     

Dependable control of uncertain linear systems based on set-theoretic methods

This article concerns the feedback control of discrete-time systems subject to disturbances and uncertainties in both model parameters and signal measurements. The uncertainties are assumed to be unknown but bounded and thus characterised by closed intervals or sets. The main result is a new approach to design a feedback controller keeping the system state in a target set. First, a method is proposed that computes minimal enclosures of the set of reachable states, which are consistent with the uncertain input and output measurements and the system dynamics. Then, a control method to keep the current state set in the target set is developed, which extends control techniques based on invariant polyhedra. The method is illustrated by a mobile robot experiment.     

Potential of diffuse scatterer interferometry for monitoring CO2 storage sites in European contexts (land cover types)

This article proposes to test the feasibility of long-term surface deformation monitoring based on synthetic aperture radar (SAR) interferometry on carbon dioxide (CO₂) storage sites with land cover representative of potential European injection sites (agricultural or forests with minimum built-up land cover). Because no operational injection site is currently active in Europe, a SAR data set (based on EnviSAT–ASAR spaceborne data) is simulated by combining SAR scenes acquired over a potential future European injection site with deformation measurements from SAR analysis carried out on the In-Salah (Algeria) CO₂ injection demonstrator site. The study shows that under such conditions, both persistent scatterer interferometry (PSI) and diffuse scatterer (DS) interferometry appear insufficient to provide a sufficiently dense measurement network to characterize surface deformation correctly. Alternative solutions, to be investigated in further studies, include the use of data archives with shorter acquisition time spans (e.g. Sentinel-1 data when available) or installation of corner reflectors. The cost of the latter mixed space/ground solution must be evaluated with respect to conventional ground-based measurement methods in the proposed context.     

Towards the automatic selection of optimal seam line locations when merging optical remote-sensing images

Image mosaicking is an important task in remote sensing due to the need for imagery with a greater spatial extent than provided by individual scenes. Merging of images requires the selection of a seam line within their area of overlap along which the scenes are merged. The seam line has a better chance of being invisible if it lies in regions where the images to be merged are very similar. The automatic detection of an optimal seam line is not a trivial task as it is difficult to find laterally continuous regions with high image similarity, and to identify image similarities when there are variations in the images, for example due to different illuminations or viewing directions, or shadow differences of tall structures. This article presents an automatic seam line location technique for remote-sensing images and achieves the following three objectives: to trace along the locations with minimal image difference so that the merged data set appears seamless; to avoid creating discontinuities within salient features within the images; and to ensure that the more accurate radiometric values that are associated with the least distance from the nadir point are better preserved in the mosaic image. Therefore, our method uses pixel-based image similarity measurement to choose the locations with high colour, edge and texture similarity; a region-based saliency map that is based on a human attention model to identify and avoid the areas with visibly dominant foreground objects; and location preference to encourage the seam line to lie as close as possible to an equal distance from the two nadir points of the images being merged. These measures are used as input to a cost function and the estimated costs are used to guide the tracing of the seam line in a dynamic programming algorithm. Our experiments demonstrate that the consideration of a combination of factors produces superior results to using just one or two of the variables as controls when merging high-resolution images containing complex structures.     

Multi-month memory effects on early summer vegetative activity in semi-arid South Africa and their spatial heterogeneity

In semi-arid African regions (annual rainfall between 200 and 600 mm), variability of vegetative activity is mainly due to the rainfall of the current rainy season. In most of South Africa, the rainy season occurs from October to March. On average, vegetative activity lags rainfall by 1 to 2 months. The interannual variability in early summer (December to September) normalized difference vegetation index (NDVI) depends primarily on precipitation at the beginning (October to November) of the rainy season. However, once this primary control is removed, the residual interannual variability in NDVI highlights a double memory effect: a 1-year effect, referred to as Mem₁, and a 7- to 10-month effect, referred to as Mem₂. This article aims at better describing the influence of soil and vegetation characteristics on these two memory effects. The data sets used in this study are as follows: (1) a 19-year NDVI time series from National Oceanic and Atmospheric Administration (NOAA) satellites, (2) rainfall records from a network of 1160 rain-gauge stations compiled by the Water Research Commission (WRC), (3) vegetation types from Global Land Cover (GLC) 2000 and (4) soil characteristics from the soil and terrain database for Southern Africa (SOTERSAF). Results indicate that among 20–30% of NDVI variance that is not explained by the concurrent rainfall, one-third is explained by the two memory effects. Mem₁ is found to have maximum effect in the northwest of our study domain, near the Botswana boundary, in the South Kalahari. Associated conditions are open grasslands growing on Arenosols. Mem₁ is less important in the southeast, particularly in open grassland with shrubs growing on Cambisols. Thus, Mem₁ mainly depends on soil texture. Mem₂ is more widespread and its influence is the greatest in the centre, the south and the east of our domain. It is related to rainfall from January to April, which controls, beyond the intervening dry season, the interannual variations of NDVI (December to September) at the beginning of the next rainy season. Through these new findings, this article emphasizes again the high potential of remote-sensing techniques to monitor and understand the dynamics of semi-arid environments.     

Estimating aboveground biomass of a mangrove plantation on the Northern coast of Vietnam using machine learning techniques with an integration of ALOS-2 PALSAR-2 and Sentinel-2A data

ABSTRACT

Aboveground biomass (AGB) of mangrove forest plays a crucial role in global carbon cycle by reducing greenhouse gas emissions and mitigating climate change impacts. Monitoring mangrove forests biomass accurately still remains challenging compared to other forest ecosystems. We investigated the usability of machine learning techniques for the estimation of AGB of mangrove plantation at a coastal area of Hai Phong city (Vietnam). The study employed a GIS database and support vector regression (SVR) to build and verify a model of AGB, drawing upon data from a survey in 25 sampling plots and an integration of Advanced Land Observing Satellite-2 Phased Array Type L-band Synthetic Aperture Radar-2 (ALOS-2 PALSAR-2) dual-polarization horizontal transmitting and horizontal receiving (HH) and horizontal transmitting and vertical receiving (HV) and Sentinel-2A multispectral data. The performance of the model was assessed using root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R²), and leave-one-out cross-validation. Usability of the SVR model was assessed by comparing with four state-of-the-art machine learning techniques, i.e. radial basis function neural networks, multi-layer perceptron neural networks, Gaussian process, and random forest. The SVR model shows a satisfactory result (R² = 0.596, RMSE = 0.187, MAE = 0.123) and outperforms the four machine learning models. The SVR model-estimated AGB ranged between 36.22 and 230.14 Mg ha^?1 (average = 87.67 Mg ha^?1). We conclude that an integration of ALOS-2 PALSAR-2 and Sentinel-2A data used with SVR model can improve the AGB accuracy estimation of mangrove plantations in tropical areas.