Remote Sensing — Open Access Journal

The normalized difference vegetation index (NDVI) is an important indicator for evaluating vegetation change, monitoring land surface fluxes or predicting crop models. Due to the great availability of images provided by different satellites in recent years, much attention has been devoted to testing trend changes with a time series of NDVI individual pixels. However, the spatial dependence inherent in these data is usually lost unless global scales are analyzed. In this paper, we propose incorporating both the spatial and the temporal dependence among pixels using a stochastic spatio-temporal model for estimating the NDVI distribution thoroughly. The stochastic model is a state-space model that uses meteorological data of the Climatic Research Unit (CRU TS3.10) as auxiliary information. The model will be estimated with the Expectation-Maximization (EM) algorithm. The result is a set of smoothed images providing an overall analysis of the NDVI distribution across space and time, where fluctuations generated by atmospheric disturbances, fire events, land-use/cover changes or engineering problems from image capture are treated as random fluctuations. The illustration is carried out with the third generation of NDVI images, termed NDVI3g, of the Global Inventory Modeling and Mapping Studies (GIMMS) in continental Spain. This data are taken in bymonthly periods from January 2011 to December 2013, but the model can be applied to many other variables, countries or regions with different resolutions. Full article

Post-classification comparison is one of the most widely used change detection methods. However, it presents several operational problems that are often ignored, such as the occurrence of impossible transitions, difficulties in accuracy assessment and results not accurate enough for the purpose. This work aims to evaluate post-classification comparison change detection results obtained from LANDSAT5/TM data in a region of the Brazilian Amazon, using three legends in different levels of detail and both pixel wise and region based classifiers. A distinctive characteristic of the used approach is that each change mapping is the result of the combination of 100 land cover classifications for each date, obtained using varied training samples. This approach allowed to account for the training samples choice into the methodology, as well as the construction of confidence mappings. We presented and discussed different approaches for evaluating change results, such as the likelihood of land cover transitions occurring within the study area and time gap, the use of rectangular matrices to incorporate the occurrence of impossible or non evaluable changes and classification uncertainty. In general, change mappings obtained from region based classifications showed better results than the ones obtained from pixel based classifications. Globally, the use of region based approaches, in contrast to pixel based ones, led to an increase in accuracy of 15.5% for the change mapping from the most detailed legend, 7.8% for the one with the legend with intermediate level of detail and 3.6% for the less detailed one. In addition, individual transitions between land cover classes were better identified using region based approaches, with the exception of transitions from a non agriculture class to an agricultural one. The proposed quality mappings are useful to help to evaluate the change mappings, mainly in legend levels with higher level of detail and if reference samples are unreliable or unavailable. It was possible to access, in a spatially explicit way, that at least 29.0% of the pixel based change mapping and 21.9% of the region based one from the most detailed legend were erroneous classified, without ground truth information on the evaluated date. These values decreased to 0.5% and 1.4% (respectively the pixel and region based approaches) for results with the legend with the intermediate level of detail and are non existent in the results from the less detailed legend. The more generalized the legend (lower number of classes), the most similar are the accuracy of region and pixel based change mappings. These accuracy values also increase as fewer classes are considered in the legend, since similar classes are assembled during clustering, which reduces the overlap between groups. However, this accuracy is still low for operational purposes in areas with few changes, even considering the very high accuracy of the land cover classifications used to generate the change mappings (land cover classification with Overall Accuracy higher than 0.98 resulted in change mappings with Overall Accuracy around 0.83). Full article

This study examines a novel extraction method for SAR imagery data of widespread flooding, particularly in the Chao Phraya river basin of central Thailand, where flooding occurs almost every year. Because the 2011 flood was among the largest events and of a long duration, a large number of satellites observed it, and imagery data are available. At that time, RADARSAT-2 data were mainly used to extract the affected areas by the Thai government, whereas ThaiChote-1 imagery data were also used as optical supporting data. In this study, the same data were also employed in a somewhat different and more detailed manner. Multi-temporal dual-polarized RADARSAT-2 images were used to classify water areas using a clustering-based thresholding technique, neighboring valley-emphasis, to establish an automated extraction system. The novel technique has been proposed to improve classification speed and efficiency. This technique selects specific water references throughout the study area to estimate local threshold values and then averages them by an area weight to obtain the threshold value for the entire area. The extracted results were validated using high-resolution optical images from the GeoEye-1 and ThaiChote-1 satellites and water elevation data from gaging stations. Full article

The unavoidable diet change in emerging countries, projected for the coming years, will significantly increase the global consumption of animal protein. It is expected that Brazilian livestock production, responsible for close to 15% of global production, be prepared to answer to the increasing demand of beef. Consequently, the evaluation of pasture quality at regional scale is important to inform public policies towards a rational land use strategy directed to improve livestock productivity in the country. Our hypothesis is that MODIS images can be used to evaluate the processes of degradation, restoration and renovation of tropical pastures. To test this hypothesis, two field campaigns were performed covering a route of approximately 40,000 km through nine Brazilian states. To characterize the sampled pastures, biophysical parameters were measured and observations about the pastures, the adopted management and the landscape were collected. Each sampled pasture was evaluated using a time series of MODIS EVI2 images from 2000–2012, according to a new protocol based on seven phenological metrics, 14 Boolean criteria and two numerical criteria. The theoretical basis of this protocol was derived from interviews with producers and livestock experts during a third field campaign. The analysis of the MODIS EVI2 time series provided valuable historical information on the type of intervention and on the biological degradation process of the sampled pastures. Of the 782 pastures sampled, 26.6% experienced some type of intervention, 19.1% were under biological degradation, and 54.3% presented neither intervention nor trend of biomass decrease during the period analyzed. Full article

Several studies have indicated that glaciers in the Qinghai-Tibet plateau are thinning, resulting in reduced water supplies to major rivers such as the Yangtze, Yellow, Lancang, Indus, Ganges, Brahmaputra in China, and south Asia. Three rivers in the upstream of Yangtze River originate from glaciers around the Geladandong snow mountain group in central Tibet. Here we used elevation observations from Ice, Cloud, and land Elevation Satellite (ICESat) and reference elevations from a 3-arc-second digital elevation model (DEM) of Shuttle Radar Terrestrial Mission (SRTM), assisted with Landsat-7 images, to detect glacier elevation changes in the western (A), central (B), and eastern (C) regions of Geladandong. Robust fitting was used to determine rates of glacier elevation changes in regions with dense ICESat data, whereas a new method called rate averaging was employed to find rates in regions of low data density. The rate of elevation change was −0.158 ± 0.066 m·a⁻¹ over 2003–2009 in the entire Geladandong and it was −0.176 ± 0.102 m·a⁻¹ over 2003–2008 in Region C (by robust fitting). The rates in Regions A, B, and C were −0.418 ± 0.322 m·a⁻¹ (2000–2009), −0.432 ± 0.020 m·a⁻¹ (2000–2003), and −0.321 ± 0.139 m·a⁻¹ (2000–2008) (by rate averaging). We used in situ hydroclimatic dataset to assess these negative rates: the glacier thinning was caused by temperature rises around Geladandong, based on the temperature records over 1979–2009, 1957–2013, and 1966–2013 at stations Tuotuohe, Wudaoliang, and Anduo. The thinning Geladandong glaciers led to increased discharges recorded at the river gauge stations Tuotuohe and Chumda over 1956–2012. An unabated Geladandong glacier melting will reduce its long-term water supply to the Yangtze River Basin, causing irreversible socioeconomic consequences and seriously degrading the ecological system of the Yangtze River Basin. Full article

The sea ice cover in the North evolves at a rapid rate. To adequately monitor this evolution, tools with high temporal and spatial resolution are needed. This paper presents IceMap250, an automatic sea ice extent mapping algorithm using MODIS reflective/emissive bands. Hybrid cloud-masking using both the MOD35 mask and a visibility mask, combined with downscaling of Bands 3–7 to 250 m, are utilized to delineate sea ice extent using a decision tree approach. IceMap250 was tested on scenes from the freeze-up, stable cover, and melt seasons in the Hudson Bay complex, in Northeastern Canada. IceMap250 first product is a daily composite sea ice presence map at 250 m. Validation based on comparisons with photo-interpreted ground-truth show the ability of the algorithm to achieve high classification accuracy, with kappa values systematically over 90%. IceMap250 second product is a weekly clear sky map that provides a synthesis of 7 days of daily composite maps. This map, produced using a majority filter, makes the sea ice presence map even more accurate by filtering out the effects of isolated classification errors. The synthesis maps show spatial consistency through time when compared to passive microwave and national ice services maps. Full article

Northwest Arkansas has undergone a significant urban transformation in the past several decades and is considered to be one of the fastest growing regions in the United States. The urban area expansion and the associated demographic increases bring unprecedented pressure to the environment and natural resources. To better understand the consequences of urbanization, accurate and long-term depiction on urban dynamics is critical. Although urban mapping activities using remote sensing have been widely conducted, long-term urban growth mapping at an annual pace is rare and the low accuracy of change detection remains a challenge. In this study, a time series Landsat stack covering the period from 1995 to 2015 was employed to detect the urban dynamics in Northwest Arkansas via a two-stage classification approach. A set of spectral indices that have been proven to be useful in urban area extraction together with the original Landsat spectral bands were used in the maximum likelihood classifier and random forest classifier to distinguish urban from non-urban pixels for each year. A temporal trajectory polishing method, involving temporal filtering and heuristic reasoning, was then applied to the sequence of classified urban maps for further improvement. Based on a set of validation samples selected for five distinct years, the average overall accuracy of the final polished maps was 91%, which improved the preliminary classifications by over 10%. Moreover, results from this study also indicated that the temporal trajectory polishing method was most effective with initial low accuracy classifications. The resulting urban dynamic map is expected to provide unprecedented details about the area, spatial configuration, and growing trends of urban land-cover in Northwest Arkansas. Full article

Processing of land surface temperature from long time series of AVHRR (Advanced Very High Resolution Radiometer) requires stable algorithms, which are well characterized in terms of accuracy, precision and sensitivity. This assessment presents a comparison of four mono-window (Price 1983, Qin et al., 2001, Jiménez-Muñoz and Sobrino 2003, linear approach) and six split-window algorithms (Price 1984, Becker and Li 1990, Ulivieri et al., 1994, Wan and Dozier 1996, Yu 2008, Jiménez-Muñoz and Sobrino 2008) to estimate LST from top of atmosphere brightness temperatures, emissivity and columnar water vapour. Where possible, new coefficients were estimated matching the spectral response curves of the different AVHRR sensors of the past and present. The consideration of unique spectral response curves is necessary to avoid artificial anomalies and wrong trends when processing time series data. Using simulated data on the base of a large atmospheric profile database covering many different states of the atmosphere, biomes and geographical regions, it was assessed (a) to what accuracy and precision LST can be estimated using before mentioned algorithms and (b) how sensitive the algorithms are to errors in their input variables. It was found, that the split-window algorithms performed almost equally well, differences were found mainly in their sensitivity to input bands, resulting in the Becker and Li 1990 and Price 1984 split-window algorithm to perform best. Amongst the mono-window algorithms, larger deviations occurred in terms of accuracy, precision and sensitivity. The Qin et al., 2001 algorithm was found to be the best performing mono-window algorithm. A short comparison of the application of the Becker and Li 1990 coefficients to AVHRR with the MODIS LST product confirmed the approach to be physically sound. Full article

Recent research has shown that using spectral–spatial information can considerably improve the performance of hyperspectral image (HSI) classification. HSI data is typically presented in the format of 3D cubes. Thus, 3D spatial filtering naturally offers a simple and effective method for simultaneously extracting the spectral–spatial features within such images. In this paper, a 3D convolutional neural network (3D-CNN) framework is proposed for accurate HSI classification. The proposed method views the HSI cube data altogether without relying on any preprocessing or post-processing, extracting the deep spectral–spatial-combined features effectively. In addition, it requires fewer parameters than other deep learning-based methods. Thus, the model is lighter, less likely to over-fit, and easier to train. For comparison and validation, we test the proposed method along with three other deep learning-based HSI classification methods—namely, stacked autoencoder (SAE), deep brief network (DBN), and 2D-CNN-based methods—on three real-world HSI datasets captured by different sensors. Experimental results demonstrate that our 3D-CNN-based method outperforms these state-of-the-art methods and sets a new record. Full article

The accurate measurement of suspended particulate matter (SPM) concentrations in coastal waters is of crucial importance for ecosystem studies, sediment transport monitoring, and assessment of anthropogenic impacts in the coastal ocean. Ocean color remote sensing is an efficient tool to monitor SPM spatio-temporal variability in coastal waters. However, near-shore satellite images are complex to correct for atmospheric effects due to the proximity of land and to the high level of reflectance caused by high SPM concentrations in the visible and near-infrared spectral regions. The water reflectance signal (ρ_w) tends to saturate at short visible wavelengths when the SPM concentration increases. Using a comprehensive dataset of high-resolution satellite imagery and in situ SPM and water reflectance data, this study presents (i) an assessment of existing atmospheric correction (AC) algorithms developed for turbid coastal waters; and (ii) a switching method that automatically selects the most sensitive SPM vs. ρ_w relationship, to avoid saturation effects when computing the SPM concentration. The approach is applied to satellite data acquired by three medium-high spatial resolution sensors (Landsat-8/Operational Land Imager, National Polar-Orbiting Partnership/Visible Infrared Imaging Radiometer Suite and Aqua/Moderate Resolution Imaging Spectrometer) to map the SPM concentration in some of the most turbid areas of the European coastal ocean, namely the Gironde and Loire estuaries as well as Bourgneuf Bay on the French Atlantic coast. For all three sensors, AC methods based on the use of short-wave infrared (SWIR) spectral bands were tested, and the consistency of the retrieved water reflectance was examined along transects from low- to high-turbidity waters. For OLI data, we also compared a SWIR-based AC (ACOLITE) with a method based on multi-temporal analyses of atmospheric constituents (MACCS). For the selected scenes, the ACOLITE-MACCS difference was lower than 7%. Despite some inaccuracies in ρ_w retrieval, we demonstrate that the SPM concentration can be reliably estimated using OLI, MODIS and VIIRS, regardless of their differences in spatial and spectral resolutions. Match-ups between the OLI-derived SPM concentration and autonomous field measurements from the Loire and Gironde estuaries’ monitoring networks provided satisfactory results. The multi-sensor approach together with the multi-conditional algorithm presented here can be applied to the latest generation of ocean color sensors (namely Sentinel2/MSI and Sentinel3/OLCI) to study SPM dynamics in the coastal ocean at higher spatial and temporal resolutions. Full article

Land surface phenology is a highly sensitive and simple indicator of vegetation dynamics and climate change. However, few studies on spatiotemporal distribution patterns and trends in land surface phenology across different climate and vegetation types in China have been conducted since 2000, a period during which China has experienced remarkably strong El Niño events. In addition, even fewer studies have focused on changes of the end of season (EOS) and length of season (LOS) despite their importance. In this study, we used four methods to reconstruct Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) dataset and chose the best smoothing result to estimate land surface phenology. Then, the phenophase trends were analyzed via the Mann-Kendall method. We aimed to assess whether trends in land surface phenology have continued since 2000 in China at both national and regional levels. We also sought to determine whether trends in land surface phenology in subtropical or high altitude areas are the same as those observed in high latitude areas and whether those trends are uniform among different vegetation types. The result indicated that the start of season (SOS) was progressively delayed with increasing latitude and altitude. In contrast, EOS exhibited an opposite trend in its spatial distribution, and LOS showed clear spatial patterns over this region that decreased from south to north and from east to west at a national scale. The trend of SOS was advanced at a national level, while the trend in Southern China and the Tibetan Plateau was opposite to that in Northern China. The transaction zone of the SOS within Northern China and Southern China occurred approximately between 31.4°N and 35.2°N. The trend in EOS and LOS were delayed and extended, respectively, at both national and regional levels except that of LOS in the Tibetan Plateau, which was shortened by delayed SOS onset more than by delayed EOS onset. The absolute magnitude of SOS was decreased after 2000 compared with previous studies, and the phenophase trends are species specific. Full article

The urbanization effects on land surface phenology (LSP) have been investigated by many studies, but few studies have focused on the temporal variations of urbanization effects on LSP. In this study, we used the Moderate-resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI), MODIS Land Surface Temperature (LST) data and China’s Land Use/Cover Datasets (CLUDs) to investigate the temporal variations of urban heat island intensity (UHII) and urbanization effects on LSP in Northeast China during 2001–2015. LST and phenology differences between urban and rural areas represented the urban heat island intensity and urbanization effects on LSP, respectively. A Mann–Kendall nonparametric test and Sen’s slope were used to evaluate the trends of urbanization effects on LSP and urban heat island intensity. The results indicated that the average LSP during 2001–2015 was characterized by high spatial heterogeneity. The start of the growing season (SOS) in old urban areas had become earlier and earlier compared to rural areas, and the differences in SOS between urbanized areas and rural areas changed greatly during 2001–2015 (−0.79 days/year, p < 0.01). Meanwhile, the length of the growing season (LOS) in urban and adjacent areas had become increasingly longer than rural areas, especially in urbanized areas (0.92 days/year, p < 0.01), but the differences in the end of the growing season (EOS) between urban and adjacent areas did not change significantly. Next, the UHII increased in spring and autumn during the whole study period. Moreover, the correlation analysis indicated that the increasing urban heat island intensity in spring contributed greatly to the increases of urbanization effects on SOS, but the increasing urban heat island intensity in autumn did not lead to the increases of urbanization effects on EOS in Northeast China. Full article

The parameterization of heat transfer based on remote sensing data, and the Surface Energy Balance System (SEBS) scheme to retrieve turbulent heat fluxes, already proved to be very appropriate for estimating evapotranspiration (ET) over homogeneous land surfaces. However, the use of such a method over heterogeneous landscapes (e.g., semi-arid regions or agricultural land) becomes more difficult, since the principle of similarity theory is compromised by the presence of different heat sources at various heights. This study aims to propose and evaluate some models based on vegetation geometry partly developed by Colin and Faivre, to retrieve the surface aerodynamic roughness length for momentum transfer ($z_{0m}$), which is a key parameter in the characterization of heat transfer. A new approach proposed by the authors consisted in the use of a Digital Surface Model (DSM) as boundary condition for experiments with a Computational Fluid Dynamics (CFD) model to reproduce 3D wind fields, and to invert them to retrieve a spatialized roughness parameter. Colin and Faivre also applied the geometrical Raupach’s approach for the same purpose. These two methods were evaluated against two empirical ones, widely used in Surface Energy Balance Index (SEBI) based algorithms (Moran; Brutsaert), and also against an alternate geometrical model proposed by Menenti and Ritchie. The investigation was carried out in the Yingke oasis (China) using very-high resolution remote sensing data (VNIR, TIR & LIDAR), for a precise description of the land surface, and a fine evaluation of estimated heat fluxes based on in-situ measurements. A set of five numerical experiments was carried out to evaluate each roughness model. It appears that methods used in experiments 2 (based on Brutsaert) and 4 (based on Colin and Faivre) are the most accurate to estimate the aerodynamic roughness length, according to the estimated heat fluxes. However, the formulation used in experiment 2 allows to minimize errors in both latent and sensible heat flux, and to preserve a good partitioning. An additional evaluation of these two methods based on another $k{B}^{-1}$ parameterization could be necessary, given that the latter is not always compatible with the CFD-based retrieval method. Full article

Spatial and temporal variability of atmospheric water vapor (H₂O) is extremely high, and therefore it is difficult to accurately evaluate the measurement precision of H₂O data by a simple comparison between the data derived from two different instruments. We determined the measurement precisions of column-averaged dry-air mole fractions of H₂O (XH₂O) retrieved independently from spectral radiances in the thermal infrared (TIR) and the short-wavelength infrared (SWIR) regions measured using a Thermal And Near-infrared Sensor for carbon Observation-Fourier Transform Spectrometer (TANSO-FTS) onboard the Greenhouse gases Observing SATellite (GOSAT), by an intercomparison between the two TANSO-FTS XH₂O data products and the ground-based FTS XH₂O data. Furthermore, the spatial variability of XH₂O was also estimated in the intercomparison process. Mutually coincident XH₂O data above land for the period ranging from April 2009 to May 2014 were intercompared with different spatial coincidence criteria. We found that the precisions of the TANSO-FTS TIR and TANSO-FTS SWIR XH₂O were 7.3%–7.7% and 3.5%–4.5%, respectively, and that the spatial variability of XH₂O was 6.7% within a radius of 50 km and 18.5% within a radius of 200 km. These results demonstrate that, in order to accurately evaluate the measurement precision of XH₂O, it is necessary to set more rigorous spatial coincidence criteria or to take into account the spatial variability of XH₂O as derived in the present study. Full article

The Andean Amazon is an endangered biodiversity hot spot but its forest dynamics are less studied than those of the Amazon lowland and forests from middle or high latitudes. This is because its landscape variability, complex topography and cloudy conditions constitute a challenging environment for any remote-sensing assessment. Breakpoint detection with Landsat time-series data is an established robust approach for monitoring forest dynamics around the globe but has not been properly evaluated for implementation in the Andean Amazon. We analyzed breakpoint detection-generated forest dynamics in order to determine its limitations when applied to three different study areas located along an altitude gradient in the Andean Amazon in Ecuador. Using all available Landsat imagery for the period 1997–2016, we evaluated different pre-processing approaches, noise reduction techniques, and breakpoint detection algorithms. These procedures were integrated into a complex function called the processing chain generator. Calibration was not straightforward since it required us to define values for 24 parameters. To solve this problem, we implemented a novel approach using genetic algorithms. We calibrated the processing chain generator by applying a stratified training sampling and a reference dataset based on high resolution imagery. After the best calibration solution was found and the processing chain generator executed, we assessed accuracy and found that data gaps, inaccurate co-registration, radiometric variability in sensor calibration, unmasked cloud, and shadows can drastically affect the results, compromising the application of breakpoint detection in mountainous areas of the Andean Amazon. Moreover, since breakpoint detection analysis of landscape variability in the Andean Amazon requires a unique calibration of algorithms, the time required to optimize analysis could complicate its proper implementation and undermine its application for large-scale projects. In exceptional cases when data quality and quantity were adequate, we recommend the pre-processing approaches, noise reduction algorithms and breakpoint detection algorithms procedures that can enhance results. Finally, we include recommendations for achieving a faster and more accurate calibration of complex functions applied to remote sensing using genetic algorithms. Full article

Spaceborne laser altimetry waveform estimates of canopy Gap Fraction (GF) vary with respect to discrete return airborne equivalents due to their greater sensitivity to reflectance differences between canopy and ground surfaces resulting from differences in footprint size, energy thresholding, noise characteristics and sampling geometry. Applying scaling factors to either the ground or canopy portions of waveforms has successfully circumvented this issue, but not at large scales. This study develops a method to scale spaceborne altimeter waveforms by identifying which remotely-sensed vegetation, terrain and environmental attributes are best suited to predicting scaling factors based on an independent measure of importance. The most important attributes were identified as: soil phosphorus and nitrogen contents, vegetation height, MODIS vegetation continuous fields product and terrain slope. Unscaled and scaled estimates of GF are compared to corresponding ALS data for all available data and an optimized subset, where the latter produced most encouraging results (R² = 0.89, RMSE = 0.10). This methodology shows potential for successfully refining estimates of GF at large scales and identifies the most suitable attributes for deriving appropriate scaling factors. Large-scale active sensor estimates of GF can establish a baseline from which future monitoring investigations can be initiated via upcoming Earth Observation missions. Full article

In this article, we report about the first results of the mapKITE system, a tandem terrestrial-aerial concept for geodata acquisition and processing, obtained in corridor mapping missions. The system combines an Unmanned Aerial System (UAS) and a Terrestrial Mobile Mapping System (TMMS) operated in a singular way: real-time waypoints are computed from the TMMS platform and sent to the UAS in a follow-me scheme. This approach leads to a simultaneous acquisition of aerial-plus-ground geodata and, moreover, opens the door to an advanced post-processing approach for sensor orientation. The current contribution focuses on analysing the impact of the new, dynamic Kinematic Ground Control Points (KGCPs), which arise inherently from the mapKITE paradigm, as an alternative to conventional, costly Ground Control Points (GCPs). In the frame of a mapKITE campaign carried out in June 2016, we present results entailing sensor orientation and calibration accuracy assessment through ground check points, and precision and correlation analysis of self-calibration parameters’ estimation. Conclusions indicate that the mapKITE concept eliminates the need for GCPs when using only KGCPs plus a couple of GCPs at each corridor end, achieving check point horizontal accuracy of ${\mu }_{E,N}\approx 1.7$ px (3.4 cm) and ${\mu }_h\approx 4.3$ px (8.6 cm). Since obtained from a simplified version of the system, these preliminary results are encouraging from a future perspective. Full article

The ability of texture models and red-edge to facilitate the detection of subtle structural vegetation traits could aid in discriminating and mapping grass quantity, a challenge that has been longstanding in the management of grasslands in southern Africa. Subsequently, this work sought to explore the robustness of integrating texture metrics and red-edge in predicting the above-ground biomass of grass growing under different levels of mowing and burning in grassland management treatments. Based on the sparse partial least squares regression algorithm, the results of this study showed that red-edge vegetation indices improved above-ground grass biomass from a root mean square error of perdition (RMSEP) of 0.83 kg/m² to an RMSEP of 0.55 kg/m². Texture models further improved the accuracy of grass biomass estimation to an RMSEP of 0.35 kg/m². The combination of texture models and red-edge derivatives (red-edge-derived vegetation indices) resulted in an optimal prediction accuracy of RMSEP 0.2 kg/m² across all grassland management treatments. These results illustrate the prospect of combining texture metrics with the red-edge in predicting grass biomass across complex grassland management treatments. This offers the detailed spatial information required for grassland policy-making and sustainable grassland management in data-scarce regions such as southern Africa. Full article

Accurate maps of surface water extent are of paramount importance for water management, satellite data processing and climate modeling. Several maps of water bodies based on remote sensing data have been released during the last decade. Nonetheless, none has a truly (90${}^{\circ }$N/90${}^{\circ }$S) global coverage while being thoroughly validated. This paper describes a global, spatially-complete (void-free) and accurate mask of inland/ocean water for the 2000–2012 period, built in the framework of the European Space Agency (ESA) Climate Change Initiative (CCI). This map results from the synergistic combination of multiple individual SAR and optical water body and auxiliary datasets. A key aspect of this work is the original and rigorous stratified random sampling designed for the quality assessment of binary classifications where one class is marginally distributed. Input and consolidated products were assessed qualitatively and quantitatively against a reference validation database of 2110 samples spread throughout the globe. Using all samples, overall accuracy was always very high among all products, between $98\%$ and $100\%$. The CCI global map of open water bodies provided the best water class representation (F-score of $89\%$) compared to its constitutive inputs. When focusing on the challenging areas for water bodies’ mapping, such as shorelines, lakes and river banks, all products yielded substantially lower accuracy figures with overall accuracies ranging between $74\%$ and $89\%$. The inland water area of the CCI global map of open water bodies was estimated to be 3.17 million km${}^2$ ± 0.24 million km${}^2$. The dataset is freely available through the ESA CCI Land Cover viewer. Full article