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Arctic sea ice change is one of the critical factors affecting the global climate environment; hence, it is crucial to obtain sea ice thickness with high accuracy while studying polar and global climate change. In the process of using altimetry data to estimate sea ice thickness, the mean sea surface height will bring greater uncertainty to the extraction of sea ice freeboard, which will affect the accuracy of sea ice thickness. Also, the influence of snow cover on radar signal penetration will bring greater uncertainty to sea ice thickness. In this paper, we present a processing chain for sea ice thickness estimation. First, we compare the effect of four different MSS models on the freeboard estimation. Then, considering the incomplete penetration of radar signals and the different speeds of radar signals penetrating the snow layer and the vacuum, the traditional sea ice thickness model is optimized to obtain the sea ice thickness. Compared with the Operation IceBridge (OIB) sea ice thickness, the accuracy of sea ice thickness obtained by the optimized model is better than 0.350 m, with a root mean square error of 0.260 m and a mean bias of 0.048 m. The comparison results show that the combination of the latest DTU18 MSS model and sea ice thickness optimization model effectively improve the accuracy of sea ice thickness. [ABSTRACT FROM AUTHOR]

Surface soil moisture (SSM) is an important variable for controlling the energy exchange between earth's surface and atmosphere. Accurate estimation of the distribution of SSM and its dynamic changes is increasingly essential in the study of hydrology, ecology, agriculture, geological hazard monitoring, and global climate change. This paper presents a new model for SSM retrieval (named, KSSM) through assimilating soil porosity with a two-dimensional feature space composed of land surface temperature and vegetation index (LST-VI). The KSSM model takes into account the physical properties of soil porosity, so it is suitable for SSM retrieval of various soil types in different regions. The KSSM model was validated using the measured data, and a comparison analysis with other data was conducted. Through the sensitivity analysis, it is concluded that the error of SSM retrieved by the KSSM model is less than 0.04 m3 m−3 and the KSSM model is robust. High accuracy of volumetric SSM estimation, error within 10% compared with the measured data, can be achieved by the KSSM model without the need for an in-situ SSM. [ABSTRACT FROM AUTHOR]

Lakes are important to the healthy functioning of the urban ecosystem. The urban lakes in Wuhan, China, which is known as 'city of hundreds of lakes', are facing substantial threats mainly due to rapid urbanization. This paper focused on detecting the spatial and temporal change of urban lakes in Wuhan, using a long time series of Landsat and HJ-1A remotely sensed data from 1987 to 2016. The permanent disappearance and seasonal fluctuation of 28 main urban lakes were analysed, and their relationships with climatic change and human activities were discussed. The results show that most lakes in Wuhan had shrunk over the past 30 years resulting in a permanent change from water to land. The shrinkage was also most apparent in the central region of the city. Seasonal fluctuations of lake area were evident for most lakes but the relative important driving variable of lake area change varied between sub-periods of time for different lakes. The explanatory power of impervious surface to five-year permanent water change is 91.75%, suggesting that urbanization – as increasing impervious surface – had led to the shrinkage of urban lakes in Wuhan. In all, 128.28 km2 five-year permanent water disappeared from 1987 to 2016. [ABSTRACT FROM AUTHOR]

Owing to the influence of global change, land cover and land use have changed significantly over the last decade in the cold and arid regions of China, such as Madoi County which is located in the source area of the Yellow River. In this paper, land-use/cover change and landscape dynamics are investigated using satellite remote sensing (RS) and a geographical information system (GIS). The objectives of this paper are to determine land-use/cover transition rates between different cover types in the Madoi County over 10 years e.g., from 1990 to 2000. Second, the changes of landscape metrics using various indices and models are quantified. The impact factors of LUCC (Land-Use land cover Change) are systematically identified by integrating remote sensing as well as statistical data, including climate, frozen soil, hydrological data and the socio-economic data. Using 30 m×30 m spatial resolution Landsat (Enhanced) Thematic Mapper (TM/ETM+) data in our study area, nine land cover classes can be discriminated. Our results show that Grassland, Marshes and Water Bodies decrease notably, while oppositely, Sands - Gobi and Barren land increase significantly. The number of lakes with an acreage larger than six hectares decreased from 405 in 1990 to 261 in 2000. Numerous small lakes dried out. The area of grassland with a high cover fraction decreased as well, while the surface area of grassland with a medium level of cover fraction increased. The medium cover fraction grassland mainly originates from high cover fraction grassland. The desertification of land is a serious issue. (ii) The inter-transformations between Grasslands, Barren Land, Sands, Gobi, Water Bodies and Marshes are remarkable. The Shannon-Weaver Diversity Index (SWDI), the Evenness Index (EI) and the extent of Landscape Heterogeneity (LH) has improved. Marshes have become more fragmented hence, with less connected patches. (iii) In the recent 30 years, average annual temperature, the power of evaporation and the index of dryness did increase significantly. Moreover, soil moisture content (SMC) decreased and the drought trend accelerated. The degradation of frozen soil has impacted on the decrease of surface water area and induced a drop in groundwater levels. Monitoring LUCC in sensitive regions would not only benefit from a study of vulnerable ecosystems in cold and high altitude regions, but would provide scientifically based decision-making tools for local governments as well. [ABSTRACT FROM AUTHOR]

The coastal marine environment is threatened by the influences of global warming and climate change and local influences brought about by the pressures of a differentially increasing human population close to coastal margins, some of the effects of which include eutrophication, marine coastal construction, pollution, destructive fishing activities and the impact of invasive species. It is increasingly recognised that remote sensing, through a range of methods operating from the sub-surface to air and space-borne sensors, offers a powerful technology to map and monitor changes in the coastal zone. Some of this recognition arises from the knowledge that traditional means of field survey, whilst remaining vital, can offer only a limited and expensive solution to the survey of the extent of impacts. However, there is a need to discover the bounds within which remote sensing techniques can usefully contribute to what is a highly variable zone, particularly where the penetrative capabilities of useful signals may be attenuated by changes in such factors as depth and turbidity.

The detection of land-cover change over large areas using short time series is a challenging and important task in global change studies. This paper introduces a novel method, called the Segment-based Detection of Trends and Change (SDTC), to determine areas that are undergoing land-cover changes. The method is illustrated in the State of Alaska over the 2001–2009 time period using three time series derived from Moderate Resolution Spectroradiometer (MODIS) imagery: the normalized difference vegetation index (NDVI), albedo, and land surface temperature (LST). SDTC extends seasonal trend analysis (STA) by using segmentation in the detection process to combine multiple variables and to incorporate the spatial context. Segments labelled as ‘change’ correspond to groups of adjacent pixels with a majority of pixels undergoing significant temporal trends as defined by the Mann–Kendall (MK) procedure and STA. Segments correspond to landscape units in a less arbitrary manner than pixels because they represent groupings of adjacent areas undergoing similar temporal behaviour. Findings indicate that the use of MK in conjunction with segmentation exploits more fully the richness of the spatial context in the process of detection. Results suggest that SDTC is a useful method for detecting and characterizing land-cover change. The technique is conservative and eliminates the ‘salt and pepper’ effect by filtering noise and identifying areas of change. Using SDTC, we found that most areas of change in Alaska undergo between one and three significant changes and that increasing LST seasonality constitutes the most widespread type of change. [ABSTRACT FROM PUBLISHER]

Feature tracking is an efficient way to derive glacier velocity. It is based on a cross-correlation algorithm that seeks offsets of the maximal correlation windows on repeated satellite images. In this paper we demonstrate that different window sizes lead to different velocities. The averaged velocity gradient (AVG) method is proposed to improve window sizes in feature tracking and to obtain the most suitable flow field. The AVG method measures velocity variation between adjacent windows on the whole glacier in the image. Different window sizes lead to different AVG values, and the best-size window corresponds to the value where the AVG changes from abrupt to gradual. Using improved feature tracking, two flow fields of the same glacier are acquired with Advanced Land Observing Satellite (ALOS) optical and synthetic aperture radar (SAR) data, respectively. The advantages, application conditions, accuracy and disadvantages of the two kinds of data using the feature tracking method are discussed. [ABSTRACT FROM AUTHOR]

This study aims to assess CO2 levels in China, based on the dry air column-averaged mixing ratios of CO2 measured by the Scanning Imaging Absorption spectrometer for Atmospheric CartograpHY (SCIAMACHY) instrument on board ENVISAT and emission inventories. A land cover and statistical data set, the Regional emission inventory in Asia (REAS), was also used in this study. The results show that the spatial variations of the CO2 concentration in the whole of China are obvious, and with clear seasonal fluctuation during 2003-2005. The monthly averaged variability of CO2 over China fluctuates, with the peak occurring in April to May and the trough occurring in August to October. The largest peak-to-trough amplitude was about 16 ppm in this period. The spatial and temporal variations of CO2 had a strong correlation with anthropogenic CO2 emissions and regional terrestrial ecosystem, as well as local meteorological conditions. This paper illustrates that satellite data are promising for obtaining a comprehensive understanding of the variations of atmospheric CO2 concentrations and of CO2 emissions to the atmosphere. [ABSTRACT FROM AUTHOR]

India's climate is changing gradually from one year to another. A significant increase in surface air temperature in different Indian regions was observed from 1998 onwards, which became all the more pronounced in 2009. Monthly mean maximum and minimum temperatures were higher than normal values over the whole of India, accompanied by a sharp increase in the duration and frequency of hot days and heatwave conditions. In the light of these observations the paper is aimed at investigating the spatial and temporal variation of air temperature at different altitudes and the interannual trend for different months over the Indian region for a period of 108 years from 1901 to 2008. It is observed that the variation in air temperature is not uniform at all places in India. The yearly mean air temperature for the period 1948-2008 exhibits an increasing trend at all atmospheric layers from the surface up to 10 km. The recently observed increase in surface air temperature and longer periods of heatwaves and hot days in India may be due to a higher occurrence of dust storms and forest fires and the influence of local factors, such as a sharp population growth accompanied by uncontrolled urbanization and rapid industrialization, resulting in high levels of pollution and imbalances in the regional climate. [ABSTRACT FROM AUTHOR]

In this paper, we quantified vegetation variations in the Qaidam Basin from 1982 to 2003 by using growing-season NDVI sequences, which were defined as the summation of monthly NDVI values from May to September, and were calculated pixel-by-pixel from a successive 8-km NDVI dataset. We adopt linear regressions to examine the trends in growing-season NDVI and the trends in climate (temperature, precipitation and sunshine duration) during this period in an attempt to depict their temporal and spatial variability. Our results indicate that climate in the Qaidam Basin has homogeneously warmed at a rate of about 0.6°C/decade during the study period, with significant trends in monthly mean temperatures in April-September. However, there were no statistically significant trends observed in precipitation and sunshine duration. We found positive growing-season NDVI trends in 31.6% of the vegetated lands in 1982-2003 and in 24.1% over the first half period, 1982-1992. In addition, few areas were shown to have negative trends during these periods. In 1993-2003, however, the percentage of land with a positive trend decreased to 13.1%, and the percentage of vegetated land with a negative trend increased to 10.2%. Growing-season NDVI trends show both temporal and spatial variability. Areas with negative trends are distributed mostly at lower elevations and near oasis boundaries, and areas with positive trends at higher elevations. Using correlation analyses we estimated the relationship between growing-season NDVI and the climatic factors with the consideration of duration and lagging effects. The results suggest that growing-season NDVI trends are more correlated to temperature increases in growing-season months when compared to variations in precipitation and sunshine duration; however increased precipitation amounts within May-August can also facilitate vegetation growth in some of this arid basin. However, we found no significant correlations between growing-season NDVI and temperature in the non-trend areas, which account for the majority of the vegetated land. We suggest that the variability in vegetation responses to the observed warming climates results from the differences in background thermal condition and moisture availability, which depend on elevation and other factors, such as hydrological conditions. [ABSTRACT FROM AUTHOR]

Although the Montreal Protocol now controls the production and emission of ozone depleting substances, the timing of ozone recovery is unclear. There are many other factors affecting the ozone layer, in particular climate change is expected to modify the speed of re-creation of the ozone layer. Therefore, long-term observations are needed to monitor the further evolution of the stratospheric ozone layer. Measurements from satellite instruments provide global coverage and are supplementary to selective ground-based observations. The combination of data derived from different space-borne instruments is needed to produce homogeneous and consistent long-term data records. They are required for robust investigations including trend analysis. For the first time global total ozone columns from three European satellite sensors GOME (ERS-2), SCIAMACHY (ENVISAT), and GOME-2 (METOP-A) are combined and added up to a continuous time series starting in June 1995. On the one hand it is important to monitor the consequences of the Montreal Protocol and its amendments; on the other hand multi-year observations provide the basis for the evaluation of numerical models describing atmospheric processes, which are also used for prognostic studies to assess the future development. This paper gives some examples of how to use satellite data products to evaluate model results with respective data derived from observations, and to disclose the abilities and deficiencies of atmospheric models. In particular, multi-year mean values derived from the Chemistry-Climate Model E39C-A are used to check climatological values and the respective standard deviations. [ABSTRACT FROM AUTHOR]

This paper examines three empirically based methods of monitoring forest growth between 1991 and 2000 from airborne synthetic aperture radar (SAR). In the first method, height change and volume change between 1991 and 2000 were estimated from the mean L-band stand backscatter difference between the two dates. Height change and volume change over the 9-year period were estimated with an accuracy of 0.23 m and 15 m3 ha-1, respectively, when the stands were below saturation point for the first date. The accuracy of the results was lower for stands beyond saturation in both data sets. In the second method, the height change is calculated from the estimated stand height in 2000 minus the estimated stand height in 1991. The second method produced poorer results than the first method, but better results than predicted by the error propagation equation. The difference between the observed accuracy and the expected error (based on the error propagation equation) appears to be due to a systematic bias in both the 1991 and 2000 estimates, as the residuals are correlated for stands below 20 years old (r = 0.71 for stand volume residuals). The third experiment investigates the utility of data from two dates to classify the stands into three age classes. The results show that, with two images separated by 9 years, 85% of stands were correctly classified compared with 69% for a single date L-HV image. [ABSTRACT FROM AUTHOR]

This paper describes a method to detect the impact of rainfall anomalies on vegetation phenology, in terms of timing (phase) and greenness, by using Fourier series to fit a time series of Normalized Difference Vegetation Index (NDVI) observations. The study was conducted in the northern semiarid region of Argentina, where rainfall is the driving factor of vegetation phenology. A 9-year time series of monthly NDVI Global Area Coverage (GAC) images, obtained with the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR), was split into nine series of 12-monthly images, each corresponding to a yearly growth cycle. A Fast Fourier Transform (FFT) algorithm was applied to each cycle, and derived parameters were analysed according to rainfall anomalies for irrigated and rainfed crops, grasslands and native forest. Derived Fourier parameters were: mean NDVI, amplitude and phase. Both negative and positive rainfall anomalies had a significant impact on the Fourier parameters. Amplitude and phase were the most sensitive parameters. Droughts modified the monomodal structure of the yearly cycle by decreasing the contribution of the 12-month periodic component and increasing the contribution of the 6-month component. The impact of drought on the Fourier parameters was highest for rainfed crops. Yearly values of Fourier parameters for grasslands and native forest were affected by prevailing hydrological conditions over the previous year. [ABSTRACT FROM AUTHOR]

The effects of climate change on northern vegetation productivity need to be fully understood in order to reduce uncertainties in predicting vegetation distributions under different climate warming scenarios. Knowledge of the relationship between northern climate and vegetation productivity will also help provide a better understanding of changes in vegetation distributions as an indicator of climate change and variability. Vegetation productivity and biomass have been monitored using long-term satellite earth observations, mostly using the Normalized Difference Vegetation Index (NDVI), as a cumulative indicator of all effects resulting from processes related to climate change, including changes in temperature, precipitation, and disturbance. In this paper, the investigation is focused on the short-term effect of temperature anomalies on arctic and tree-line transition vegetation productivity in both dry and humid regions of Canada. The analysis shows that several land-cover types composed mainly of trees and shrubs exhibit a significant increase in NDVI with higher-than-normal temperatures in the preceding 10-40-day period, while land-cover types consisting of lichen and moss growing on mostly barren surfaces show a significant NDVI decrease with increased temperature. These trends are consistent with results reported in plot-warming experiments in the north, which have shown that certain vegetation communities increase, while others decrease in cover fraction and biomass in response to warming. When land cover is grouped into increasing and decreasing NDVI with temperature and stratified by dry and humid regions of Canada, much of the dry region of northern Canada does not exhibit significant NDVI response to preceding temperature anomalies. It is expected that in the absence of disturbance or other limiting factors, an increased frequency of elevated temperature anomalies may eventually contribute to changes in vegetation biomass. A map of land-cover types that have the potential to increase in biomass with climate warming and those that are vulnerable to decline is presented. [ABSTRACT FROM AUTHOR]

The EU-CLOUDMAP project took place between 1997-2000 as a collaboration between five university and government research groups in the UK, Germany, the Netherlands and Switzerland. The original scientific motivation of the EU-CLOUDMAP Project was to improve the measurement and characterization of cirrus and contrail cloud properties. IPCC (Penner et al. 1999) demonstrated that contrail clouds could play a small but significant role in changing the radiative balance of the atmosphere based on work from one of the CLOUDMAP partners (DLR) using AVHRR data over Europe. However, the scope was broadened to include properties of clouds at all altitudes as (Cess et al. 1993) had shown that depending on how cloud processes are parameterised can lead to an order of magnitude difference in predictions of surface temperature due to changes in CO2 radiative forcing. This error is by far the largest uncertainty in making accurate forecasts of global warming. The primary technological motivation of the Project was to develop new techniques for deriving cloud-top properties (cloud-top height, amount, microphysics and winds) from a new series of meteorological sensors based on the use of either cloud-top stereo (ATSR-2 and MISR) or Oxygen A-band (MOS) and their application to the generation of new cloud climatology products. A secondary goal was to develop an automated technique, based on fuzzy logic, to detect contrails in non-thermal imagery where contrails can only be detected through their unique spatial characteristics. Validation of cloud products was perceived as a crucial central issue to any adoption of these products by the Numerical Weather Prediction (NWP) and climate forecasting communities. This presents unique challenges as validation must be conducted simultaneously with satellite data acquisition. Ground-based remote sensing techniques exploiting continuously operating radars and lidars were used and a new technique based on visible stereo digital cameras for retrieving cloud-base properties was pioneered. A technology spin-off from the CLOUDMAP work at UCL was the development of an uncooled thermal IR fish-eye lens camera for continuously monitoring cloud cover from the ground to replace existing manual observations of cloud cover (Chapman et al. 2007). Eight papers in this issue show some of the highlights of the project including fundamental aspects of the algorithms for retrieval of cloud-top height using stereo photogrammetry from ATSR-2 (Muller et al. 2007, Denis et al. 2007) and MOMS (Drescher 2007) and Oxygen A-band from MOS (Preusker et al. 2007). Seiz et al. (2007) describe the development of a novel technique for the automated retrieval of cloud-base height from stereo visible digital cameras. Naud et al. (2007) describe the validation of cloud-top heights from ATSR-2 and MOS using ground-based radar and lidar. Hetzheim (2007) shows how fuzzy logic systems can be employed for contrail detection when thermal imagery is not available using MOS and ATSR-2 to demonstrate his techniques. Finally, Meyer et al. (2007) show results from applying the thermal IR contrail detection technique to AVHRR over Asia. [ABSTRACT FROM AUTHOR]

Remote sensing plays a vital role in climate change studies by providing us with a record of 'snapshots' of the Earth's environment through time. Almost 30 years have past since the launch of the first routine and repetitive remote sensing systems. We are on the brink of being able to use remotely sensed imagery to define climatic normals using features of the Earth's surface environment as proxy indicators. Before we can do this, however, we need to develop new image analysis strategies—hypertemporal image analysis techniques—which we can use to measure variability and identify changes in long sequences of remotely sensed imagery. In this paper we explore one such technique: statistical time series analysis (TSA). We apply AutoRegressive-Moving Average (ARMA) time series models to a 108-month sequence of passive microwave images of Arctic sea ice concentrations. TSA is traditionally a manual technique applied to one or two temporal variables. Through this research we demonstrate how, by making two simple assumptions about the data, the approach can be automated to process the thousands of time series in a temporal image sequence. ARMA models are shown to be useful in deriving a spatial summary of the temporal characteristics of the modelled phenomenon, for identifying changes within that system, and for forecasting into the future with statistical confidence. [ABSTRACT FROM AUTHOR]

Understanding the spatiotemporal characteristics of photosynthetic vegetation (PV) and non-photosynthetic vegetation (NPV) is critical for the study of vegetation in arid and semiarid regions. However, the evaluation of the fractional cover of PV (fPV) and NPV (fNPV) in Xinjiang has received very little attention. Thus, this study aims to evaluate the spatiotemporal variation characteristics of PV and NPV and their responses to precipitation and temperature in Xinjiang from 2001 to 2020. The results showed that the overall stability of fPV and fNPV were of the low fluctuation and high fluctuation types, respectively. A significant increase trend of fPV was found in most regions (P < 0.01), while fNPV showed an insignificant increase trend in general, except for eastern Xinjiang (P > 0.05). The comparison of fPV and fNPV shows that the vegetation of Xinjiang has improved over time, except for the Yili region, showing the need to strengthen the environmental protection of this region. Through the spatial analysis, some agricultural oasis areas experienced an expansion in the distribution of vegetation, while some sparse vegetation distribution area has not improved. From the climate perspective, the response degree of fPV and fNPV with precipitation is higher than that of temperature. The responses of fPV and fNPV to precipitation are greater in the summer and spring phases, respectively. Whereas, the responses of both fPV and fNPV to temperature are greater in the autumn phase. In the time phase, precipitation mainly promotes the distribution of PV and NPV, while temperature mainly inhibits their distribution. This study provides a theoretical basis for ecological restoration and conservation in Xinjiang and other arid and semiarid regions worldwide. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the author discusses various reports featured at the 13th Biennial Pan Ocean Remote Sensing Conference (PORSEC) including coastal impacts, climate change, and ocean-atmosphere interactions.

Climate change monitoring requires decades-long time-series radiometric measurements using multiple optical sensors in multiple platforms covering the globe. The problem of achieving traceability to SI units for these measurements is discussed. A major challenge is to determine the result of a measurement and its associated uncertainty using various calibration and validation processes. These processes are plagued by systematic (non-statistical) uncertainties that are not well understood. In particular, different, but in principle equivalent, SI traceable measurements may differ by more than would be expected from the uncertainties associated with the individual measurements. We propose a methodology based on the International Organization for Standardization (ISO) Guide to the Expression of Uncertainty in Measurement (GUM) for the analysis of uncertainties in such measurements along with consistency checking. This allows the measurement result and its associated uncertainty to evolve as new knowledge is gained from additional experiments, and it promotes greater caution in drawing conclusions in view of the sparse measurements. We use data from ongoing total solar irradiance measurements from various instruments in orbit to illustrate the principles. [ABSTRACT FROM AUTHOR]

Highly accurate and detailed information on land cover products is crucial in studying global climate change and sustainable development. GlobeLand30 is the first global 30 m resolution Land cover (LC) product based on remote sensing data developed by Chinese scientists. GlobeLand30 has 10 first-level classes of products. However, no second-level classification products have been released. This study presents an integration method based on fuzzy theory and combines three 30 m resolution LC products, namely, National Land Cover Database 2011 (NLCD 2011), Fine Resolution Observation and Monitoring of Global Land Cover Segmentation 2010 (FROM-GLC-Seg) and global forest cover data (treecover2010) products, using the European Environment Information and Observation Network Action Group on Land Monitoring in the European (EAGLE) system of semantic translation. The conterminous United States region is adopted as the research area, and the GlobeLand30 (2010) forest class is subdivided into coniferous, broadleaf and mixed forests. Three different weighted voting methods are applied. The difference is whether the user or local accuracy of the source product is considered. The result shows that the respective accuracies of broadleaf, coniferous, and mixed forests are approximately 65.8%, 57.0%, and 40.9% of the weighted voting method without considering any product's accuracy; 79.3%, 65.9%, and 58.4% of the weighted voting method considering the user accuracy; and 79.9%, 69.9%, and 59.3% of the weighted voting method considering the local accuracy of each product, respectively. The proposed method for refining the GlobeLand30 first class forest can be applied to other classes and land cover products. [ABSTRACT FROM AUTHOR]

Due to high spatial and temporal resolution and near real-time accessibility of satellite precipitation data, the necessity of using these data in the hydrological application seems to be more pressing than ever. In this study, the skill of six post real-time (Climate Hazards Group Infrared Precipitation with Station data (CHIRPS); CPC MORPHing technique (CMORPH); Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN); PERSIANN Climate Data Record (PERSIANN-CDR); precipitation produced from the inversion of the satellite soil moisture (SM) observations derived from the European Space Agency (ESA) Climate Change Initiative (SM2RAIN); Tropical Rainfall Measuring Mission (TRMM 3B42-V7)) and two near real-time (PERSIANN Cloud Classification System (PERSIANN-CCS); TRMM real-time (TRMM 3B42-RT)) satellite daily precipitation products are evaluated by comparing with 28 rain gauges in Karkheh River Basin, located in the semi-arid region of Iran. The evaluation is performed for two types of quantiles (lower quantile (< Q10 and < Q25) and upper quantile (> Q50, > Q75, and > Q95)) and rainy seasons using categorical and quantitative metrics for the period March 2003 to December 2014. The spatial analysis indicated that there is not remarkable variation in the skill of satellite precipitation products across the study area. Results showed that the satellite precipitation estimates are more accurate in lower than upper quantile. The seasonal analysis presented that the skill of satellite precipitation products for fall and spring is slightly higher than winter. For post real-time satellite, in terms of POD (VHI), PERSIANN-CDR in spring (winter and spring), SM2RAIN in winter and spring (fall) shows the best skill, and according to FAR and CSI, CMORPH is the best in all seasons. In addition, VHI and POD of PERSIANN-CCS have better skill than 3B42-RT for near real-time satellite for all seasons. Generally, PERSIANN-CCS (PERSIANN-CDR and SM2RAIN) shows the best skill for near (post) real-time satellite precipitation estimations when whole data are included in the analysis. [ABSTRACT FROM AUTHOR]

Proper estimation and spatiotemporal trend analysis of net primary productivity (NPP) are very important for forest ecosystem monitoring and management. In this article, we investigate the spatiotemporal trend and variability of the Boreal Ecosystem Productivity Simulator (BEPS)-derived forest NPP over Nepal during the period 2000–2015. This study is the first attempt to simulate nation-wide NPP at 500 m spatial resolution at the daily step for the recent 16-year period. In our work, we use a process-based model BEPS, to estimate the NPP in the forest of Nepal. All the meteorological, forest cover, soil data, leaf area index (LAI), tree cover, biomass, and biophysical parameters, required for the model, were downloaded from different sources or prepared, pre-processed and used in the BEPS model to come up with the daily NPP. In spite of some fluctuations in NPP in some years, there is an overall positive change in the NPP throughout the study period. The annual average national NPP has been raised from 1.34 g m−2 day−1 to 1.74 g m−2 day−1 from 2000 to –2015. Examining the inter-annual variation, we found almost 87% of the total forested area has a positive inter-annual variation and 13% negative. The overall average daily NPP per m2 over the entire forest in the entire period ranges from 0.1 to 4.7 g m−2 day−1 with an average of 1.65 g m−2 day−1. Similarly, by physiographic regions, the average NPP in the plain was 2.43 g m−2 day−1, hills 1.83 g m−2 day−1, and mountain 1.41 g m−2 day−1. The total daily NPP for the entire forest area ranges from 3 to 257 tons with an average of 96 tons over the study period. NPP was found to be highly influenced by rainfall, solar radiation, and temperature. While in case of the variation of NPP on different forest types, broad-leaved forest was found to have almost 1.7 times more (NPP 1.97 g m−2 day−1) than that of needle-leaved forest (NPP 1.18 g m−2 day−1). The slope per cent of <15% is favourable (NPP 2.15 g m−2 day−1) for higher NPP among different slope per cent. We found that this method could be more convenient for estimation and studies of the NPP in Nepal. The result from this study gives us important information on intra and inter-annual spatiotemporal trends and variability of NPP in the forest of overall Nepal of different physiographic regions. It also gives us information on the variation in trends of NPP with the change in various climatic and vegetation parameters, which can be important for the proper forest ecosystem monitoring, management, and planning operations. [ABSTRACT FROM AUTHOR]

Sichuan Province, China, is a typical ecologically fragile area that is sensitive to global climate change. Studies regarding the spatial-temporal variations and driving factors of FVC (Fractional Vegetation Coverage) in Sichuan Province's vegetation ecosystem are of important theoretical and practical significance for revealing the relationship between global climate change and vegetation ecosystems. These studies are also important theoretical and practical significance for the evaluation of environmental quality and service function adjustment of terrestrial ecosystems. In existing studies, there is a lack of detailed depictions of the FVC response to climatic factors in the context of different vegetation types and different landform features in Sichuan Province. In this study, the spatial-temporal patterns and change trends for the FVC of the growing seasons during the 2000–2017 period for Sichuan Province were analysed based on the FVCs that were inversely determined from MODIS (MODerate-resolution Imaging Spectroradiometer) NDVI (Normalized Difference Vegetation Index) remote sensing data, and they were combined with air temperature, relative humidity and precipitation data. Moreover, the GRA (Grey Relational Analysis) method was used to study the response of the FVC to climate changes. Based on the results of the GRA, the zoning of climatic factors as driving forces for the FVC was performed, and the differences in the spatial-temporal characteristics of the FVC response to different climatic factors were presented in quantitative form. Here, we found that the vegetation coverage in Sichuan province showed a slight degradation trend, and that the medium to low altitude woody plants were significantly degraded. The proportion of regions in which relative humidity (17.3%) and precipitation (17.4) were strong drivers for FVC changes, was much greater than the regions in which air temperature (1.8%) and other co-drivers were the force. [ABSTRACT FROM AUTHOR]

Long term and consistent records of near-surface soil freeze/thaw (F/T) status are required for understanding hydrological, ecological, and biogeochemical responses of land surface to global warming. To create such a record, we compiled and inter-calibrated satellite observations from the Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) and its successor, AMSR2, using linear regression models, and then applied a discriminant algorithm to the calibrated observations to map global F/T status from 2002 to 2018. The new global F/T dataset was rigorously assessed using in situ air and surface temperatures, and modelled soil temperature. Results show that agreement between remotely sensed F/T status and that determined by in situ or modelled temperature exceeds 85% and 79% for ascending and descending orbits, respectively. Moreover, consistency between the F/T datasets derived from two sensors is around 0.8 after calibration, in nonoverlapping time frames. With such an accuracy and consistency, we calculated frost days and frost trends using the F/T dataset. The mean annual number of frost days of high northern latitudes (>45° N) is 279.2 ± 44.1 days. Based on Mann-Kendall's tau-b test, 7.7% of global lands show a significant warming trend, and most of which are concentrated in the Western United States, Northern and Eastern Canada, Northern Europe and Western China. Such a spatial distribution was found to be consistent with the global land surface temperature anomalies trend from 2002 to 2018. Both the results of applications and favourable accuracy indicate the potential of this long, consistent F/T record to track global temperature change. [ABSTRACT FROM AUTHOR]

Gridded precipitation products have been widely used in scientific literatures, e.g., weather forecasting, hydrological process modelling and disaster simulating. However, it is necessary to evaluate their accuracies before using them for practical purposes. In this study, three mainstream different-spatial-scale daily gridded precipitation data (GPD), including [Global Satellite Mapping of Precipitation–gauge adjusted (GSMAP_Gauge), TRMM 3B42 version-7 (TRMM 3B42V7) and Global Precipitation Climatology Project version 1.2 (GPCP-V1.2)] were systematically evaluated against local rain gauges for their ability to detect precipitation characteristics of Shanghai in 2008 based on upscaling of rain gauge. In general, the gridded precipitation products overestimate precipitation compared to upscaled ground observations (UGOs). However, all of the products have an obvious underestimation on extreme precipitation (>50 mm day−1). In contrast, the results show that GSMaP_Gauge has the highest correlation coefficient, TRMM 3B42V7 has the lowest RMSE, and Probability of Detection (POD) in GPCP-V1.2 is better than that in TRMM 3B42V7 and GSMaP_Gauge. As the spatial resolution decreases, the frequency and amount of extreme precipitation events decrease regardless of detection from UGOs and GPD. We also find that those heavy precipitation events detected from TRMM 3B42V7 at 0.25° and 1.0° are significantly underestimated. Overall, we believe that it is necessary to evaluate gridded precipitation products at the urban scale based on upscaling of rain gauge. [ABSTRACT FROM AUTHOR]

Optical remote sensing images with high temporal resolution can be used to monitor lake ice phenology, a periodic freezing and thawing cycle of ice resulting from seasonal and inter-annual climate variations. In the research reported here, we used MODIS satellite data to establish the time series of lake ice extent and extracted lake ice phenology dates and durations for eight large typical lakes in Northeast China for the hydrological years from 2003 to 2016. The MODIS-derived results were validated against ice records at hydrological stations. The mean absolute error for a freeze-up start (FUS), freeze-up end (FUE), break-up start (BUS) and break-up end (BUE) was 3.1, 4.8, 6.6 and 6.6 days, respectively. Our findings indicated that the investigated lakes were tending to freeze later and melt earlier and were frozen for a shortened period over time. FUS was experiencing a delay of 0.65 days per year and BUE was advancing by 0.19 days per year, implying a decrease of frozen duration (FD) of 0.84 days per year taking all eight lakes into consideration. The lake ice duration increased with latitude, and the lakes with a relatively smaller area had a higher yearly rate of change and were more variable compared with the larger ones. The relationship between lake ice phenology and other influencing factors was evaluated using correlation coefficients and partial least squares regression. The results showed that the freeze-up process was more dependent on the lake morphometry, while the break-up process was more dependent on climate changes, particularly on air temperature, which had the highest correlation coefficient (r = −0.69, p < 0.01). [ABSTRACT FROM AUTHOR]

Arctic areas have experienced greening and changes in permafrost caused by climate change during recent decades. However, there has been a lack of automated methods in mapping changes in fine-scale patterns of permafrost landscapes. We mapped areal coverage of bare peat areas and changes in them in a peat plateau located in north-western Russia between 2007 and 2015. We utilized QuickBird and WorldView-3 satellite image data in an object-based setting. We compared four different one-class classifiers (one-class support vector machine, binary support vector machine, random forest, rotation forest) both in a fully supervised binary setting and with positive and unlabelled training data. There was notable variation in classification performance. The bare peat area F-score varied between 0.77 and 0.96 when evaluated by cross-validated training data and between 0.22 and 0.57 when evaluated by independent test data. Overall, random forest performed the most robustly but all classifiers performed well in some classifications. During the 8 year period, there was a 21%–26% decrease in the bare peat areal coverage. We conclude that (1) tested classifiers can be used in one-class settings and (2) there is a need to develop methods for tracking changes in single land cover types. [ABSTRACT FROM AUTHOR]

Surface soil moisture (SSM) is one of the key parameters in the study of global climate change, water, and energy exchanges at both the land surface and atmospheric interface and drought and acidification measure. The temperature vegetation dryness index (TVDI) is an effective index from optical remote sensing imagery to monitor regional surface soil moisture status. Due to the disturbance of multiple factors, the coefficients of determination (R2) of the dry and wet edge of the surface temperature - normalized difference vegetation index (Ts-NDVI) feature space of the traditional TVDI method are quite low and unstable in karst area. Therefore, this article developed an improved Ts-NDVI feature space by conducting elevation correction to the land surface temperature (Ts) to monitor soil moisture in the karst area of Guangxi, China. After digital elevation model (DEM) correction, the coefficients of determination of the wet edge were improved obviously. The drought distribution of Guangxi in spring and autumn of 2009 were analysed using the modified Ts-NDVI space of the TVDI method (MTVDI) and verified by the in situ data. The results showed that the MTVDI can reasonably reflect the distribution of soil moisture in the study area, and the drought expression is in line with the in situ data and the actual situation. [ABSTRACT FROM AUTHOR]

Vegetation green-up signals the timing of available nutritious plants and shrubs providing high-quality forage for ungulates. In this study, we characterized spatial and temporal patterns of spring phenology and explored how they were related to preceding temperature and moisture conditions. We tested correlations between late winter weather and indicators of the onset and the length of the spring growing period with 250-m resolution time-series satellite data (2001 - 2013) for Wyoming, USA. In western Wyoming mountains, drier and warmer conditions during late winter were associated with earlier spring green-up onset of growth in forests, shrubs, and grasses. In the northeast mountains, onset of spring correlated positively with preceding warmer temperatures, but not with precipitation. In most basin and plains shrublands and grasslands, spring onset was not correlated with temperature, although earlier onset of spring was correlated with drier conditions in 25% of shrub/scrub areas. Results about the length of spring were less definitive, with warmer temperatures related to longer green-up time for 12-30% of the land cover in western mountains but to shorter green-up time periods for 10-20% of the grasses and shrubs in basins and plains. Complex phenological patterns are likely to affect ungulate foraging behaviour on a local scale. [ABSTRACT FROM AUTHOR]

Climate change has caused a great impact on vegetation growth, production and distribution through variations of precipitation, temperature and sunshine. In this study, a categorization of zones for vegetation responses to climatic variability was conducted. Seasonal and annual crop responses to climate change in each region were analysed with multiple linear regression. The results show that the annual impact of climatic factors on crop growth was most significant in lower North China (R2 = 0.48) and most insignificant in Northeast China (R2 = 0.22). Temperature is the limiting climatic factor for crop growth annually in North China and Northeast China (zones 1-3), (p ≤ 0.05), while sunshine duration plays an important role for crop growth in zones which are more southern (zones 3 ~ 5). Precipitation significantly affects the annual crop growth in Inner Mongolia-Hebei-Shandong zone (zone 2) and Southeast zone (zone 5). Therefore, more attention should be paid to these zones. The spring temperature is the limiting climatic factor for crop growth in all the zones (p ≤ 0.05). Spring warming is helpful for crop growth in mainland China. Different agricultural and administrative measures should be taken in each zone to adapt to future climate change. [ABSTRACT FROM AUTHOR]

In the epoch of the human-induced climate change, seagrasses can mitigate the resulting negative impacts due to their carbon sequestration ability. The endemic and dominant in the Mediterranean Posidonia oceanica seagrass contains the largest stocks of organic carbon among all seagrass species, yet it undergoes a significant regression in its extent. Therefore, suitable quantitative assessment of its extent and optically shallow environment are required to allow good conservation and management practices. Here, we parameterise a semi-analytical inversion model which employs above-surface remote sensing reflectance of Sentinel-2A to derive water column and bottom properties in the Thermaikos Gulf, NW Aegean Sea, Greece (easternMediterranean). In the model, the diffuse attenuation coefficients are expressed as functions of absorption and backscattering coefficients. We apply a comprehensive pre-processing workflow which includes atmospheric correction using C2RCC (Case 2 Regional CoastColour) neural network, resampling of the lower spatial resolution Sentinel-2A bands to 10m/ pixel, as well as empirical derivation of water bathymetry and machine learning-based classification of the resulting bottom properties using the Support Vector Machines. SVM-based classification of benthic reflectance reveals ~300 ha of P. oceanica seagrass between 2 and 16 mof depth, and yields very high producer and user accuracies of 95.3% and 99.5%, respectively. Sources of errors and uncertainties are discussed. All in all, recent advances in Earth Observation in terms of optical satellite technology, cloud computing and machine learning algorithms have created the perfect storm which could aid high spatiotemporal, large-scale seagrass habitat mapping and monitoring, allowing for its integration to the Analysis Ready Data era and ultimately enabling more efficientmanagement and conservation in the epoch of climate change. [ABSTRACT FROM AUTHOR]

Long-term time series of spatially explicit cropland maps are essential for global crop modelling and climate change studies. The spatial resolution and temporal continuity of global cropland maps have been improving and several global data sets are released recently. Here, we calculated country-level cropland areas from the annual land-cover (LC) maps produced by the European Space Agency Climate Change Initiative (ESA-CCI) project and from the Food and Agricultural Organization of the United Nations statistical data (FAOSTAT) from 1992 to 2014. Because these two data sets used different approaches for generating the cropland data, we further quantified the consistency/difference in cropland areas and temporal changes between both products. Using log-transformed the time-averaged country-level cropland areas, a good linear relationship was found between these two products across different countries. However, only 8% of countries (mostly Organization for Economic Co-operation and Development countries) showed cropland area difference smaller than 1% between ESA-CCI and FAOSTAT. The cropland areas (without mosaic cropland types) from ESA-CCI are lower than the areas from FAOSTAT in 26% of countries but higher in 66% of countries. The magnitude of the latter difference (i.e. higher estimates of ESA-CCI than FAOSTAT) would be further amplified if crop intensity was taken into account. In addition, opposite temporal trends of cropland areas were found between these two data sets in 41% of countries. Although there are uncertainties in ESA-CCI LC maps, resulting from remote-sensing techniques such as mixed pixels, spectral similar objects, and same subject with different spectrum, the long time series and relatively high resolution of this product help us to understand the differences between satellite-based and inventory-based data sets and thus identify the possible strategies to improve the accuracy of satellite-based LC products. [ABSTRACT FROM AUTHOR]

Given the ability of effectively observing vegetation at a variety of spatial and temporal scales, remote sensing has been widely used to monitor and understand the change of mangrove forest extent. Yet, a systematic cognition of the unique contribution of remote sensing on studying mangrove forest extent dynamic is lacking, which prevents us from clearly identifying and further overcoming current deficiencies of studying the change of mangrove forest extent with remote sensing. Therefore, in this review, we summarized remotely sensed extraction methods and data related to mangrove forest extent change monitoring and all discoveries from mangrove forest extent change observation and driver analysis. We found that mangrove forest in most of the study areas has declined over the past decades, and the loss rate is accelerating. We also found that remote sensing plays an important role in revealing how the extent of mangrove forest responds to climate change and human impacts. In addition, we identified several recurring limitations from previous studies. This review highlights the role of remote sensing on studying mangrove forest extent change, and is helpful to make better use of remotely sensed data in this field. [ABSTRACT FROM AUTHOR]

Satellite and ground-based observations are used to explore the composite oceanic-atmospheric link known as the El Niño/La Niña Southern Oscillation (ENSO) phenomenon, which is closely associated with extreme weather events (e.g. heat waves, tornadoes, floods, and droughts), incidence of epidemic diseases (e.g. malaria), severe coral bleaching, etc. The ENSO temporal evolution depends on the energy exchange within the coupled ocean/atmosphere system. Its cycle has an average period of about 4 years, but there is considerable modulation of it from several sources and this is not yet fully understood. In this article, we attempted to explore the intrinsic features of the Best ENSO Index (BEI) from 1870 to 2017. Studying the distribution that characterizes BEI increments, the asymptotic power-law scaling was revealed in their extreme fluctuations. Additionally, in order to study the evolution of BEI data over time, the detrended fluctuation analysis was used, which showed positive long-range correlations of power-law type and multifractal behaviour. These results aim to give a better insight into the global signature of ENSO evolution, considering both the continuous natural interactions taking place between the oceans and the atmosphere and anthropogenic effects. Furthermore, the results obtained could be employed to elucidate the development of more accurate advanced modelling of ocean-atmosphere interactions, thereby improving climate change projections. [ABSTRACT FROM AUTHOR]

Arid regions are highly vulnerable to climate change and human activity. Global warming has the potential to increase their area. In most arid regions, desertification, land degradation, and drought are frequent. An early warning and monitoring system based on numerical models, remote sensing, and weather forecasts is urgently needed to guard human well-being in those regions. This study defined degraded land area on the basis of dust erodibility determined only from satellite data, and measured seasonal variations over each continent. During the 5 years from 2012 to 2016, degraded land area showed a tendency to increase in South America and Oceania and to decrease in Asia, Europe, Africa, and North America. Degraded land area covered up to 19% of the world’s total land area, almost exactly the percentage of hyper-arid (7.3%) plus arid regions (11.6%) determined by the widely used aridity index, and down to 7%. [ABSTRACT FROM AUTHOR]

The increasing pressures and hazards derived from human action and climate change make the implementation of coastal monitoring plans a requirement for assessing coastal responses and evolution. For this reason, this work aims to use unmanned aerial systems (UAS) in combination with Structure from Motion algorithms for detecting morphodynamic changes caused by a storm in Camposoto beach (SW Spain). This approach provided pre- and post-storm digital elevation models of the study area with root mean square vertical errors of 6.89 and 5.54 cm, respectively, that allowed the identification of storm-induced gain and loss of sediment using Geomorphic Change Detection software. Furthermore, it also captured the response of the system against the action of specific marine processes such as storm-induced swash, collision, and overwash. The results obtained highlight the importance of an optimum flight plan design (e.g. the use of 85-75% front and side image overlap, respectively) and enhance the suitability of the UAS technology to satisfy the demands and needs in coastal monitoring studies in comparison with the traditional techniques, namely aerial photogrammetry, satellite imagery, and/or light detection and ranging. [ABSTRACT FROM AUTHOR]

Airborne laser scanning (ALS) and image matching are the two main techniques for generating point clouds for large areas. While the classification of ALS point clouds has been well investigated, there are few studies that are related to image matching point clouds. In this study, point clouds of multiple resolutions from high-resolution aerial images (ground sampling distance, GSD, of 6 cm) over the city of Vienna were generated and investigated with respect to point density and processing time. Three different study sites with various urban structures are selected from a bigger dataset and classified based on two different approaches: machine learning and a traditional operator-based decision tree. Classification accuracy was evaluated and compared with confusion matrices. In general, the machine learning method results in a higher overall accuracy compared to the simple decision tree method, with accuracies of 87% and 84%, respectively, at the highest resolution. At lower-resolution levels (GSDs of 12 cm and 24 cm), the overall accuracy of machine learning drops by 4% and that of the simple decision tree by 7% for each level. Classifying rasterized data instead of the original point cloud resulted in an accuracy drop of 5%. Thus, using machine learning on point clouds at the highest available resolution is suggested for classification of urban areas. [ABSTRACT FROM AUTHOR]

Satellite and reanalysis precipitation products can be potentially used for monitoring and analysing drought patterns in tropical regions. In this study, Tropical Measuring Mission Multisatellite Precipitation Analysis monthly product (3B43) and the product from National Centers for Environmental Prediction Climate Forecast System Reanalysis (NCEP-CFSR) are analysed for precipitation estimation and drought assessment over Singapore. The evaluation is conducted by comparing the field observations from 22 precipitation gauges from 1998 to 2014. The Standardized Precipitation Index recommended by the World Meteorological Organization was used for the drought assessment. Results show that 3B43 performed better in monthly, seasonal, and annual precipitation estimations compared to NCEP-CFSR. Both products show an underestimation of monthly and annual precipitation. Similarly, 3B43 also outperformed NCEP-CFSR in drought monitoring over Singapore. The 3B43 product successfully detected major droughts that occurred in 2005, 2009, and 2014. Nevertheless, 3B43 reported a higher drought intensity compared to gauge measurements in recent drought events. Application of the NCEP-CFSR in drought monitoring is not recommended due to the low correlations with gauge measurements. In addition, NCEP-CFSR is unable to detect the major drought events correctly, except for the 2009 drought. This study serves as a reference for satellite algorithm and drought index designers to develop better products in the future. [ABSTRACT FROM PUBLISHER]

The agricultural impacts of tropical cyclones remain the primary threat to livelihoods in Southeast Asia and Latin America. The impacts take two forms, one is windthrow, i.e. uprooted or snapped trees, caused by strong wind, and the other is hydrogeological effects from heavy precipitation. The empirical effects are different for the two forms. However, little previous research has been devoted to distinguishing the two effects to estimate agricultural losses, and even fewer have used moderate-resolution (10 m resolution) images. This study presents a methodological progression to address this deficiency. First, an object-based image analytical method distinguishes the two effects using Satellite Pour l’Observation de la Terre – 5 satellite images. Various object-based features are compared to acquire their spectral, textural, and geometric characteristics for the interpretation. Second, a public participation geographical information system (PPGIS) approach is developed that combines community empowerment and collaborative field survey to rebuild and represent ground truth during disasters for image validation. The method is applied to a case study of typhoon Bopha that struck Compostela Valley, eastern Mindanao, the Philippines in December 2012. Our assessment indicates that the producer accuracy reaches 88.9% for debris and mud flows and 83.3% for windthrow, and user accuracy reaches 94% and 81%, respectively. The result indicates that the proposed methods have great potential for distinguishing the two effects. It also highlights the efficacy of integrating PPGIS with remote sensing, for image validation purposes and in practice to enhance local residents’ environmental consciousness for enhancing adaptive capacity in resource limited regions. [ABSTRACT FROM PUBLISHER]

Seaweed and seagrass beds play a multiplicity of functions within ecosystems, and have both ecological and economical value. However, anthropogenic activities, as well as climate change have contributed on the degradation. This study used data from Advanced Land Observing Satellite (ALOS) Advanced Visible and Near Infrared Radiometer Type 2 (AVNIR-2) orbital images to detect seaweeds and seagrasses. Qualitative and quantitative samples were used to validate the images, classified using the kappa coefficient (κ). The supervised classifications performed by the algorithms fuzzy logic – Fuzclass, maximum likelihood – Maxlike, minimum distance to means – Mindist, parallelepiped – Piped, showed an accuracy level of 0.93, 0.84, 0.83 (both excellent), and 0.62 (substantial), respectively. The results of the hard classifiers (Maxlike, Mindist, and Piped) submitted a new classification based on fuzzy logic (Fuzclass) demonstrated accuracy level of 0.74, 0.61 (both substantial), and 0.50 (moderate), respectively. Considered superior to the others, the Fuzclass classifier exhibited the best tendency in representing reef bottom–type distribution. Maxlike generated a map of seaweed and seagrass spatial distribution and abundance of the Maracajaú reef, identifying seven classes: (1) dense seaweeds; (2) sand; (3) dense seagrasses; (4) sparse seagrasses; (5) calcareous seaweeds; (6) sparse seaweeds; and (7) fine sand. The map of Maracajaú reef bottom type showed that it was possible to apply image processing and digital classification methodologies to distinguish submerged organisms, revealing information to help in planning and management of these ecosystems, enabling future monitoring. [ABSTRACT FROM AUTHOR]

A call for papers regarding the topics listed in the 2011 Annual Conference on Earth Observation in a Changing World of the Remote Sensing and Photogrammetry Society (RSPSoc) including natural environment, climate change, and data management is presented.

Remotely derived environmental variables, including sea surface height anomaly (SSHA), sea surface temperature (SST), chlorophyll-a (chl-a), eddy kinetic energy (EKE), mixed layer data set of argo float (MLD), Niño 3.4 index, and bigeye tuna catch data for the period 1997-2008, were used to analyse ocean climate variability and how they relate to the hotspots of bigeye tuna catch in the eastern Indian Ocean off Java. The empirical orthogonal function (EOF) was performed to obtain a more detailed structure of the spatio-temporal ocean variability in the region. The results showed that the first EOF modes of chl-a, SSHA, and SST accounted for 42.8%, 36.5%, and 27.4% of total variance, respectively, and these corresponded to the interannual signal. The maps of spatial patterns of the first EOF modes of SSHA, SST, and chl-a gave very typical values for cold-water SSHA, low SST, and high chl-a concentration along the southern coast of the Indonesian archipelago; and warm-water SSHA, high SST, and low chl-a concentration in the offshore region to make frontal areas along the latitudinal line around 10-12° S. The EOF analysis further revealed a strong relationship between the El Niño event and favourable oceanographic conditions, resulting in a significant increase in bigeye tuna catch. The average hook rate of 0.71 (0.43) was recorded during El Niño (La Niña). Major hotspots were located away from the confluence region and frontal areas around 11-16° S and 110-118° E and were thus demonstrated as the most favourable oceanographic conditions for bigeye tuna fishing in the eastern Indian Ocean off Java. [ABSTRACT FROM AUTHOR]

Glaciers and ice sheets are sensitive indicators of climate change. The changes of outlines and texture features on the glacier surface are useful indicators of glacier change monitoring. The sensitivity of synthetic aperture radar (SAR), which is independent of weather and solar illumination, provides an efficient approach for glacier monitoring and the development of high-resolution SAR makes it possible to study glaciers in more detail. In this article, the decision-tree classification method, the Canny operator, and the phase congruency method are applied and compared to extract glacier outlines and water-eroded stripes using X-band high-resolution SAR imagery of the Dongkemadi glacier area in the Qinghai–Tibet Plateau, collected by the TerraSAR satellites. The experimental results are presented to illustrate the accuracy and efficiency of the phase congruency method as compared to other methods used for the extraction of linear features (outline and stripes) on glaciers. The phase congruency method, which is independent of image illumination and magnification, is used for the first time in glacier outline and stripe extraction. It is shown that, when combined with high-resolution SAR imagery, this method has the potential to provide valuable support for glacier mapping and change monitoring in the future. [ABSTRACT FROM AUTHOR]

Ecological time series data are widely used in ecological research thanks to the development of remote-sensing technologies and fixed ecological research stations. However, the serial correlation issue with time series, which violates the fundamental assumption of independence for traditional statistical models or analysis, is rarely considered by ecologists in vegetation–climate relationship research. In addition, the issue of time lags between climate change and vegetation response is also often ignored. Inadequate consideration of these issues produces misleading results in some cases. In this article, we propose an approach based on the Autoregressive Integrated Moving Average (ARIMA) model and the nonparametric test to address serial correlation issue and distribution requirements for the valid statistical analysis of time series data. With Hulunber meadow steppe as a case, we applied this approach to analyse the role of climate factors in vegetation dynamics based on leaf area index (LAI) data and climatic data. The results showed that the LAI dynamics of Hulunber meadow steppe were mainly related to temperature with the time lag of zero, whereas the impact of precipitation on LAI dynamics was not statistically obvious. The comparison of regression models that deal with serial correlation and residual normality to different extents showed that ignoring the serial correlation issue with time series data likely produces misleading results, highlighting the importance of serial correlation removal. The combination of nonparametric correlation tests with ARIMA-based cross-correlation analysis also proved quite useful in reducing the chance of spurious correlation and time lags resulting from outlier values in ARIMA-based cross-correlation. [ABSTRACT FROM PUBLISHER]

Quantification of land-surface evapotranspiration (ET) is highly significant in water resources management, climate change studies, and numerical weather prediction. The constant reference evaporative fraction method (EFr, the ratio of the actual to reference ET), which assumes that the daily EFris equal to that at the satellite overpass time, is a scheme that has been widely applied to upscale remotely sensed instantaneous ET to daily ET. To overcome the difficulties encountered in the acquisition of tower-based meteorological variables, this study investigates the feasibility of using publicly available weather forecast information to estimate the daily reference ET using the constant EFrmethod. A two-source energy balance model is adopted to compute the instantaneous ET using Moderate-Resolution Imaging Spectroradiometer (MODIS) remote-sensing data acquired between January 2011 and October 2012 at the Yucheng Comprehensive Experimental Station in the North China Plain. The results show that the daily maximum and minimum air temperatures from weather forecast information are consistent with the corresponding ground-based measurements, with a bias of 0.8 K and a root mean square error (RMSE) of <2.0 K. The daily global solar radiation and daily wind speed were poorly forecast when compared with the ground-based measurements. Using the meteorological variables from the daily weather forecast information produced a small bias of 0.1 mm day–1and an RMSE of 0.6 mm day–1when the estimated daily reference ET was compared with that derived using the ground-based meteorological measurements. When the remotely sensed instantaneous ET and half-hourly reference ET were as accurate as the ground-based measurements, the upscaling method produced the daily ET, using the meteorological variables from the weather forecast information, with a bias of 0.1 mm day–1and an RMSE of 0.7 mm day–1. [ABSTRACT FROM PUBLISHER]

Accurate, reliable, and up-to-date forest stand volume information is a prerequisite for a detailed evaluation of commercial forest resources and their sustainable management. Commercial forest responses to global climate change remain uncertain, and hence the mapping of stand volume as carbon sinks is fundamentally important in understanding the role of forests in stabilizing climate change effects. The aim of this study was to examine the utility of stochastic gradient boosting (SGB) and multi-source data to predict stand volume of aEucalyptusplantation in South Africa. The SGB ensemble, random forest (RF), and stepwise multiple-linear regression (SMLR) were used to predictEucalyptusstand volume and other related tree-structural attributes such as mean tree height and mean diameter at breast height (DBH). Multi-source data consisted of SPOT-5 raw spectral features (four bands), 14 spectral vegetation indices, rainfall data, and stand age. When all variables were used, the SGB algorithm showed that stand volume can be accurately estimated (R2 = 0.78 and RMSE = 33.16 m3 ha−1(23.01% of the mean)). The competing RF ensemble produced anR2value of 0.76 and a RMSE value of 37.28 m3 ha−1(38.28% of the mean). SMLR on the other hand, produced anR2value of 0.65 and an RMSE value of 42.50 m3 ha−1(42.50% of the mean). Our study further showed thatEucalyptusmean tree height (R2 = 0.83 and RMSE = 1.63 m (9.08% of the mean)) and mean diameter at breast height (R2 = 0.74 and RMSE = 1.06 (7.89% of the mean)) can also be reasonably predicted using SGB and multi-source data. Furthermore, when the most important SGB model-selected variables were used for prediction, the predictive accuracies improved significantly for mean DBH (R2 = 0.81 and RMSE = 1.21 cm (6.12% of the mean)), mean tree height (R2 = 0.86 and RMSE = 1.39 m (7.02% of the mean)), and stand volume (R2 = 0.83 and RMSE = 29.58 m3 ha−1(17.63% of the mean)). These results underscore the importance of integrating multi-source data with remotely sensed data for predictingEucalyptusstand volume and related tree-structural attributes. [ABSTRACT FROM PUBLISHER]

We have examined 16 years (1998–2013) of particulate organic carbon (POC) concentrations derived from remotely sensed ocean colour. POC concentrations vary spatially from more than 300 mg m−3in the northern North Atlantic in summer to about 20 mg m−3in the oligotrophic South Pacific (16-year global average = 67.7 mg m−3). The seasonal variability is weak at lower latitudes and stronger at higher latitudes. The annual mean surface POC concentrations show statistically significant regional trends (p < 0.05, 95% confidence level), and are decreasing in the North Atlantic and North Pacific and increasing in the South Pacific and Southern Oceans. The global trend is not significant. The 16-year global average water column POC biomass integrated over the euphotic depth, the mixed layer depth, or based on a combination of these two depths is estimated to be about 3.97, 3.92, and 5.03 g m−2, respectively. Water column integrated biomass shows different spatial and seasonal patterns than the surface POC concentrations, and is increasing in many ocean regions. Globally averaged POC biomass is also increasing. At the same time ocean colour data indicate a decrease in the global oceanic productivity (PP). This means that there is a negative trend in the ratio of PP to POC biomass almost everywhere in the ocean. Such a decrease could indicate that the biological pump in the ocean is weakening, but longer time series of the ocean colour data are needed to confirm this observation. [ABSTRACT FROM PUBLISHER]

Detailed information about the prediction of within-field potential in terms of yield at the field scale is an attractive goal that would allow useful applications in precision agriculture. Biophysical variables characterizing crop canopies, such as the leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), fractional ground cover (Fcover) and the concentration of chlorophyll-aand -b(Cab), can be estimated from satellite remote-sensing data through the application of a neural network inversion of a radiative transfer model, such as PROSAIL. The knowledge of the temporal and spatial variability of these variables can enhance the possibilities of estimating yield at the field scale. The aim of this study is to investigate the influence of acquisition time and spatial resolution of biophysical variables estimated from satellite data on the grain yield estimation of wheat crops. We used SPOT 4 (spatial resolution: 20 m) and SPOT 5 (spatial resolution: 10 m) images, acquired at six different dates during the wheat growing season in 2012, to obtain LAI, Fcover, FAPAR, and Cab on five fields in Maccarese (Central Italy). A preliminary survey was carried out to correlate spatially biophysical variables with the final grain yield at each acquisition date. Biophysical variables estimated at a spatial resolution of 10 m during the stem elongation stage showed the best simple and spatial correlation with yield. At this stage, all the biophysical variables showed the highest correlation values as compared to the other dates. Subsequently, we used the variables estimated from SPOT data at each growth stage to calibrate multiple linear regression (MLR) and cubist regression (CR) models for two fields, which were then validated on five independent fields. Although the CR calibration models provided better accuracy than MLR, the best validation statistics were gained from MLR models, obtaining a root mean square error (RMSE) of about 1 t ha−1for three of these fields, using remote data having a spatial resolution of 10 metres and acquired between steam elongation and booting stage. The optimal acquisition time is affected,ceteris paribus, by the agricultural management and in particular by the variety that can influence the trend of crop growth. However, the optimal growth stage for yield estimation seems to be quite similar over the study area during a growth season. The validation of models on field data collected in another growing season is mainly affected by the climate conditions. These results highlight the importance of spatial resolution and the influence of acquisition time of satellite images on the estimation of yield at the field scale by remote-sensing data. [ABSTRACT FROM AUTHOR]

Understanding changes in monsoon variability over a decade requires thorough knowledge of the seasonal and inter-annual variability in surface energy flux and its forcing parameters (land surface and meteorology) in response to climate change. In the present study, the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua climate model gridded global products (0.05° × 0.05° spatial resolution) of land surface temperature (LST;Ts), normalized difference vegetation index (NDVI), and surface albedo (α) were used to generate seasonal (June–September) and inter-annual (2003–2012) variation in surface energy flux and its forcing parameters over different agro-climatic regions (ACRs) of India. Energy fluxes were retrieved using a single-source surface energy balance model (here vegetation and soil is considered as a single unit). Energy flux observations over different ACRs allowed comparison of the seasonal transition of latent heat flux (LE), net radiation (Rn), soil heat flux (G), available energy (Q = Rn – G), and evaporative fraction (EF) as terrestrial links to the atmosphere. The seasonal and inter-annual variation in EF was investigated by plotting against the soil moisture information retrieved from the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) global monthly data product (1° × 1° spatial resolution). Decadal and seasonal analysis showed that energy fluxes vary widely in time and space due to variability in surface radiation parameters (Ts,α), vegetation cover, soil moisture, and air temperature (Ta), which influence the seasonal transition of monsoon through LE and EF. Among the ACRs, LE and EF were found lowest in the Western Dry Region (WDR) and highest in the Western Himalayan Region (WHR). The spatiotemporal depiction of MODIS LE and MODIS EF over a span of 10 years can identify the hotspots and monsoon intensity over different ACRs. Climatic parameters that are susceptible to changes resulting from climate change are thoroughly studied in the present analysis. [ABSTRACT FROM AUTHOR]

Accurate spatial interpolation (SI) of climate data is vital for the management and supervision of natural resources and agriculture. Owing to the lack of an adequate number of meteorological stations, meteorological-station-data-based SI methods may not always reflect the real climatic conditions of an interpolated point. Land surface temperature (LST) data obtained from satellite sensors enable the characterization of meteorological conditions of areas without meteorological stations. The aim of this article is to present a new modified inverse distance weighting (M-IDW) SI method for air temperature (Ta), total precipitation (Pt), and relative humidity (RH) by integrating Landsat LST data with meteorological station data for the interpolation process. The M-IDW approach is based on the correlation relationship between the climate data and LST at each meteorological station, which is incorporated into the traditional IDW to improve the estimation of the climate data at an interpolation location of interest. The proposed method, M-IDW, is applied for the interpolation of long years’ (i.e. long term) monthly average (LYMA)Ta,Pt, and RH climate data from meteorological stations in the Eastern Thrace region, which is 23,764 km2, located in southeast Europe. The LYMA of theTa,Pt, and RH has been constructed using data obtained from 27 meteorological stations that had functioned at least 10 years between 2000 and 2012 and from the corresponding satellite data. The outputs of the interpolation are in the form of LYMA, so are the analysed climate data. The spatial resolution of the predicted surface was taken as 30 m, similar to the original data presented by United States Geological Survey. The results were compared with those of the standard IDW, ordinary kriging (OK), and ordinary cokriging (OCK) methods to analyse the performance and accuracy of the proposed method. The results show that the proposed M-IDW method has the potential for SI of climate data, if enough number of images and cloudless pixels are incorporated in the LYMA LST computation. The proposed method, in general, yields better results than standard IDW and OK methods, especially during spring, summer, and partially in autumn for the interpolation ofTa(with 0.72°C, 0.53°C, and 0.66°C root mean square error (RMSE) values, respectively) andPt(with 11.07 mm, 7.64 mm, and 4.85 mm RMSE values, respectively). OCK and M-IDW results were comparable in spring, summer, and autumn where M-IDW was slightly better forTain autumn and spring and was slightly better forPtin summer. For the RH interpolation, although M-IDW results were found to be close to the results of IDW, OK, and OCK in spring, summer, and autumn, for the overall seasonal interpretation, the RMSE values of M-IDW were worse than the others. In general, M-IDW yields worse results for the winter months, which in turn is related to cloudiness and availability of satellite images. [ABSTRACT FROM AUTHOR]