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In line with the United Nations Sustainable Development Goal (SDG) 6, the main goal of the Special Issue on "Remote sensing for water resources and environmental management" was to solicit papers from a diverse range of scientists around the world on the use of cutting-edge remote sensing technologies to assess and monitor freshwater quality, quantity, availability, and management to ensure water security. Special consideration was given to scientific manuscripts that covered, but were not limited to, the development of geospatial techniques and remote sensing applications for detecting, quantifying, and monitoring freshwater water resources, identifying potential threats to water resources and agriculture, as well as other themes related to water resources and environmental management at various spatial scales. The Special Issue attracted over thirteen peer-reviewed scientific articles, with the majority of manuscripts originating from China. Most of the studies made use of satellite datasets, ranging from coarse spatial resolution data, such as the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO), to medium spatial resolution data, such as the Landsat series, ERA5, Modern-Era Retrospective Analysis for Research and Application Land version 2 reanalysis product (MERRA2), CLSM and NOAH ET, and MODIS (Moderate Resolution Imaging Spectroradiometer). Google Earth Engine (GEE) data, together with big data processing techniques, such as the remote sensing-based energy balance model (ALEXI/DisALEXI approach) and the STARFM data fusion technique, were used for analyzing geospatial datasets. Overall, this Special Issue demonstrated significant knowledge gaps in various big data image processing techniques and improved computing processes in assessing and monitoring water resources and the environment at various spatial and temporal scales. [ABSTRACT FROM AUTHOR]

The present paper has used a comprehensive approach to study atmosphere pollution sources including the study of vertical distribution characteristics, the epicenters of occurrence and transport of atmospheric aerosol in North-West China under intensive dust storm registered in all cities of the region in April 2014. To achieve this goal, the remote sensing data using Moderate Resolution Imaging Spectroradiometer satellite (MODIS) as well as model-simulated data, were used, which facilitate tracking the sources, routes, and spatial extent of dust storms. The results of the study have shown strong territory pollution with aerosol during sandstorm. According to ground-based air quality monitoring stations data, concentrations of PM 10 and PM 2.5 exceeded 400 μg/m 3 and 150 μg/m 3 , respectively, the ratio PM 2.5 /PM 10 being within the range of 0.123–0.661. According to MODIS/Terra Collection 6 Level-2 aerosol products data and the Deep Blue algorithm data, the aerosol optical depth (AOD) at 550 nm in the pollution epicenter was within 0.75–1. The vertical distribution of aerosols indicates that the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) 532 nm total attenuates backscatter coefficient ranges from 0.01 to 0.0001 km −1  × sr −1 with the distribution of the main types of aerosols in the troposphere of the region within 0–12.5 km, where the most severe aerosol contamination is observed in the lower troposphere (at 3–6 km). According to satellite sounding and model-simulated data, the sources of pollution are the deserted regions of Northern and Northwestern China. [ABSTRACT FROM AUTHOR]

Abstract: That the multi-source remote sensing information integrates knowledge-based geospatial constraints to develop efficient and practical Land cover classification algorithm has become one of the most important developing directions in the field of remote sensing ground object classification. Remote sensing classification is a strictly incompatible problem, but the spectra distribution of remote sensing data has compatible attributes especially in mountainous regions, and such contradiction is one of the main reasons leading to uncertainties in remote sensing classification. In this paper, the remote sensing spectra feature compatible information is transformed into the probability of the association degree firstly, and then the matter-element theory is introduced to establish models to achieve the integrated classification of multi-source data to fuse knowledge-based geographical constrained condition probability. Taking the grassland–wetland fragile ecosystem in Ruoergai plateau of China as a case study, this paper selected the multi-source data including images of Landsat TM and CBERS, ASTER-GDEM and MODIS-NDVI to construct a comprehensive classifier, in which the relationship between topography and land cover, and the prior knowledge on vegetation growth difference were taken as constraints to support the decision-making. The classification accuracy was evaluated by a field investigation and existing land cover map. The test result shows that, the overall accuracy (89.89%) and Kappa coefficient (0.8870) are better than those derived by the Maximum Likelihood method. It indicates that the proposed classification method is not subject to the dimensionality and form of data sources, and it can integrate the data source information to improve the classification accuracy, so that it is very useful to apply multi-source data and prior knowledge to land cover classification in mountainous regions. [Copyright &y& Elsevier]

In order to extract quantitative water-leaving information from the Thematic Mapper (TM) image accurately in inland waters, atmospheric correction is a necessary step. Based on former researchers' results, the paper presents two atmospheric correction algorithms based on meteorological data (MD) and on Moderate Resolution Imaging Spectroradiometer (MODIS) Vicarious Calibration (MVC) for TM image in inland waters according to the theory of radiative transfer. Studying Taihu lake, China, in this paper we derived water remote sensing reflectance from a TM image of 26 July 2004 by these two atmospheric correction algorithms and we compare the results with that of dark object subtraction (DOS) and 6S code. The results show that the effect of atmospheric correction based on meteorological data and MODIS Vicarious Calibration is much better than that of DOS and 6S code. Although the MD is more accurate, MVC may be an ideal choice for TM images in inland water because TERRA MODIS images can be acquired easily than collecting meteorological data at the time of satellites passing over. [ABSTRACT FROM AUTHOR]

Estimating gross primary production (GPP) and net primary production (NPP) are significant important in studying carbon cycles. Using models driven by multi-source and multi-scale data is a promising approach to estimate GPP and NPP at regional and global scales. With a focus on data that are openly accessible, this paper presents a GPP and NPP model driven by remotely sensed data and meteorological data with spatial resolutions varying from 30 m to 0.25 degree and temporal resolutions ranging from 3 hours to 1 month, by integrating remote sensing techniques and eco-physiological process theories. Our model is also designed as part of the Multi-source data Synergized Quantitative (MuSyQ) Remote Sensing Production System. In the presented MuSyQ-NPP algorithm, daily GPP for a 10-day period was calculated as a product of incident photosynthetically active radiation (PAR) and its fraction absorbed by vegetation (FPAR) using a light use efficiency (LUE) model. The autotrophic respiration (Ra) was determined using eco-physiological process theories and the daily NPP was obtained as the balance between GPP and Ra. To test its feasibility at regional scales, our model was performed in an arid and semi-arid region of Heihe River Basin, China to generate daily GPP and NPP during the growing season of 2012. The results indicated that both GPP and NPP exhibit clear spatial and temporal patterns in their distribution over Heihe River Basin during the growing season due to the temperature, water and solar influx conditions. After validated against ground-based measurements, MODIS GPP product (MOD17A2H) and results reported in recent literature, we found the MuSyQ-NPP algorithm could yield an RMSE of 2.973 gC m-2 d-1 and an R of 0.842 when compared with ground-based GPP while an RMSE of 8.010 gC m-2 d-1 and an R of 0.682 can be achieved for MODIS GPP, the estimated NPP values were also well within the range of previous literature, which proved the reliability of our modelling results. This research suggested that the utilization of multi-source data with various scales would help to the establishment of an appropriate model for calculating GPP and NPP at regional scales with relatively high spatial and temporal resolution. [ABSTRACT FROM AUTHOR]

Historically, change detection reviews have examined and categorized algorithms based on their techniques for the remote sensing community. Here, we synthesize urban land change algorithms by the types of information they provide to a diverse and growing set of user communities. Two goals of the paper are first to synthesize past and current change detection studies to examine urban land change to help users of remote sensing algorithms understand and navigate the vast variety of available methods, and second to identify gaps in knowledge for the urban remote sensing community. We analyzed 644 peer-reviewed research papers published in English-language journals and conducted a systematic review of urban land change algorithms. All papers included in our study focus on urban land change ; studies that concentrate on single-date urban classification or mapping for one point in time without an explicit urban land change component are not included in this analysis. The review showed five key results and knowledge gaps. First, most urban change detection algorithms are being developed and applied for only a few regions, with 75% of studies focused on high or upper-middle-income countries and the majority of these on China or the United States. This suggests a major gap in geographic coverage as well as the need for more studies on cities in low and lower-middle-income countries. Second, the results show that 41% of the algorithms have been developed or applied for cities of over 5 million inhabitants. This focus on large cities is problematic given that only 11% of the world's urban population lives in cities with populations greater than 5 million and that most future urban growth will occur in small- and medium-sized towns and cities with populations of fewer than 1 million people. Third, our analysis shows that 62% of the studies use three or fewer time points to measure urban land change with an average study length of 17 years. Since rapidly growing urban areas are highly dynamic, this suggests that existing algorithms using only a few time points are likely missing urban transitions that can only be captured with high temporal frequency analysis. Fourth, we find that urban expansion is the most commonly monitored type of urban land change. Comparatively fewer studies characterized intra-urban change or three-dimensional structural change. Fifth and finally, the results show that an overwhelming majority—87%—of all studies identify only one urban class, highlighting a need for more studies that distinguish intra-urban variation and differentiate multiple urban classes. Our analysis shows that it is very difficult—nearly impossible—to compare across algorithms. Thus, for users of urban land change information, it is difficult to navigate the literature and know which algorithms are most appropriate for a particular use. Taken together, this points to the need for improved reproducibility, replicability, and comparability of studies in order to help harmonize urban land change information across regions and countries. This is especially important given the growing user communities of urban land change products and analysis, especially from policy and practice. • We conduct a systematic review of the urban land change algorithm literature. • Most studies monitor urban change at low temporal frequencies. • The majority of urban land change is characterized using only one urban class. • Over 75% of studies focus on high or upper-middle level income countries. • Key knowledge gaps include in geographic coverage, city size, and land transitions. [ABSTRACT FROM AUTHOR]

Currently, remote sensing technologies were widely employed in the dynamic monitoring of the land. This paper presented an algorithm named fuzzy nonlinear proximal support vector machine (FNPSVM) by basing on ETM+ remote sensing image. This algorithm is applied to extract various types of lands of the city Da’an in northern China. Two multi-category strategies, namely “one-against-one” and “one-against-rest” for this algorithm were described in detail and then compared. A fuzzy membership function was presented to reduce the effects of noises or outliers on the data samples. The approaches of feature extraction, feature selection, and several key parameter settings were also given. Numerous experiments were carried out to evaluate its performances including various accuracies (overall accuracies and kappa coefficient), stability, training speed, and classification speed. The FNPSVM classifier was compared to the other three classifiers including the maximum likelihood classifier (MLC), back propagation neural network (BPN), and the proximal support vector machine (PSVM) under different training conditions. The impacts of the selection of training samples, testing samples and features on the four classifiers were also evaluated in these experiments. [ABSTRACT FROM AUTHOR]

Abstract: To deal with water resources crisis caused by climate change and intensified human activities, the Hetao Irrigation District of Inner Mongolia in North China has been experiencing rehabilitation for water-saving since 1999. This has significantly changed its regional hydrological cycle and thus water use patterns over different land use types. In this paper, Surface Energy Balance Algorithm for Land (SEBAL) model fed with MODIS data was applied in Hetao area from 2000 to 2010 to examine the spatial and temporal patterns of evapotranspiration (ET). The SEBAL estimated ET agreed well with that from other methods in the study area. The results indicate that inter-annual variability in ET over agricultural land, water body, woodland and irrigated grassland are primarily explained by the variation of reference ET. So it is with the seasonal variability in agricultural land ET on monthly basis. However, the inter-annual variations of ET over sandy land, Gobi desert and mountain areas are mainly controlled by precipitation. Over the study period, a reduction in river water diversion for irrigation has not reduced the agricultural land ET, indicating no significant impact on agricultural production. But ET over the non-irrigated grassland tends to decrease, which was likely caused by declining groundwater table in recent years. [Copyright &y& Elsevier]

Abstract: The availability of precipitation data with high spatial resolution is of fundamental importance in several applications such as hydrology, meteorology and ecology. At present, there are mainly two sources of precipitation estimates: raingauge stations and remote sensing technology. However, a large number of studies demonstrated that traditional point measurements based on raingauge stations cannot reflect the spatial variation of precipitation effectively, especially in ungauged basins. The technology of remote sensing has greatly improved the quality of precipitation observations and produced reasonably high resolution gridded precipitation fields. These products, derived from satellites, have been widely used in various parts of the world. However, when applied to local basins and regions, the spatial resolution of these products is too coarse. In this paper, we present a statistical downscaling algorithm based on the relationships between precipitation and other environmental factors in the Qaidam Basin such as topography and vegetation, which was developed for downscaling the spatial precipitation fields of these remote sensing products. This algorithm is demonstrated with the Tropical Rainfall Measuring Mission (TRMM) 3B43 dataset, the Digital Elevation Model (DEM) from the Shuttle Radar Topography Mission (SRTM) and SPOT VEGETATION. The statistical relationship among precipitation, DEM and Normalized Difference Vegetation Index (NDVI), which is a proxy for vegetation, is variable at different scales; therefore, a multiple linear regression model was established under four different scales (0.25°, 0.50°, 0.75° and 1.00°, respectively). By applying a downscaling methodology, TRMM 3B43 0.25°×0.25° precipitation fields were downscaled to 1×1km pixel precipitation for each year from 1999 to 2009. On the basis of three criteria, these four downscaled results were compared with each other and the regression model established at the resolution of 0.50° was selected as the final downscaling algorithm in this study. The final downscaled results were validated by applying the observations for a duration of 11years obtained from six raingauge stations in the Qaidam Basin. These results indicated that the downscaled result effectively captured the trends in inter-annual variability and the magnitude of annual precipitation with the coefficient of determination r 2 ranging from 0.72 to 0.96 at six different raingauge stations. [Copyright &y& Elsevier]

Abstract: As a major component of the water budget of a wetland, evaporation has proven difficult to measure evapotranspiration accurately. This paper developed a scheme to assess actual evapotranspiration (ETa) across a range of land uses in Nansi Lake Wetland, China, using a remote sensing technique and the Surface Energy Balance Algorithm for Land (SEBAL) model. The estimation of pixel-scaled ETa was conducted via SEBAL using Landsat-7 ETM+ images, DEM and meteorological data. Compared with the recorded pan evaporation, the estimated evapotranspiration calculated by SEBAL agreed well with the results derived from pan observations. Then, the spatial distribution characteristic of daily evapotranspiration was analyzed by referencing the land-use map of 2000. It was found that the open water body and creed swamp were at a high evapotranspiration rate, while the crop land, grassland and rural residential land took the second place, and the overflow land, town constructed land and bare soil were at the lowest evapotranspiration rate, which accorded with the evapotranspiration theory. The research demonstrates the considerable potential of SEBAL model for estimation of spatial ETa with little ground-based weather data over large areas such as wetlands. [Copyright &y& Elsevier]

In this paper we present an improved watershed segmentation algorithm for settlement mapping from medium resolution satellite data over plain areas in China. The algorithm can increase the computational efficiency of the fastest reported watershed segmentation algorithm by 30-40%. We apply this method to a selected study area in southern Hebei Province, China. We acquired a Landsat Enhanced Thematic Mapper Plus (ETM + ) image over this area in May 2000, two Landsat Thematic Mapper (TM) images in August 2004 and April 2005, and two Beijing-1 satellite images in May 2006 and May 2007. The three types of images have three similar spectral bands (green, red and near-infrared) with similar spatial resolution (30-32 m). Only the red and near-infrared bands were used in image segmentation for settlement area extraction. The extracted settlement results are compared with manual interpretation results by two people. We assumed the human interpretation results are of higher accuracy than the segmentation results. Our results indicated that our settlement area extraction method is effective. With high quality images, the overall accuracies are nearly 94%, the kappa coefficient can be greater than 0.85. [ABSTRACT FROM AUTHOR]

A remote sensing approach is proposed in this paper for monitoring instantaneous underwater light climate in Dahuchi Lake, China. Water constituents that are optically significant to the lake water can be retrieved through this method based on an artificial neural network (ANN) algorithm with a satisfactory accuracy. In this study, the ANN was trained by spectral simulations which were generated by a water colour simulation model (WASI). An underwater light attenuation model has been built by fitting the field measurements and applied to calculate photosynthetically available radiation (PAR) at the bottom of the lake from the inputs of hyperspectral reflectance above the water surface and the water depth. Results show that this method can predict the concentration of suspended particle matter (SPM) with a high accuracy and explain more than half of the underwater PAR variation with a standard deviation of 94.8 μmol s-1 m-2. In order to test the portability of the algorithm, it has also been extended to three bands of Landsat TM image. Compared with hyperspectral inputs, ANN with broadband inputs has lower but still acceptable accuracy on SPM retrieval. The advantage of this method lies in its time-efficient and cost-efficient monitoring of underwater light climate, i.e. the simulations generated by a semi-physical model can be used for ANN training, instead of using a large number of field measurements. [ABSTRACT FROM AUTHOR]

The MODerate resolution Imaging Spectroradiometer (MODIS) and the Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) are onboard the same satellite platform NASA TERRA. Both MODIS and ASTER offer routine retrieval of land surface temperatures (LSTs), and the ASTER- and MODIS-retrieved LST products have been used worldwide. Because a large fraction of the earth surface consists of mountainous areas, variations in elevation, terrain slope and aspect angles can cause biases in the retrieved LSTs. However, terrain-induced effects are generally neglected in most satellite retrievals, which may generate discrepancy between ASTER and MODIS LSTs. In this paper, we reported the terrain effects on the LST discrepancy with a case examination over a relief area at the Loess Plateau of China. Results showed that the terrain-induced effects were not major, but nevertheless important for the total LST discrepancy. A large local slope did not necessarily lead to a large LST discrepancy. The angle of emitted radiance was more important than the angle of local slope in generating the LST discrepancy. Specifically, the conventional terrain correction may be unsuitable for densely vegetated areas. The distribution of ASTER-to-MODIS emissivity suggested that the terrain correction was included in the generalized split window (GSW) based approach used to rectify MODIS LSTs. Further study should include the classification-induced uncertainty in emissivity for reliable use of satellite-retrieved LSTs over relief areas. [ABSTRACT FROM AUTHOR]

Leaf area index (LAI) is an important ecological and environmental parameter. A new LAI algorithm is developed using the principles of ground LAI measurements based on canopy gap fraction. First, the relationship between LAI and gap fraction at various zenith angles is derived from the definition of LAI. Then, the directional gap fraction is acquired from a remote sensing bidirectional reflectance distribution function (BRDF) product. This acquisition is obtained by using a kernel driven model and a large-scale directional gap fraction algorithm. The algorithm has been applied to estimate a LAI distribution in China in mid-July 2002. The ground data acquired from two field experiments in Changbai Mountain and Qilian Mountain were used to validate the algorithm. To resolve the scale discrepancy between high resolution ground observations and low resolution remote sensing data, two TM images with a resolution approaching the size of ground plots were used to relate the coarse resolution LAI map to ground measurements. First, an empirical relationship between the measured LAI and a vegetation index was established. Next, a high resolution LAI map was generated using the relationship. The LAI value of a low resolution pixel was calculated from the area-weighted sum of high resolution LAIs composing the low resolution pixel. The results of this comparison showed that the inversion algorithm has an accuracy of 82%. Factors that may influence the accuracy are also discussed in this paper. [Copyright &y& Elsevier]

Yue, Y.L.; Qing, S.; Diao, R.X., and Hao, Y.L., 2020. Remote sensing of suspended particulate matter in optically complex estuarine and inland waters based on optical classification. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Geospatial Research of Coastal Environments. Journal of Coastal Research, Special Issue No. 102, pp. 303-317. Coconut Creek (Florida), ISSN 0749-0208. Accurate suspended particulate matter (SPM) concentration retrieval across complex estuarine to inland waters from ocean color remote sensing reflectance (Rrs(λ)) faces challenges. In this paper, an optical classification-based SPM retrieval algorithm in optically complex estuarine and inland waters was proposed and tested in the Yellow River Estuary and Daihai Lake, China. Firstly, the in situ measured Rrs(λ) (n = 204) were classified into two optical water types with the method defined by Matsushita et al. (2015). Secondly, we designed several mathematical models and selected the optimal algorithm according to the goodness of fit. Optimal algorithms were developed for each water type to achieve accurate SPM retrieval. Through the construction of the optimal retrieval algorithm in each water type, the uncertainty of SPM retrievals has been reduced from 95 % to about 39 % compared with the algorithm without optical classification. The retrieval algorithm based on optical water classification was further applied to the Sentinel-2 MSI L2A data over the study area and produced reliable SPM maps. Independent validation with the in situ-satellite match-ups further demonstrates the algorithm's validity (uncertainty of about 47 %). In contrast, applications of other SPM retrieval algorithms resulted in less reliable SPM results with either unsatisfactory retrieval accuracy in class1 (the lowest value of r can reach 0.02). The optical classification, together with the optimal retrieval algorithm for each optical type, is proved to be a feasible way for SPM retrieval in high accuracy over optically complex waters. [ABSTRACT FROM AUTHOR]