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In recent years, high resolution (HR) remote sensing images have brought significant changes to land cover monitoring. Monitoring water resources, a vital and scarce commodity for human survival, is a crucial aspect of land cover monitoring and forms the foundation for water resource allocation in regions facing shortages. Hence, a universal method is necessary to address this problem and handle large-scale water resources monitoring. We explore the network structure via improving the interpretability to reduce the network layers and propose a lightweight pixel-wise semantic segmentation model, which achieves waterbody extraction from GF-1 remote sensing images with high accuracy and high speed. The proposed model structure addresses both semantic segmentation for large and small waterbodies, extracting fine-grained edge and contour information from high-resolution feature maps. Additionally, it efficiently extracts multi-scale high-level semantic information using residual convolutional blocks and dilated convolutional blocks. The multi-scale feature maps are fused, and binary classification is predicted through Support Vector Machines (SVM). Furthermore, the paper introduces a model training method with an adaptive learning rate, reducing the overall training time. To validate the model's performance, remote sensing images from GF-1 are utilized to construct a dataset. Experimental results, compared with five models, demonstrate that the proposed method achieves the highest accuracy and least training time. [ABSTRACT FROM AUTHOR]

To better understand how contour-levee irrigation practice impacts water resources for formulating effective water management policies, it is important to obtain its application on large-scale data sets, e.g. state-wide. Automatic classification of contour levee croplands from high-resolution aerial images is of great potential given the success of deep neural networks and the availability of high-resolution remote sensing imagery. This paper proposes a gradient CNN model to classify fields with contour levees from remote sensing images. Our model produces high-quality segmentation masks that are refined with superpixel-based segmentation post-processing. Our method is evaluated using images by the National Agriculture Imagery Program (NAIP) for the counties in Arkansas. A comparison with the state-of-the-art methods demonstrates the improved performance of our proposed method. Our method demonstrates superior performance in the classification of challenging cases and achieves an overall 3.08% of accuracy improvement and 28.57% BER error reduction, compared to the second-best method. The p-value with respect to the second-best method is 0.005, which indicates great statistical significance. In addition, the results for data of different counties demonstrate the exceptional generalization ability of our method. [ABSTRACT FROM AUTHOR]

Water monitoring is an important part of water resource protection. The extraction of water body from multispectral remote-sensing images has been proven to be an efficient and fast way for water monitoring. This paper presents a water body extraction algorithm from multispectral remote-sensing image based on region similarity and boundary information by combining adaptive band selection and over-segmentation. First of all, three bands are adaptively chosen by similarity-based band selection algorithm. Then, the image domain is partitioned into a series of homogeneous sub-regions by over-segmentation incorporating spectral and spatial information. On the sub-regions, the regional similarity is defined with respect to the similarities of texture and spectral features which are extracted using structure analysis method. After that, boundary information is extraction by Canny algorithm, then the water body is extracted by using the Fractal Net Evolution Approach (FNEA) which combines regional similarity and boundary information. The proposed algorithm is used to extract six water bodies with different complex texture backgrounds from multispectral sensors. According to the accuracy evaluation of water body extraction results, the overall accuracy (OA) is higher than 97.9100% and all Kappa coefficients (K) are up to 0.9436. We calculated the relative error (RE) of the area between the reference water body and the water body extracted by the proposed algorithm, the minimum and maximum relative error range is between [0.6180%, 7.7050%]. The experiments show that the proposed algorithm is feasible and effective. [ABSTRACT FROM AUTHOR]

The studies on snow depth comprise a crucial area of research in the Indian Himalayas, where the seasonal snow cover primarily drives the rivers and significant water resources. In this paper, the initial estimates of the line of sight displacement obtained using differential interferometric phase in VV and VH polarizations using Sentinel-1 bi-temporal dual polarimetric SAR data corresponding to snow covered and snow free land cover, are improved by applying bias corrections for the snow phase and for residual errors in displacement derived from the corrected snow phase. The bias for the snow phase is computed from the observed phase in VV and VH polarizations for the snow free area and the bias for the residual errors is computed by observing the stationary pixels identified in the snow free area using a digital elevation model. The snow depth is computed as a weighted sum of the corrected displacements in the VV and VH polarization, with the weights derived using the local incidence angle. The snow depth results based on the proposed approach was evaluated with respect to field measurements and a coefficient of determination of 0.628 was observed with an improvement of ~0.4 as compared to the displacement observed in the VV and VH channel using the conventional method. [ABSTRACT FROM AUTHOR]

The physical properties of a snowpack strongly influence the emissions from the substratum, making snow property retrievals feasible by means of the surface brightness temperature observed by passive microwave sensors. Depending on the spatial resolution observed, time series records of daily snow coverage and critical snowpack properties such as snow depth (SD) and snow water equivalent (SWE) could be helpful in applications ranging from modelling snow variations for water resources management in a catchment to global climatologic studies. However, the challenge of including spaceborne SWE products in operational hydrological and hydroclimate modelling applications is very demanding with limited uptake by these systems, mostly attributed to insufficient SWE estimation accuracy. The root causes of this challenge include the coarse spatial resolution of passive microwave (PM) observations that observe highly aggregated snowpack properties at the spaceborne scale, and inadequacies during the retrieval process caused by uncertainties with the forward emission modelling of snow and challenges to find robust parameterizations of the models. While the spatial resolution problem is largely in the realm of engineering design and constrained by physical restrictions, a better understanding of developed and adopted retrieval methodologies can provide the clarity needed to enhance our knowledge in this field. In this paper, we review snow depth and SWE retrieval methods using PM observations, taking only dry snow retrieval processes into consideration. Snow properties using PM observations can be modelled by purely empirical relations based on underlying physical processes, and SD and SWE can be estimated by regression-based approaches. Snow property retrievals have been refined gradually throughout four decades use of PM observations in tandem with better understanding of physical processes, inclusion of better snowpack parameterizations, improved uncertainty analysis frameworks, and applying better inversion algorithms. Studying available methods, we conclude that snowpack parameterization is key to accurate retrieval. By improving retrieval algorithm architectures to better capture dynamic snowpack evolution processes, SWE estimates are likely to improve. We conclude that this challenge can be addressed by coupling emission models and land surface models or integrating weather-driven snowpack evolution into emission models and performing inversion in a Bayesian framework. [ABSTRACT FROM AUTHOR]

Monitoring the spatial and temporal extents of permanent and temporary bodies of surface water is important for various applications such as water resource management, climate modelling, and biodiversity conservation. Satellite remote sensing is an effective source of information to detect surface water over large areas and document their evolution in time. Recently, the European Space Agency (ESA) launched freely available SAR (Synthetic Aperture Radar) and optical sensors (Sentinel-1 & 2) with high revisiting time and spatial resolution. The objective of this paper is to explore the contribution of multi-temporal and multi-source (passive and active) Sentinel observations for improving the detection and mapping of surface waters by applying decision-level image fusion techniques. The approach is tested over Central Ireland using a time series of 16 Sentinel-1 images and a few Sentinel-2 images for the period 2015–2016. Compared to a mono-date approach, the combination of Sentinel-1 & 2 observations provides better accuracy for mapping permanent surface water. Decision level fusion technique allows mapping temporary surface water (such as flooding) with a high accuracy. It also gives the possibility to monitor their dynamics by providing the probability of occurrence of flooded areas at the pixel level. [ABSTRACT FROM AUTHOR]

During 2015, sea surface temperature (SST) in the central tropical Pacific (TP) was warmer than normal, what indicated about the potential for the development of El Niño Southern Oscillation (ENSO). By December 2015, El Niño intensified when SST anomaly in the Niño-3.4 tropical Pacific area reached +2.9 °C, which indicated about the strongest event of the past 36 years. El Niño normally impacts weather, ecosystems, and socioeconomics (agriculture, fisheries, energy, human health, water resource etc.) on all continents. However, the current El Niño is much stronger than the recent strong 1997–1998 event. Therefore, this paper investigates how the strength of El Niño impacts world ecosystems and which areas are affected. The vegetation health (VH) method and 36-year of its data have been used as the criteria of the impact. Specifically, the paper investigates VH-ENSO teleconnection, focusing on estimation of vegetation response to El Niño intensity and transition of the impact from boreal winter to spring and summer. Two types of ecosystem response were identified. In boreal winter, ecosystems of northern South America, southern Africa, eastern Australia, and Southeast Asia experienced strong vegetation stress, which will negatively affect agriculture, energy, and water resources. In Argentina, southeastern USA and the Horn of Africa ecosystem response is opposite. One of the worst disasters associated with ENSO is drought. The advantages of this study are in derivation of vegetation response to moisture, thermal, and combined conditions including an early detection of drought-related stress. For the first time, ENSO impact was evaluated based on all events with |SSTa|> 0.5 ºC and >2.0 ºC. The current strong El Niño has already triggered drought in Brazil, southern Africa, southeastern Asia, and eastern Australia during December–February. Such conditions will be transitioned from boreal winter to spring but not to summer 2016, except for two regions: northern Brazil and southeastern Asia. [ABSTRACT FROM PUBLISHER]