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This paper seeks to identify opportunities to integrate Earth observations (EO) into flood forecast-based early action and propose future directions for research and collaboration between EO and humanitarian communities. Forecast-based early action (FbA) is an approach to shift disaster response toward anticipation to mitigate impacts to at-risk communities; however, timely and accurate information is needed in the development of data-based triggers and thresholds for action. Therefore, this paper considers the readiness of a wide range of EO for flood monitoring and forecasting in the design, operations, and evaluation phases of FbA. The most significant opportunities for EO to inform FbA efforts lie in the design and evaluation phases, as EO can aid in the development of impact-based triggers. The EO products most readily applicable include precipitation, streamflow estimates, and exposure mapping, and those requiring the greatest amount of further research include vulnerability and impact assessments. This paper identifies collaboration opportunities for the EO and humanitarian communities to create tailored products, such as overlays combining flood extents with exposure maps. Such collaboration opportunities can be fostered by open data sharing, data verification efforts, and incentives for supporting boundary organizations capable of enabling the use of EO for FbA.

The dispersion characteristics of the background ionosphere and the random fluctuations of the ionospheric irregularities are an important source of phase error that seriously damages the quality of radar images. To mitigate the ionospheric distortions of P-band spaceborne synthetic aperture radar (SAR) images, a super-resolution deep learning method is proposed in this paper. Different from the traditional imaging method based on the prior knowledge of imaging, the method proposed in this paper directly trains the ionospheric implicit imaging model of spaceborne SAR without complicated iterative processes. First, the phase errors, which are caused by the dispersion and the scintillation in the range and azimuth directions, respectively, are analyzed. Second, an improved u-net structure that embeds the residual network between the encoder and decoder is briefly proposed. Finally, the range doppler algorithm is used to preprocess radar image as the input of the convolutional neural network (CNN) and is compare with the predicted output of the CNN. Experimental results prove the effectiveness of the proposed method in radar image focusing.

Currently, due to the limited amount of data and the difficulty of designing a network, there are few papers on constructing a new convolutional neural network for scene classification using the publicly available datasets of high-resolution remote sensing images. Considering the existing problems, the current scene classification methods of high-resolution remote sensing images are summarized, and the IMFNet model is constructed to classify scenes of high-resolution remote sensing images in this paper. The IMFNet is an end-to-end network, which can learn features from data automatically. The main characteristic of the IMFNet network structure is that the Inception module is used to extract the details of remote sensing images and the multifeature fusion strategy is proposed to ensure the integrity of information. In addition, optimization methods are adopted to improve the classification accuracy. In order to verify the effectiveness of the method proposed in this paper, the two benchmark datasets—the UC Merced dataset and the SIRI-WHU dataset were adopted for experiments. The classification accuracy of the two datasets reaches 92.14% and 90.43%, respectively. Experimental results show that the method proposed has certain advantages over the classification methods based on low-level and middle-level visual features and even some classification methods based on high-level visual features.

Region map is the sparse representation of a high-resolution synthetic aperture radar (SAR) image on the middle-level semantic layer in its semantic space. Based on the semantic information of the region map, the high-resolution SAR image is divided into hybrid, structural, and homogeneous pixel subspaces. The segmentation of SAR images can be divided into these three subspaces segmentation, of which the segmentation of hybrid subspace has more challenge because of complex structures. There are often many extremely inhomogeneous areas in the hybrid pixel subspace. Are these nonconnected areas in the same or different classes? To solve this problem, a Bayesian learning model with the constraint of sketch characteristic and an initialization method is proposed to construct a structural vector that can reflect the essential features of each extremely inhomogeneous area. Then, the unsupervised segmentation of the hybrid pixel subspace can be realized by using the structural vectors of these areas in this paper. Theoretical analysis and experimental results show that the performance of the hybrid pixel subspace segmentation realized by the structural vectors based on the Bayesian learning model proposed in the paper is better than that only used by hand designing features.

Long-term data records obtained from Earth observing sensors depend not only on the calibration accuracy of individual sensors but also on the consistency across instruments and platforms. Hence, sensor calibration intercomparison plays a vital role for a better understanding of various science products. The Moderate Resolution Imaging Spectroradiometer (MODIS) and enhanced thematic mapper plus (ETM+) on the Terra and Landsat 7 platforms have operated successfully since their launch, collecting measurements in the reflective solar and infrared parts of the spectrum. Terra MODIS has employed a reflectance-based calibration since beginning its mission. In the case of ETM+, a radiance-based calibration was employed until recent years, when a reflectance-based calibration was introduced. Being in the AM constellation with less than 30 min difference in overpass times, near-simultaneous Earth scene measurements can be effectively used to assess the calibration differences between the spectrally matching bands of these two instruments. The pseudoinvariant calibration sites (PICS) in the North African desert are widely used for on-orbit calibration and validation of satellite sensors. Four PICS from this region have been employed to assess the multitemporal reflectance differences. Correction for bidirectional reflectance, spectral response function mismatch, and impacts of atmospheric water-vapor have been incorporated to provide an assessment of the long-term stability of each spectral band and reflectance differences amongst them. Results indicate that the spectral bands of both instruments show a long-term stability to within 2% from 2000 to 2017. The top-of-atmosphere reflectances between the two instruments postcorrection agree to within 4%. Also included in this paper is a detailed discussion of various parameters contributing to the uncertainties of this cross-calibration. The techniques presented in this paper can be further extended to perform similar intercomparison between Landsat 8 Operational Land Imager, Aqua MODIS, and Suomi-NPP VIIRS.

Aerial images can greatly facilitate rescue efforts and recovery in the aftermath of hurricane disasters. Although supervised classification methods have been successfully applied to aerial imaging for building damage evaluation, their use remains challenging since supervised classifiers have to be trained using a large number of labeled samples, which are not available soon after disasters. However, rapid response is crucial for rescue tasks, which places greater demands on classification methods. To accelerate their deployment, a semisupervised classification method is proposed in this paper using a large number of unlabeled samples and only a few labeled samples that could be rapidly obtained. The proposed approach consists of three steps: segmentation, unsupervised pretraining using convolutional autoencoders (CAE), and supervised fine-tuning using convolutional neural networks (CNN). Leveraging the representation capability of CAE, the learned knowledge from CAE could be transferred to the counterparts of CNN. After pretraining, the CNN classifier is further refined with a few labeled samples to improve feature discrimination. To demonstrate this methodology, a recognition strategy of damaged buildings based on context information using only vertical postevent aerial two-dimensional images is presented in this paper. As a case study, a coastal area affected by the 2012 Sandy hurricane is investigated. Experimental results show that the proposed semisupervised method produces an overall accuracy of 88.3% and obtains an improvement of up to 9% against a CNN classifier trained from scratch.

Continual advances in hyperspectral technology have allowed researchers to easily and rapidly estimate the copper content of Seriphidium terrae-albae, an effective indicator plant for the exploration of copper deposits. Seriphidium terrae-albae is a small semishrub with relatively small leaves. Therefore, the canopy spectra are intensely affected by the surrounding soil. This significantly affects the inversion accuracy of copper content. Spectral data transformation is important in eliminating background noise and extracting spectral features. This paper focuses on researching the effects of different spectral transformation methods on the copper content inversion accuracy in Seriphidium terrae-albae and selecting the most effective method. It begins by conducting 12 spectral transformations for original spectra and then inputting a spectrum that has been transformed into the partial least squares model (PLS) for further modeling the input parameters. The final step is selecting cross-validation R2 as a standard for the most suitable spectral transformation method. The results indicate that different spectral transformation methods significantly affect the accuracy of the copper content inversion. The original spectrum that has undergone the first and second derivations can dramatically increase the copper content inversion accuracy. This includes the optimum spectral transformation as the second-order derivative of the square root of the raw spectrum. This paper also examines the variation trends of correlation between the same band and copper content under different spectral transformations. The results indicate that each band always has an optimum spectral transformation method. Therefore, this paper proposes using different spectral transformation methods at different wavelengths to form a combined spectrum within the whole wavelength. The experiments conducted as a part of this study demonstrate that the combined spectrum can greatly improve the copper content inversion accuracy in comparison with the single spectral transformation method. These findings can provide some reference for scholars, who will use hyperspectral data to estimate the metal content of plants similar to Seriphidium terrae-albae.

This paper addresses the optimization problem of frequency division phase-coded (FDPC) waveforms for improving the detection performance in multiple-input multiple-output over-the-horizon radars (MIMO-OTHR). A sparse frequency division complementary chaotic modulation (SFDCCM) waveform is proposed and analyzed. First, the basic structure of the FDPC waveform is presented, and its MIMO ambiguity function is derived to provide guidance on further waveform optimization. It turns out that the modulation codes and carrier frequencies are two key factors for good waveform performance. Then, the chaotic sequences are introduced due to their unique advantages, and an adaptive clonal selection algorithm is utilized to obtain a pair of complementary chaotic sequences to suppress the range sidelobes. The optimized complementary pair is modulated on different carrier frequencies. As the available spectrum of the MIMO-OTHR is discontinuously distributed, the carrier frequencies are generally sparse and should be properly selected according to the real-time frequency spectrum monitoring (RTFSM) system. Finally, the SFDCCM is proposed based on the FDPC structure. The pulse compression results of the SFDCCM are compared with typical waveforms. Four cases of carrier frequency selection in the SFDCCM and the corresponding doppler tolerance are analyzed. Simulation results validate the superiority of the proposed waveform in this paper.

Spectral unmixing (SU) of hyperspectral images (HSIs) is one of the important areas in remote sensing (RS) that needs to be carefully addressed in different RS applications. Despite the high spectral resolution of the hyperspectral data, the relatively low spatial resolution of the sensors may lead to mixture of different pure materials within the image pixels. In this case, the spectrum of a given pixel recorded by the sensor can be a combination of multiple spectra each belonging to a unique material in that pixel. Spectral unmixing is then used as a technique to extract the spectral characteristics of the different materials within the mixed pixels and to recover the spectrum of each pure spectral signature, called endmember. Block-sparsity exists in hyperspectral images as a result of spectral similarity between neighboring pixels. In block-sparse signals, the nonzero samples occur in clusters and the pattern of the clusters is often supposed to be unavailable as prior information. This paper presents an innovative spectral unmixing approach for HSIs based on block-sparse structure. Hyperspectral unmixing problem is solved using pattern coupled sparse Bayesian learning strategy (PCSBL). To evaluate the performance of the proposed SU algorithm, it is tested on both synthetic and real hyperspectral data and the quantitative results are compared to those of other state-of-the-art methods in terms of abundance angle distance and mean squared error. The achieved results show the superiority of the proposed algorithm over the other competing methods by a significant margin., 25 pages, 8 figures, 2 tables, accepted for publication in journal

Automatic extraction of buildings from remote sensing data is an attractive research topic, useful for several applications, such as cadastre and urban planning. This is mainly due to the inherent artifacts of the used data and the differences in viewpoint, surrounding environment, and complex shape and size of the buildings. This paper introduces an efficient deep learning framework based on convolutional neural networks (CNNs) toward building extraction from orthoimages. In contrast to conventional deep approaches in which the raw image data are fed as input to the deep neural network, in this paper the height information is exploited as an additional feature being derived from the application of a dense image matching algorithm. As test sites, several complex urban regions of various types of buildings, pixel resolutions and types of data are used, located in Vaihingen in Germany and in Perissa in Greece. Our method is evaluated using the rates of completeness, correctness, and quality and compared with conventional and other “shallow” learning paradigms such as support vector machines. Experimental results indicate that a combination of raw image data with height information, feeding as input to a deep CNN model, provides potentials in building detection in terms of robustness, flexibility, and efficiency.

The ability of sensors to detect changes in the Earth’s environment is dependent on retrieving radiometrically consistent and calibrated measurements from its surface. Intercalibration provides consistency among satellite instruments and ensures fidelity of scientific information. Intercalibration is especially important for spaceborne satellites without any on-board calibration, as accuracy of instruments is significantly affected by changes that occur postlaunch. To better understand the key parameters that impact the intercalibration process, this paper describes a simulation environment that was developed to support the primary mission of the Algodones Dunes campaign. Specifically, measurements obtained from the campaign were utilized to create a synthetic landscape to assess the feasibility of using the Algodones Dunes system as an intercalibration site for spaceborne instruments. The impact of two key parameters (differing view-angles and temporal offsets between instruments) on the intercalibration process was assessed. Results of these studies indicate that although the accuracy of intercalibration is sensitive to these parameters, proper knowledge of their impact leads to situations that minimize their effect. This paper concludes with a case study that addresses the feasibility of performing intercalibration on the International Space Station’s platform to support NASA’s CLARREO, the climate absolute radiance and refractivity observatory, mission.

The Suomi national polar-orbiting partnership Visible Infrared Imaging Radiometer Suite (VIIRS) instrument has successfully operated since its launch in October 2011. The VIIRS day-night band (DNB) is a panchromatic channel covering wavelengths from 0.5 to 0.9 microns that is capable of observing Earth scenes during both daytime and nighttime at a spatial resolution of 750 m. To cover the large dynamic range, the DNB operates at low-, middle-, and high-gain stages, and it uses an on-board solar diffuser (SD) for its low-gain stage calibration. The SD observations also provide a means to compute the gain ratios of low-to-middle and middle-to-high gain stages. This paper describes the DNB on-orbit calibration methodology used by the VIIRS characterization support team in supporting the NASA Earth science community with consistent VIIRS sensor data records made available by the land science investigator-led processing systems. It provides an assessment and update of the DNB on-orbit performance, including the SD degradation in the DNB spectral range, detector gain and gain ratio trending, and stray-light contamination and its correction. Also presented in this paper are performance validations based on Earth scenes and lunar observations, and comparisons to the calibration methodology used by the operational interface data processing segment.

Soil salinity is a complex problem that affects groundwater aquifers and agricultural lands in the semiarid regions. Remote sensing and spectroscopy database systems provide accuracy for salinity autodetection and dynamical delineation. Salinity detection techniques using polychromatic wavebands by field geocomputation and experimental data are time consuming and expensive. This paper presents an automated spectral detection and identification of salt minerals using a monochromatic waveband concept from multispectral bands—Landsat 8 Operational Land Imager (OLI) and Thermal InfraRed Sensor (TIRS) and spectroscopy United States Geological Survey database. For detecting mineral salts related to electrolytes, such as electronical and vibrational transitions, an integrated approach of salinity detection related to the optical monochromatic concept has been addressed. The purpose of this paper is to discriminate waveband intrinsic spectral similarity using the Beer–Lambert and Van 't Hoff laws for spectral curve extraction such as transmittance, reflectance, absorbance, land surface temperature, molar concentration, and osmotic pressure. These parameters are primordial for hydrodynamic salinity modeling and continuity identification using chemical and physical approaches. The established regression fitted models have been addressed for salt spectroscopy validation for suitable calibration and validation. Furthermore, our analytical tool is conducted for better decision interface using spectral salinity detection and identification in the Oran watershed, Algeria.

This paper presents a practical method for landslide detection, using single-temporal Landsat8 image at a spatial resolution of 30 m, which is publicly available. The method introduces the concept “saliency” to express potential landslide regions in the image. Based on the saliency calculations, landslides are further extracted by object-based contours using morphological operations. The experimental area covers 2 deg×2 deg, which is more practical for most cases, and the performance validates the efficiency and robustness of this method in practical applications. The overall accuracy in terms of landslide/background classification reaches 99.87%, indicating the proposed method is viable. Even though the commission error and omission error of the detected landslides are both above 50%, the proposed method is able to remove ∼99.9% background, thus reducing manual interpretation to a large extent. Moreover, since it does not need training data or many experienced parameters, it is much easier to be applied to other cases. This paper moves forward a step in landslide detection toward practical applications, such as emergency response.

A method for estimating the parameters of the sinusoidal frequency modulation (SFM) signal is presented in this paper. Based on the modified discrete sinusoid frequency modulation transform (DSFMT), the SFM signal can be transformed into the DSFMT domain where it is energy-concentrated and then the parameters can be estimated by the global maximum. To search for the location of the global maximum with less computational load, particle swarm optimization is used in this paper. Then the algorithm is used in the synthetic aperture radar imaging with high frequency vibration of the platform, and the focus performance can be improved significantly. Simulation results demonstrate the effectiveness of the method proposed in this paper.

This paper integrates decision tree–based data mining (DM) and object-based image analysis (OBIA) to provide a transferable model for the detailed characterization of urban land-cover classes using WorldView-2 (WV-2) satellite images. Many articles have been published on OBIA in recent years based on DM for different applications. However, less attention has been paid to the generation of a transferable model for characterizing detailed urban land cover features. Three subsets of WV-2 images were used in this paper to generate transferable OBIA rule-sets. Many features were explored by using a DM algorithm, which created the classification rules as a decision tree (DT) structure from the first study area. The developed DT algorithm was applied to object-based classifications in the first study area. After this process, we validated the capability and transferability of the classification rules into second and third subsets. Detailed ground truth samples were collected to assess the classification results. The first, second, and third study areas achieved 88%, 85%, and 85% overall accuracies, respectively. Results from the investigation indicate that DM was an efficient method to provide the optimal and transferable classification rules for OBIA, which accelerates the rule-sets creation stage in the OBIA classification domain.

This paper proposes a method that combined hyperspectral remote sensing with super-low-frequency (SLF) electromagnetic detection to extract oil and gas reservoir characteristics from surface to underground, for the purpose of determining oil and gas exploration target regions. The study area in Xinjiang Karamay oil–gas field, China, was investigated. First, a Hyperion dataset was used to extract altered minerals (montmorillonite, chlorite, and siderite), which were comparatively verified by field survey and spectral measurement. Second, the SLF electromagnetic datasets were then acquired where the altered minerals were distributed. An inverse distance weighting method was utilized to acquire two-dimensional profiles of the electrical feature distribution of different formations on the subsurface. Finally, existing geological data, field work, and the results derived from Hyperion images and SLF electromagnetic datasets were comprehensively analyzed to confirm the oil and gas exploration target region. The results of both hyperspectral remote sensing and SLF electromagnetic detection had a good consistency with the geological materials in this study. This paper demonstrates that the combination of hyperspectral remote sensing and SLF electromagnetic detection is suitable for the early exploration of oil and gas reservoirs, which is characterized by low exploration costs, large exploration areas, and a high working efficiency.

Agro-geoinformatics, the agricultural-related geoinformation technology, is critical for agricultural policy formulation and decision making, land use monitoring, agricultural sustainability, crop acreage and yield estimation, disaster assessment, bioenergy crop inventory, food security policy, environmental assessment, carbon accounting, and other research topics that are of vital importance to American agriculture and its economy. Agro-geoinformatics addresses issues such as collecting (including field surveys and remote sensing), processing, storing, archiving, preservation, retrieving, transmitting, accessing, visualization, analyzing, synthesizing, presenting, and disseminating geospatially distributed agricultural geoinformation. The geospatially distributed in situ data of crop plants and fields are collected in person or measured by sensor networks while large scale field level or regional measurement of crops growth and land cover condition could be remotely sensed via satellite or aerial sensors. Recent advances in remote sensing and sensor networks have created a suite of new opportunities as well as challenges in agro-geoinformatics, and incurred an increased interest in recent years to further promote this technology, as evidenced by numerous new research and applications on this topic. This special section provides a snapshot of the most recent research developments in methods, technologies, and applications of remote sensing and sensor networks in agro-geoinformation. Many of the papers included in this special section are extended versions of materials that were presented at the Third International Conference on Agro-Geoinformatics (Agro-Geoinformatics 2014) in Beijing, China, in August 2014. The special section covers a wide spectrum of research applications. The first part of the section contains six papers. “Three-dimensional geospatial information service based on cloud computing” by P. Yue et al. investigated how performance of the OGC Web 3D Service (W3DS) for geospatial data could be improved by cloud computing technologies and how W3DS could be developed in a cloud computing environment. They presented a design and implementation of the three-dimensional geospatial information cloud service using the Apache Hadoop as the framework. “Does simultaneous variable selection and dimension reduction improve the classification of Pinus forest species?” by K. Peerbhay et al. compared using the sparse partial least squares discriminant analysis (SPLS-DA) and variable importance in the projection (VIP) methods to perform simultaneous variable selection and dimension reduction of hyperspectral remotely sensed data in classifying three commercial Pinus tree species. They concluded that the SPLS-DA yielded better classification accuracies and selected fewer wavebands within visible spectrum region. “Estimating plant area index for monitoring crop growth dynamics using Landsat-8 and RapidEye images” by J. Shang et al. investigated using Landsat-8 and RapidEye images to map within-field variability of crop growth conditions and tracking the seasonal growth dynamics. The study showed that two sensors had a good agreement. They also observed that the plant area index (PAI) and Enhanced Vegetation Index (EVI2) had a good linear relationship and EVI2 was more resistant to saturation at high biomass range than NDVI. “Spatial evaluation of crop maps by the spatial production allocation model in China” by Z. Li et al. evaluated the performance of the Spatial Production Allocation Model (SPAM) by comparing spatial pixel distribution maps allocated by the SPAM from the known spatial statistical data of rice, maize, and wheat crop production in China with remote sensing results. They found the SPAM allocation could provide the limited spatial accuracy. “Assessing

Wuhan University, State Key Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing, Wuhan 430079, ChinaAbstract. A comprehensive and effective parallel airborne synthetic aperture radar (SAR)processing system using the architecture of central processing units (CPU) and graphicalprocessing units (GPU) is proposed first in this paper. By using the techniques of ComputeUnified Device Architecture, the SAR processing system is much more efficient and robust,thereby enabling it to work with high efficiency. This paper focuses on the optimizations ofmotioncompensation,subaperturechirpscalingalgorithm,phasegradientautofocus(PGA),andevenvisualizationwithOpenSourceComputerVision Library.Apart from this,itisalsothe firsttime the PGA has been run on the architecture of CPU and GPU. Experimental results show aspeedup of ∼37 times compared with a nonoptimized CPU-based approach.

Plastic-mulched landcover (PML) is the land surface covered by thin plastic films. PML has been expanding rapidly worldwide and has formed a significant agriculture landscape in the last two decades. Large-scale PML may impact the regional and global climate, ecosystem, and environment because it changes the energy balance and water cycles of the land surfaces, reduces the biodiversity, deteriorates the soil structure, etc. To study its impact, the spatial and temporal distributions and dynamics of PML have to be obtained. This paper presents a threshold model (TM) for PML detection and mapping with moderate-resolution imaging spectroradiometer (MODIS) time series data. Based on the temporal-spectral features of PML in the early stage of a growing season after planting, a TM was designed with the number of days (d) when the normalized difference vegetation index (NDVI) value is larger than a threshold value (x) as the discriminator. The model has been successfully applied to map PML in southern Xinjiang, China, from the interpolated MODIS NDVI time series (from 90th to 125th day of each year). Results indicate that when TM parameter x is set to 0.2 and d to 8, the overall accuracy and kappa coefficient (κ) are >0.84 and 0.65, respectively. We believe this classification accuracy can meet the PML mapping for large geographic areas. Furthermore, visual comparison between the PML maps from TM classification of MODIS time series and that from the maximum likelihood classification of Landsat ETM+ and OLI images shows they are consistent both in the pattern and location of PML. Therefore, detection and mapping of PML by using MODIS time series with the TM method is feasible. The PML mapping in this study used a cropland mask derived from Landsat images using a maximum likelihood classifier to mask out non-cropland when applying the TM algorithm. The accuracy of such a mask is subject to further study. Because of frequent global coverages of MODIS data, the method presented in this paper could potentially be used for PML detecting and mapping at continental and global scales.

Oriented object detection is a challenging task in aerial images since the objects in aerial images are displayed in arbitrary directions and are frequently densely packed. The mainstream detectors describe rotating objects using a five-parament or eight-parament representations, which suffer from representation ambiguity for orientated object definition. In this paper, we propose a novel representation method based on area ratio of parallelogram, called ARP. Specifically, ARP regresses the minimum bounding rectangle of the oriented object and three area ratios. Three area ratios include the area ratio of a directed object to the smallest circumscribed rectangle and two parallelograms to the minimum circumscribed rectangle. It simplifies offset learning and eliminates the issue of angular periodicity or label point sequences for oriented objects. To further remedy the confusion issue of nearly horizontal objects, the area ratio between the object and its minimal circumscribed rectangle is employed to guide the selection of horizontal or oriented detection for each object. Moreover, the rotated efficient Intersection over Union (R-EIoU) loss with horizontal bounding box and three area ratios are designed to optimize the bounding box regression for rotating objects. Experimental results on remote sensing datasets, including HRSC2016, DOTA, and UCAS-AOD, show that our method achieves superior detection performance than many state-of-the-art approaches.

The current paper investigates the long-term global impacts on crop productivity under different climate scenarios using the AgMIP approach (Agricultural Model Intercomparison and Improvement Project). The paper provides horizontal model intercompar- ison from 11 economic models as well as a more detailed analysis of the simulated effects from the Common Agricultural Policy Regionalized Impact (CAPRI) model to systematically compare its performance with other AgMIP models and specifically for the Chinese agriculture. CAPRI is a comparative static partial equilibrium model extensively used for medium and long-term economic and environmental policy impact applications. The results indicate that, at the global level, the climate change will cause an agricultural productivity decrease (between �2% and �15% by 2050), a food price increase (between 1.3% and 56%) and an expansion of cultivated area (between 1% and 4%) by 2050. The results for China indicate that the climate change effects tend to be smaller than the global impacts. The CAPRI-simulated effects are, in general, close to the median across all AgMIP models. Model intercomparison analyses reveal consistency in terms of direction of change to climate change but relatively strong hetero- geneity in the magnitude of the effects between models. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. (DOI: 10.1117/1.JRS.9

This paper analyzes the in-flight performance of the Suomi National Polar-orbiting Partnership Ozone Mapping & Profiling Suite (OMPS) nadir instruments and evaluates sensors’ on-orbit calibrations after sensors’ two-year operation. All uncertainty values quoted in this paper are 1−σ values unless stated otherwise. With the data collected from in-flight nominal calibration, our results have demonstrated that sensor performance complies with the system specifications in most cases. The largest term in the wavelength-dependent albedo calibration uncertainty for Nadir Mapper is the cross-track position difference effect of 2.5%. Final adjustments of stray light and wavelength variation are still being made to optimize OMPS sensor data records before reaching the validation mature level.

Snow plays an important role in the energy and water balance of drainage basins in alpine regions. Contribution of snowmelt to runoff is one of the important water resources in mountainous regions, in addition to rainfall and glacier melt. Snow stores at least one-third of the water usage for irrigation and growth of crops worldwide. Snow covered area and snow depth/water equivalent estimations are important parameters for regional climate change studies, agriculture, and water resource management. Traditional in situ measurements provide critical snow observations in limited areas and for calibration and validation (cal/val) of remotely sensed estimation of snow parameters. Satellite-based snow measurements have revolutionized the monitoring of spatiotemporal variations of snow cover and snow depth/water equivalent in complex natural conditions at regional and global scales. This special section provides a picture of state-of-the-art research in snow remote sensing, algorithm development and improvement, and snow modeling and application. It includes 20 papers that cover four major topics: snow cover and snow depth/snow water equivalent (SWE) mapping using optical and passive microwave remote sensing (10 papers), algorithm developments for snow cover and glacier mapping (6 papers), snow cover and snow melt-runoff modeling (3 papers), and one review paper. Many of the papers are extended versions of materials that were presented in the first Snow Remote Sensing of China Workshop (Lanzhou, China, August 2013). The review paper “Remote sensing for snow hydrology in China: challenges and perspectives” by Wang et al. (CID 084687) provides a detailed summary of the development of snow remote sensing, snow parameters retrieving and applications in China since the 1980s. In particular, since 2007, two major large-scale field-based experiments: (1) Watershed Allied Telemetry Experimental Research (WATER) in the Heihe River Basin of Northwest China and (2) Cooperative Observation Series for Snow Properties in three major snowfall regions (Northwest China, Northeast China and the Tibetan Plateau), have been initiated and have obtained many first-hand data for algorithm development and validation of remotely sensed snow parameters retrievals. One particular application of remote sensing snow products in China is the early warning system of snow-caused livestock death (disasters) in pastoral areas. The system is a unique application and development, particularly for undeveloped high mountainous areas such as the Tibetan Plateau (TP), where mean elevations are over 4000 m and there is little or no infrastructure and industry. There are 10 papers about snow cover and snow depth/SWE mapping and applications. Of these, 5 papers use optical remote sensing, primarily MODIS snow cover products; 4 papers use passive microwave remote sensing; and one paper evaluates the accuracy of the U.S. National Ice Center’s Multisensor Snow and Ice Mapping System (IMS) snow cover product in China. Two papers by Chen et al. and by Li and Ke present applications of different MODIS snow cover products for northeast China. Both find an increased trend of snow covered area (SCA) in the most recent 10 years, primarily correlated with the recent decreased tendency of air temperature in the region, although there is a clear long-term increased trend of air temperature since 1960. The recent decreasing trend in air temperature in the region is interesting and deserving offurther

A pixel-based cropland classification study based on the fusion of data from satellite images with different resolutions is presented. It is based on a time series of multispectral images acquired at different resolutions by different imaging instruments, Landsat-8 and RapidEye. The proposed data fusion method capabilities are explored with the aim of overcoming the shortcomings of different instruments in the particular cropland classification scenario characterized by the very small size of crop fields over the chosen agricultural region situated in the plains of Vojvodina in northern Serbia. This paper proposes a data fusion method that is successfully utilized in combination with arobust random forest classifier in improving the overall classification performance, as well as in enabling application of satellite imagery with a coarser spatial resolution in the given specific cropland classification task. The developed method effectively exploits available data and provides an improvement over the existing pixel-based classification approaches through the combination of different data sources. Another contribution of this paper is the employment of crowdsourcing in the process of reference data collection via dedicated smartphone application.

University of Pavia, Department of Electrical, Computer and Biomedical Engineering, ItalyRemote sensing is a very useful tool in retrieving urban information in a timely, detailed, andcost-effective manner to assist various planning and management activities. Hyperspectralremote sensing has been of great interest to the scientific community since its emergence inthe 1980s, due to its very high spectral resolution providing the potential of finer material detec-tion, classification, identification, and quantification, compared to the traditional multispectralremote sensing. With the advance of computing facilities and more airborne high-spatial-reso-lution hyperspectral image data becoming available, many investigations on its real applicationsare taking place. In particular, urban environments are characterized by heterogeneous surfacecovers with significant spatial and spectral variations, and airborne hyperspectral imagery withhigh spatial and spectral resolutions offers an effective tool to analyze complex urban scenes.The objectiveof this special section of the Journal of Applied Remote Sensing is to provide asnapshot of status, potentials, and challenges of high-spatial-resolution hyperspectral imagery inurban feature extraction and land use interpretation in support of urban monitoring and man-agement decisions. This section includes twelve papers that cover four major topics: urban landuse and land cover classification, impervious surface mapping, built-up land analysis, and urbansurface water mapping.There are nine papers about urban land use and land cover classification. “Hyperspectralimage classification with improved local-region filters” by Ran et al. proposes two local-regionfilters, i.e., spatial adaptive weighted filter and collaborative-representation-based filter, for spa-tial feature extraction, thereby improving classification of urban hyperspectral imagery. “Edge-constrained Markov random field classification by integrating hyperspectral image with LiDARdata over urban areas” by Ni et al. adopts an edge-constrained Markov random field method foraccurate land cover classification over urban areas with hyperspectral image and LiDAR data.“Combining data mining algorithm and object-based image analysis for detailed urban mappingof hyperspectral images” by Hamedianfar et al. explores the combined performance of a datamining algorithm and object-based image analysis, which can produce high accuracy of urbansurfacemapping.“Dynamicclassifierselectionusingspectral-spatial information forhyperspec-tralimageclassification”bySuetal.proposestheintegrationofspectralfeatureswithvolumetrictextural features to improve the classification performance for urban hyperspectral images.“Representation-based classifications with Markov random field model for hyperspectralurban data” by Xiong et al. improves representation-based classification by considering spa-tial-contextualinformationderivedfromaMarkovrandomfield.“Classificationofhyperspectralurban data using adaptivesimultaneous orthogonal matching pursuit” by Zou et al. improves theclassification performance of a joint sparsity model, i.e., simultaneous orthogonal matching pur-suit, by using a priori segmentation map.Othertechniques,suchaslinearunmixinganddimensionalityreduction,arealsoinvestigatedin conjunction with urban surface mapping.Among the nine papersonclassification,twopapersconsider linear unmixing, which are “Unsupervised classification strategy utilizing an endmem-ber extraction technique for airborne hyperspectral remotely sensed imagery” by Xu et al., and“Endmembernumberestimationforhyperspectralimagerybasedonvertexcomponentanalysis”by Liu et al. One paper studies the impact of dimensionality reduction (through band selection)on classification accuracy, which is “Ant colony optimization-based supervised and unsuper-vised band selections for hyperspectral urban data classification” by Gao et al.

In recent years, satellite imaging technologies have resulted in an increased number of bands acquired by hyperspectral sensors, greatly advancing the field of remote sensing. Accordingly, owing to the increasing number of bands, band selection in hyperspectral imagery for dimension reduction is important. This paper presents a framework for band selection in hyperspectral imagery that uses two techniques, referred to as particle swarm optimization (PSO) band selection and the impurity function band prioritization (IFBP) method. With the PSO band selection algorithm, highly correlated bands of hyperspectral imagery can first be grouped into modules to coarsely reduce high-dimensional datasets. Then, these highly correlated band modules are analyzed with the IFBP method to finely select the most important feature bands from the hyperspectral imagery dataset. However, PSO band selection is a time-consuming procedure when the number of hyperspectral bands is very large. Hence, this paper proposes a parallel computing version of PSO, namely parallel PSO (PPSO), using a modern graphics processing unit (GPU) architecture with NVIDIA’s compute unified device architecture technology to improve the computational speed of PSO processes. The natural parallelism of the proposed PPSO lies in the fact that each particle can be regarded as an independent agent. Parallel computation benefits the algorithm by providing each agent with a parallel processor. The intrinsic parallel characteristics embedded in PPSO are, therefore, suitable for parallel computation. The effectiveness of the proposed PPSO is evaluated through the use of airborne visible/infrared imaging spectrometer hyperspectral images. The performance of PPSO is validated using the supervised K-nearest neighbor classifier. The experimental results demonstrate that the proposed PPSO/IFBP band selection method can not only improve computational speed, but also offer a satisfactory classification performance.

The emerald ash borer (EAB) poses a significant economic and environmental threat to ash trees in southern Ontario, Canada, and the northern states of the USA. It is critical that effective technologies are urgently developed to detect, monitor, and control the spread of EAB. This paper presents a methodology using multisourced data to predict potential infestations of EAB in the town of Oakville, Ontario, Canada. The information combined in this study includes remotely sensed data, such as high spatial resolution aerial imagery, commercial ground and airborne hyperspectral data, and Google Earth imagery, in addition to nonremotely sensed data, such as archived paper maps and documents. This wide range of data provides extensive information that can be used for early detection of EAB, yet their effective employment and use remain a significant challenge. A prediction function was developed to estimate the EAB infestation states of individual ash trees using three major attributes: leaf chlorophyll content, tree crown spatial pattern, and prior knowledge. Comparison between these predicted values and a ground-based survey demonstrated an overall accuracy of 62.5%, with 22.5% omission and 18.5% commission errors.

Pansharpening aims to fuse a low-resolution multispectral image with a high-resolution panchromatic image to create a multispectral image with high spatial and spectral resolution. The intensity-hue-saturation (IHS) fusion method transforms an image from RGB space to IHS space. This paper reports a method to improve the spectral resolution of a final multispectral image. The proposed method implies two modifications on the basic IHS method to improve the sharpness of the final image. First, the paper proposes a method based on a genetic algorithm to find the weight of each band of multispectral image in the fusion process. Later on, a texture-based technique is proposed to save the spectral information of the final image with respect to the texture boundaries. Spectral quality metrics in terms of SAM, SID, Q-average, RASE, RMSE, CC, ERGAS and UIQI are used in our experiments. Experimental results on IKONOS and QuickBird data show that the proposed method is more efficient than the original IHS-based fusion approach and some of its extensions, such as IKONOS IHS, edge-adaptive IHS and explicit band coefficient IHS, in preserving spectral information of multispectral images.

Most existing multispectral fusion algorithms often suffer from spectral or spatial information distortion. Driven by this motivation, we propose an edge-guided multispectral (MS) image fusion algorithm. In particular, it combines the advantages of generative adversarial networks and improved fusion frameworks, so the merged image can better preserve the spectral information of the original multispectral image while injecting spatial detail information. Specifically, first, an MS image with more image detail is generated using the generated confrontation network for preliminary reconstruction. The panchromatic image edge information and the antagonistic learning strategy are introduced for the robust multispectral image reconstruction. Then, using the reconstructed MS image and the general component substitution image fusion framework, the whole fusion system of this paper is constructed. An enhancement operator is introduced to inject spatial details. Our extensive dataset evaluations show that our approach performs better in terms of high objective quality and human visual perception than several of the most advanced fusion methods.

The wide application of agricultural greenhouses globally has brought economic benefits; however, it has also led to many environmental problems. The timely and accurate acquisition of greenhouse areas and distribution is valuable for authorities seeking to optimize regional agricultural management and mitigate environmental pollution. Automatic extraction of the greenhouses from high spatial resolution remote sensing (RS) imagery based on deep learning can reduce labor costs and improve operational efficiency to have better application prospects. In this paper, we propose a multi-channel fused fully convolutional network (FCN) optimized by the optimal scale object-oriented segmentation results for agricultural greenhouse extraction from high spatial resolution RS imagery. First, to make full use of remote sensing feature images of target objects and to not increase the complexity of the deep learning network, we constructed a decision-level fusion FCN network that can simultaneously input multiple remote sensing images for preliminary extraction of greenhouse. Second, to address a defect in the classical FCN network causing the easy loss of ground object details, we optimized the preliminary extraction results from FCN by the results of object-oriented segmentation. Finally, the optimized greenhouse extraction results were processed by the mathematical morphology, and the final extraction results were obtained. The experimental results demonstrate that: (1) Multi-channel fused FCN model could use the unique spectral characteristics of different ground objects. (2) Optimized initial extraction results from FCN based on the optimal scale object-oriented segmentation results could fully maintain the edge details of the greenhouse. Experimental results show that the proposed method can extract the greenhouse effectively. The precision and F value of our proposed method are 92.68% and 0.94.

This paper proposes the design of an ecological classification management method based on ecological sensitivity and disturbance of road network construction that reflects the characteristics of an island ecosystem using geographical information system and remote sensing technologies. This method evaluates the ecological impacts during the high-intensity development of the island, classifies different ecological management types, and shows the key areas and management strategies for improving the ecological and environmental quality of the island and implementing ecological restoration. The proposed method is applied to Haitan Island, which has been in a period of high-intensity development since 2012. The results show that (1) the ecological sensitivity evaluation method of the island established in this study fully considers the key ecological issues of an island ecosystem. The evaluation results of highly and moderately sensitive areas cover surface water and its conservation area, woodland, and beach, objectively reflecting the relative spatial heterogeneity of ecological sensitivity within Haitan Island. (2) The distribution of high disturbance areas of road network construction is in line with the overall planning pattern of Pingtan Comprehensive Experimental Zone, which matched with the objectivity reality. The high and moderate disturbance areas of the Haitan Island increased by 6.80 and 27.60 km2, respectively, in the last 10 years, and the no disturbance area decreased by 73.70 km2. The impact on highly ecologically sensitive areas is little, and the key ecological areas are strictly protected in the high-intensity development activities. (3) The areas of ecological protection priority area, ecological restoration priority area, general control zone, and ecological reconstruction area on Haitan Island are 92.74, 9.27, 113.57, and 34.42 km2, respectively. The ecological classification management should be implemented for different areas, and targeted ecological protection and restoration strategies should be adopted, which is conducive to improving the overall ecological environment quality of Haitan Island.

Monitoring of the coastal environment is vitally important as these areas are of economic value and at the same time highly exposed to anthropogenic influence, in addition to variation of environmental variables. In this paper we show how the combination of bio-optical data from satellites, analysis of water samples, and a ship-mounted automatic flow-through sensor system (Ferrybox) can be used to detect and monitor phytoplankton blooms both spatially and temporally. Chlorophyll a (Chl a) data and turbidity from Ferrybox are combined with remotely sensed Chl a and total suspended matter from the MERIS instrument aboard the satellite ENVISAT (ENVIronmental SATellite) European Space Agency. Data from phytoplankton speciation and enumeration obtained by a national coastal observation network consisting of fish farms and the Norwegian Food Safety Authority are supplemented with data on phytoplankton pigments. All the data sets are then integrated in order to describe phytoplankton bloom dynamics in a Norwegian fjord over a growth season, with particular focus on Emiliania huxleyi. The approach represents a case example of how coastal environmental monitoring can be improved with existing instrument platforms. The objectives of the paper is to present the operative phytoplankton monitoring scheme in Norway, and to present an improved model of how such a scheme can be designed for a large part of the world's coastal areas.

This paper reviews the researches and developments in the last decade on near lossless satellite data compression techniques at the Canadian Space Agency (CSA). After briefly describing the two vector quantization based near lossless hyperspectral data compression techniques, it reviews the activities that assessed the near lossless properties of the compression techniques. The assessment results demonstrated that the compression errors introduced by the compression techniques are smaller than the intrinsic noise of the original data. This level of compression errors is considered as near lossless, as it has no impact or minor impact on the afterwards application utilization comparing the original data. This paper summarizes the activities of evaluating how satellite data product level impacts the compression performance for making decision whether or not on-board data processing is required or radiometric conversion should be applied on-board before compression. These evaluations examined the impact of the anomalies in raw hyperspectral data and the impact of on-board pre-processing and radiometric conversion on compression performance. The studies on the effect of spatial and spectral distortion of hyperspectral sensors on compression performance are also reviewed. This paper summarizes a multi-disciplinary user acceptability study that systematically assessed the impacts of the compression techniques on remote sensing products and applications. Eleven user groups covered a wide range of application areas and a variety of hyperspectral sensors participated in the study. This paper reviews the effort to explore the benefits of employing forward error correction to further enhance the resilience to bit-errors of the compressed data. The hardware developments are reported. Two versions of hardware compressor prototypes that implement the CSA near lossless compression techniques for on-board processing have been built. Finally, this paper reports the CSA's participation in the development of international standards for satellite data systems within the CCSDS organization.

In this paper, we propose two compression methods for hyperspectral images with discriminant features enhanced. Generally, when hyperspectral images are compressed with conventional image compression algorithms, which mainly minimize mean squared errors, discriminant features of the original data may not be well preserved since they may not be necessarily large in energy. In this paper, we propose two compression methods that do preserve the discriminant information. In the first method, we enhanced the discriminant features and then compressed the enhanced data using conventional image compression algorithms such as 3D JPEG 2000. In the second method, we applied a feature extraction method and extracted the discriminantly dominant feature vectors. By examining the dominant feature vectors, we determined the discriminant usefulness of each spectral band. Based on these findings, we determined the bit allocation of each spectral band assuming 2D compression methods are used. Experiments show that the proposed methods effectively preserved the discriminant information and yielded improved classification accuracies compared to existing compression algorithms.

Building damage assessment plays an essential role during post-disaster rescue operations. Given that labeled samples are difficult to timely obtain after a disaster, transfer learning attracts increasing attention. However, different sensors employed cause considerable discrepancies not only between historical and current scenes but also among historical scenes, which could exert an effect on transfer performance. Therefore, a multi-source adversarial domain adaptation (MADA) method is proposed in this paper to fulfill the task of post-disaster building assessment. This method consists of two phases. First, imageries of several historical scenes are transformed into the same style of the current scene through the CycleGAN model with a classifier, ensuring class invariance, to be fused to make an adapted source domain. Second, feature alignment between adapted source and target domains is executed based on adversarial discriminative domain adaptation. The MADA method enhances the transformed image quality, fully utilizes relevant information in historical scenes, solves inter-scene interference problems among historical images, and improves the transfer efficiency from historical to the current disaster scene. Two experiments are conducted with Hurricane Sandy, Irma, and Maria datasets as multi-source and target domains to validate MADA’s effectiveness. Results show that the classification performance is better than other methods.

The monitoring of rivers based on remote sensing data provides an opportunity for the observation of the natural dynamics of water and flood conditions. Taking advantage of the availability of radar data, we describe a methodology used to detect the river’s water surface flooding patterns and the size of floods using observations from Sentinel-1 imagery. With the proposed methodology, characteristics of the river’s normal average riverbed surface are determined, as well as the flood footprint. As a case study, it shows the annual and interannual water variability at the lower Coatzacoalcos Basin in the state of Veracruz, Mexico. This site was chosen after considering the evidence of seasonal flooding and the changes that happened during the 2015 to 2017 period. Our paper details the results on surface changes during this time period. The results on water surface cover have been validated through different sources of information to confirm the ability to differentiate flood areas and know the maximum flood extensions. An agreement above 92% is estimated to be reached with the use of the evaluated maps of binary water/no-water. Recurrent flood areas were detected where water remained during one or several weeks during the month of October, both in 2015 and 2017. Finally, a change in the extent of water maps in the framework of the Sustainable Development Goals is achieved with the integration of historical data over time and space of the natural behavior of the rivers in the case study.

Soft classification methods can be used for mixed-pixel classification on remote sensing imagery by estimating different land cover class fractions of every pixel. However, the spatial distribution and location of these class components within the pixel remain unknown. To map land cover at subpixel scale and increase the spatial resolution of land cover classification maps, in this paper, a prediction model combining wavelet transform and Radial Basis Functions (RBF) neural network, abbreviated as Wavelet-RBFNN, is constructed by predicting high-frequency wavelet coefficients from low-frequency coefficients at the same resolution with RBF network and taking wavelet coefficients at coarser resolution as training samples. According to different land cover class fraction images obtained from mixed-pixel classification, based on the assumption of neighborhood dependence of wavelet coefficients, subpixel mapping on remote sensing imagery can be accomplished through two steps, i.e., prediction of land cover class compositions within subpixels and hard classification. The experimental results obtained with artificial images, QuickBird image and Landsat 7 ETM+ image indicate that the subpixel mapping method proposed in this paper can successfully produce super-resolution land cover classification maps from remote sensing imagery, outperforming cubic B-spline and Kriging interpolation method in visual effect and prediction accuracy. The Wavelet-RBFNN model can also be applied to simulate higher spatial resolution image, and automatically identify and locate land cover targets at the subpixel scales, when the cost and availability of high resolution imagery prohibit its use in many areas of work.

More than 69,000 people died following the magnitude 8.0 Wenchuan earthquake of May 12, 2008. Bad weather hampered relief efforts, and in some cases rescuers had to trek into the disaster area on foot and search for trapped survivors by hand as roads were blocked by debris. Due to travel difficulties, spatial information needs to be extracted in the disaster area by remote sensing techniques. The main problem focused on in this paper is how to use the all-weather and all-day/night capability of Synthetic Aperture Radar (SAR) to extract primary seismic disaster information after the earthquake. Using air- and space-borne SAR images with different bands, polarizations and incidence angles, including multi-polarization X-band air-borne data, C-band polarimetric Radarsat-2, X-band TerraSAR-X with high resolution, multi-polarization X-band COSMO-SkyMed and L-band multi-polarization ALOS-PalSAR space-borne data, we perform image characteristics analysis of landslides. Obvious differences can be recognized between old and new landsides in SAR images with different bands. Multi-polarization SAR can play an important role in landslide discrimination. Two SAR images at different incidence angles do not provide much more information if the difference between the two angles is small. Landslide recognition accuracy strongly depends on the direction of view, especially for large incidence angles, in which case the characteristic difference for landslide recognition is great. There are different polarization responses between a landslide and its surroundings that can be used to recognize the landslide. Interferometric SAR images, on the other hand, do not provide good recognition capability due to temporal decorrelation and resolution. Meanwhile, information extraction of barrier lakes using different resolution and incidence angle SAR images is analyzed in this paper; small incidence angles and high resolutions improve the object recognition and information extraction of barrier lakes.

Precise registration and orthorectification of remote sensing images are the basic processes for quantitative remote sensing applications, especially for multi-temporal image analysis. In this paper, we present an automated precise registration and orthorectification package (AROP) for Landsat and Landsat-like data processing. The Landsat and Landsat-like satellite images acquired from different sensors at different spatial resolutions and projections can be re-projected, co-registered, and orthorectified to the same projection, geographic extent, and spatial resolution using a common base image through a combined resampling strategy; this allows us to perform multi-temporal image analysis directly. This paper presents and tests the AROP package on Landsat and Landsat-like data. The package is now freely available from our research web site.

The goal of this paper was to develop methods and protocols for water productivity mapping (WPM) using remote sensing data at multiple resolutions and scales in conjunction with field-plot data. The methods and protocols involved three broad categories: (a) Crop Productivity Mapping (CPM) (kg/m2); (b) Water Use (evapotranspiration) Mapping (WUM) (m3/m2); and (c) Water Productivity Mapping (WPM) (kg/m3). First, the CPMs were determined using remote sensing by: (i) Mapping crop types; (ii) modeling crop yield; and (iii) extrapolating models to larger areas. Second, WUM were derived using the Simplified Surface Energy Balance (SSEB) model. Finally, WPMs were produced by dividing CPMs and WUMs. The paper used data from Quickbird 2.44m, Indian Remote Sensing (IRS) Resoursesat-1 23.5m, Landsat-7 30m, and Moderate Resolution Imaging Spectroradiometer (MODIS) 250m and 500m, to demonstrate the methods for mapping water productivity (WP). In terms of physical water productivity (kilogram of yield produced per unit of water delivered), wheat crop had highest water productivity of 0.60 kg/m3 (WP), followed by rice with 0.5 kg/m3, and cotton with 0.42 kg/m3. In terms of economic value (dollar per unit of water delivered), cotton ranked highest at $ 0.5/m3 followed by wheat with $ 0.33/m3 and rice at $ 0.10/m3. The study successfully delineated the areas of low and high WP. An overwhelming proportion (50+%) of the irrigated areas were under low WP for all crops with only about 10% area in high WP.

The wildfires which occurred in April 2007 in southern Georgia lasted for almost two months. Approximately 386,722 acres were burned. In this paper, we explored the strategy to use MODIS products to study fire impacts on vegetation. Vegetation variations caused by the fires are studied using these MODIS products: 8-day composite fire products, 16-day composite vegetation indices, and land cover types. Several tiles of MODIS products from dates immediately before and after the fire were employed to monitor vegetation changes. Unburned control plots were selected to establish new variables QNDVI and QEVI which are used to evaluate the vegetation recovery status. The results show that vegetation indices: NDVI and EVI decreased dramatically after the fires. The variations of QNDVI and QEVI indicate that vegetation status still has a disparity between fire spots and surrounding undisturbed area, even though commonly used vegetation indices of fire spots have attained pre-fire levels. The appropriate distance outside the fire spots for selecting control plots is related to the size of the burned area. The larger the burned area is, the bigger distance we should choose for control plots. EVI and QEVI are better indicators to show vegetation changes due to fires. The method presented in this paper can be employed to monitor vegetation change to fires as well as to indicate different recovery rate. It also can be useful to identify the fire severity and assess the ecological consequence of fires.

Earth observation with Landsat and other moderate resolution sensors is a vital component of a wide variety of applications across disciplines. Despite the widespread success of the Landsat program, recent problems with Landsat 5 and Landsat 7 create uncertainty about the future of moderate resolution remote sensing. Several other Landsat-like sensors have demonstrated applicability in key fields of earth observation research and could potentially complement or replace Landsat. The objective of this paper is to review the range of applications of 5 satellite suites and their Landsat-like sensors: SPOT, IRS, CBERS, ASTER, and ALI. We give a brief overview of each sensor, and review the documented applications in several earth observation domains, including land cover classification, forests and woodlands, agriculture and rangelands, and urban. We conclude with suggestions for further research into the fields of cross-sensor comparison and multi-sensor fusion. This paper is significant because it provides the remote sensing community a concise synthesis of Landsat-like sensors and research demonstrating their capabilities. It is also timely because it provides a framework for evaluating the range of Landsat alternatives, and strategies for minimizing the impact of a possible Landsat data gap.

In this paper, we evaluate the possibility of a wireless sensor network concept operating in the oceanic environment. A population of underwater sensor nodes, termed "optical plankton," form a distributed sensor network characterized by the deployment of many miniature and low-cost sensors capable of profiling the particulate composition in the immediate marine environs. The stimulation of backscatter signals from the surrounding medium is one principle of operation studied, but the potential for additional probing methods is discussed. The optically probed signal is communicated to a base station, where the large number of received signals is fused to obtain an accurate estimate of the nature of the local aqueous medium. The many similarities and differences between the atmospheric and oceanic sensing and wireless communication environments are discussed and the distinctive features of an oceanic probing system are underlined. Specific scientific applications are briefly reviewed. The challenges to be met are addressed in general and a focused analysis of the specific issue of multi-access interference (MAI), common to all optical wireless-based sensor networks, is presented. Novel analytical approaches have been employed to evaluate and quantify the MAI. It is the object of this paper to assess the feasibility of a novel miniaturized oceanic probing system and explore some of the challenges. It would appear that the proposed scheme could be the basis of an innovative oceanic probing method and fill a niche not as yet catered for with existing technologies.

This paper describes and illustrates frameworks for societal benefits associated with data products from the Multi-angle Imaging SpectroRadiometer (MISR), one of five instruments launched into polar orbit aboard NASA's Terra spacecraft in December 1999. The objective of framing and illustrating benefits is impelled by the January, 2007, U.S. National Research Council's seminal decadal survey of U.S. Earth science. The survey urges that all missions explicitly identify potential societal benefits in mission planning and operation. In this paper, our retrospective look at MISR offers approaches which mission planning for future instruments could operationalize. "Societal benefit" generally refers to practical applications of data and data products beyond their intrinsic science merit. The paper demonstrates how societal benefit frameworks work, highlights some of these benefits in the case of MISR, and seeks to provide useful guidance for benefit descriptions in future multi-angle and other Earth observation research programs.

Urban forest is of great interest to a variety of scientific and urban planning applications. This paper presents a strategy for monitoring urban forest using Landsat TM/ETM+ (Thematic Mapper / Enhanced Thematic Mapper Plus) imagery time series and calculating its ecological benefits. And the strategy is applied to the temporal analysis of the zone inside the 4th Ring Road in Beijing. The analysis consists of two key steps in: the first is to extract urban forest from Landsat images; the second is to calculate the ecological benefits of urban forest. The extraction of urban forest from Landsat imagery is accomplished implementing classification models, which are based on empirical relationships between forest coverage and the spectrum on Landsat imagery, and are generated using regression tree techniques. Quickbird images and field investigations are applied to generate classification models and to assess their accuracies. Subsequently, the ecological benefits calculation about urban forest is carried out introducing CITYgreen model. This paper mainly concerns carbon storage and the function in air pollution reduction. In the following part, the results of the analysis are presented, as well as the figures that illustrate their variations. At the end, the advantages and disadvantages of this strategy are discussed.

The high-spectral-resolution AIRS (Atmospheric InfraRed Sounder) instrument onboard the NASA (National Aeronautics and Space Administration) Earth Observing System (EOS)-Aqua satellite represents the most advanced sounding system in space and provides unprecedented wealth of highly accurate radiance measurements. This paper describes a standalone and fast single field-of-view (FOV) algorithm to retrieve atmospheric sounding profiles (temperature, humidity, ozone) and surface parameters (surface skin temperature, surface emissivity) from AIRS Level 1B (L1B) clear only infrared radiance measurements. The retrieval algorithm is part of the International MODIS (Moderate Resolution Imaging Spectroradiometer)/AIRS Processing Package (IMAPP) software package, which provides international users with the capability of receiving and processing direct broadcast data in real-time. The IMAPP AIRS retrieval algorithm is based on principal component regression to obtain fast and accurate estimates of the atmospheric state at single FOV. This algorithm is designed specifically for real-time direct broadcast applications where sounding products can be processed efficiently at highest possible spatial resolution. Simulated radiance data is trained on a global set of profiles, representative of a wide variety of atmospheric scenes, which makes the algorithm globally applicable. The results presented and discussed in this paper demonstrate that the IMAPP AIRS retrieval product is rigorously evaluated by various product sources such as numerical weather prediction model analysis fields, retrieved parameters from the operational AIRS L2 product and data from other instruments.

The global interaction of CO2 flux was highly dynamic under seasonal and inter-annual variability. Thus, precise estimation of gross primary productivity (GPP) and net photosynthesis (PsnNet) under seasonal variations was important to understand the global carbon cycle and ecosystem response to climate variability. Considering this significance, in our paper, we have conducted a comparative investigation of correlation (i.e., Pearson product, Spearman rank, and Kendall rank) and supervised machine learning models (i.e., random forest, conditional inference forests, and quantile regression forests) to explore the agreement and prediction of GPP and PsnNet under seasonal variability from spectral indices, tasseled cap transformations, and reflectances over a mixed ecosystem assembling moderate resolution imaging spectroradiometer and Landsat data. Associated uncertainties and errors have been also compared. Our study was carried out in a tropical moist ecosystem under pre-monsoon (March) and post-monsoon (October) conditions. Overall outcome revealed that seasonal differentiation does not affect the simple correlations; however, it has a significant impact on the GPP/PsnNet prediction process. Near-infrared (NIR)-based spectral indices have been found with the best agreements and statistically significant (p

The Advanced Baseline Imager (ABI) aboard Geostationary Operational Environmental Satellite (GOES)-16 and -17 satellites represent the next-generation geostationary multispectral imaging instrument. Since GOES-16 ABI imagery data became available, stray light was observed in ABI visible, near-infrared (VNIR), and 3.9 μm, i.e., CH07, channels. A stray-light characterization scheme was developed to quantitatively monitor stray-light variation in ABI imagery. The stray-light analysis is focused on ABI CH07, whose nighttime radiometric performance being impacted by stray light is of main concern. It is found that the stray light in the ABI imagery occurs over ∼3 months around spring and fall equinox each year. The maximum stray light of GOES-16 ABI CH07 is ∼0.65 K at 300 K scene in zone of normal performance (ZONP), i.e., region with relative solar angle >7.5 deg, which is within the radiometric requirement of 1 K. The analysis of Himawari-8 Advanced Himawari Imager (AHI) CH07 data indicates that its maximum stray light is ∼3.35 K at 300 K scene in ZONP, much higher than that of GOES-16. This confirms the effectiveness of reducing the major stray-light leaking path in ABI as a result of lessons learned from Himawari-8 AHI. The magnitude of GOES-17 ABI CH07 stray light is shown to be ∼0.45 K in ZONP, slightly lower than GOES-16. The analysis of AHI 3.9 and 6.2 μm channel stray-light radiance ratio is shown to match the ratio of solar irradiance spectrum, which suggests that the stray-light issue is due to direct leakage of solar radiation through the instrument. Further characterization and monitoring of GOES-16 ABI VNIR channel stray light also help understand the solar origin of ABI stray light. This paper also investigated cases of strong atmospheric refraction-induced stray-light contamination onto ABI detectors during solar eclipse seasons and presents scheme to reduce such potentially harmful contamination.

Airborne laser remote sensing is extensively used in geophysical explorations. Laser-remote-sensing studies are often undertaken to predict the availability of mineral resources in a target area. A major issue encountered in such studies concerns the acquisition of the ground-vibration information and analysis of the optoelectric characteristics of the corresponding target vibration. Our paper presents a scheme involving the use of a laser remote sensing system (LRSS) to realize the geophone-detection function and experimentally verify the optoelectric characteristics of the target ground vibration. As observed, the proposed LRSS demonstrates a 19- to 98-V s / cm sensitivity range for the low- to medium-frequency vibrations in the 0- to 600-Hz range. Overall, the LRSS could sense a wide range (0 to 4 kHz) of ground-vibration frequencies. Our research is expected to open avenues for electro-optical sensing technologies to be used in applications such as hidden-structure detection and non-line-of-sight imaging.