Multi-feature combined cloud and cloud shadow detection in GaoFen-1 wide field of view imagery

The wide field of view (WFV) imaging system onboard the Chinese GaoFen-1 (GF-1) optical satellite has a 16-m resolution and four-day revisit cycle for large-scale Earth observation. The advantages of the high temporal-spatial resolution and the wide field of view make the GF-1 WFV imagery very popular. However, cloud cover is an inevitable problem in GF-1 WFV imagery, which influences its precise application. Accurate cloud and cloud shadow detection in GF-1 WFV imagery is quite difficult due to the fact that there are only three visible bands and one near-infrared band. In this paper, an automatic multi-feature combined (MFC) method is proposed for cloud and cloud shadow detection in GF-1 WFV imagery. The MFC algorithm first implements threshold segmentation based on the spectral features and mask refinement based on guided filtering to generate a preliminary cloud mask. The geometric features are then used in combination with the texture features to improve the cloud detection results and produce the final cloud mask. Finally, the cloud shadow mask can be acquired by means of the cloud and shadow matching and follow-up correction process. The method was validated using 108 globally distributed scenes. The results indicate that MFC performs well under most conditions, and the average overall accuracy of MFC cloud detection is as high as 96.8%. In the contrastive analysis with the official provided cloud fractions, MFC shows a significant improvement in cloud fraction estimation, and achieves a high accuracy for the cloud and cloud shadow detection in the GF-1 WFV imagery with fewer spectral bands. The proposed method could be used as a preprocessing step in the future to monitor land-cover change, and it could also be easily extended to other optical satellite imagery which has a similar spectral setting.Comment: This manuscript has been accepted for publication in Remote Sensing of Environment, vol. 191, pp.342-358, 2017. (http://www.sciencedirect.com/science/article/pii/S003442571730038X

Mapping crop phenology using NDVI time-series derived from HJ-1 A/B data

With the availability of high frequent satellite data, crop phenology could be accurately mapped using time-series remote sensing data. Vegetation index time-series data derived from AVHRR, MODIS, and SPOT-VEGETATION images usually have coarse spatial resolution. Mapping crop phenology parameters using higher spatial resolution images (e.g., Landsat TM-like) is unprecedented. Recently launched HJ-1 A/B CCD sensors boarded on China Environment Satellite provided a feasible and ideal data source for the construction of high spatio-temporal resolution vegetation index time-series. This paper presented a comprehensive method to construct NDVI time-series dataset derived from HJ-1 A/B CCD and demonstrated its application in cropland areas. The procedures of time-series data construction included image preprocessing, signal filtering, and interpolation for daily NDVI images then the NDVI time-series could present a smooth and complete phenological cycle. To demonstrate its application, TIMESAT program was employed to extract phenology parameters of crop lands located in Guanzhong Plain, China. The small-scale test showed that the crop season start/end derived from HJ-1 A/B NDVI time-series was comparable with local agro-metrological observation. The methodology for reconstructing time-series remote sensing data had been proved feasible, though forgoing researches will improve this a lot in mapping crop phenology. Last but not least, further studies should be focused on field-data collection, smoothing method and phenology definitions using time-series remote sensing data

Applications of satellite ‘hyper-sensing’ in Chinese agriculture:Challenges and opportunities

Ensuring adequate food supplies to a large and increasing population continues to be the key challenge for China. Given the increasing integration of China within global markets for agricultural products, this issue is of considerable significance for global food security. Over the last 50 years, China has increased the production of its staple crops mainly by increasing yield per unit land area. However, this has largely been achieved through inappropriate agricultural practices, which have caused environmental degradation, with deleterious consequences for future agricultural productivity. Hence, there is now a pressing need to intensify agriculture in China using practices that are environmentally and economically sustainable. Given the dynamic nature of crops over space and time, the use of remote sensing technology has proven to be a valuable asset providing end-users in many countries with information to guide sustainable agricultural practices. Recently, the field has experienced considerable technological advancements reflected in the availability of ‘hyper-sensing’ (high spectral, spatial and temporal) satellite imagery useful for monitoring, modelling and mapping of agricultural crops. However, there still remains a significant challenge in fully exploiting such technologies for addressing agricultural problems in China. This review paper evaluates the potential contributions of satellite ‘hyper-sensing’ to agriculture in China and identifies the opportunities and challenges for future work. We perform a critical evaluation of current capabilities in satellite ‘hyper-sensing’ in agriculture with an emphasis on Chinese sensors. Our analysis draws on a series of in-depth examples based on recent and on-going projects in China that are developing ‘hyper-sensing’ approaches for (i) measuring crop phenology parameters and predicting yields; (ii) specifying crop fertiliser requirements; (iii) optimising management responses to abiotic and biotic stress in crops; (iv) maximising yields while minimising water use in arid regions; (v) large-scale crop/cropland mapping; and (vi) management zone delineation. The paper concludes with a synthesis of these application areas in order to define the requirements for future research, technological innovation and knowledge exchange in order to deliver yield sustainability in China

Detection, Emission Estimation and Risk Prediction of Forest Fires in China Using Satellite Sensors and Simulation Models in the Past Three Decades—An Overview

Forest fires have major impact on ecosystems and greatly impact the amount of greenhouse gases and aerosols in the atmosphere. This paper presents an overview in the forest fire detection, emission estimation, and fire risk prediction in China using satellite imagery, climate data, and various simulation models over the past three decades. Since the 1980s, remotely-sensed data acquired by many satellites, such as NOAA/AVHRR, FY-series, MODIS, CBERS, and ENVISAT, have been widely utilized for detecting forest fire hot spots and burned areas in China. Some developed algorithms have been utilized for detecting the forest fire hot spots at a sub-pixel level. With respect to modeling the forest burning emission, a remote sensing data-driven Net Primary productivity (NPP) estimation model was developed for estimating forest biomass and fuel. In order to improve the forest fire risk modeling in China, real-time meteorological data, such as surface temperature, relative humidity, wind speed and direction, have been used as the model input for improving prediction of forest fire occurrence and its behavior. Shortwave infrared (SWIR) and near infrared (NIR) channels of satellite sensors have been employed for detecting live fuel moisture content (FMC), and the Normalized Difference Water Index (NDWI) was used for evaluating the forest vegetation condition and its moisture status

Vicarious Methodologies to Assess and Improve the Quality of the Optical Remote Sensing Images: A Critical Review

Over the past decade, number of optical Earth observing satellites performing remote sensing has increased substantially, dramatically increasing the capability to monitor the Earth. The quantity of remote sensing satellite increase is primarily driven by improved technology, miniaturization of components, reduced manufacturing, and launch cost. These satellites often lack on-board calibrators that a large satellite utilizes to ensure high quality (e.g., radiometric, geometric, spatial quality, etc.) scientific measurement. To address this issue, this work presents “best” vicarious image quality assessment and improvement techniques for those kinds of optical satellites which lacks on-board calibration system. In this article, image quality categories have been explored, and essential quality parameters (e.g., absolute and relative calibration, aliasing, etc.) have been identified. For each of the parameters, appropriate characterization methods are identified along with its specifications or requirements. In cases of multiple methods, recommendation has been made based-on the strengths and weaknesses of each method. Furthermore, processing steps have been presented, including examples. Essentially, this paper provides a comprehensive study of the criteria that needs to be assessed to evaluate remote sensing satellite data quality, and best vicarious methodologies to evaluate identified quality parameters such as coherent noise, ground sample distance, etc

Developments in Earth observation for the assessment and monitoring of inland, transitional, coastal and shelf-sea waters

The Earth's surface waters are a fundamental resource and encompass a broad range of ecosystems that are core to global biogeochemical cycling and food and energy production. Despite this, the Earth's surface waters are impacted by multiple natural and anthropogenic pressures and drivers of environmental change. The complex interaction between physical, chemical and biological processes in surface waters poses significant challenges for in situ monitoring and assessment and often limits our ability to adequately capture the dynamics of aquatic systems and our understanding of their status, functioning and response to pressures. Here we explore the opportunities that Earth observation (EO) has to offer to basin-scale monitoring of water quality over the surface water continuum comprising inland, transition and coastal water bodies, with a particular focus on the Danube and Black Sea region. This review summarises the technological advances in EO and the opportunities that the next generation satellites offer for water quality monitoring. We provide an overview of algorithms for the retrieval of water quality parameters and demonstrate how such models have been used for the assessment and monitoring of inland, transitional, coastal and shelf-sea systems. Further, we argue that very few studies have investigated the connectivity between these systems especially in large river-sea systems such as the Danube-Black Sea. Subsequently, we describe current capability in operational processing of archive and near real-time satellite data. We conclude that while the operational use of satellites for the assessment and monitoring of surface waters is still developing for inland and coastal waters and more work is required on the development and validation of remote sensing algorithms for these optically complex waters, the potential that these data streams offer for developing an improved, potentially paradigm-shifting understanding of physical and biogeochemical processes across large scale river-sea continuum including the Danube-Black Sea is considerable

Estimating daily evapotranspiration based on a model of evaporative fraction (EF) for mixed pixels

Currently, applications of remote sensing evapotranspiration (ET) products are limited by the coarse resolution of satellite remote sensing data caused by land surface heterogeneities and the temporal-scale extrapolation of the instantaneous latent heat flux (LE) based on satellite overpass time. This study proposes a simple but efficient model (EFAF) for estimating the daily ET of remotely sensed mixed pixels using a model of the evaporative fraction (EF) and area fraction (AF) to increase the accuracy of ET estimate over heterogeneous land surfaces. To accomplish this goal, we derive an equation for calculating the EF of mixed pixels based on two key hypotheses. Hypothesis 1 states that the available energy (AE) of each sub-pixel is approximately equal to that of any other sub-pixels in the same mixed pixel within an acceptable margin of error and is equivalent to the AE of the mixed pixel. This approach simplifies the equation, and uncertainties and errors related to the estimated ET values are minor. Hypothesis 2 states that the EF of each sub-pixel is equal to that of the nearest pure pixel(s) of the same land cover type. This equation is designed to correct spatial-scale errors for the EF of mixed pixels; it can be used to calculate daily ET from daily AE data. The model was applied to an artificial oasis located in the midstream area of the Heihe River using HJ-1B satellite data with a 300&thinsp;m resolution. The results generated before and after making corrections were compared and validated using site data from eddy covariance systems. The results show that the new model can significantly improve the accuracy of daily ET estimates relative to the lumped method; the coefficient of determination (R2) increased to 0.82 from 0.62, the root mean square error (RMSE) decreased to 1.60 from 2.47&thinsp;MJ&thinsp;m−2(decreased approximately to 0.64 from 0.99&thinsp;mm) and the mean bias error (MBE) decreased from 1.92 to 1.18&thinsp;MJ&thinsp;m−2 (decreased from approximately 0.77 to 0.47&thinsp;mm). It is concluded that EFAF can reproduce daily ET with reasonable accuracy; can be used to produce the ET product; and can be applied to hydrology research, precision agricultural management and monitoring natural ecosystems in the future.</p

Remote Sensing of Ecology, Biodiversity and Conservation: A Review from the Perspective of Remote Sensing Specialists

Remote sensing, the science of obtaining information via noncontact recording, has swept the fields of ecology, biodiversity and conservation (EBC). Several quality review papers have contributed to this field. However, these papers often discuss the issues from the standpoint of an ecologist or a biodiversity specialist. This review focuses on the spaceborne remote sensing of EBC from the perspective of remote sensing specialists, i.e., it is organized in the context of state-of-the-art remote sensing technology, including instruments and techniques. Herein, the instruments to be discussed consist of high spatial resolution, hyperspectral, thermal infrared, small-satellite constellation, and LIDAR sensors; and the techniques refer to image classification, vegetation index (VI), inversion algorithm, data fusion, and the integration of remote sensing (RS) and geographic information system (GIS)

Going hyperspectral: the 'unseen' captured?

All objects, name them soil, water, trees, vegetation, structures, metals, paints or fabrics, create a unique spectral fingerprint. A sensor determines these fingerprints by measuring reflected light, most of which registers in wavelengths, or bands, invisible to humans. This is what the crime scene investigation (CSI) television programs have popularized how DNA or fingerprints can be used to solve crimes. Similarly, forest CSI of “seeing” the trees in the deep high mountain tropical forest is now a major focus in the air and spaceborne hyperspectral sensing technology and in other different applications such as agriculture, environment, geology, transportation, security, and several others. The availability of sub-meter resolution colour imagery from satellites coupled with internet based services like Google Earth and Microsoft Virtual Earth have resulted in an enormous interest in remote sensing among the general public. The ability to see one’s home or familiar landmarks in an image taken from hundreds of kilometers above the earth elicits wonder and awe. Deciding where, when, what and how to sense or measure the DNA of individual trees from the air or space is a crucial question in the sustainable development and management of our Malaysian tropical forest ecosystems. However, to monitor, quantify, map and understand the content and nature of our forest, one would ideally like to monitor it everywhere and all the time too. This is impossible, and consequently, forest engineers must select relatively very high to high near to real time resolution sensors with the ability to transcend boundaries, capabilities, features and interfacing realms for such measurement. The dynamic interplay of these elements is precisely coordinated by signaling networks that orchestrate their interactions. High-throughput experimental and analytical techniques now provide forest engineers with incredibly rich and potentially revealing datasets from both air and spaceborne hyperspectral sensors (also known as imaging spectrometers). However, it is impossible to exhaustively explore the full experimental and operational hyperspectral sensors available in the market out there and so forest engineers must judiciously choose which one is the best to perform and fulfill their project objectives and missions. The complexity and high-dimensionality of these systems makes it incredibly difficult for forest engineers and other users alone to manage and optimize sensing processes. In order to add or derive value from a hyperspectral remotely sensed image several factors such as resolution, swath, and signal to noise ratio, amongst others need to be considered. A grand challenge for the forest engineer’s scientific discovery in the 21st Century is therefore, to devise very high real-time ultra-spatial and spectral air and space borne sensors that automatically measure and adapt sensing operations in large-scale and economical systems with the unseen captured. This lecture therefore focuses on the emerging theory, origin of the hyperspectral sensors, research, practice, limitations and identifies future challenge and outlook of hyperspectral sensing systems in the quest towards a sustainable Malaysian forestry context and other different applications to capture the “unseen”. It is quite certain that advances in hyperspectral remote sensing and more sophisticated analytical methods will resolve any “unseen” issues in time with the best approach of transcending boundaries and interfacing remote sensing data with precise information from the field plots. Unfortunately, as a relatively new analytical technique, the full potential of air and spaceborne hyperspectral imaging has not yet been realized in Malaysi