Over the past decade, the number of studies that investigate aerosol–cloud interactions has increased considerably. Although tremendous progress has been made to improve the understanding of basic physical mechanisms of aerosol–cloud interactions and reduce their uncertainties in climate forcing, there is still poor understanding of 1) some of the mechanisms that interact with each other over multiple spatial and temporal scales, 2) the feedbacks between microphysical and dynamical processes and between local-scale processes and large-scale circulations, and 3) the significance of cloud–aerosol interactions on weather systems as well as regional and global climate. This review focuses on recent theoretical studies and important mechanisms on aerosol–cloud interactions and discusses the significances of aerosol impacts on radiative forcing and precipitation extremes associated with different cloud systems. The authors summarize the main obstacles preventing the science from making a leap—for exampl...

Review of Aerosol–Cloud Interactions: Mechanisms, Significance, and Challenges

Rapid and uncontrolled population growth along with economic and industrial development, especially in developing countries during the late twentieth and early twenty-first centuries, have increased the rate of land-use/land-cover (LULC) change many times. Since quantitative assessment of changes in LULC is one of the most efficient means to understand and manage the land transformation, there is a need to examine the accuracy of different algorithms for LULC mapping in order to identify the best classifier for further applications of earth observations. In this article, six machine-learning algorithms, namely random forest (RF), support vector machine (SVM), artificial neural network (ANN), fuzzy adaptive resonance theory-supervised predictive mapping (Fuzzy ARTMAP), spectral angle mapper (SAM) and Mahalanobis distance (MD) were examined. Accuracy assessment was performed by using Kappa coefficient, receiver operational curve (RoC), index-based validation and root mean square error (RMSE). Results of Kappa coefficient show that all the classifiers have a similar accuracy level with minor variation, but the RF algorithm has the highest accuracy of 0.89 and the MD algorithm (parametric classifier) has the least accuracy of 0.82. In addition, the index-based LULC and visual cross-validation show that the RF algorithm (correlations between RF and normalised differentiation water index, normalised differentiation vegetation index and normalised differentiation built-up index are 0.96, 0.99 and 1, respectively, at 0.05 level of significance) has the highest accuracy level in comparison to the other classifiers adopted. Findings from the literature also proved that ANN and RF algorithms are the best LULC classifiers, although a non-parametric classifier like SAM (Kappa coefficient 0.84; area under curve (AUC) 0.85) has a better and consistent accuracy level than the other machine-learning algorithms. Finally, this review concludes that the RF algorithm is the best machine-learning LULC classifier, among the six examined algorithms although it is necessary to further test the RF algorithm in different morphoclimatic conditions in the future.

Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review

Research on aerosol sources and chemical composition: Past, current and emerging issues

Agriculture provides for the most basic needs of humankind: food and fiber. The introduction of new farming techniques in the past century (e.g., during the Green Revolution) has helped agriculture keep pace with growing demands for food and other agricultural products. However, further increases in food demand, a growing population, and rising income levels are likely to put additional strain on natural resources. With growing recognition of the negative impacts of agriculture on the environment, new techniques and approaches should be able to meet future food demands while maintaining or reducing the environmental footprint of agriculture. Emerging technologies, such as geospatial technologies, Internet of Things (IoT), Big Data analysis, and artificial intelligence (AI), could be utilized to make informed management decisions aimed to increase crop production. Precision agriculture (PA) entails the application of a suite of such technologies to optimize agricultural inputs to increase agricultural production and reduce input losses. Use of remote sensing technologies for PA has increased rapidly during the past few decades. The unprecedented availability of high resolution (spatial, spectral and temporal) satellite images has promoted the use of remote sensing in many PA applications, including crop monitoring, irrigation management, nutrient application, disease and pest management, and yield prediction. In this paper, we provide an overview of remote sensing systems, techniques, and vegetation indices along with their recent (2015–2020) applications in PA. Remote-sensing-based PA technologies such as variable fertilizer rate application technology in Green Seeker and Crop Circle have already been incorporated in commercial agriculture. Use of unmanned aerial vehicles (UAVs) has increased tremendously during the last decade due to their cost-effectiveness and flexibility in obtaining the high-resolution (cm-scale) images needed for PA applications. At the same time, the availability of a large amount of satellite data has prompted researchers to explore advanced data storage and processing techniques such as cloud computing and machine learning. Given the complexity of image processing and the amount of technical knowledge and expertise needed, it is critical to explore and develop a simple yet reliable workflow for the real-time application of remote sensing in PA. Development of accurate yet easy to use, user-friendly systems is likely to result in broader adoption of remote sensing technologies in commercial and non-commercial PA applications.

Applications of Remote Sensing in Precision Agriculture: A Review

Strong earthquakes are among the prime triggering factors of landslides. The 2008 Wenchuan earthquake (Mw = 7.9) triggered tens of thousands of landslides. Among them, the Daguangbao landslide is the largest one, which covered an area of 7.8 km2 with a maximum width of 2.2 km and an estimated volume of 7.5 × 108 m3. The landslide is located on the hanging wall of the seismogenic fault, the Yingxiu–Beichuan fault in Anxian town, Sichuan Province. The sliding mass travelled about 4.5 km and blocked the Huangdongzi valley, forming a landslide dam nearly 600 m high. Compared to other coseismic landslides in the study area, the Daguangbao landslide attained phenomenal kinetic energy, intense cracking, and deformation, exposing a 1-km long head scarp in the rear of the landslide. Based on the field investigation, we conclude that the occurrence of the landslide is controlled mainly by the seismic, terrain, and geological factors. The special location of the landslide and the possible topographic amplification of ground motions due to the terrain features governed the landslide failure. The effects of earthquakes on the stability of slopes were considered in two aspects: First, the ground shaking may reduce the frictional strength of the substrate by shattering of rock mass. Second, the seismic acceleration may result in short-lived and episodic changes of the normal (tensile) and shear stresses in the hillshopes during earthquakes. According to the failure mechanism, the dynamic process of the landslide might contain four stages: (a) the cracking of rock mass in the rear of the slope mainly due to the tensile stress generated by the ground shaking; (b) the shattering of the substrate due to the ground shaking, which reduced the frictional strength of the substrate; (c) the shearing failure of the toe of the landslide due to the large shear stress caused by the landslide gravity; and (d) the deposition stage.

The characteristics and failure mechanism of the largest landslide triggered by the Wenchuan earthquake, May 12, 2008, China

With the advent of new satellite technology, the radiative energy exchanges between Sun, Earth, and space may now be quantified accurately. Nevertheless, much less is known about the magnitude of the energy flows within the climate system and at the Earth’s surface, which cannot be directly measured by satellites. This review surveys the basic theories, observational methods, and different surface energy balance algorithms for estimating evapotranspiration (ET) from landscapes and regions with remotely sensed surface temperatures, and highlights uncertainties and limitations associated with those estimation methods. Although some of these algorithms were built up for specific land covers like irrigation areas only, methods developed for other disciplines like hydrology and meteorology, are also reviewed, where continuous estimates in space and in time are needed. Temporal and spatial scaling issues associated with the use of thermal remote sensing for estimating evapotranspiration are also discussed. A review of these different satellite based remote sensing approaches is presented. The main physical bases and assumptions of these algorithms are also discussed. Some results are shown for the estimation of evapotranspiration on a rice paddy of Chiayi Plain in Taiwan using remote sensing data.

https://www.mdpi.com/1996-1073/7/5/2821/pdf

Evapotranspiration Estimation with Remote Sensing and Various Surface Energy Balance Algorithms—A Review

Aerosol effects on the enhancement of cloud-to-ground lightning over major urban areas of South Korea

Study of cloud-to-ground lightning and precipitation and their seasonal and geographical characteristics over Taiwan

A large earthquake struck Wenchuan County, China on 12 May 2008 and is one of the most severe natural disasters of recent decades that has changed the entire landscape of Wenchuan County. This paper presents a satellite image analysis of the landslide following the Wenchuan earthquake and its consequences, and demonstrates how remote sensing techniques can be used for civilian crisis-management purpose and disaster relief. Real-time high resolution 2-dimensional and 3-dimensional information on structural deformation of land areas, directional changes of rivers, creation of new lakes, and the water levels of rivers and lakes in the earthquake affected area have been provided using the FORMOSAT-2 and Satellite pour l'Observation de la Terre (SPOT)-5 satellite images. Satellite images taken before and after the 12 May 2008 Wenchuan Earthquake have been used for landslide investigation. Possible flooding and rupture of natural dams during the post-earthquake period have been indicated through the analysis of 3-dimensional images of FORMOSAT-2. Some rescue methods have been suggested for immediate recovery of the bereaved persons and to locate the corresponding area accurately using remote sensing techniques. Some precautionary measures have also been suggested to avoid further destruction. Comparisons of panchromatic and multispectral images of FORMOSAT-2 for a particular region have been made to quantify the location accuracy. Our results show that the real-time high-spatial resolution FORMOSAT-2 satellite images could be an efficient and useful resource for decision-makers to prepare rescue and post-event recovery operation plans, especially for some isolated earthquake areas where damage distribution is often very uneven and hard to reach in time.

S.K. Kar

Papers

Evapotranspiration Estimation with Remote Sensing and Various Surface Energy Balance Algorithms—A Review

Aerosol effects on the enhancement of cloud-to-ground lightning over major urban areas of South Korea

Study of cloud-to-ground lightning and precipitation and their seasonal and geographical characteristics over Taiwan

Use of high-resolution FORMOSAT-2 satellite images for post-earthquake disaster assessment: a study following the 12 May 2008 Wenchuan Earthquake

Enhancement of cloud-to-ground lightning activity over Taipei, Taiwan in relation to urbanization