Cloudy Climate Research Articles

Systematic bias in reanalysis-derived solar power profiles & the potential for error propagation in long duration energy storage studies

This study consolidates the literature relating to the systematic bias of model-derived irradiance data and further characterises the same for ERA-5 and MERRA-2, two reanalysis datasets that are frequently used to model renewable power in energy system modelling studies. The bias errors yielded when modelling Solar PV generation for solar parks in Australia and Northern Ireland are compared under the optics of typically clear versus typically cloudy respective climates. Evidence is presented that MERRA-2 exhibits a significant global overestimation bias, contrary to some literature that suggests local variation and underestimation in some locations. Based on the trends identified, it is proposed that overestimation bias proliferates in more cloudy climates for both reanalysis datasets. The implications of such bias for studies involving long-duration energy storage with low round-trip efficiency are investigated and an unfortunate mechanism for error propagation is highlighted. A simplified power system consisting of wind, solar, and energy storage is modelled to demonstrate this effect with solar power profiles derived from metered generation, ERA-5, and MERRA-2. It shown that greater systematic bias of MERRA-2, in combination with the opportunity for error propagation through low-round trip efficiency, has the potential to significantly distort the charging & discharging pattern, overall utilization, and total energy requirement of long duration energy storage infrastructure. ERA-5, despite also exhibiting systematic bias, was better able to reproduce the simulated results of the metered generation scenario. Finally, it is recommended that energy modellers using MERRA-2 data to simulate solar PV outputs should now migrate to ERA-5 if reanalysis data is to be used.

Characteristics of thermal comfort in the offices of North-East India

In the context of climate change and global warming, the nexus between energy and buildings is self-explanatory. As per the ministry of statistics and program implementation, Govt. of India data, the primary energy consumption in the building sector is 37% of total primary energy consumption and about 24% to total CO2 emissions. It is evident from the research that thermal comfort, energy efficiency in buildings and sustainable architecture are interlinked and interdependent. The present study is carried out in the Naturally ventilated offices of North-East India at three representative locations i.e., Tezpur, Imphal and Shillong, from warm and humid, Cool and humid and Cold and cloudy climates, respectively. Year-long thermal comfort surveys were carried out in 81 naturally ventilated office buildings, collecting 2326 samples spread over three locations from July 2016 to June 2017. Data analysis shows that neutral temperature through regression analysis is 26.4°C, 24.7°C, and 23.4°C for Tezpur, Imphal and Shillong, respectively. Preferred temperature and relative humidity in Tezpur, Imphal and Shillong offices are 24°C, 23.5 °C and 22 °C and 55%, 55% and 63%, respectively. Probit analysis showed that occupants are more adaptive toward the warmer side of the thermal sensation scale.

The combined influence of rootstock and vintage climate on the grape and wine flavonoids of Vitis vinifera L. cv. Cabernet Sauvignon in eastern China.

Rootstocks are commonly utilized owing to their resistance to abiotic and biotic stress in viticulture. This study evaluated the effects of three rootstocks (1103P, SO4, and 5A) on the Cabernet Sauvignon (CS) vine growth, and their berries and wines flavonoids profiles in four consecutive vintages. The results showed that 1103P increased the pruning weight of CS and decreased the anthocyanin concentration in berries and wines, especially in the vintages with more rainy and cloudy days. 5A tended to decrease the pruning weight of CS and increase the anthocyanin concentration in berries and wines. Orthogonal partial least squares discriminant analysis (OPLS-DA) showed that the concentrations of total anthocyanins, F3’H-anthocyanins, malvidin-3-O-glucoside (Mv-glu), and malvidin-3-O-acetylglucoside (Mv-acglu) were the key substances affected by the rootstocks in CS berries and were significantly decreased by 1103P. Total anthocyanins, pinotins, Mv-glu, epicatechin, and vitisins were the rootstock-sensitive compounds that commonly differed in wines among the three comparison groups in the two vintages. Furthermore, 1103P brought more brightness to the wine and 5A gave the wine more red tones. In conclusion, rootstock 5A was recommended in the rainy and cloudy climate regions with regard to the berry flavonoids accumulation and the wine color.

Heating and Cooling Feasibility of Absorption Heat Pumps Driven by Evacuated Tube Solar Collectors: An Energy and Economic Analysis

Thermal compression-driven heat pump plants usually use solar collectors only providing direct heating when possible and low-temperature (but higher than outside air) cold sources for the heat pump evaporator otherwise. At the same time, solar collectors drive absorption chillers only during summer. In this paper, the possibility of employing evacuated tube collectors to drive an absorption heat pump that operates in summer as a chiller was evaluated from both energy and economic points of view. The ground and solar energy at low temperature were used as cold sources. The ground can be recharged by the cooling of the absorber/condenser in the summer and by solar energy during the mid-seasons. The yearly operation analysis in two different climates here proposed also considered also the utilization of suitable storage capabilities varying the size of the ground probes, solar field, and Generator Tank. A primary energy ratio of 4.75 in a cold and cloudy climate and of 5.9 in a warmer and clearer sky climate was obtained in the best plant configuration. An economic analysis on the actual investment costs was presented. The final evaluation considered a cost reduction on the basis of recent price lists available on the Asian market.

Citrus Canker—Distribution, Taxonomy, Epidemiology, Disease Cycle, Pathogen Biology, Detection, and Management: A Critical Review and Future Research Agenda

Xanthomonas citri subsp. citri, a causative agent of the citrus canker (CC) disease, belongs to one of the essential groups of the bacterial phytopathogen family, Xanthomonadaceae. It has been a potential threat to the globally significant citrus fruit crop, which has remained under investigation for disease management and epidemiology since the 1980s. In Pakistan, the average yield of citrus is 11 t/ha, which is lower than other countries, including China, Brazil, and India, having average productions of 27, 26, and 22 tons/hectare, respectively. Citrus canker is one of the most devastating diseases, posing a significant threat to crop yield and fruit quality. To date, five distinct types (or forms) of the citrus canker have been recognized; the Asiatic (Canker A) form is most destructive and affects most citrus cultivars. Severe infection outcomes include dieback, defoliation, severely blemished fruit, premature fruit drop, and reduced fruit quality. The infection increases under humid, warm, cloudy climate, wind, and heavy rainfall. The analysis of plasmid and chromosomal DNA of X. citri subsp. citri depicted an evolutionary relationship among pathovars of Xanthomonas. The extensive study on the genome of X. citri subsp. citri has contributed to the current knowledge of plant host recognition of pathogens, host specificities, dissemination, and propagation. Regulatory programs, i.e., quarantine or exclusion, continued to be practiced, prohibiting infected citrus plant material into the existing stock. Other measures include removal of inoculums sources, resistant hosts, protective copper-containing sprays, and windbreak systems. In this review, we explored the latest trends in the areas of epidemiology, pathogenome, detection, host–pathogen interaction, biofilm formation, and management of X. citri subsp. citri.

Magnitude and distribution of the untapped solar space-heating resource in U.S. climates

Space heating is the single greatest source of building-related greenhouse gas emissions in the industrialized world, giving urgency to the development of strategies for carbon-free heating. Recent advances have shown that the direct capture, storage, and deployment of solar energy, without conversion to electricity, has considerable potential to address space-heating needs even in cold and cloudy climates. However, the solar energy available for direct heating at climatic and metropolitan scales is both unquantified and widely assumed to be negligible, impeding further investigation, development, and policy responses. To estimate the magnitude and distribution of solar resources concurrent with space-heating needs, we spatially integrate datasets characterizing solar radiation, outdoor temperature, and heating energy use across U.S. climates. Results show that the median resource incident upon collectors of residential scale (10m2) and distribution is much greater than previously realized, equaling 7MWh per household annually; by comparison, the median household heating need is currently 10.3MWh. Unexpectedly, cloud-diffused solar radiation accounts for over one-quarter of this resource in all but semi-arid climates. Metropolitan residential resources exceed 5TWh in areas including Detroit and Boston (cold continental), WashingtonD.C. (humid subtropical), Seattle and San Francisco (Mediterranean), and Denver (semi-arid), and national resources exceed 750TWh annually, compared to approximately 1200TWh of annual heating need. Current technology is able to capture and retain over half of a direct solar heating resource, revealing that the untapped U.S. solar heating potential is comparable to one-third of the national residential space-heating need and implying that analogous resources exist in analogous climates worldwide.

A Comparison of RSOC-Battery Hybrid Energy Storage for Microgrids in the Netherlands and India

The importance of energy storage is steadily rising as electricity grids around the world become more reliant on intermittent renewable power sources such as solar PV and wind turbines. Apart from pumped hydro storage, which forms the bulk of existing energy storage, several other technologies are in development, such as batteries, super-capacitors, flywheels and synthetic e-fuels. Li-ion batteries are highly efficient and dynamic, while e-fuels have many advantages for large-scale seasonal storage. Reversible solid oxide cells (rSOC) are efficient electrochemical devices, which can play an important role in energy storage in the form of e-fuels in the near future. This paper seeks to show the advantages of rSOC systems in long-term energy storage. In particular, it seeks to compare the capital cost of a hybrid system to a purely Li-ion battery system.This paper focuses on the design of energy storage for isolated sustainable micro-grids. A hybrid system is designed, using Li-ion batteries for instantaneous grid balancing, and rSOC system for long-term storage in the form of hydrogen. The paper also focuses on the effect of the wind-solar energy mix and climate. For temperate and cloudy climates, Groningen in the Netherlands is used as an example. The Netherlands has a renewable energy mix of around 70% wind and 30% solar. For sunny tropical climates, Jodhpur in India is used, and India has a renewable energy mix of around 55% wind and 45% solar. Using hourly local data for solar irradiation and wind velocities, a simple algorithm is developed for system sizing. The generation as well as storage systems are sized for a constant consumption load of 20 kW (a constant load is considered to be a reasonable assumption for industrial loads, for example). The capital cost calculation includes the wind turbine(s), solar panels, battery bank, rSOC stack, and compressed hydrogen tank.The results show that the use of an rSOC-hydrogen module in a hybrid-energy storage system leads to significant capital cost reduction compared to a purely Li-ion battery-based system. As expected, this reduction is a result of the fact that power and energy-storage capacities are decoupled in an rSOC/e-fuel system, which means that a small rSOC stack can be used with a large tank, which is suitable for long-term seasonal storage. Hydrogen tanks also cost significantly less than batteries for the same energy capacity. The cost reduction for Jodhpur is more significant than for Groningen.The study also shows the significant effect of the local energy mix on the design of energy storage systems. Naturally, Jodhpur has more solar radiation and less seasonal variation in solar energy. But contrary to initial expectations, tropical Jodhpur requires larger energy seasonal storage facilities than temperate Groningen. This is because the small seasonal solar variation in Jodhpur is further compounded by the seasonal wind pattern, where the wind is strong in summer and weak in winter. On the other hand, the wind patterns are much more stable in Groningen, which largely neutralises the solar variation, especially since wind makes up 70% of the energy mix in Groningen. Figure 1

The Effect of Climate on the Solar Radiation Components on Building Skins and Building Integrated Photovoltaics (BIPV) Materials

The business model of building-integrated photovoltaics (BIPV) is developing expeditiously and BIPV will soon be recognised as a building envelope material for the entire building skins, among other alternatives such as brick, wood, stone, metals, etc. This paper investigates the effect of climate on the solar radiation components on building skins and BIPV materials in the northern hemisphere. The selected cities are Stavanger in Norway, Bern in Switzerland, Rome in Italy, and Dubai in the UAE. The study showed that for all the studied climates, the average incident radiation on the entire building skins is slightly more than the average incident radiation on the east or west facades, regardless of the orientations of the building facades. Furthermore, the correlation between solar radiation components and different BIPV technologies is discussed in this paper. It is also found that when it comes to the efficiency of different BIPV cells, the impact of the climate on some of the BIPV technologies (such as DSC and OSC) is much more significant than others (such as c-Si, mc-Si and CIGS). The evidence from this study suggests that in climates with higher diffuse radiation-or with more overcast days per year-the contribution of IR radiation decreases. Therefore, the efficiency of BIPV materials that their spectral responses are dependent on the IR radiation (like Si and CIGS) in such a climate would drop down meaningfully. On the other hand, the DSC and OSC solar cells could be a good option for cloudy climates since they have more stable performance, even in such a climate. Although, their efficiency compared to other BIPV materials such as Si-based BIPV solar cells is still significantly less thus far.

Artificial neural network models for global solar energy and photovoltaic power forecasting over India

ABSTRACT For design and development of solar-based photovoltaic (PV) system, accuracy in obtaining data of solar energy has been a prime concern. Forecasting of global solar energy in clear or sunny weather conditions are easily calculated; however, the challenge lies in estimating under the impact of foggy and cloudy conditions. Therefore, this research investigates and presents the Artificial Neural Network (ANN) models for global solar energy forecasting for distinct sky-conditions such as sunny, hazy, partly, and fully cloudy conditions. This research further applied for distinct climatic conditions namely composite, warm & humid, moderate, hot & dry, and cold & cloudy climate zone using meteorological parameters. Further, PV power forecasting has been essential for planning the operating capability of energy systems and its reliability. The present work explores the implementation of proposed model for short-term photovoltaic power forecast in solar energy systems. Lastly, comparative analysis of the developed model with other neural network models are carried out using statistical evaluation indices. It has been observed from the overall analysis that by using the Radial-Basis-Function-Neural-Network (RBFNN) model, a significant reduction in error has been noticed as compared to other methods. Further, it is noted that the sunny model provides improved performance for hot & dry climate zone; hazy model gives favorable output for composite climate zone; partly cloudy model is favorable for warm & humid climate zone; and lastly, fully cloudy model provides favorable results for cold & cloudy climate zone. The model has been implemented for broader application series and it has been observed that for short-term photovoltaic power forecasting under composite climatic zone, hazy model emerges out to provide better performance with Mean Absolute Percentage Error (MAPE) of 0.0019% followed by sunny, partly, and fully cloudy model with MAPE 0.024%, 0.054%, and 0.109% respectively. The models presented in this research will be useful for scholars to design and engineer the solar energy systems.

Surface Mapping of Earth-like Exoplanets using Single Point Light Curves

Spatially resolving exoplanet features from single-point observations is essential for evaluating the potential habitability of exoplanets. The ultimate goal of this protocol is to determine whether these planetary worlds harbor geological features and/or climate systems. We present a method of extracting information from multi-wavelength single-point light curves and retrieving surface maps. It uses singular value decomposition (SVD) to separate sources that contribute to light curve variations and infer the existence of partially cloudy climate systems. Through analysis of the time series obtained from SVD, physical attributions of principal components (PCs) could be inferred without assumptions of any spectral properties. Combining with viewing geometry, it is feasible to reconstruct surface maps if one of the PCs are found to contain surface information. Degeneracy originated from convolution of the pixel geometry and spectrum information determines the quality of reconstructed surface maps, which requires the introduction of regularization. For the purpose of demonstrating the protocol, multi-wavelength light curves of Earth, which serves as a proxy exoplanet, are analyzed. Comparison between the results and the ground truth is presented to show the performance and limitation of the protocol. This work provides a benchmark for future generalization of exoplanet applications.

The dual angle solar harvest (DASH) method: An alternative method for organizing large solar panel arrays that optimizes incident solar energy in conjunction with land use

Plummeting costs of solar panels along with rising costs of land makes it imperative to consider both the per-panel and unit-area incident solar energy. An alternative approach of organizing large solar panel arrays that considers this co-optimization problem is suggested and a new dual-angle technique is introduced, called the dual-angle solar harvest (DASH) method, in which a solar array is composed of two tilt angles. This paper evaluates the DASH method at two climatically and geographically different locations: Akron, OH and Barstow, CA. Use of the DASH method can enable potential better handling of complex radiation fields in partly cloudy climates, allow for preferential weighting of irradiation during different seasons, and may be a tool to increase array-wide incident irradiation at a pre-existing array in a cost-effective manner. This latter example is tested nationwide, in which one angle is constrained at the single optimum tilt angle. Results show the lowest percentage of panel-rows that require an adjustment to a different tilt angle to achieve the desired gain in array-wide incident energy. The more cost-effective solutions tend to be located in cloudier climates and at higher latitudes.

Health Beliefs About UV and Skin Cancer Risk Behaviors.

Our purpose was to examine the beliefs of college students about UV exposure and sunscreen use and their associations with skin cancer risk and protective behaviors in a cloudy climate. The sample was online survey participants (N = 334) recruited from a large university in Oregon. After fitting an initial measurement model, we fit a structural equation model including Health Beliefs About UV (HBAU) subscales (Health Benefits of Tanning, Seasonal Effects, Tanning Through the Winter, and Sunscreen Toxicity), outcome variables (sunscreen use, indoor tanning, and outdoor tanning), and covariates (eg, tanning and sunscreen use). A minority of participants held the beliefs represented by 3 HBAU subscales, but beliefs about negative health effects of the local weather (Tanning Through the Winter) were common. The measurement and adjusted models provided good fit to the data (χ2 = 143.30; P = .29; df = 136; Root-Mean Square Error of Approximation = .014; Comparative Fit Index = .992; Tucker-Lewis Index = .981). After adjusting for covariates, Sunscreen Toxicity predicted reduced sunscreen use (β = −.12, P = .021), Health Benefits of Tanning predicted outdoor tanning (β = .43, P < .001), and Tanning Through the Winter predicted indoor tanning (β = .31, P = .02). The small sample size, nonresponse rate, and cross-sectional nature of this study mean these findings should be interpreted cautiously. Beliefs about health benefits of sun exposure, the regional weather, and sunscreen safety play a role in skin cancer risk and protective behaviors.

Solar energy potential according to climatic and geometrical parameters of cities and buildings: A case-study from Tabriz City- Iran

This paper focused on the solar energy utilization in cities and buildings as exemplified by Tabriz City in Iran. All the geographic and climatic factors have been considered to estimate the solar energy potential for Tabriz City using angstrom model in SPSS. Then, the solar energy potential in the Blue Mosque (BM) of Tabriz investigated in detail. Decisive parameters in solar energy evaluation such as geographic parameters, climatic factors, radiation types, and orientation techniques and geometry analysis of buildings, were considered based on Tabriz regarding Duffie Beckman and Stephenson's cousin methods. The method of Lagrange interpolation regarded to define the elements geometrical equations. The rate of irradiation solar energy was analyzed for domed, horizontal and vertical surfaces of the BM in Tabriz City using Ladybug for Rhino and MS Excel software programs. The results of this study showed that it is possible to domesticate noticeable rate of solar energy in a cloudy climate like Tabriz by appropriate orientation and geometry of building elements.

Tanning benefits, seasonal effects, and concerns about sunscreen: Measuring health beliefs about UV among college students

Objective: To develop and validate a scale measuring health beliefs about UV in cloudy climates that may impact UV exposure behaviors. Participants: Students at a large university in Oregon completed pilot (N = 115) and final (N = 335) scales online March–July, 2016. Five participants underwent cognitive interviews. Methods: Expert feedback, cognitive interviews, and pilot data guided item development and refinement. We conducted factor analysis and invariance testing. Results: The final four-factor model fit well (χ2 = 37.97, df = 37, RMSEA = 0.000, CFI = 1.000). HBAU subscales are Sunscreen Toxicity, Seasonal Effects, Health Benefits of Tanning, and Tanning Through the Winter. Invariance testing supported strong invariance across sex and tanning status. Conclusion: The HBAU measures beliefs that encourage UV exposure and discourage protection (eg, the belief that sunscreen ingredients are toxic). This scale will enable more comprehensive measurement of cognitive predictors of UV exposure for student health, clinical, and research purposes.

Thermal comfort in naturally ventilated office buildings in cold and cloudy climate of Darjeeling, India – An adaptive approach

Thermal comfort standards are essential to determine a good indoor climate as well as to optimize energy use inside a building. The Predicted Mean Vote (PMV) model is used to determine the indoor comfort limits in a conditioned building. However, PMV model often exaggerates thermal sensation, as it does not include adaptation undertaken by the subjects in the real environment. These adaptive effects depend upon factors like local climatic condition, ethnicity, culture, etc. In this paper the results of adaptive thermal comfort based field studies conducted in 3 naturally ventilated office buildings of cold and cloudy climate in north east India are presented. The variation in thermal sensation, thermal preference, clothing insulation, neutral temperatures and other behavioral adaptive measures undertaken by the subjects like taking hot and cold beverages, number of showers in order to feel comfortable are discussed. The comfortable thermal sensation votes are plotted on the psychometric chart and a comparison with the comfort zone prescribed by ASHRAE is made. The subjects were found to be comfortable in cooler temperature than that prescribed in the standard, and thereby a modification in the comfort zone, which reflects the adaptive action of the subjects for the region is proposed.

Agrobiodiversidad: Validación E Identificacion De Flora En La Comunidad Piñancay, Parroquia Capsol, Chunchi-Ecuador

Ecuador is considered as a country with great biodiversity, in spite of its small territory. This is due to multiple factors including geographic location, geological, topographical and climatic factors, precipitation, temperature, and location in the tropical belt of the planet. On the equinoctial line, with territories located both in the northern hemisphere and in the southern hemisphere, all this forces us to maintain a constant responsibility towards our flora and fauna for its conservation and wealth. Piñancay is one of the six (6) communities of the Capsol parish, which belongs to the Chunchi canton. It has a cold, humid, and cloudy climate that allows the development of agricultural activities on a wide range. It however serves as a form of income to the inhabitants of the sector. Thus, based on the validation and identification of the flora inventory of the Piñancay community and in addition to the morphological and taxonomic identification of the plants, a study was carried out on the current agrobiodiversity of the Piñancay community.

Summer activity patterns for mosses and lichens in Maritime Antarctica

AbstractWithin Antarctica there are large gradients both in climate and in vegetation which offer opportunities to investigate links between the two. The activity (% total time active) of lichens and bryophytes in hydric and xeric environments was monitored at Livingston Island (62°39'S). This adds a northern site with a maritime, cloudy climate to previous studies in the southern Antarctic Peninsula and the Dry Valleys (78°S). Annual activity increases northwards from less than 1% to nearly 100%. There is a major and consistent difference between hydric sites which, with snow melt, can be 100% active in summer months even in the Dry Valleys, and xeric sites which, depending on precipitation, rarely exceed 80% activity even at Livingston Island. Mosses dominate hydric sites and lichens the xeric sites all along the gradient. Mean temperatures when active are 2–4°C at all sites, as liquid water is required. Light is a potential major stress reaching 880 µmol m-2s-1when active in continental sites. The lack of extremes in temperatures and light combined with high activity levels means that summer at Livingston Island is one of the better sites for lichen and bryophyte growth in the world.

Daylight Utilization with Light Pipe in Farm Animal Production: A Simulation Approach

Light pipes offer a passive way to bring daylight inside deep buildings, such as agricultural buildings. However, the lack of reliable performance predictability methods for light pipes represents a major obstacle preventing their widespread use. This paper evaluates a simulation approach for performance prediction and identifies key light pipe design parameters affecting their daylight transmission performance. The study was carried out through continuous monitoring of daylight in two full-scale, identical pig stables fitted with two light pipe systems, Solatube® and Velux®. The experiment included three continuously measuring sensors in each stable and an outdoor sensor during 2013 and 2014. A forward raytracing tool, TracePro®, was used for illuminance prediction and parametric simulations. The simulation results for overcast skies indicated discrepancies between the simulated and average measurement results below 30% in all cases. The discrepancies for clear skies were somewhat higher, i.e., below 30% for 67% of the cases. The higher discrepancies with clear skies were due to the overestimation of absolute sunlight levels and absence of an advanced and detailed optical characterization of the dome collector’s surface. The parametric results have shown that light pipes’ performance is better during summer time, in sunny climates, at low to mid-latitudes, which provides higher solar altitudes than during winter and cloudy climates at high latitudes. Methods to improve the luminous transmittance for low solar altitudes occurring in Scandinavia include: bending or tilting the pipe, increasing the aspect ratio, improving the pipe specular reflectance, tilting the collector to the south, and using optical redirecting system in the collector. (Less)

Temperature Microclimates of Plants in a Tropical Alpine Environment: How Much does Growth Form Matter?

ABSTRACTIn the aseasonal tropical alpine environment, plants experience frequent oscillations of air temperature around zero, but little is known about the leaf temperatures of different plant growth forms in dry versus humid climatic conditions. During July–August 2007, we measured air temperature at 100 cm and 20 cm above the ground, soil temperature at 1 cm and 10 cm depth, and temperatures of leaves and stems of tropical alpine plants on the eastern (windward and cloudy) and western (leeward and sunny) sides of the Antisana volcano (Ecuador) between 4100 m and 4600 m, with the aim of examining the effects of climate and growth forms on leaf temperature. The sunnier climate on the western side of the mountain provided a much broader thermal envelope, in which only leaves of low-statured plants showed significant departure from air temperature during the day. In contrast, most plants had warmer leaves than was the air temperature on the eastern side, and except for cushion plants, the difference in temperature was progressively greater in leaves of taller plants. Plants warmed up significantly faster on the western side and at higher elevations, with the fastest warming rates of 13–15 K h−1 observed in erect herbs. Night cooling rates did not differ between the opposite mountain sides or between elevations. Erect herbs cooled at the fastest rates (3 K h−1), whereas cushion plants cooled at the slowest rates (about 1 K h−1). Height aboveground along with aspect (west vs. east) were the most significant determinants of the leaf thermal microclimate during the day, with elevation having no effect. Low-statured plants experienced more extreme and more variable microclimates than taller plants in sunnier and drier conditions, but the effect of plant height was almost negligible in humid and cloudy climates. In all climatic conditions, cushion plants performed better than any other growth form by achieving higher temperature during the day and preventing rapid cooling during the night.

Object-Oriented Classification of Sugarcane Using Time-Series Middle-Resolution Remote Sensing Data Based on AdaBoost.

Most areas planted with sugarcane are located in southern China. However, remote sensing of sugarcane has been limited because useable remote sensing data are limited due to the cloudy climate of this region during the growing season and severe spectral mixing with other crops. In this study, we developed a methodology for automatically mapping sugarcane over large areas using time-series middle-resolution remote sensing data. For this purpose, two major techniques were used, the object-oriented method (OOM) and data mining (DM). In addition, time-series Chinese HJ-1 CCD images were obtained during the sugarcane growing period. Image objects were generated using a multi-resolution segmentation algorithm, and DM was implemented using the AdaBoost algorithm, which generated the prediction model. The prediction model was applied to the HJ-1 CCD time-series image objects, and then a map of the sugarcane planting area was produced. The classification accuracy was evaluated using independent field survey sampling points. The confusion matrix analysis showed that the overall classification accuracy reached 93.6% and that the Kappa coefficient was 0.85. Thus, the results showed that this method is feasible, efficient, and applicable for extrapolating the classification of other crops in large areas where the application of high-resolution remote sensing data is impractical due to financial considerations or because qualified images are limited.