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Records of the past years showed that the climate of built-up regions differs significantly from rural regions and one of the most obvious and important modifying effects of urbanization on local climate is the urban heat island (UHI). In this paper, four types of land cover, namely urban bare concrete cover, urban woods or the shade of trees, urban water areas and urban lawn, were selected to study their microclimate, and the UHI was also analyzed using air temperature data measured at four fixed observation spots in Nanjing, China, during hot weather from July to September, 2005. Dry and wet bulb temperature data were obtained by whirling psychrometers, and wind speed data by cup anemometers. Our observed data focused on the detailed statistical analysis of the microclimate variation in the four types of land cover during the whole day. The results showed that: (1) the microclimate of these four types of land cover had significant differences among different observation sites. In general, the air temperature of these four types of land cover complied with the order during daytime: bare concrete cover>lawn>water areas>woods or the shade of trees, with reversed order during nighttime when the air temperature of the lawn became the lowest. Compared with the bare concrete cover, the other three types of land cover showed the effect of dropping air temperature ranging between 0.2 and 2.9 °C. There were some instant dynamic characteristics in detailed temporal series among these four types of cover in the different observation sites. (2) The UHI effect could be detected obviously by the air temperature difference between the urban center area and the rural area. The average UHI intensity during the monitoring period was between 0.5 and 3.5 °C; however, there were also significant day-to-day variations. A strong UHI effect usually occurred around midnight; while about 2–3 h after sunrise the UHI began to decrease till midday time; and during 13:00–15:00, the UHI effect had a sudden increase and then decreased again; after sunset, a peak UHI effect was frequently observed during 18:00–21:00. (3) Finally, by means of the standard deviation (SD), this paper provides a concise and comprehensive understanding for the temporal and spatial microclimatic dynamics of these four kinds of urban cover in the four observation sites. Air temperature at the height of 1.5 m in Nanjing showed that the nocturnally horizontal temperature gradient was somewhat different from that reported in other large cities, and a marked heterogeneity in a smaller ground cover scale could be detected from the microclimatic spatial pattern. There is no doubt that the analysis of these four types of land cover presents the insight into possible countermeasures to decrease the high air temperature in hot summers, and is relevant to the urban planning redevelopment.

In order to integrate urban microclimatic pattern, heat island intensity, land cover and cooling effect of vegetation corridors in a more comprehensive way, both fixed and mobile observations at two types of scale have been made simultaneously in different weather conditions during hot weather in Nanjing, China. Then the air temperature distribution of Nanjing and its impact factor were detected and quantified, and vegetation cooling models were developed in this paper. There was a significant day-to-day variation of urban heat island (UHI), and the average intensity of UHI during the measurement period was 2.0 °C. The strong UHI usually occurred around midnight; however, a peak UHI was frequently observed 2–3 h after sunset. During daytime, several isolated hot areas were observed next to cooler ones, indicating a negative UHI. The air temperature distributions of the vegetation corridors at mesoscale in the evening were similar to those at micro-scale at noon. The influence of the Purple Mountain was very obvious with the maximum cooling effect up to 3.0 °C/100 m at microscale and 0.4 °C/km at mesoscale. This research indicated that the pattern of the UHI in Nanjing is a function of the climatic conditions, the local topography, the effect of the Purple Mountain and the Yangtze River and the urban characteristics such as land uses, vegetation, building density, traffic loads, construction materials and anthropogenic heat sources, which should be taken into account for urban planning and ecologically comfortable residence. The results have further relevance for environmental implications in a subtropical city on the Yangtze River.

Remote sensing is a powerful tool for obtaining important agronomic information about field crops. Many spectral vegetation indices (VIs) have been developed in the past three decades to provide more sensitive measurements of plant biophysical parameters and to reduce external noise interferences such as those related to soil and the atmosphere. Some VIs were developed based on narrowband spectral data and others on broadband sensors. Therefore, although the mathematical equations defining VIs are the same, their calculated values are different, thus affecting their stability in predicting agronomic variables such as total green leaf area index. The objective of this study was to compare the ability of VIs derived from broad and narrowbands and to determine the optimum red–NIR bands for VIs used in predicting leaf area index (LAI) and canopy chlorophyll density (CCD) of cotton canopies. A completely randomized experiment was conducted in a cotton (Gossypium hirsutum L. cv. Sumian 3) field treated with four nitrogen application rates: 0%, 50%, 100% and 200% of the recommended rate. Hyperspectral reflectance was measured at 2.3 m above the cotton canopy on July 15, August 14 and October 1, 2002 using a FieldSpec® FR spectroradiometer. Corresponding leaf area index values and CCD were also measured on these dates. A large number (i.e. 22,500) of two-band combinations in the Normalized Difference Vegetation Index (λ2 − λ1)/(λ1 + λ2) and the Ratio Vegetation index λ2/λ1 was used for a linear and exponential regression analysis against LAI and CCD values. Moreover, traditional broadband vegetation indices based on simulated spectra were compared with their narrowband versions in predicting LAI and CCD. The results suggest that 640–660 nm and 800–870 nm, the centers of the red and NIR channels of several multi-spectral sensors on the current generation of earth-orbiting satellites, were not always the optimum wavelength position of red–NIR bands for VIs. Although different in formula, both the NDVI (normalized difference vegetation index) and RVI (ratio vegetation index) calculated from narrowbands at 690–710 nm and 750–900 nm were closely correlated with LAI (R2 > 0.8) and CCD (R2 > 0.85). The red–NIR band position was more important than band width for modeling LAI and CCD. In summary, hyperspectral remotely sensed data provide more alternative red–NIR bands compared to multi-spectral data and, therefore, can provide greater flexibility in predicting LAI and CCD.