Assessment of total columnar ozone climatological trends over the Indian sub-continent

In the present study, long term satellite and Dobson spectrophotometer Total Column Ozone (TCO) data have been used to study the interannual variability and also to assess climatological trends in TCO over different geographical locations of Indian sub-continent. TCO data were analyzed for the period 1957 to 2015 over New Delhi (28.63° N, 77.18° E), Varanasi (25.30° N, 83.02° E), Pune (18.53° N, 73.84° E) and Kodaikanal (10.0° N, 77.47° E). An extensive validation was performed for Total Ozone Mapping Spectrometer (TOMS) and Ozone Monitoring Instrument (OMI) retrieved TCO data independently with Dobson Spectrophotometer TCO measurements over New Delhi, Varanasi, Pune and Kodaikanal. The results of this exercise showed good correlation coefficient (r) of 0.87 (0.88), 0.84 (0.82), 0.91 (0.80) and 0.84 (Data not available) respectively. Climatological mean TCO over New Delhi, Varanasi, Pune and Kodaikanal are 275.02 ± 6.44 DU, 269.03 ± 7.34 DU, 260.78 ± 5.07 DU and 258.71 ± 6.36 DU respectively for the period 1957 to 2015. An increasing trend over New Delhi (0.20 DU year^–1), Pune (0.18 DU year^–1), Kodaikanal (0.14 DU year^–1) and decreasing trend over Varanasi (0.01 DU year^–1) were observed. High significance of TCO trend was found at New Delhi (p-value < 0.0001), Pune (p-value = 0.002) and Kodaikanal (p-value = 0.003) with negligible trend over Varanasi with p-value of 0.84. The TCO variations at different geographical locations associated with upper atmospheric meteorological parameters such as lower Stratospheric Temperature (ST) at 65 hPa and Tropopause Height (TH) were also addressed. Annual lower stratospheric temperature shows positive relationship with TCO and Stratospheric ozone over the study sites. Further, decadal variability in TCO with respect to solar activity at New Delhi was also analyzed.     

Satellite-sensed tropospheric NO2 patterns and anomalies over Indus,Ganges, Brahmaputra,and Meghna river basins

This study presents trends, seasonality, hot spots, and anomalies of tropospheric NO₂ pollution over four basins of Indus, Ganges, Brahmaputra, and Meghna rivers in South Asia using observations from Ozone Monitoring Instrument (OMI) on-board Aura satellite during 2004–2015. For the first time this area, a highly populated and industrialized region with significant emissions of air pollutants, has been discussed collectively. OMI data reveal significantly elevated NO₂ column over the region averaged at (1.9 ± 0.1) × 10¹⁵ molecules cm^–2 (average ± standard deviation of observations) with an increase of 21.12% (slope (0.036 ± 0.004) × 10¹⁵ molecules cm^–2, y-intercept (1.705 ± 0.024) × 10¹⁵ molecules cm^–2, R² = 0.92) during the study period. According to MACCity anthropogenic emissions inventory transportation, energy, residential, and industrial sectors are the major contributors of high NO_x emissions. NO₂ pollution hot spots are identified and their tendencies have been discussed. The hot spots of megacities Lahore (Pakistan) and Dhaka (Bangladesh) are found to be strengthening and expanding over the time. Eastern Ganges Basin shows the highest NO₂ concentration at (2.63 ± 0.22) × 10¹⁵ molecules cm^–2 and growth rate of 3.22% per year mainly linked to power generation, fossil fuel extraction, mining activities, and biomass burning. NO₂ over Indus–Ganges–Brahmaputra–Meghna Basin exhibits seasonal maximum in winter and minimum in monsoon. The highest seasonality is found over Meghna Basin due to large variations in meteorological conditions and large-scale crop-residue burning. Some anomalies in NO₂ levels have been detected linked to intense crop-residue burning events. During these anomalies, exceptionally high levels of daily NO₂ reaching up to 76.23 × 10¹⁵ molecules cm^–2 have been observed over some places in Indus and Meghna Basins.     

Total ozone column measurements using an ultraviolet multi-filter radiometer

We have developed and used a method to retrieve total ozone column (TOC), from Ultraviolet Multi-filter Rotating Shadowband Radiometer (UVMFR) measurements in combination with radiative transfer model calculations. Look-up tables of ratios of the direct solar irradiance at (DI) 305 and 325nm in terms of TOC, solar zenith angle, and aerosol optical depth (AOD) have been constructed and compared with TOC retrievals estimated directly from UVMFR irradiance measurements. Sensitivity analysis of the influence of AOD on the calculated TOC has been investigated and found to be 1 Dobson unit per 0.1 change in AOD. We also examined the impact of ozone effective temperature on the TOC retrieval and found that it leads to a 0.9% change in TOC per K. UVMFR direct irradiance measurements in Athens, Greece, during the period July 2009–May 2014 were used to create a time series of high-temporal-frequency measurements (1 min for cloudless conditions) of TOC, which facilitated an analysis of the diurnal variation of TOC. Comparison of the TOC retrievals from the UVMFR with co-located and synchronous daily TOC retrievals from a Brewer MKIV spectrophotometer showed very good agreement (correlation coefficient 0.98). Daily TOC retrievals from the UVMFR were within ±3% compared with the ones measured by the Ozone Monitoring Instrument overpasses on board the Aura satellite.     

Satellite remote sensing of total ozone column (TOC) over Pakistan and neighbouring regions

Total ozone column (TOC) obtained from the Ozone Monitoring Instrument (OMI) on board the Aura satellite was utilized to examine the spatio-temporal distribution of atmospheric ozone over Pakistan and adjoining regions of Afghanistan, India, and Iran for October 2004 to March 2014. This region has not yet been evaluated in greater detail. A yearly spatial averaged value of 278 ± 2 DU was found over the region. A decadal increase of 1.3% in TOC value over study region was observed for the first time. Large spatial and temporal variability of TOC was found over the study region. Elevated ozone columns were observed over the regions with high NO₂ and CO concentrations. Analysis indicated that Srinagar city has the highest averaged value of 290 ± 3 DU whereas Jodhpur city showed the highest increasing trend of 1.9% per decade. A monthly averaged maximum value of 289 ± 8 DU and a minimum of 264 ± 5 DU were found during April and November, respectively, over the region. January showed a decreasing trend of ?0.8% and February exhibited the highest increasing trend of 5.1% per decade. Forward trajectory analysis showed the possibility of ozone transport from eastern parts of the study region towards the Indian Ocean (Bay of Bengal) through the subtropical jet stream creating low values at higher meridians in October. TOC data deduced from OMI and the Atmospheric Infrared Sounder were compared to check the level of correlation and the results showed significant correlation (r = 0.75) and an acceptable average relative difference of 4.2%.     

Analysis of lidar depolarization calibration procedure and application to the atmospheric aerosol characterization

A Raman lidar system is used to monitor the aerosol depolarization features of the urban atmosphere at the Andalusian Centre for Environmental Research (CEAMA), in Granada, southeastern Spain. The lidar system was upgraded in 2010 to enable the application of the ±45° calibration method, which does not require any external optical device. We analyse the method and classify the atmospheric aerosol following the criteria based on depolarization. Backscatter coefficient, backscatter-related Angström exponent (å _β), volume linear depolarization ratio (δ^v), and particle linear depolarization ratio (δ^p) profiles are studied in Saharan dust and biomass burning smoke events during the summer of 2010. The strength of these events was visualized in the aerosol optical depth (AOD) series obtained by Sun and star photometers operated at CEAMA. During the analysed events, the AOD at 440 nm ranged between 0.2 and 0.3, although the Angström exponent (å _AOD) retrieved by the Sun photometer was considerably lower during the Saharan dust event (å _AOD = 0.4 ± 0.1) than during the biomass burning event (å _AOD = 1.4 ± 0.1). Regarding å _β profiles, å _β values were similar along the vertical profiles and comparable to å _AOD values for each event. In contrast, the particle linear depolarization ratio (δ^p) at 532 nm showed an opposite behaviour to å _β, changing along the vertical profiles. In fact, the aerosol layers located in the free troposphere showed mean values of δ^p of 0.13 ± 0.08 and 0.03 ± 0.01 in the Saharan dust and biomass burning events, respectively. These results show that the use of depolarization techniques enables an accurate aerosol typing and the understanding of the layer's composition in the atmosphere.     

Spectral identification of ozone‐damaged pine needles

Needles were collected from ponderosa and Jeffrey pine trees at three sites in the Sierra Nevada, and were assembled into 504 samples and grouped according to five dominant live needle conditions – green, winter fleck, sucking insect damage, scale insect damage, and ozone damage – and a random mixture. Reflectance and transmittance measurements of abaxial and adaxial surfaces were obtained at ca 0.3 nm spectral resolution from 400–800 nm, and binned to simulate Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) data. There were no significant differences in optical properties between the two surfaces. Ozone‐damaged needles were collected from Jeffrey pine trees at one site, and exhibited significantly different (family‐wise α = 0.01) reflectance and transmittance signatures – and significantly different signature slopes – at both spectral resolutions, from green and winter fleck needles from the same site. Ozone‐damaged needles had significantly different (family‐wise α = 0.01) abaxial surface reflectance and reflectance slope signatures from all other groups of needles, at both spectral resolutions. In comparison with three chlorophyll reflectance indices, a new red fall index (RFI) provides high classification accuracies for ozone‐damaged and non‐ozone‐damaged pine needles (overall acc. = 94%; κ = 59%). Thus, ozone‐damaged Jeffrey pine needles have a unique spectral signature in relation to dominant needle conditions of ponderosa and Jeffrey pine trees.     

Observation of an agricultural biomass burning in central and east China using merged aerosol optical depth data from multiple satellite missions

Agricultural biomass burning (ABB) in central and east China occurs every year from May to October and peaks in June. During the period from 26 May to 16 June 2007, one strong ABB procedure happened mainly in Anhui, Henan, Jiangsu and Shandong provinces. This article focuses on analysis of this ABB procedure using a comprehensive set of aerosol optical depth (AOD) data merged by using the optimal interpolation method from the Moderate Resolution Imaging Spectroradiometer, the Multi-angle Imaging Spectroradiometer (MIRS) as well as Sea-viewing Wide Field-of-view Sensor (SeaWiFS)-derived AOD products. In addition, the following additional data are used: fire data from the National Satellite Meteorological Centre of China Meteorological Administration, the mass trajectory analyses from hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model and ground-based AOD and Ångström data derived from the Aerosol Robotic Network and China Aerosol Remote Sensing Network. The results show that merged satellite AOD values can expand the spatial coverage of agricultural biomass aerosol distributions with good accuracy (R = 0.93, root mean square error = 0.37). Based on the merged AOD images, the highest AOD values were found concentrated in central China as well as in eastern China before 6 June and further extended to northeast China after 12 June. AODs from ground measurement show that eastern China always keeps high AOD values (>1.0), with a maximum exceeding 3.0 and extending as high as nearly 5.0 during this ABB event. With the help of the HYSPLIT model, we analysed the ABB sources and examined how transport paths affect the concentrations of air pollutants in some sites. The results show that Henan, Jiangsu and Anhui provinces are the three main sources in this ABB.     

Predicting Eucalyptus spp. stand volume in Zululand,South Africa: an analysis using a stochastic gradient boosting regression ensemble with multi-source data sets

Accurate, reliable, and up-to-date forest stand volume information is a prerequisite for a detailed evaluation of commercial forest resources and their sustainable management. Commercial forest responses to global climate change remain uncertain, and hence the mapping of stand volume as carbon sinks is fundamentally important in understanding the role of forests in stabilizing climate change effects. The aim of this study was to examine the utility of stochastic gradient boosting (SGB) and multi-source data to predict stand volume of a Eucalyptus plantation in South Africa. The SGB ensemble, random forest (RF), and stepwise multiple-linear regression (SMLR) were used to predict Eucalyptus stand volume and other related tree-structural attributes such as mean tree height and mean diameter at breast height (DBH). Multi-source data consisted of SPOT-5 raw spectral features (four bands), 14 spectral vegetation indices, rainfall data, and stand age. When all variables were used, the SGB algorithm showed that stand volume can be accurately estimated (R² = 0.78 and RMSE = 33.16 m³ ha^?1 (23.01% of the mean)). The competing RF ensemble produced an R² value of 0.76 and a RMSE value of 37.28 m³ ha^?1 (38.28% of the mean). SMLR on the other hand, produced an R² value of 0.65 and an RMSE value of 42.50 m³ ha^?1 (42.50% of the mean). Our study further showed that Eucalyptus mean tree height (R² = 0.83 and RMSE = 1.63 m (9.08% of the mean)) and mean diameter at breast height (R² = 0.74 and RMSE = 1.06 (7.89% of the mean)) can also be reasonably predicted using SGB and multi-source data. Furthermore, when the most important SGB model-selected variables were used for prediction, the predictive accuracies improved significantly for mean DBH (R² = 0.81 and RMSE = 1.21 cm (6.12% of the mean)), mean tree height (R² = 0.86 and RMSE = 1.39 m (7.02% of the mean)), and stand volume (R² = 0.83 and RMSE = 29.58 m³ ha^?1 (17.63% of the mean)). These results underscore the importance of integrating multi-source data with remotely sensed data for predicting Eucalyptus stand volume and related tree-structural attributes.     

Modelling gross primary production in semi-arid Inner Mongolia using MODIS imagery and eddy covariance data

We evaluate the modelling of carbon fluxes from eddy covariance (EC) tower observations in different water-limited land-cover/land-use (LCLU) and biome types in semi-arid Inner Mongolia, China. The vegetation photosynthesis model (VPM) and modified VPM (MVPM), driven by the enhanced vegetation index (EVI) and land-surface water index (LSWI), which were derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) surface-reflectance product (MOD09A1), were used to model and validate the temporal changes in gross primary production (GPP) from the EC towers during the 2006 and 2007 growing seasons. The annual GPP predicted by the VPM model (GPP_VPM) was predicted reasonably well in 2006 and 2007 at the cropland (coefficient of determination, R ² = 0.67 and 0.71, for 2006 and 2007, respectively) and typical steppe (R ² = 0.80 and 0.73) sites. The predictive power of the VPM model varied in the desert steppe, which includes an irrigated poplar stand (R ² = 0.74 and 0.68) and shrubland (R ² = 0.31 and 0.49) sites. The comparison between GPP obtained from the eddy covariance tower (GPP_tower) and GPP obtained from MVPM (GPP_MVPM) (predicted GPP) showed good agreement for the typical steppe site of Xilinhaote (R ² = 0.84 and 0.70 in 2006 and 2007, respectively) and for the Duolun steppe site (R ² = 0.63) and cropland site (R ² = 0.63) in 2007. The predictive power of the MVPM model decreased slightly in the desert steppe at the irrigated poplar stand (R ² = 0.56 and 0.47 in 2006 and 2007 respectively) and the shrubland (R ² = 0.20 and 0.41). The results of this study demonstrate the feasibility of modelling GPP from EC towers in semi-arid regions.     

Absorption characteristics of ocean water in the Arabian Sea during winter bloom from in situ measurements and Oceansat 2 OCM and MODIS data

In the northern Arabian Sea, blooms usually occur during the northeast monsoon (November–January) and inter-monsoon (February–April) periods. After death, these phytoplankton blooms produce massive subsurface zones of low dissolved oxygen levels that have a major impact on the ocean water ecosystem. Many studies have been done to identify the bloom in this region, but those on the optical properties of bloom water are scarce. The present study emphasizes the optical properties (inherent) of the bloom water in the study region using in situ and satellite data. The total absorption coefficient of ocean water was measured from in situ radiance data collected in the northern Arabian Sea from the Sagar Sampada cruise (SS-286) during March 2011. The same data were also derived from the top-of-atmosphere radiance and remote sensing reflectance of the Oceansat 2 Ocean Colour Monitor (OCM-2) and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) sensors, respectively. A comparison between measured (in situ) and retrieved total absorption coefficients from OCM-2 was made. The measured and retrieved absorption coefficients are in good agreement. Root mean square errors between measured and retrieved absorption coefficients are 0.018 m^?1, 0.026 m^?1, and 0.034 m^?1 for 490 nm, 510 nm, and 555 nm, respectively. An inter-comparison of total absorption properties retrieved from OCM-2 and MODIS data in the region of one degree radius around the stations was also made. A fairly good match was observed on 10, 14, and 16 March 2011 (coefficient of determination, R² = 0.75, 0.87, and 0.62, respectively) for the blue band (490 nm) and (R² = 0.77, 0.79, and 0.71, respectively) the green band (555 nm). The study demonstrates the potential of using remote-sensing optical data for identifying bloom waters.     

Annual and seasonal variations in tropospheric ozone concentrations around Varanasi

This study examines the annual, seasonal and diurnal variations in the ambient concentrations of ozone at a suburban site of Varanasi, India, during 2002–2006. Prominent seasonal variations in ozone concentrations were recorded. Ozone concentrations were higher during the warmer months. Daytime 12‐hourly mean monthly ozone concentrations varied from 45.18 to 62.35 ppb during summer, from 28.55 to 44.25 ppb during winter and from 24 to 43.85 ppb during the rainy season from 2002 to 2006. Distinct diurnal variations in ozone concentrations were also observed. Daytime maxima in ozone concentration were recorded between 1200 and 1400 h, whereas morning and evening hours showed lower concentrations of ozone. Ozone concentrations in the atmosphere depended on several meteorological factors. Monthly average ozone concentration was significantly correlated with maximum temperature (p<0.001) and mean monthly temperature (p<0.05), maximum relative humidity (p<0.001), minimum relative humidity (p<0.001) and mean monthly relative humidity (p<0.001), and sunshine hours (p<0.001). Ozone concentrations in the ambient air have shown an increase in the past decade that was more in the winter and rainy seasons than in the summer. This study suggests that ozone concentrations around Varanasi were sufficiently high to cause significant damage to agricultural production. The present work can be extended to a regional level by incorporating modelling studies using recent remote sensing tools.     

Chlorophyll content estimation in arid grasslands from Landsat-8 OLI data

In this study, an arid grassland was selected, and the chlorophyll content of the leaf and canopy level was estimated based on Landsat-8 Operational Land Imager (OLI) data using the PROSAIL radiative transfer (RT) model. Two vegetation indices (green chlorophyll index, CI_green, and greenness index, G) were selected to estimate the leaf and canopy chlorophyll content (LCC and CCC). By analysing the effect of soil background on the two indices, the LCC was divided into low and moderate-to-high levels. A different combination of the two indices was adopted at each level to improve the chlorophyll content estimation accuracy. The results suggested that the chlorophyll content estimated using the proposed method yielded a higher accuracy with coefficient of determination, R² = 0.84, root-mean-square error, RMSE = 9.67 μg cm^?2 for LCC and R² = 0.85, RMSE = 0.43 g m^?2 for CCC than that using CI_green alone with R² = 0.62, RMSE = 20.04 μg cm^?2 for LCC and R² = 0.85, RMSE = 0.71 g m^?2 for CCC. The results also confirmed the validity of this approach to estimate the chlorophyll content in arid areas.     

Sensitivity study of Radarsat-2 polarimetric SAR to crop height and fractional vegetation cover of corn and wheat

Increasing studies have been conducted to investigate the potential of polarimetric synthetic aperture radar (SAR) in crop growth monitoring due to the capability of penetrating the clouds, haze, light rain, and vegetation canopy. This study investigated the sensitivity of 16 parameters derived from C-band Radarsat-2 polarimetric SAR data to crop height and fractional vegetation cover (FVC) of corn and wheat. The in-situ measured crop height and FVC were collected from 29 April to 30 September 2013, at the study site in southwest Ontario, Canada. A total of 10 Radarsat-2 polarimetric SAR images were acquired throughout the same growing season. It was observed that at the early growing stage, the corn height was strongly correlated with the SAR parameters including HV (R² = 0.88), HH-VV (R² = 0.84), and HV/VV (R² = 0.80), and the corn FVC was significantly correlated with HV (R² = 0.79) and HV/VV (R² = 0.92), but the correlation became weaker at the later growing stage. The sensitivity of the SAR parameters to wheat variables was very low and only HV and Yamaguchi helix scattering showed relatively good but negative correlations with wheat height (R² = 0.57 and R² = 0.39) at the middle growing stage. These findings indicated that Radarsat-2 polarimetric SAR (C-band) has a great potential in crop height and FVC estimation for broad-leaf crops, as well as identifying the changes in crop canopy structures and phenology.     

Estimating plot volume using lidar height and intensity distributional parameters

This study explored the feasibility of height distributional metrics and intensity values extracted from low-density airborne light detection and ranging (lidar) data to estimate plot volumes in dense Korean pine (Pinus koraiensis) plots. Multiple linear regression analyses were performed using lidar height and intensity distributional metrics. The candidate variables for predicting plot volume were evaluated using three data sets: total, canopy, and integrated lidar height and intensity metrics. All intensities of lidar returns used were corrected by the reference distance. Regression models were developed using each data set, and the first criterion used to select the best models was the corrected Akaike Information Criterion (AIC_c). The use of three data sets was statistically significant at R² = 0.75 (RMSE = 52.17 m³ ha^?1), R² = 0.84 (RMSE = 45.24 m³ ha^?1), and R² = 0.91 (RMSE = 31.48 m³ ha^?1) for total, canopy, and integrated lidar distributional metrics, respectively. Among the three data sets, the integrated lidar metrics-derived model showed the best performance for estimating plot volumes, improving errors up to 42% when compared to the other two data sets. This is attributed to supplementing variables weighted and biased to upper limits in dense plots with more statistical variables that explain the lower limits. In all data sets, intensity metrics such as skewness, kurtosis, standard deviation, minimum, and standard error were employed as explanatory variables. The use of intensity variables improved the accuracy of volume estimation in dense forests compared to prior research. Correction of the intensity values contributed up to a maximum of 58% improvement in volume estimation when compared to the use of uncorrected intensity values (R² = 0.78, R² = 0.53, and R² = 0.63 for total, canopy, and integrated lidar distributional metrics, respectively). It is clear that the correction of intensity values is an essential step for the estimation of forest volume.     

A simple empirical method for estimating surface water vapour pressure using MODIS near-infrared channels: applications to northern Spain's Asturias region

In this article, we present a simple methodology for obtaining algorithms to estimate surface water vapour pressure (e ₀) over cloud-free land areas using Moderate Resolution Imaging Spectroradiometer (MODIS) data. The algorithm obtained in this case is adapted to the particular climatic characteristics of the Asturias region, but the methodology can easily be extrapolated and used to obtain algorithms for other regions around the world. The proposed method estimates e ₀ from a simple linear combination of the radiances of the MODIS near-infrared (NIR) channels more commonly applied to total precipitable water (W) estimations. Comparison between the e ₀ data measured at the ground-based meteorological stations in Asturias (daily data from 2004) versus the values predicted using the proposed algorithm gives R ² = 0.76 and residual standard error (RSE) = 2.07 hPa (16%). The algorithm was tested using the data from 2008 obtained in Asturias and in two sites outside of Asturias with similar latitudes and radiosonde observations (La Coruña and Santander). The resulting validation demonstrates that the algorithm gives good results in Asturias (root-mean-square deviation (RMSD) = 2.50 hPa (19%) and bias = 1.26 hPa, with R ² = 0.65) and when La Coruña is included (R ² = 0.61), but that its validity is decreased when Santander is also included (R ² = 0.56).

The possibility of obtaining e ₀ from three global MODIS algorithms for W retrieval was also tested and compared to our algorithm. The results show that our algorithm gives better results than the International MODIS/Atmospheric InfraRed Sounder Processing Package (IMAPP) Water Vapour Near-Infrared (WVNIR) product and the Sobrino algorithm. The MODIS Total Precipitable Water (MOD05) product is worse than that obtained with our algorithm in Asturias (R ² = 0.61 vs. R ² = 0.65), but the two values are similar if the stations in La Coruña (R ² = 0.60) and Santander (R ² = 0.56) are included in the comparison. The dominant advantage of the novel algorithm proposed in this study is that it is simpler and can be produced quickly in real time.     

Satellite and ground-based monitoring of smoke in the atmosphere during the summer wildfires in European Russia in 2010 and Siberia in 2012

Using Moderate Resolution Imaging Spectroradiometer (MODIS) (Aqua and Terra satellites) and in situ observations, a comparative analysis of two large-scale smoke events caused by the summer wildfires in European Russia (ER) in 2010 and Western Siberia (WS) in 2012 was carried out. In the 5-day periods of the extreme smoke pollution (5–9 August 2010 in ER and 27–31 July 2012 in WS), the number of active fires in the equal territories, confined by the coordinates 47°–65° N, 25°–55° E and 51°–70° N, 71°–104° E, was found to be 4754 for ER and 3823 for WS. With this, the regional mean aerosol optical depths (AODs) were found to be (1.02 ± 0.02) and (1.00 ± 0.04), not much differing for both the events. The regional mean aerosol radiative forcing effects at the top (R₁) and the bottom (R₂) of the atmosphere over ER/WS according to MODIS observations were estimated to be (?61 ± 1) and (?54 ± 2) W m^?2, and (?107 ± 2) and (?96 ± 3) W m^?2, respectively. At the same time, the local values of AOD and the local absolute values of R₁ and R₂ over WS were considerably higher than those over ER. MODIS AOD (L3) data during the wildfires of 2010 were validated by AOD data obtained by the sun-sky photometer CIMEL, operating at the AERONET station Zvenigorod. The rates of radiative heating of the smoky atmosphere over ER and WS were also estimated and compared with the existed temperature anomalies, obtained using National Centers for Environmental Prediction National Center for Atmospheric Research reanalysis data. Optical and microphysical properties of smoke aerosols during the wildfires in ER and WS also revealed some similar characteristics. The aerosols were mostly found in the submicron-size fraction and characterized by very high single-scattering albedos (0.95–0.98). In the dense smoke conditions, the degree of linear polarization at the scattering angle 90° during both the events decreased to negative values ranging between ?0.1 and ?0.15. The optical properties of smoke aerosols were mainly conditioned by unusually narrow particle size distribution.     

Canopy level estimations of chlorophyll and LAI for two tropical species (teak and bamboo) from Hyperion (EO1) data

Current economic development in tropical regions (especially in India, China, and Brazil) is putting tremendous pressure on tropical forest cover. Some of the dominant and economically important species are planted at large scale in these countries. Teak and bamboo are two important species of tropical regions because of their commercial and conservation values. Accurate estimates of foliar chemistry can help in evaluating the health status of vegetation in these regions. An attempt has been made to derive canopy level estimation of chlorophyll and leaf area index (LAI) for these species utilizing Hyperion data. Partial least square (PLS) regression analysis was carried out to identify the correlation between measured parameters (chlorophyll and LAI) and Hyperion reflectance spectra. PLS regression identified 600–750 nm as a sensitive spectral region for chlorophyll and 1000–1507 nm for LAI. The PLS regression model tested in this study worked well for the estimation of chlorophyll (R ² = 0.90, root mean square error (RMSE) = 0.182 for teak and R ² = 0.84, RMSE = 0.113 for bamboo) and for the estimation of LAI (R ² = 0.87, RMSE = 0.425). The lower predictive error obtained indicates the robustness of the data set and also of the applicability of the PLS regression analysis. Wavelengths recognized by the PLS regression model were utilized for the development of vegetation indices for estimating chlorophyll and LAI. Predictive performances of the developed simple ratios (SRs) were evaluated using the cross-validation method. SR 743/692 gave the best results for the prediction of chlorophyll with the leave-one-out cross-validation (LOO-CV) method (R ² = 0.73, RMSE = 0.28 for teak and R ² = 0.71, RMSE = 0.15 for bamboo). The normalized difference ratio (ND 1457/1084) gave the best results for the prediction of LAI with LOO-CV (R ² = 0.66, RMSE = 0.57). Ratios developed here can be tested for teak and bamboo cover spread in tropical regions with similar environmental conditions.     

Improvements in the estimation of daily minimum air temperature in peninsular Spain using MODIS land surface temperature

Air temperature (T_a) is a key variable in many environmental risk models and plays a very important role in climate change research. In previous studies we developed models for estimating the daily maximum (T_max), mean (T_mean), and minimum air temperature (T_min) in peninsular Spain over cloud-free land areas using Moderate Resolution Imaging Spectroradiometer (MODIS) data. Those models were obtained empirically through linear regressions between daily T_a and daytime Terra-MODIS land surface temperature (LST), and then optimized by including spatio-temporal variables. The best T_mean and T_max models were satisfactory (coefficient of determination (R²) of 0.91–0.93; and residual standard error (RSE) of 1.88–2.25 K), but not the T_min models (R² = 0.80–0.81 and RSE = 2.83–3.00 K). In this article T_min models are improved using night-time Aqua LST instead of daytime Terra LST, and then refined including total precipitable water (W) retrieved from daytime Terra-MODIS data and the spatio-temporal variables curvature (c), longitude (λ), Julian day of the year (JD) and elevation (h). The best T_min models are based on the National Aeronautics and Space Administration (NASA) standard product MYD11 LST; and on the direct broadcast version of this product, the International MODIS/AIRS Processing Package (IMAPP) LST product. Models based on Sobrino’s LST1 algorithm were also tested, with worse results. The improved T_min models yield R² = 0.91–0.92 and RSE = 1.75 K and model validations obtain similar R² and RSE values, root mean square error of the differences (RMSD) of 1.87–1.88 K and bias = 0.11 K. The main advantage of the T_min models based on the IMAPP LST product is that they can be generated in nearly real-time using the MODIS direct broadcast system at the University of Oviedo.     

Comparison of Landsat 8 OLI and Landsat 7 ETM+ for estimating grassland LAI using model inversion and spectral indices: case study of Mpumalanga,South Africa

The leaf area index (LAI) is the key biophysical indicator used to assess the condition of rangeland. In this study, we investigated the implications of narrow spectral response, high radiometric resolution (12 bits), and higher signal-to-noise ratio of the Landsat 8 Operational Land Imager (OLI) sensor for the estimation of LAI. The Landsat 8 LAI estimates were compared to that of its predecessors, namely Landsat 7 Enhanced Thematic Mapper Plus (ETM+) (8 bits). Furthermore, we compared the radiative transfer model (RTM) and spectral indices approaches for estimating LAI on rangeland systems in South Africa. The RTM was inverted using artificial neural network (ANN) and lookup table (LUT) algorithms. The accuracy of the models was higher for Landsat 8 OLI, where ANN (root mean squared error, RMSE = 0. 13; R² = 0. 89), LUT (RMSE = 0. 25; R² = 0. 50), compared to Landsat 7 ETM+, where ANN (RMSE = 0. 35; R² = 0. 60), LUT (RMSE = 0. 38; R² = 0. 50). Compared to an empirical approach, the RTM provided higher accuracy. In conclusion, Landsat 8 OLI provides an improvement for the estimation of LAI over Landsat 7 ETM+. This is useful for rangeland monitoring.     

Comparison of different regression models and validation techniques for the assessment of wheat leaf area index from hyperspectral data

Leaf area index (LAI) is one of the most important plant parameters when observing agricultural crops and a decisive factor for yield estimates. Remote-sensing data provide spectral information on large areas and allow for a detailed quantitative assessment of LAI and other plant parameters. The present study compared support vector regression (SVR), random forest regression (RFR), and partial least-squares regression (PLSR) and their achieved model qualities for the assessment of LAI from wheat reflectance spectra. In this context, the validation technique used for verifying the accuracy of an empirical–statistical regression model was very important in order to allow the spatial transferability of models to unknown data. Thus, two different validation methods, leave-one-out cross-validation (cv) and independent validation (iv), were performed to determine model accuracy. The LAI and field reflectance spectra of 124 plots were collected from four fields during two stages of plant development in 2011 and 2012. In the case of cross-validation for the separate years, as well as the entire data set, SVR provided the best results (2011: R²_cv = 0.739, 2012: R²_cv = 0.85, 2011 and 2012: R²_cv = 0.944). Independent validation of the data set from both years led to completely different results. The accuracy of PLSR (R²_iv = 0.912) and RFR (R²_iv = 0.770) remained almost at the same level as that of cross-validation, while SVR showed a clear decline in model performance (R²_iv = 0.769). The results indicate that regression model robustness largely depends on the applied validation approach and the data range of the LAI used for model building.