Impact of emissions from anthropogenic sources on satellite-derived reflectance

The present study deals with the impact of extensive anthropogenic activities associated with festivities and agricultural crop residues burning in the Indo-Gangetic Plains (IGP) on satellite-derived reflectance during November 2007. Intense smoke plumes were observed in the IRS-P4 OCM satellite data over IGP associated with agricultural crop residue burning during the study period. Terra-MODIS AOD and CALIPSO LIDAR backscatter datasets were analysed over the region to understand the spatial and temporal variation of the aerosol properties. Ground-based measurements on aerosol optical properties and black carbon (BC) mass concentration were carried out during 7–14 November 2007 over urban region of Hyderabad, India. Top of the Atmosphere (TOA) reflectance estimated from IRS-P4 Ocean Color Monitor (OCM) data showed large variations due to anthropogenic activities associated with crop residue burning and fireworks. Atmospheric corrections to OCM satellite data using 6S radiative transfer code with inputs from ground and satellite measurements could account for the variations due to differential aerosol loading. Results are discussed in this paper.     

Study on extraction of crop information using time-series MODIS data in the Chao Phraya Basin of Thailand

In order to acquire the crop-related information in Chao Phraya Basin, time-series MODIS data were used in this paper. Although the spatial resolution of MODIS data is not very high, it is still useful for detecting very large-scale phenomenon, such as changes in seasonal vegetation patterns. After the data processing a general crop-related LULC (land use and land cover) map, cropping intensity map and cropping patterns map were produced. Analysis of these maps showed that the main land use type in the study area was farmland, most of which was dominated by rice. Rice fields mostly concentrated in the flood plains and double or triple rice-cropping system was commonly employed in this area. Maize, cassava, sugarcane and other upland crops were mainly distributed in the high alluvial terraces. Because these area often have water shortage problem particularly in the dry season which can support only one crop in a year, the cropping intensity was very low. However, some upland areas can be cultivated twice a year with crops which have short growing seasons. The crop information extracted from MODIS data sets were assessed by CBERS data, statistic data and so on. It was shown that MODIS derived crop information coincided well with the statistic data at the provincial level. At the same time, crop information extracted by MODIS data sets and CBERS were compared with each other which also showed similar spatial patterns.     

Seasonal variability of atmospheric aerosol over the North Indian region during 2005–2009

The Indo-Gangetic basin (IGB) extends 2000 km in length along NW–SE and has 400 km width, in the north the basin is bounded by towering Himalaya. High aerosol optical depth (AOD) is observed over the IGB throughout the year. The Himalaya restricts the transport of aerosols across Tibet and China. We have used ground based Kanpur and Gandhi College Aerosol Robotic Network (AERONET) stations and Multiangle Imaging SpectroRadiometer (MISR) and Moderate Resolution Imaging Spectroradiometer (MODIS) Terra level-3 AOD products for the years 2005–2009 to study the variability of aerosol over the Indo-Gangetic (IG) plains. An increase in both satellite-derived as well as ground observed aerosol loading during 2005–2009 has been found over major cities located in the IG plains. The correlation coefficients between AERONET and MISR data are found to be 0.70, 0.36 0.82, in contrast the correlation coefficients between AERONET and MODIS 0.49, 0.68, and 0.43, respectively during summer, winter and monsoon seasons. The AOD estimation using MISR is found to be close to AERONET data during summer and monsoon seasons, in contrast MODIS estimation is better during winter season.     

Monitoring of rice cropping intensity in the upper Mekong Delta,Vietnam using time-series MODIS data

Information on rice growing areas is important for policymakers to devise agricultural plans. This research explores the monitoring of rice cropping intensity in the upper Mekong Delta, Vietnam (from 2001 to 2007) using time-series MODIS NDVI 250-m data. Data processing includes three steps: (1) noise is filtered from the time-series NDVI data using empirical mode decomposition (EMD); (2) endmembers are extracted from the filtered time-series data and trained in a linear mixture model (LMM) for classification of rice cropping systems; and (3) classification results are verified by comparing them with the ground-truth and statistical data. The results indicate that EMD is a good filter for noise removal from the time-series data. The classification results confirm the validity of LMM, giving an overall accuracy of 90.1% and a Kappa coefficient of 0.7. The lowest producer and user accuracies were associated with single crop rain-fed rice class due to the mixed pixel problems. A strong yearly correlation at the district level was revealed in the MODIS-derived areas (R² ? 0.9). Investigation of interannual changes in rice cropping intensity from 2001 to 2007 showed a remarkable conversion from double to triple crop irrigated rice from 2001 to 2003, especially in the Thoai Son and Phu Tan districts. A big conversion from triple crop rice back to double crop rice cultivation was also observed in Phu Tan from 2005 to 2006. These changes were verified by visual interpretation of Landsat images and examination of NDVI profiles.     

Estimating irrigation water use and withdrawal of ground water on the High Plains,U.S.A.

In four decades following the Dust Bowl days of the 1930's, extensive areas of dry farming and rangeland on the semi-arid U.S. High Plains were transformed into a vast region of irrigated oases, producing meat and grain for much of the world. The agricultural economy has experienced such rapid growth in part because of the availability of ground water and because of development of new irrigation technology to use that water for agriculture. However, more water is being used than is being replaced. To estimate both the volume of water withdrawn and the regional scope of the problem a technique has been developed that combines multispectral data from Earth-orbiting satellite with known pumpage data for the same growing season. The location and extent of irrigated cropland—some with different crops watered at different times—is inventoried using computer-assisted analysis of the data from Landsat. The amount of water used is estimated by multiplying and summing surface area of irrigated agriculture and the average measured pumpage from sampled sites. Published findings to date are cited in the Selected References. All suggest transferability of a promising technology to the study of land transformation processes elsewhere.     

PM2.5/PM10 ratio characteristics over urban sites of India

The PM_2.5/PM₁₀ ratio (PM_2.5 and PM₁₀ are defined as mass concentration of particles having aerodynamic diameter less than 2.5 and 10 µm respectively) is one of the important parameters in understanding the severity of the fine mode surface particulate matter pollution. The present study characterises PM_2.5/PM₁₀ ratio estimates from eight Indian urban sites with varying levels of urbanization. Five years (2015–2019) of collocated PM_2.5, PM₁₀, and meteorological (ambient temperature, relative humidity (RH), and wind speed) measurements are used to understand the spatial and temporal variability in the PM_2.5/PM₁₀ ratio at different scales and to investigate its relationship with meteorological parameters. Over the study sites, the seasonal mean PM_2.5/PM₁₀ ratio varied between 0.31 ± 0.08 (mean ± standard deviation) and 0.65 ± 0.13. Seasonally, the highest PM_2.5/PM₁₀ ratio was observed during winter and post-monsoon seasons. Sites in the Indo-Gangetic Plain (IGP) exhibited higher PM levels (PM_2.5 and PM₁₀) and higher PM_2.5/PM₁₀ ratios than the corresponding values recorded at other sites. The seasonal mean PM_2.5/PM₁₀ ratio estimated (over the study sites) using MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, version 2) ranged between 0.25 ± 0.08 and 0.77 ± 0.16, and exhibited consistent overestimation (when compared to values derived from measurements) during winter and pre-monsoon seasons. Grossly, the PM_2.5/PM₁₀ ratio exhibited a weak association with meteorological parameters. Interestingly, despite variations in geography, population, anthropogenic activities and PM concentrations across seasons and sites, the PM_2.5/PM₁₀ ratio showed low variability.     

Dust storms detection over the Indo-Gangetic basin using multi sensor data

Dust storms are common during the summer season over the Indo-Gangetic basin and are considered to be a major health hazard to millions of people living in the basin. In countries like India, there is no early warning made for dust outbreaks and as a result the day-to-day life is affected by these dust events. In this paper, efforts have been made to utilize multi sensor data to study the characteristics of dust storms. Moderate Resolution Imaging Spectroradiometer and Multiangle Imaging SpectroRadiometer images clearly show dust storm events over the Indo-Gangetic basin. The Total Ozone Mapping Spectroradiometer (TOMS) Aerosol Index and the Advanced Microwave Sounding Unit (AMSU) data over the western end of the Indo-Gangetic basin have been analyzed. These data show characteristic behavior of brightness temperature and Aerosol Index due to dust, which change significantly as dust migrates towards east over Kanpur. The TOMS Aerosol Index and the AMSU brightness temperature (T_b) show a characteristic anti-correlation, which confirms the presence of a dust storm over Indo-Gangetic basin.     

Sweetpotato vine management for confined food production in a space life-support system

Sweetpotato (Ipomea batatas L.) ‘Whatley–Loretan’ was developed for space life support by researchers at Tuskegee University for its highly productive, nutritious storage roots. This promising candidate space life-support crop has a sprawling habit and aggressive growth rate in favorable environments that demands substantial growing area. Shoot pruning is not a viable option for vine control because removal of the main shoot apex drastically inhibits storage-root initiation and development, and chemical growth retardants typically are not cleared for use with food crops. As part of a large effort by the NASA Specialized Center of Research and Training in Advanced Life Support to reduce equivalent system mass (ESM) for food production in space, the dilemma of vine management for sweetpotato was addressed in effort to conserve growth area without compromising root yield. Root yields from unbranched vines trained spirally around wire frames configured either in the shapes of cones or cylinders were similar to those from vines trained horizontally along the bench, but occupying only a small fraction of the bench area. This finding indicates that sweetpotato is highly adaptable to a variety of vine-training architectures. Planting a second plant in the growth container and training the two vines in opposite directions around frames enhanced root yield and number, but had little effect on average length of each vine or bench area occupied. Once again, root yields were similar for both configurations of wire support frames. The 3–4-month crop-production cycles for sweetpotato in the greenhouse spanned all seasons of multiple years during the course of the study, and although electric lighting was used for photoperiod control and to supplement photosynthetic light during low-light seasons, there still were differences in total light available across seasons. Light variations and other environmental differences among experiments in the greenhouse had more effects on vine length than on root yield. Average vine length correlated positively with total hours of daylight received across seasons, and responses for one plant per container were higher above a threshold duration of solar exposure, suggesting that the vines of two plants per container compete for available light. In addition to the adaptability of sweetpotato to various vine-training architectures and across seasons in terms of maintaining root productivity, the open, interior volumes of the support frames tested in this study will provide future opportunity to enhance sweetpotato root yield in space by adding novel interior lighting, such as from intracanopy arrays of light-emitting diodes. This work was sponsored by NASA grant NAG 5 1286.     

Development of a regional rain retrieval algorithm for exclusive mesoscale convective systems over peninsular India

The present study emphasize the development of a region specific rain retrieval algorithm by taking into accounts the cloud features. Brightness temperatures (T_bs) from various TRMM Microwave Imager (TMI) channels are calibrated with near surface rain intensity as observed from the TRMM – Precipitation Radar. It shows that T_b–R relations during exclusive-Mesoscale Convective System (MCS) events have greater dynamical range compared to combined events of non-MCS and MCS. Increased dynamical range of T_b–R relations for exclusive-MCS events have led to the development of an Artificial Neural Network (ANN) based regional algorithm for rain intensity estimation. By using the exclusive MCSs algorithm, reasonably good improvement in the accuracy of rain intensity estimation is observed. A case study of a comparison of rain intensity estimation by the exclusive-MCS regional algorithm and the global TRMM 2A12 rain product with a Doppler Weather Radar shows significant improvement in rain intensity estimation by the developed regional algorithm.     

Mapping recent agricultural developments in China from satellite data

Landsat data have been employed to study and map agricultural developments in three regions of China: 1) Pearl River delta; 2) Nen River basin; and 3) Xinjiang Autonomous Region. Manual interpretation procedures used in conjunction with multi-date Landsat images and collateral information permitted rice yields to be estimated for the Pearl River delta in 1978. A combination of manual and computer-assisted analyses of Landsat data of Northeast China revealed that more than 15,000 km² of agricultural land in a 184,500 km² study area had been reclaimed from rangeland and marshland. These analyses also indicated a shift in cropping practices, with the foodcrops wheat and corn replacing cash crops such as soybeans. In the arid west, Landsat image data provided valuable input to a geographic information system (GIS). It appears the GIS approach will prove useful for evaluating agricultural land potential in the remote areas of China.     

A study of forest vegetation dynamics in the south of the Krasnoyarskii Krai in spring

Remote sensing applications have greatly enhanced ability to monitor and manage in the areas of forestry. Accurate measurements of regional and global scale vegetation dynamics (phenology) are required to improve models and understanding of inter-annual variability in terrestrial ecosystem carbon exchange and climate–biosphere interactions. Study of vegetation phenology is required for understanding of variability in ecosystem. In this paper, monitoring of vegetation dynamics using time series of satellite data is presented. Vegetation variability (vegetation rate) in different topoclimatic areas is investigated. Original software using IDL interactive language for processing of satellite long-term data series was developed. To investigate growth dynamics vegetation rate inferred from remote sensing was used. All estimations based on annual time series of Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. Vegetation rate for Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) was calculated using MODIS data. The time series covers spring seasons of each of 9 years, from 2000 to 2008. Comparison of EVI and NDVI derived growth rates has shown that NDVI derived rates reveal spatial structure better. Using long-term data of vegetation rates variance was estimated that helps to reveal areas with anomalous growth rate. Such estimation shows sensitivity degree of different areas to different topoclimatic conditions. Woods of heights depend on spatial topoclimatic variability unlike woods of lowlands. Principal components analysis shows vegetation with different rate conditions. Also it reveals vegetation of same type in areas with different conditions. It was demonstrated that using of methods for estimating the dynamic state of vegetation based on remote sensing data enables successful monitoring of vegetation phenology.     

Trends in foF2 and the 24th solar activity cycle

The trends in foF2 are analyzed based on the data of Juliusruh and Boulder ionospheric stations. It is shown that using the traditional solar activity index F10.7 leads to an impossible trend in foF2 when the data for the 24th solar activity cycle are included into the analysis. It is assumed that the F10.7 index does not describe correctly the solar ultraviolet radiation variations in that cycle. A correction of this index using the Rz (sunspot number) and Ly (intensity of the Lyman-α line in the solar spectrum) is performed, and it is shown that in that case reasonable values of the foF2 trends are obtained.     

Remote sensing techniques for monitoring and managing irrigated lands

Agriculture in semi-arid tracts of the world depends on water to sustain its irrigation systems. Such agricultural systems either derive from government investments in the control of surface flow or they have been developed through the exploitation of groundwater sometimes by a large community of unsupervised individuals seeking to maximise their own advantage without concern for the resource upon which they depend in the medium and long term. In both cases government agencies need data on the area irrigated and the volume of water used. In countries with highly developed scientific and agricultural institutions the contribution of remote sensing, though significant, may only provide between five and ten per cent of the data required to guide regional and national managers. In countries without such institutions the proportion contributed by remote sensing can be very much higher, as shown in a recent study in North Africa. The paper will emphasise the importance of carefully structured sampling procedures, both to improve the areal estimates from satellite imagery and the estimates of water use based upon them. The role of satellite imagery in providing information on the status of water resources, on trends in water use and in the implementation of policies to extend or diminish irrigated land are discussed.     

Classification of multitemporal Sentinel-2 data for field-level monitoring of rice cropping practices in Taiwan

Cadastral information of rice fields is important for monitoring cropping practices in Taiwan due to official initiatives. Remote sensing based rice monitoring has been a challenge for years because the size of rice fields is small, and crop mapping requires information of crop phenology, relating to spatiotemporal resolution of satellite data. This study aims to develop an approach for mapping rice-growing areas at field level using multi-temporal Sentinel-2 data in Taiwan. The data were processed for 2018, following four main steps: (1) construct time-series Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI), (2) noise filtering of the time-series data using wavelet transform, (3) rice crop classification using information of crop phenology, and (4) parcel-based accuracy assessment of the mapping results. The parcel-to-parcel comparisons between mapping results and ground reference data indicated satisfactory results. These findings were confirmed by close agreement between satellite-derived rice area and government’s statistics. Although some factors, including mixed-pixel issues and cloud-cover effects, lowered the mapping accuracies of townships along the coastline, this study has demonstrated the efficacy of using multitemporal Sentinel-2 data to create a reliable database of rice-growing areas over a large and heterogeneous region. Such a quantitative information was important for updating rice crop maps and monitoring cropping practices.     

Improving the modeling of bottomside thickness parameters over midlatitudes and high latitudes

This paper investigates bottomside thickness parameters at Digisonde stations over midlatitude and high latitude regions, and compares the diurnal, seasonal, and solar activity variations in 2014 and 2009. The geographic latitudes of high latitude considered in this work are located beyond ±60° and those of midlatitude are located between ±40° and ±60°. The IRI-modeled B0 with ABT-2009 option (B0_IRI) are also examined and compared with four kinds of the B0 values, i.e., the observed B0 (B0_obs) from GIRO, the computed B0 following to Jamjareegulgarn et al. (2017a) (B0_old), the calculated B0 with a correction factor regarding to Jamjareegulgarn et al. (2017b) (B0_new), and the B0 with an average correction factor (B0_new_c_av). The average correction factors are proposed additionally in this work so as to assist occasionally the experimental B0 nonexistence of Digisonde which are equal to 0.2658 and 0.2058 for midlatitudes and high latitudes, respectively. Results show that the diurnal variations of B0_new and B0_new_c_av are in a good agreement with those of B0_obs evidently compared with those of B0_IRI and B0_old at every station during the three seasons over high and middle latitudes. During the three seasons, the diurnal variations of B0_new_c_av show similar trends and are close to one another with the B0_obs and the B0_new with small deviations. The differences between the B0obs and the B0_new_c_av also show similar trends and are close to one another with those between the B0obs and the B0_new. In contrast, the B0_IRI with ABT-2009 option seems to predict the B0 values poorly during the three seasons at high latitudes and some seasons at midlatitudes. The proposed B0_new is useful for computing approximately the observed B0 and the ionogram-based total electron content (ITEC) of Digisonde, and the plasma scale height over midlatitudes and high latitudes.     

Surface temperature estimation in Singhbhum Shear Zone of India using Landsat-7 ETM+ thermal infrared data

Land surface temperature (LST) is an important factor in global change studies, heat balance and as control for climate change. A comparative study of LST over parts of the Singhbhum Shear Zone in India was undertaken using various emissivity and temperature retrieval algorithms applied on visible and near infrared (VNIR), and thermal infrared (TIR) bands of high resolution Landsat-7 ETM+ imagery. LST results obtained from satellite data of October 26, 2001 and November 2, 2001 through various algorithms were validated with ground measurements collected during satellite overpass. In addition, LST products of MODIS and ASTER were compared with Landsat-7 ETM+ and ground truth data to explore the possibility of using multi-sensor approach in LST monitoring. An image-based dark object subtraction (DOS3) algorithm, which is yet to be tested for LST retrieval, was applied on VNIR bands to obtain atmospheric corrected surface reflectance images. Normalized difference vegetation index (NDVI) was estimated from VNIR reflectance image. Various surface emissivity retrieval algorithms based on NDVI and vegetation proportion were applied to ascertain emissivities of the various land cover categories in the study area in the spectral range of 10.4–12.5 μm. A minimum emissivity value of about 0.95 was observed over the reflective rock body with a maximum of about 0.99 over dense forest. A strong correlation was established between Landsat ETM+ reflectance band 3 and emissivity. Single channel based algorithms were adopted for surface radiance and brightness temperature. Finally, emissivity correction was applied on ‘brightness temperature’ to obtain LST. Estimated LST values obtained from various algorithms were compared with field ground measurements for different land cover categories. LST values obtained after using Valor’s emissivity and single channel equations were best correlated with ground truth temperature. Minimum LST is observed over dense forest as about 26 °C and maximum LST is observed over rock body of about 38 °C. The estimated LST showed that rock bodies, bare soils and built-up areas exhibit higher surface temperatures, while water bodies, agricultural croplands and dense vegetations have lower surface temperatures during the daytime. The accuracy of the estimated LST was within ±2 °C. LST comparison of ASTER and MODIS with Landsat has a maximum difference of 2 °C. Strong correlation was found between LST and spectral radiance of band 6 of Landsat-7 ETM+. Result corroborates the fact that surface temperatures over land use/land cover types are greatly influenced by the amount of vegetation present.     

Spatiotemporal variations in litter mass and their relationships with climate in temperate grassland: A case study from Xilingol grassland,Inner Mongolia (China)

Clarifying spatiotemporal variations of litter mass and their relationships with climate factors will advance our understanding of ecosystem structure and functioning in grasslands. Our objective is to investigate the spatiotemporal variations of litter mass in the growing season and their relationships with precipitation and temperature in the Xilingol grassland using MOD09A1 data. With widely used STI (simple tillage index), we firstly estimated the litter mass of Xilingol grassland in the growing season from 2000 to 2014. Then we investigated the variations of litter mass in the growing season at regional and site scales. We further explored the spatiotemporal relationships between litter mass and precipitation and temperature at both scales. The litter mass increased with increasing mean annual precipitation and decreasing mean annual temperature at regional scale. The variations of litter mass at given sites followed quadratic function curves in the growing season, and litter mass generally attained maximums between August 1 and September 1. Positive spatial relationship was observed between litter mass variations and precipitation, and negative spatial relationship was found between litter mass variations and temperature in the growing season. There was no significant relationship between inter-annual variations of litter mass and precipitation and temperature at given sites. Results illustrate that precipitation and temperature are important drivers in shaping ecosystem functioning as reflected in litter mass at regional scale in the Xilingol grassland. Our findings also suggest the action of distinct mechanism in controlling litter mass variations at regional and sites scales.     

Estimating canopy LAI and chlorophyll of tropical forest plantation (North India) using Sentinel-2 data

With the free and full access to images from Sentinel-2 satellite, the interest to use this data for quantitative retrieval of vegetation parameters is ever-increasing. LAI and chlorophyll are two key variables which are desired for studying productivity, nutrient and stress status of vegetation. Studies carried out on croplands using simulated Sentinel-2 MSI and parametric approach have identified vegetation indices (VIs) with high sensitivity to LAI and chlorophyll. To test how Sentinel-2 red-edge based VIs perform for retrieval of LAI and Chlorophyll of tropical mixed forest canopies, this study has been performed. The field measurements of LAI and chlorophyll content were recorded in a total of 28 ESUs (Elementary Sampling Units) in Bhakra range in the Tarai Central Forest Division, Uttarakhand (India). The in-situ measurements were statistically correlated with Sentinel-2VIs and strength of correlation was validated using Predicted Residual Error Sum of Squares (PRESS) statistic. Field LAI corrected for foliage clumpiness effect improved correlation of VIs with LAI. Among all VIs tested, Normalized Difference Index (NDI) offered highest positive correlation (R² = 0.79, p < 0.05) with LAI while Red-Edge Chlorophyll Index (RECI) (R² = 0.83, RMSE = 0.24 g/m², p < 0.05) and Simple Ratio (SR) 740/705 (R² = 0.79, RMSE = 0.27 g/m², p < 0.05) were the most closely related to chlorophyll content. VIs with red-edge and NIR combinations offered best results.     

Soil moisture mapping over the Chinese Loess Plateau using ENVISAT/ASAR data

In this paper, we used the available algorithm for soil moisture estimation over LOPEX05 (the Loess Plateau land surface process Experiment (2005)) area. The available algorithm used ENVISAT/ASAR AP mode VV polarization observational data at a low incidence angle and ground measured soil moistures. The ground measurements were performed in the summer of the 2005 during the LOPEX05 field campaign. The validated results indicate that an average difference between the soil moistures estimated from the microwave remote sensing and ground measurements is less than 0.02 cm³/cm³, with a RMS error of 2.0%, and a maximum less than 0.04 cm³/cm³. The algorithm was applied to the surface soil moisture mapping later. The results show that this algorithm is suitable for monitoring soil moisture information of the agricultural fields over the Chinese Loess Plateau, when ground land cover situation and the resolution of imagery data are taken into account. However, we also find that there are large differences over the steep slope region, the edge of mesa. The results are not acceptable for surface soil moisture estimation in these regions. Thus, the surface soil moisture retrieval in the steep slope region of the Loess Plateau need to be further investigated in the future.