Integrated drought monitoring index: A tool to monitor agricultural drought by using time-series datasets of space-based earth observation satellites

In this study, integrated drought monitoring index (IDMI) was proposed as a tool to assess and monitor the spatio-temporal dynamics of agricultural drought during the northeast monsoon season for the period from 2000 to 2016 in Tamil Nadu state, south-eastern part of Indian peninsula. The IDMI is characterized as the principal component of precipitation condition index (PCI), soil moisture condition index (SMCI), temperature condition index (TCI), and vegetation condition index (VCI) derived from time-series satellite observations of climate hazards group infra-red precipitation with stations (CHIRPS), European space agency climate change initiative (ESA-CCI) and moderate resolution imaging spectroradiometer (MODIS). The study shows that in the year 2016, about 44.4 and 17.8% of Tamil Nadu state was under extreme and severe drought conditions, respectively. Sensitivity analysis of the study shows that PCI is the most influential parameter to IDMI, followed by VCI and TCI. The validation of IDMI with 3-month standardized precipitation index (SPI) by using Pearson correlation test shows a strong positive correlation between IDMI and 3-month SPI with correlation coefficient (r) value of 0.73 and 0.77 for the wet (2005) and dry year (2016), respectively. The study clearly demonstrates the potential of IDMI derived from time-series datasets of earth observation satellites as a tool in assessment and monitoring of spatio-temporal dynamics of agricultural drought. The proposed IDMI could be effectively used as a reliable tool to monitor agricultural drought and develop its mitigation strategies to minimise the adverse effects of drought on agriculture, water resources, and livelihoods of the people.     

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.     

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.     

Satellite derived aerosol optical depth climatology over Bangalore,India

Climatological aerosol optical depths (AOD) over Bangalore, India have been examined to bring out the temporal heterogeneity in columnar aerosol characteristics. AOD values at 550 nm derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard NASA’s Terra and Aqua satellites, for the period of 2002–2011 have been analyzed (independently) for the purpose. Frequency distributions of the AOD values are examined to infer the monthly mean values. Monthly and seasonal variations of AOD are investigated in the light of regional synoptic meteorology. Climatological monthly and seasonal mean Terra and Aqua AOD values exhibited similar temporal variation patterns. Monthly mean AOD values increased from January, peaks during May and thereafter (except for a secondary peak during July) fall off to reach a minimum during December. Monsoon season recorded the highest climatological seasonal mean AOD, while winter season recorded the lowest. AOD values show an overall increasing trend on a yearly basis, which was found mainly due to sustained increase in the seasonal averaged AOD during summer. The results obtained in the present study are compared with that of the earlier studies over the same location and also with AOD over various other Indian locations. Finally, the radiative and climatic impacts are discussed.     

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.     

Chlorophyll,calcite, and suspended sediment concentrations in the Bay of Bengal and the Arabian Sea at the river mouths

Chlorophyll and suspended sediment concentrations (SSC) and sea surface temperature (SST) are important parameters in assessing the productivity of coastal regions. Numerous rivers flow into the eastern (Ganga, Subernarekha, Mahanadi, Godavari, Krishna, Penner, and Kaveri) and western (Narmada, Tapti, and Indus) coasts of the Indian sub-continent. Using IRS P4 (Oceansat-1) Ocean Color Monitor (OCM) and Moderate Resolution Imaging Spectroradiometer (MODIS) data, we have retrieved chlorophyll, calcite, and SSC near the mouth of these rivers for the period during 2000–2004. The maxima of chlorophyll-a concentrations at the river mouth is much higher for the Himalayan and north India rivers (Ganga, Subernarekha, Mahanadi, and Indus) (10–14 mg/m³) compared to rivers in the southern parts of India (Kaveri and Penner) (∼4 mg/m³). The maxima of calcite concentration (∼45 moles/m³), chlorophyll (∼14 mg/m³), and sediment concentrations (∼9 g/m³) near river mouth are found to be influenced by river discharges (Ganga and Brahmaputra) during the monsoon season. The calcite concentration (∼45 moles/m³) at the mouth of Ganga river shows a major peak with the onset of monsoon season (June–July) followed by a maxima in chlorophyll-a with a time lag of 1–2 months. The Krishna, Kaveri, and Penner rivers show low chlorophyll concentrations (3–8 mg/m³), high calcite (0–40 moles/m³), and low SSC (<3 g/m³) compared to Narmada and Tapti rivers (chlorophyll-a 12–14 mg/m³, calcite 0–2 moles/m³, and SSC 13–19 g/m³). The Indus river shows similar behavior (maxima of chlorophyll ∼13 mg/m³ and SSC ∼8 g/m³) with respect to Ganga river except for high calcite concentration during winter months (∼25 moles/m³). The characteristics of the chlorophyll, calcite, and SSC at the mouth of these rivers show spatial and temporal variability along the eastern and westerns coasts of India which are found to differ widely. A comparison of the chlorophyll concentrations using OCM and MODIS data shows low chlorophyll concentrations in the Bay of Bengal as compared to the Arabian Sea.     

Evaluation of MODIS GPP over a complex ecosystem in East Asia: A case study at Gwangneung flux tower in Korea

Moderate Resolution Imaging Radiometer (MODIS) gross primary productivity (GPP) has been used widely to study the global carbon cycle associated with terrestrial ecosystems. The retrieval of the current MODIS productivity with a 1 × 1 km² resolution has limitations when presenting subgrid scale processes in terrestrial ecosystems, specifically when forests are located in mountainous areas, and shows heterogeneity in vegetation type due to intensive land use. Here, we evaluate MODIS GPP (MOD17) at Gwangneung deciduous forest KoFlux tower (deciduous forest; GDK) for 2006–2010 in Korea, where the forests comprise heterogeneous vegetation cover over complex terrain. The monthly MODIS GPP data overestimated the GDK measurements in a range of +15% to +34% and was fairly well correlated (R = 0.88) with the monthly variability at GDK during the growing season. In addition, the MODIS data partly represented the sharp GPP reduction during the Asian summer monsoon (June–September) when intensive precipitation considerably reduces solar radiation and disturbs the forest ecosystem. To examine the influence of subgrid scale heterogeneity on GPP estimates over the MODIS scale, the individual vegetation type and its area within a corresponding MODIS pixel were identified using a national forest type map (∼71-m spatial resolution), and the annual GPP in the same area as the MODIS pixel was estimated. This resulted in a slight reduction in the positive MODIS bias by ∼10%, with a high degree of uncertainty in the estimation. The MODIS discrepancy for GDK suggests further investigation is necessary to determine the MODIS errors associated with the site-specific aerodynamic and hydrological characteristics that are closely related to the mountainous topography. The accuracy of meteorological variables and the impact of the very cloudy conditions in East Asia also need to be assessed.     

A surface reflectance correction model to improve the retrieval of MISR aerosol optical depth supported by MODIS data

Combined use of different satellite sensors are known to improve retrievals of aerosol optical depth (AOD). In this study, we propose a new method for retrieving Multi-angle Imaging SpectroRadiometer (MISR) AOD data supported by Moderate Resolution Imaging Spectroradiometer (MODIS) data in Jiangsu Province, China, over the period of 2016–2018 using MODIS L1B, bidirectional reflectance distribution function (BRDF), MISR 1B2T, and ground-measured AOD data. This method is based on the surface reflectance determined by the MODIS V5.2 algorithm. Through the observation angle and spectral conversion between different sensors, the MISR AOD can be obtained. The correlation coefficient (R) and root-mean-square error (RMSE) between the retrieved MISR and ground-measured AOD data varied between different seasons. The accuracy of the MISR AOD retrieval was notably improved after correcting the MISR surface reflectance. Therefore, the method proposed in this study is feasible for the retrieval of MISR AOD supported by MODIS data, and will be applicable to atmospheric environmental monitoring over large areas in the future.     

Land use/land cover classification using time series Landsat 8 images in a heavily urbanized area

It is of great significance to timely, accurately, and effectively monitor land use/cover in city regions for the reasonable development and utilization of urban land resources. The remotely sensed dynamic monitoring of Land use/land cover (LULC) in rapidly developing city regions has increasingly depended on remote-sensing data at high temporal and spatial resolutions. However, due to the influence of revisiting periods and weather, it is difficult to acquire enough time-series images with high quality at both high temporal and spatial resolution from the same sensor. In this paper we used the temporal-spatial fusion model ESTARFM (Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model) to blend Landsat8 and MODIS data and obtain time-series Landsat8 images. Then, land cover information is extracted using an object-based classification method. In this study, the proposed method is validated by a case study of the Changsha City. The results show that the overall accuracy and Kappa coefficient were 94.38% and 0.88, respectively, and the user/producer accuracies of vegetation types were all over 85%. Our approach provides an accurate and efficient technical method for the effective extraction of land use/cover information in the highly heterogeneous regions.