bluuebunny/crossref_metadata_2025 huggingface dataset:
DETERIORATION OF AGRICULTURAL YIELD OVER 35 YEARS: EVIDENCE FROM SIMULATED SEDIMENT EXPORT AND NUTRIENT DISCHARGE DATA WITHIN MICRO ENVIRONMENT
Authors: Hasan M. S. U. , Bandopadhyay S. , Akhtar S. , Saha P.
Published: July 2019

Abstract. The present study is an evidence that depicts the degradation of agricultural yield in terms of soil nutrient (NH4, NO3, NO2) deficit over 35 years (1979–2014) due to river sedimentation process within a small-scale basin. Literatures showed a significant decline in amount of nutrients from croplands in each year. In best of our knowledge, no such studies till date have examined small river basin environment to understand this inverse relationship between sedimentation process and soil nutrient loss using open source earth observation (EO) data and hydrological modelling at Indian context. SWAT model was applied to evaluate the basin hydrology and sedimentation process and associated soil nutrient conditions over time using historical flow and long-term meteorological data. Simulated sediment import and export significantly correlates (p < 0.00) with annual discharge values with the R2 of 0.93 and 0.99 respectively. However, over the time different nutrient contents of soil such as NH4, NO3 etc. decreased drastically. Output reviles that NO3 decreased from 32660 kg N to 5689 kg N within the observation period. Similarly, NH4, and NO2 also reduced with the 12020 kg N to 1577 kg N and 49.44 kg N to 6.85 kg N respectively. Total nitrogen and mineral phosphorus concentration also decreased over time. The Overall scenario showed that within 35 years, the increasing rate of sedimentation process through Gandheshwari river, adversely impact the agricultural yield concentration of the region. This study will help stakeholders, farmers to plan appropriate agricultural strategies at grass root level for small basin areas.
AUTOMATION OF THE DSSAT CROP GROWTH SIMULATION MODEL
Authors: Sachin G. , Mohammed Ahamed J. , Nagajothi K. , Rana M. , Murugan B. S.
Published: July 2019

Abstract. Crop Simulation Models (CSM) simulate the growth, development, and yield of crops using various inputs such as soil water, carbon and nitrogen processes, and management practices. DSSAT (Decision Support System for Agrotechnology Transfer) is a software program that comprises dynamic crop growth simulation models for over 42 crops. It incorporates modules for crop, soil, and weather to simulate long-term outcomes of crop management strategies. DSSAT-CSM requires various data for model operation. This includes data on the site where the model is to be operated, on the daily weather during the growth cycle, on the characteristics of the soil at the beginning of the growing cycle or crop sequence, and on the management of the crop. Acquisition of the data and providing the data to the DSSAT model is tedious and time-consuming as each individual value has to be manually entered. Additionally, crop simulation models can only be run for specific points and not for entire locations. Sometimes site-specific data especially weather data cannot be obtained. The output thus produced is difficult to analyze spatially at a large scale. The main purpose of this paper is to take the required dataset directly from spatial data. This is done by dividing locations into grids and taking the data from each grid. Python scripts are then used to convert this data into crop model format which is then run through DSSAT on an individual basis. The output thus obtained is be entered back into their respective grids as spatial data.
A BASELINE QUANTIFICATION OF AGRICULTURAL WATER USE EFFICIENCY OVER INDIAN REGION USING GEO-LEO SATELLITES
Authors: Choudhury I. , Bhattacharya B. K. , Eswar R. , Sekhar M.
Published: July 2019

Abstract. The agricultural water use efficiency (AWUE) over India was evaluated during 2009–2014 through three steps e.g. productivity mapping using MODIS GPP; consumptive water use (CWU) mapping in terms of evapotranspiration (ET) partioned into green (CWUg; ET streaming from rainfall) and blue (CWUb; ET streaming from irrigation) water; AWUE mapping i.e. the ratio of the seasonal sum of GPP and CWU in terms of green (AWUFg) and blue (AWUFb) water. The CWU was estimated from seasonal sum of crop ET (ETc). ETc was obtained from composite crop co-efficient from INSAT and corresponding reference ET (ET0), where daily ET0 was estimated using daily solar insolation (Kalpana-1VHRR) and metrological data from Weather Research and Forecasting (WRF) variables through data fusion in FAO-56 framework. Monthly effective rainfall (ER) was estimated from daily NOAA CPC rainfall data using USDA SCS method. Actual ET (ETa) was estimated using single-source surface energy balance framework with thermal observations from MODIS and MERRA reanalysis data. The CWUg at seasonal scale was determined using ER, ETa and CWU, whereas CWUb was estimated as the difference between ETa and CWUg followed by estimation of AWUEg and AWUEb. The average AWUEg and AWUEb during kharif and rabi seasons were found to be 1.13 kg Cm−3 and 1.25 kg Cm−3 respectively with highest share goes to Indo-gangetic plains due to high crop intensification. The arid and semiarid tract showed low AWUE. The baseline assessment of satellite based AWUE at country scale providesa scope for better management of agricultural water with respect to food security.
SENSITIVITY OF FOUR CONTEXTUAL REMOTE SENSING BASED SURFACE ENERGY BALANCE MODELS TO SPATIAL DOMAIN
Authors: Bhattarai N. , Mallick K. , Jain M.
Published: July 2019

Abstract. It remains unclear how the selection of a spatial domain affects the accuracy of evapotranspiration (ET) estimates from contextual remote sensing based surface energy balance (SEB) models, particularly at large spatial scales. We thus tested the effect of spatial domain on four widely implemented contextual remote sensing based SEB models: Surface Energy Balance Algorithm for Land (SEBAL), Mapping ET with Internalized Calibration (METRIC), Simplified Surface Energy Balance Index (S-SEBI), and Triangular ET models. We applied these models on 44 near cloud-free Moderate Resolution Imaging Spectroradiometer (MODIS) thermal images across all of India from 2004 to 2006. Four spatial domains were considered: all of India, agro-ecological regions, 300 km × 300 km grids, and 600 km × 600 km grids and we compared hourly ET estimates from the models against observed ET data at four Bowen Ratio sites in India. Model performance varied across all models and spatial domains. ET values of neighboring pixels across spatial domains formed sharp edges along the boundaries of agro-ecological regions, 300 km × 300 km grids, and 600 km × 600 km grids suggesting that all ET models are highly sensitive to the selection of spatial domain. No single spatial domain was found to be optimal for all models and hence potential uncertainties associated with the selection of spatial domain should be taken into consideration when implementing these models at regional scales. The results from this study provide guidance for future regional-scale implementation of ET models and potential approaches to overcome these challenges.
SENTINEL 2 AND LANDSAT-8 BANDS SENSITIVITY ANALYSIS FOR MAPPING OF ALKALINE SOIL IN NORTHERN DRY ZONE OF KARNATAKA, INDIA
Authors: Meti S. ,   Hanumesh, Lakshmi P. D. , Nagaraja M. S. , Shreepad V.
Published: July 2019

Abstract. Soil salinization is most common land degradation process occurring in deep vertisol of northern dry zone of Karnataka, India. Accurate and high resolution spatial information on salinization can assist policy makers to better target areas for interventions to avoid aggravation of soil degradation process. Digital soil mapping using satellite data has been identified as a potential means of obtaining soil information. This paper focuses on exploring possibility of using new generation medium resolution Landsat-8 and Sentinel-2 satellite data to map alkaline soils of Ramthal irrigation project area in north Karnataka. Surface soil salinity parameters of zone 20 were correlated with reflectance values of different band and band combination and traditional salinity indices and result has indicated that SWIR bands of both satellite showed significant negative correlation with soil pH, EC (r = −0.39 to −0.45) whereas visible and NIR bands did not show significant relation. However rationing of SWIR bands with visible blue band has significantly improved the correlation with soil pH and EC (r = +0.60 to +0.70). Traditional salinity index based on visible bands failed to show significant correlation with soil parameters. It is interesting to note that SWIR bands alone did not show significant correlation with soil sodicity parameters like exchangeable Na, SAR, RSC but band rationing with blue bands has significantly improved the correlation (r = 0.45). High resolution soil salinity map was prepared using simple linear regression model and using this map will serve as base map for the policy makers.
IMPACT OF CANAL RESTRUCTURING ON AGRICULTURAL LAND USE IN 23 DOWN HAIDERGARH CANAL COMMAND SYSTEM, UTTAR PRADESH, INDIA
Authors: Yadav P. K. , Singh P. , Kumar N. , Upadhyay R. K. , Jadaun S. P. S.
Published: July 2019

Abstract. The 23 Down Haidergarh Canal command system of Uttar Pradesh is situated in Indo-Gangetic Plain of India. It has huge potential of crop productivity. The canal system was unlined; due to which a huge amount of water waste as seepage, which results, waterlogging and sodic land development. This leads reduction in the productivity of crop land. To overcome with this problem government started the restructuring/lining of canal in 23 Down Haidergarh canal command system.The present study was an attempt to find out the changes in agricultural land of rabi season because during rabi season canal is important source of irrigation in 23 Down Haidergarh canal command system. Remote sensing and GIS techniques were used to monitor the changes after the restructuring of canal system. The LISS-III data (Linear Imaging Self-Scanner) of Rabi season for the year 2011–12 and 2017–18 was used for mapping of agricultural land use changes in rabi season for 23 Down Haidergarh canal command system. This study is useful to find out the change in agricultural land after the restructuring of canal command system.
DISCRIMINATION AND CHARACTERIZATION OF PROMINENT DESERTIC VEGETATIONS USING HYPERSPECTRAL IMAGING DATA
Authors: Borana S. L. , Yadav S. K. , Paturkar R. T.
Published: July 2019

Abstract. Imaging Hyperspectral data are advent as potential solutions in modeling, discrimination and mapping of vegetation species. Hyperspectral remote sensing provides valuable information about vegetation type, leaf area index, chlorophyll, and leaf nutrient concentration. Estimation of these vegetation parameters has been made possible by calculating various vegetation indices (VIs), usually by ratioing, differencing, ratioing differences and combinations of suitable spectral band. This paper presents a ground-based hyperspectral imaging system for characterizing vegetation spectral features. In this study, a ground-based hyperspectral imaging data (AISA VNIR 400–960 nm, Spectral Resolution @ 2.5 nm) was used for spectral vegetation discrimination and characterization of natural desertic tree species. This study assessed the utility of hyperspectral imagery of 240 narrow bands in discrimination and classification of desert tree species in Jodhpur region using ENVI software. Vegetation indices derived from hyperspectral images used in the Analysis for tree species classification discrimination study. Prominent occurring two desertic tree species, viz., Neem and Babul in Jodhpur region could be effectively discriminated. Study demonstrated the potential utility of narrow spectral bands of Hyperspectral Imaging data in discriminating vegetation species in a desertic terrain.
SPATIAL DISAGGREGATION OF THE BIOENERGY POTENTIAL FROM CROP RESIDUES USING GEOSPATIAL TECHNIQUE
Authors: Chakraborty A. , Biswal A. , Pandey V. , Murthy C. S. , Rao P. V. N. , Chowdhury S.
Published: July 2019

Abstract. Limited national fossil fuel resources and sustained increases in energy prices have resulted in nationwide efforts to study and deploy alternative energy sources. Despite high prospect, biomass resources has not been effectively utilized in India due to the lack of information on amount, type and time of its availability on a geospatial frame work to facilitate its transportability, establishment of bio-fuel plants tailor made for specific crop residues. Hence, a comprehensive approach towards geospatial mapping of bio-energy potential from surplus crop residues of selected crops (rice. wheat, cotton and sugarcane) over the Haryana state of India is implemented by utilizing a hybrid model combining both statistical and remote sensing technique. Bioenergy potential was calculated from crop production statistics collected at district level. The grain production data were converted into gross residue potential using residue production ratio. The crop residue was further converted into collectable crop residue using collectable coefficient. To generate the spatial map of the selected crops, potential crop masks were prepared using multi-temporal satellite data. These crop masks were then converted to crop fraction at 1 km grid level. MODIS NPP data product was then processed and converted into same 1 km to account the spatial variability of biomass potential. Using these crop fractions as independent variables, relationship was established with NPP as dependent variable using a machine learning technique (Random Forest algorithm). These crop specific response curves (crop fraction vs NPP) were utilized as a weight to disaggregate district level gross biomass potential to 1 km grid level. The spatial map thus generated provided spatial details of the type and amount surplus crop residues and could be vital input for planning and policy making of utilization of the surplus biomass resources of India.
REMOTE SENSING FOR ESTIMATION OF INTENSITY AND EXTENT OF PLANT RESIDUE COVER
Authors: Prajapati R. , Chakrborty D. , Saha S. , Gupta V. K. , Sahoo R. N.
Published: July 2019

Abstract. Left-over crop residue on the surface is a measure of tillage intensity and soil management, and is an integral part of the conservation agriculture practice. Remote sensing can be successfully used to monitor the large area crop residue cover which is otherwise difficult through the conventional way, provided the spectrally similar crop residues and soil can be suitably differentiated. Hyperspectral reflectance (350–2500 nm) of various quantities of crop residue cover over red soil was measured with varying moisture contents in the residue. A broad spectrum near 2100 mm was identified for dry residue, which was not recorded in soil spectra. This could possibly be linked to the cellulose-lignin content. The cellulose absorption index (CAI) was evaluated for crop residue cover with moderate to good correlations, with strong dependency on the residue water content. A few narrow bands were identified to characterize both the cellulose-lignin (i.e. the CAI) and the water content, and could be incorporated in on-board multispectral sensors for regional estimation of crop residue over the soil surface.
EXPLORATION OF LAND RESOURCES FOR ANALYSING THE SUITABILITY OF SELECTED CROPS IN AN ADMINISTRATIVE UNIT OF THRISSUR DISTRICT, KERALA
Authors: Shyju K. , Kumaraswamy K.
Published: July 2019

Abstract. The fitness of land for a defined use is termed as land suitability. The aim of the study is to find the land suitability of for selected crops examining the indicators of land suitability. The study focuses on analysing suitability of agricultural crops in Pazhayannur Block of Thrissur District. Pazhayannur is the administrative unit (block) in the east of the Talapilli Taluk of Thrissur District in Kerala. The physical and chemical characteristics of the soils of Pazhayannur block like texture, depth, slope, erosion, pH, salinity and soil available primary nutrients (NPK), secondary nutrients and micronutrients are identified. The slope of the terrain using Cartosat-Digital Elevation Model, soil erosion, rainfall and land capability is analysed for suitability classification. Land suitability is studied for the selected crops like paddy, banana, coconut, and rubber. The weighted overlay analysis in GIS is adopted for the analysis. The parameters are weighted based on its class and its influence in suitability of specific crops. The results on agricultural land suitability for individual crops were divided into 5 categories according to the land suitability classification of the United Nations Food and Agriculture Organization (FAO). It is identified that Pazhayannur has potential for the cultivation of the selected crops. The crops show high suitability (S1) in minor proportion and moderate suitability (S2) is found in 30 percent and above and marginal suitability (S3) is noticed in lesser areas. There is few portion of land which is currently not suitable for cultivation (N1) because of its topographic limitations. The restricted areas of forest are permanently not suited for agriculture (N2).
EVALUATION OF MODIS NDVI AND RAINFALL RELATION OVER THE LAND USE AND LAND COVERS OF TAMIL NADU, INDIA
Authors: Prajesh P. J. , Kannan B. , Pazhanivelan S. , Kumaraperumal R. , Ragunath K. P.
Published: July 2019

Abstract. Vegetation is a part of terrestrial ecosystem that plays an important role in stabilizing global environment. Their needs to be a reliable information on the status of vegetation, which is needed for solving environmental problems. In this present study, vegetation growth and development were monitored over the land covets of Tamil Nadu, India during the crop growing season viz., Kharif season and Rabi season of 2017 using MODIS satellite derived surface reflectance product (MOD09A1) which is available at 500 m resolution and 8-days temporal period. Based on the surface reflectance data, NDVI was extracted for monitoring vegetation greening and browning. In order to correlate the relation between vegetation growth and influence of rainfall over the land covers, averaged seasonal rainfall was extracted from TRMM based rainfall product. It was noticed that NDVI response from the land covers showed a good range of temporal variations in vegetation biomass condition, however NDVI values appears to have increasing variations which indicated presence of high biomass intensity in Tamil Nadu. The seasonal NDVI response under non-vegetated/barren land class and moderate vegetation class was well related to shortage of dry spell and deficiency in precipitation that occurs due to abrupt changes in climate within the season. While the seasonal rainfall distribution over the land covers suggested that, compared to Kharif season, Rabi season received maximum amount of rainfall in Tamil Nadu during the cropping season of 2017. However, it was also observed that due fluctuation in intensity and duration of rainfall, the seasonal rainfall distribution over the land covers suggested that, compared to Kharif season, Rabi season received maximum amount of rainfall in Tamil Nadu during the cropping season of 2017.
TRENDS AND VULNERABILITY ASSESSMENT OF METEOROLOGICAL AND AGRICULTURAL DROUGHT CONDITIONS OVER INDIAN REGION USING TIME-SERIES (1982–2015) SATELLITE DATA
Authors: Das R. , Das P. K. , Bandyopadhyay S. , Raj U.
Published: July 2019

Abstract. The vulnerability and trends of meteorological as well as agricultural drought conditions over Indian region was studied using long-term (1982–2015) gridded precipitation and time-series normalized difference vegetation index (NDVI) data. The Climate Hazards Group Infra-Red Precipitation with Station (CHIRPS) precipitation data (~5 km) was used to compute Standardized precipitation index (SPI) at 3-month time scale for Indian summer monsoon season (June-September). Subsequently, the long-term Global Inventory Modelling and Mapping Studies (GIMMS) time-series NDVI data (~8 km) was interpolated at daily scale and smoothened using Savitzky and Golay filtering method. Further, the time-series NDVI data was transformed into several phenological parameters using threshold and derivative approach. As integrated NDVI, i.e. the area under seasonal NDVI curve, is able to represent the anomalies in seasonal agricultural production, it was transformed into standardized vegetation index (SVI) using empirical distribution. Several drought parameters, e.g. magnitude and extent, were computed at district level based on the SPI and SVI values, where values with SPI or SVI less than minus one was considered as meteorological and agricultural drought year, respectively. The trends of drought magnitude and extent for both the meteorological and agricultural drought were estimated using Sen’s slope. The direction of trends and magnitude were found to be varying spatially across different parts of Indian region. Further, the mean SPI/SVI values along with drought frequency were utilized to categorize entire Indian agricultural area into different vulnerable zones during three decades separately. The overall drought vulnerability was found to be decreasing over time.
QUALITY CHECKING OF CROP CUTTING EXPERIMENTS USING REMOTE SENSING DATA: A CASE STUDY FOR RICE CROP IN ODISHA
Authors: Dubey S. K. , Mandloi D. , Gavli A. S. , Latwal A. , Das R. , Ray S. S.
Published: July 2019

Abstract. Under Pradhan Mantri Fasal Bima Yojana (PMFBY), a large number of Crop Cutting Experiments (CCEs) were conducted by Odisha State for Kharif Rice in the year 2016 and 2017. The present study was carried out to examine the quality of the performed CCEs using statistical methods and Remote Sensing (RS) technique. Total 24389 and 34725 CCEs were conducted. After removing outliers, 22083 and 26848 CCE points were analyzed for the year 2016 and 2017, respectively. Multi-date RISAT-1 (2016) and Sentinel-1A (2017) satellite data were used for generating the Kharif Rice crop mask, which was used to get NDVI and NDWI values for Rice pixels, from MODIS VI products. The values of these indices were divided into four strata from highest A, followed by B, C, and D (Lowest Value) based on the range (minimum and maximum) of values. The CCE based yield data were then divided into four yield strata of equal proportion. Yield and RS (NDVI+NDWI) based strata were combined to examine whether the CCE Points having high yield fall under good NDVI zone or vice versa. The results showed that there was strong match between CCE strata and the vegetation index strata in both the years. Therefore, it could be be concluded that RS based indices have the capability to assess the quality/accuracy of CCEs. Furthermore, the large variety of information available with CCEs such that crop variety, crop condition, water sources, stress conditions etc., can be used as input parameters to train any model to predict better results.
QUANTITATIVE ASSESSMENT OF RICE CROP DAMAGE POST TITLI CYCLONE IN SRIKAKULAM, ANDHRA PRADESH USING GEO-SPATIAL TECHNIQUES
Authors: Ponnurangam G. G. , Setiyono T. D. , Maunahan A. , Satapathy S. S. , Quicho E. , Gatti L. , Romuga G. , Garcia C. , Prasadini P. , Kumar M. S. , Rani P. P. , Kumar C. K. , Reddy K. J. R. , Holecz F.
Published: July 2019

Abstract. Mapping the extent of damage due to natural calamities remains one of the thrust areas in monitoring resource inventory through geo-spatial techniques. The effect of the cyclone ‘Titli’ and heavy rains during first fortnight of October 2018 in Srikakulam district, Andhra Pradesh State has been demonstrated using geo-spatial technology in terms of flood inundated rice area and corresponding yield and production loss. The pre- and post-cyclone (5 and 13 October 2018) flood inundation maps were generated using Sentinel-1A and TerraSAR-X Synthetic Aperture Radar (SAR) data respectively. The pre-cyclone rice area estimates were derived from multi-temporal Sentinel-1A SAR data, while yield forecast is based on the combination of satellite observations and yield simulation using ORYZA crop growth model. An intensive ground truth data collection had been carried out for the validation of satellite-derived rice area estimation of pre-cyclone event. An accuracy assessment has been carried out for district, mandal and village level. An overall accuracy of 96% with kappa coefficient 0.92 has been achieved. With the help map flood inundation and rice area maps, mandal-wise flood affected rice area and corresponding yield loss have been estimated. The post-cyclone ground truth data had been collected for quantitative assessment of crop damaged area. An overall accuracy of the flood affected rice map was 85% with kappa coefficient 0.70. It was estimated that rice crop damage assessment with SAR data indicated 53312 ha out of 205174 ha were affected and corresponding estimated yield as well as production are 0.8 t/ha and 189160 t respectively.
COMPARISON OF AGRICULTURAL SITUATION OF INDIA FOR TWO YEARS (2017 AND 2018) USING VARIOUS DROUGHT ASSESSMENT INDICATORS DURING SOUTH WEST MONSOON SEASON IN INDIA
Authors: Saxena S. , Choudhary K. , Saxena R. , Rabha A. , Tahlani P. , Ray S. S.
Published: July 2019

Abstract. Agricultural drought is concerned with the soil moisture deficiency in relation to meteorological droughts and climatic factors and their impacts on agricultural production and economic profitability. Present study is based on two years kharif seasons i.e. 2018 and 2017, comparison of drought assessment using remote sensing, soil moisture indices, rainfall and crop sown area as per the New Drought Manual, December, 2016. The drought assessment was carried out at district and sub-district level under National Agricultural Drought Assessment and Monitoring System (NADAMS) project. Drought trigger-1 is checked with rainfall deviation and dry spell. During 2017, the final drought categories were defined on the basis of Rainfall, Moisture and Vegetation Condition Index. During 2018, the final district level drought categories are defined using 3 indicators, where sown area upto end of August was also considered. Based on the approach defined in the New Drought Manual, analysis was carried out at district level for 17 major agricultural drought prone states of the country. State wise Rainfall deviation, dry spell, NDVI/NDWI situation was compared for both the years. Remote sensing based vegetation and water indices are important impact indicator out of 4 because it gives an idea of crop profile and surface wetness condition respectively. Thus the present study is an attempt to compare the drought situation in kharif season of years 2017 and 2018 on the basis of different impact indicators.
AGRICULTURAL DROUGHT AND SOIL MOISTURE ANALYSIS USING SATELLITE IMAGE BASED INDICES
Authors: Sreekesh S. , Kaur N. , Sreerama Naik S. R.
Published: July 2019

Abstract. The deficiency in rainfall leads to meteorological droughts. Its manifestations are visible both in the vegetation cover and soil moisture. The present study assessed the characteristics of agricultural drought following meteorological droughts. The study also assessed the severity of meteorological droughts and their manifestation on the agriculture and soil moisture in a semi-arid area. The study has been carried out for the Malaprabha sub-basin which partly covers three districts of North Interior Karnataka, India. India Meteorological Department’s (IMD) criteria have been used to identify the drought years, and its severity has been assessed through the Standard Precipitation Index (SPI). The IMD’s monthly rainfall data were used to identify the drought years and periods for the region. Among the drought years, the mild, moderate, and severe drought along with deficit and excess rainfall years were considered to assess and characterize the soil moisture conditions and the agricultural drought. The satellite image based indices for these selected years were constructed to determine the soil moisture conditions and the agricultural drought severity. The Temperature-Vegetation Dryness Index (TVDI) was used to determine the soil moisture conditions. The indices employed to determine the agriculture drought are NDVI, Thermal Condition Index (TCI), Vegetation Condition Index (VCI), and Vegetation Health Index (VHI). These satellite-based indices were calculated using the Landsat images of the selected drought and non-drought years. The results showed that the seasonal and annual drought are frequent in the study area. There are spatial and temporal variations in the drought years and their severity. The satellite-based indices clearly indicate the spatial variation in the agriculture droughts and its intensity. It has been found that the impact of drought on agriculture has significantly reduced due to the development of well-irrigation in the sub-basin. VHI is more appropriate in determining the agricultural drought and its characteristics.
EFFECT OF CLIMATE CHANGE ON POTENTIAL EVAPOTRANSPIRATION IN THE UPPER BEAS BASIN OF THE WESTERN INDIAN HIMALAYA
Authors: Rani S. , Sreekesh S. , Krishnan P.
Published: July 2019

Abstract. Appraisal of potential evapotranspiration (PET) is needed for estimating the agricultural water requirement and understanding hydrological processes in an arena. Therefore, aim of the paper was to estimate the PET in the upper Beas basin, situated in the Western Indian Himalaya, under future climate change scenarios (by mid-21st century). Climate data (1969–2010) of Manali, Bhuntar and Katrain were obtained from India Meteorological Department (IMD) and the Indian Agricultural Research Institute (IARI). Landsat data were used for mapping land use/land cover (LULC) conditions of the basin through decision tree technique. Elevation detail of the catchment area is derived from the Cartosat-1 digital elevation model (DEM). Simulations of PET were done by the Soil and Water Assessment Tool (SWAT) model. The model was calibrated using the average monthly discharge data from Thalout station. The study found fluctuations in PET under different climate change scenarios. It is likely to increase in near future owing to the rise in temperature. The higher water demand can be met from the excess snowmelt water reaching the lower basin area during the cropping seasons. This study will be helpful to understand water availability conditions in the upper Beas basin in the near future.
MODIS DERIVED NDVI BASED TIME SERIES ANALYSIS OF VEGETATION IN THE JODHPUR AREA
Authors: Yadav S. K. , Borana S. L.
Published: July 2019

Abstract. Arid region of India shows vast variation in climate and vegetation during last two decades. In order to analysis impact of monsoonal patterns on the vegetation indices of the arid zone, a three years (2015–2017) temporal series Moderate Resolution Image Spectrometer (MODIS) data for Pre & Post Monsoon was used for computing Normalized Difference Vegetation Index (NDVI). The cloud-free NDVI time series data are used to study the relationship between the rainfall pattern and the vegetation changes in Jodhpur District. ENVI and ArcGIS image processing software are used to evaluate and monitor the vegetation for the pre-monsoon and post-monsoon seasons for three years. Enormous changes were observed during pre and post monsoon temporal analysis. This study shows that MODIS NDVI data is best suited for quick vegetation assessment in arid region.
MACHINE LEARNING BASED CROP CYCLE MAPPING USING MULTI TEMPORAL SPACE DATASETS
Authors: Gupta P. K. , Verma P. A. , Chauhan P.
Published: July 2019

Abstract. The Indo-Gangetic region is an important source of food grain produced in India. With advent of advanced crop management practices, there has been a recent increase in production. This could be due to improved yield and/or multiple crops per year. Understanding cropping patterns is, therefore, a fundamental requirement to strategize food security in the country. The long running space based earth observation programmes, provides a powerful tool to study the change in vegetation, through use of indices, synoptically and temporally. This study proposes a machine learning based technique to identify distributed (pixel-wise) cropping cycles, based on the temporal trend in normalized difference vegetation index (NDVI). The technique is applied to several time series NDVI data sets (OCM, MODIS and GIMMS) and results are discussed. There is tremendous scope to further extend this study to predict drought conditions, change in cropping pattern, and estimate production statistics, by using advanced artificial intelligence methods (such as deep learning).
ASSESSING THE TRANSFERABILITY OF MACHINE LEARNING ALGORITHMS USING CLOUD COMPUTING AND EARTH OBSERVATION DATASETS FOR AGRICULTURAL LAND USE/COVER MAPPING
Authors: Praveen B. , Mustak S. , Sharma Pritee
Published: July 2019

Abstract. Mapping of agricultural land use/cover was initiated since the past several decades for land use planning, change detection analysis, crop yield monitoring etc. using earth observation datasets and traditional parametric classifiers. Recently, machine learning, cloud computing, Google Earth Engine (GEE) and open source earth observation datasets widely used for fast, cost-efficient and precise agricultural land use/cover mapping and change detection analysis. Main objective of this study was to assess the transferability of the machine learning algorithms for land use/cover mapping using cloud computing and open source earth observation datasets. In this study, the Landsat TM (L5, L8) of 2018, 2009 and 1998 were selected and median reflectance of spectral bands in Kharif and Rabi season were used for the classification. In addition, three important machine learning algorithms such as Support Vector Machine with Radial Basis Function (SVM-RBF), Random forest (RF) and Classification and Regression Tree (CART) were selected to evaluate the performance in transferability for agricultural land use classification using GEE. Seven land use/cover classes such as built-up, cropland, fallow land, vegetation etc. were selected based on literature review and local land use classification scheme. In this classification, several strategies were employed such as feature extraction, feature selection, parameter tuning, sensitivity analysis on size of training samples, transferability analysis to assess the performance of the selected machine learning algorithms for land use/cover classification. The result shows that SVM-RBF outperforms the RF and CART for both spatial and temporal transferability analysis. This result is very helpful for agriculture and remote sensing scientist to suggest promising guideline to land use planner and policy-makers for efficient land use mapping, change detection analysis, land use planning and natural resource management.
RELATIONSHIP BETWEEN SAR/SENTINEL-1 POLARIMETRIC AND INTERFEROMETRIC DATA WITH BIOPHYSICAL PARAMETERS OF AGRICULTURAL CROPS
Authors: Prudente V. H. R. , Oldoni L. V. , Vieira D. C. , Cattani C. E. V. , Sanches I. D.
Published: July 2019

Abstract. The use of SAR (Synthetic Aperture Radar) data in agricultural applications has not yet been adequately explored, due to the complexity of the processing required, the lower diversity and availability of SAR data, and the difficulty in interpreting this data. The interactions between microwave energy and vegetation are influenced by factors related to plant structure, dielectric properties of the canopy, planting density and line orientation, and the angle of incidence and polarization of the wave. These differences between the recorded phases are used in data analysis techniques such as polarimetry and interferometry. Thus, the objective of this research is to analyse the relationships between the biophysical attributes of two agricultural crops (soybean and wheat) and the polarimetric and interferometric parameters of the SAR/Sentinel-1 data. The backscatter coefficients sigma and gamma in the VV polarization have inversely direct relation with the height of the crops, that is, as cultures grow, the interaction of energy in this polarization increases and the return signal decreases. For the cross polarization VH, the behaviour is the opposite, the larger the canopy height, the greater the interaction of vertical polarization and the greater the return on horizontal polarization. The interferometric coherence had small values, characterising a temporal decorrelation between the image pairs, due the canopy development. This preliminary study serves as a basis for future research with SAR/Sentinel-1 data focused on crops.
RETRIVAL OF BIO-PHYSICAL PARAMETERS IN SUNFLOWER CROP (HELIANTHUS ANNUUS) USING FIELD BASED HYPERSPECTRAL REMOTE SENSING
Authors: Routh P. K. , Sarkar N. C. , Das P. K. , Debnath D. , Bandyopadhyay S. , Raj U.
Published: July 2019

Abstract. Information on several crop bio-physical parameters is important as inputs for crop growth modelling, leaf stress analysis, crop health study and productivity point of view. Conventionally, biophysical parameters are measured in laboratory methods which are time consuming, laborious and destructive in nature. With the advent of remote sensing technology, the limitations of conventional methods can be overcome. Moreover, due to its narrow absorption bands at different wavelength, use of hyperspectral remote sensing becomes very useful in retrieving several bio-physical parameters. In the present study, field as well as laboratory based spectro-radiometer observations were carried out at Agronomy Department of VisvaBharati University, West Bengal, on Sunflower crop at its peak vegetation stage towards retrieving different bio-physical parameters, specifically leaf area index (LAI), chlorophyll content index (CCI), fluorescence etc. Different foliar boron (no boron, 0.15% and 0.20%) and irrigation (4–6 irrigations) treatments, i.e. total nine treatments with three replications, were applied on sunflower crop during different phenological stages to achievemaximum ranges of the bio-physical parameters. The LAI, CCI and fluorescence parameters were collected using canopy analyzer,chlorophyll content meter and portable gas exchange system, respectively. In each of the treatments, total four hyperspectral measurements were collected, which were further corrected for noise and smoothened using Savitzky-Golay filtering. Total thirty-four narrow band indices were computed based on the hyperspectral data, and the regression analysis was carried out among the indices and bio-physical parameters. The regression parameters were further deployed on the hyperspectral indices to retrieve the bio-physical parameters. The Gitelson & Merzylak-1 (GM-1) and Carter Indices-1 (CI-1) were found to the best indices for retrieving the LAI and CCI, respectively with correlation correlation (r) values of 0.87 and 0.80. On the other hand, Normalized Phaenophytinization Index (NPQI) and GM-1 were found to best for retrieving the Fv/Fm (dark) and Fvˈ/Fmˈ (light) with correlation(r)values of 0.92 and 0.76, respectively. Hence, the hyperspectral remote sensing be successfully utilized for retrieving several bio-physical parameters both at field (canopy level) and laboratory (leaf level) conditions.
POTENTIAL OF C-BAND HYBRID POLARIMETRIC RISAT-1 SAR DATA FOR WHEAT CROP HEIGHT ESTIMATION
Authors: Srivastava H. S. , Sivasankar T.
Published: July 2019

Abstract. The advancement of hybrid polarimetry and the launch of Radar Imaging SATellite-1 (RISAT-1), the first Earth observing satellite with capability to acquire hybrid polarimetric SAR data, have given opportunity to explore the potentials of this data for various geo-spatial applications. Among these, crop growth monitoring and soil moisture estimation is one of the important application for proper irrigation management, yield forecasting and crop insurance systems. The objective of this paper is to provide a better understanding of the sensitivity of RISAT-1 hybrid polarimetric SAR data for wheat height. However, backscattering coefficients have shown less sensitive towards wheat height, polarimetric parameters such as Stokes parameters and scattering decomposition parameters have shown higher sensitivity. The analysis observed the highest correlation coefficient (|r|) and lowest RMSE (in m) for g3 with 0.61 and 0.18 respectively followed by odd bounce with 0.59 and 0.2 respectively.
RICE CROP MONITORING USING SENTINEL-1 C-BAND DATA
Authors: Dineshkumar C. , Satishkumar J.
Published: July 2019

Abstract. Among the cereal grains, rice is staple food for ½ of the world population. In Asian countries as population increases, the demand for rice also increases. For this future demand rice, biophysical variables are monitored for agricultural management and yield prediction using space borne satellite platform. While in satellite remote sensing there are numerous trouble in mapping and monitoring rice field, particularly in multi season paddy in rainy season, incorporating the changes in crop phenology, the impact of climate and farmland variability. To determine these issues Sentinel-1 was launched and provide opportunity to monitor rice crop, in 10 m spatial resolution, C-band, dual polarization image with 12 days revisit. A rice field in Kulithalai, Tamilnadu is utilized as examination. In this study Sentinel-1 data, which can recognize little vegetation difference at firmly found ground truth (GT) point. While observing the different growing stages of rice, the volume scattering segment proportion was increased though the surface scattering segment proportion for the most part diminished. During rice growing stages, surface scattering component ratio decreased, while volume scattering ratio will be increased. This study describes how multi temporal observation by SAR has extent capacity for estimating rice growing land and monitoring growing stages and also interpret σ0VV and σ0VH back scattering co-efficient. These are examined by knowing crop parameters like leaf area index, plant height, no. of panicle/m2 and no. of grains/m2.
AN ANALYSIS OF NATIONAL-STATE-DISTRICT LEVEL ACREAGE AND PRODUCTION ESTIMATES OF RABI SORGHUM UNDER FASAL PROJECT
Authors:   Dolly, Saxena S. , Dubey S. K. , Choudhary K. , Sehgal S. ,   Neetu, Ray S. S.
Published: July 2019

Abstract. Sorghum (Sorghum vulgare Pers.) popularly known as jowar is the most important food and fodder crop of dry land agriculture. It is generally grown in both Kharif and Rabi season, with sown area of 25.3 and 38.3 lakh ha, respectively, at National level. The major states contributing to national sorghum acreage are Karnataka and Maharashtra. Crop acreage and yield estimation is of great significance to develop food policies and economic plans for a country. In this regard, under FASAL project, Sorghum production estimation is being carried out since 2013-14. Data from December to January is used for classification considering the peak season of crop. The satellite data used for classification has been gradually changed from Resourcesat 2 LISS III/ Landsat 8 OLI to Sentinel 2. Ground Truth has played major role to support the classification methodology. Total 1,409 GT points have been collected so far for both the states, with the help of state agriculture departments. NDVI based Crop Cutting Experiment (CCE) points are also generated and given to State for conducting the experiments at ground level. District level Sorghum crop yield is estimated using two procedures - i) Agro-meteorological regression models, ii) Remote sensing index based empirical models. The state and district level estimates were compared with government estimates. At state level the RMSE was found ranging from 6.5% to 34.7% and correlation (r) was found ranging from 071 to 0.87. At district level, the correlation between FASAL project estimates and government estimates ranged between 0.80 to 0.95. Overall, the paper discusses, the experience gained from the Rabi sorghum production estimation, in last 5 years, under FASAL project and the scope of improvement.
EVAPOTRANSPIRATION AND EVAPORATION/TRANSPIRATION RETRIEVAL USING DUAL-SOURCE SURFACE ENERGY BALANCE MODELS INTEGRATING VIS/NIR/TIR DATA WITH SATELLITE SURFACE SOIL MOISTURE INFORMATION
Authors: Boulet G. , Delogu E. , Chebbi W. , Rafi Z. , Le Dantec V. , Mallick K. , Mougenot B. , Olioso A. , Zribi M. , Lili-Chabaane Z. , Er-Raki S. , Merlin O.
Published: July 2019

Abstract. Evapotranspiration is an important component of the water cycle. For the agronomic management and ecosystem health monitoring, it is also important to provide an estimate of evapotranspiration components, i.e. transpiration and soil evaporation. To do so, Thermal InfraRed data can be used with dual-source surface energy balance models, because they solve separate energy budgets for the soil and the vegetation. But those models rely on specific assumptions on raw levels of plant water stress to get both components (evaporation and transpiration) out of a single source of information, namely the surface temperature. Additional information from remote sensing data are thus required. This works evaluates the ability of the SPARSE dual-source energy balance model to compute not only total evapotranspiration, but also water stress and transpiration/evaporation components, using either the sole surface temperature as a remote sensing driver, or a combination of surface temperature and soil moisture level derived from microwave data. Flux data at an experimental plot in semi-arid Morocco is used to assess this potentiality and shows the increased robustness of both the total evapotranspiration and partitioning retrieval performances. This work is realized within the frame of the Phase A activities for the TRISHNA CNES/ISRO Thermal Infra-Red satellite mission.
Factors Influencing E-Procurement Practice in Pakistan
Authors: Aqeel Amish , Asim Muhammad
Published: July 2019

The concept of E-procurement has been widely spread with the growing use of informational technology in every field. Organizations are adopting methods of procurements that are more reliable, less time consuming and relatively stronger than the traditional methods. This quantitative study investigates factors influencing e-procurement practice in Karachi, Pakistan. A sample of 319 respondents was taken to observe the influence of internal organizational support, integration with suppliers’ electronic systems, supplier willingness, perceived improvements to purchasing tasks and supplier pressure on e-procurement intention. The results information indicates that internal organizational support, supplier willingness and perceived improvements to purchasing tasks significantly influences e-procurement intentions. However, the factors integration with suppliers’ electronic systems and supplier pressure does not have any significant impact on e-procurement intention. The result recommends the use of e-procurement system for organizations to facilitate the current practices of procurement.
Manifestations and Causes of Civil Service Corruption in the of Developing Countries
Authors: Desta Yemane
Published: July 2019

This article examines the manifestations and causes of corrupt practices in the civil service of developing countries based on review of theoretical and empirical literature regarding corruption and development.  Among the corrupt practices identified are:  bribery, embezzlement, fraud, extortion, abuse of power, conflict of interest, insider trading/abuse of privileged information, favoritism, collusion with business interests, procurement contract/bid rigging and influence peddling  This article also has identified the main underlying causes of corruption in the civil service of developing countries to be politicization of the civil service, poor pay, lack of accountability/transparency in public organizations, weak enforcements mechanisms, lack of merit-based career advancement, and excessive/ opaque regulations.  The findings reported this article reveal that ethical violations in the civil service and overall corruption levels in politics, economy, and society are very high, with the negative outcome of hampering economic growth, sabotaging social justice and contributing to political instability in the context of developing countries. Keywords: accountability, civil service, corruption, corruption perception index, developing countries, development, ethics, transparency