Qualification: 
Ph.D
alkasingh@am.amrita.edu

Dr. Alka Singh currently serves as an Assistant Professor (SLG) at the Amrita Center for Wireless Networks & Applications (Amrita WNA), Amritapuri Kerala.

Dr. Alka’s area of expertise is in remote sensing of hydrology. She is interested in solving Earth Science-related problems with a holistic and sustainable approach.

Education

2017-2020   Postdoc from NASA USA NASA Goddard Space Flight Centre, Greeenbelt, Maryland, USA   Jet Propulsion Laboratory (NASA) & California Institute of Technology (Caltech), Pasadena, California (U.S.A)
2010-2017 Ph. D. from TUM Germany The Technical University of Munich, Germany on hydrological mass variations
2007-2009 M. Sc. Geioinformatics from ITC Netherlands Faculty Geo-Information Science and Earth Observation, Enschede, the Netherlands and Indian Institute of Remote Sensing, Dehradun.
2005- 2009 Adv Diploma in RS and GIS from JMI New Delhi Jamia Milia Islamia New Delhi

Publications

Publication Type: Journal Article

Year of Publication Title

2021

Alka Singh, Reager, J. Thomas, and Behrangi, A., “Estimation of hydrological drought recovery based on precipitation and Gravity Recovery and Climate Experiment (GRACE) water storage deficit”, Hydrology and Earth System Sciences, vol. 25, no. 2, pp. 511–526, 2021.[Abstract]


Drought is a natural extreme climate phenomenon that presents great challenges in forecasting and monitoring for water management purposes. Previous studies have examined the use of Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage anomalies to measure the amount of water missing from a drought-affected region, and other studies have attempted statistical approaches to drought recovery forecasting based on joint probabilities of precipitation and soil moisture. The goal of this study is to combine GRACE data and historical precipitation observations to quantify the amount of precipitation required to achieve normal storage conditions in order to estimate a likely drought recovery time. First, linear relationships between terrestrial water storage anomaly (TWSA) and cumulative precipitation anomaly are established across a range of conditions. Then, historical precipitation data are statistically modeled to develop simplistic precipitation forecast skill based on climatology and long-term trend. Two additional precipitation scenarios are simulated to predict the recovery period by using a standard deviation in climatology and long-term trend. Precipitation scenarios are convolved with water deficit estimates (from GRACE) to calculate the best estimate of a drought recovery period. The results show that, in the regions of strong seasonal amplitude (like a monsoon belt), drought continues even with above-normal precipitation until its wet season. The historical GRACE-observed drought recovery period is used to validate the approach. Estimated drought for an example month demonstrated an 80 % recovery period, as observed by the GRACE.

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2019

A. Behrangi, Alka Singh, Song, Y., and Panahi, M., “Assessing gauge undercatch correction in Arctic basins in light of GRACE observations”, Geophysical Research Letters, vol. 46, no. 20, pp. 11358–11366, 2019.[Abstract]


Precipitation measurements at gauges are often considered as reference truth for evaluation of satellite precipitation products. However, gauges may contain large errors. A major source of gauge‐measurement error is snowfall undercatch in high latitudes. We show that the two popular correction factors (CFs) used in the Global Precipitation Climatology Centre monitoring and the Global Precipitation Climatology Project products are different by more than 50%. The CFs can be as large as 3; thus, the choice of CF introduces large uncertainties. Here, in light of observation of storage change from the Gravity Recovery and Climate Experiment (GRACE) and by using the mass conservation principle, we assess the two popular CFs over six Arctic basins. By investigating monthly time series and multiyear precipitation rates over the studied basins using GRACE‐based analysis, the CF based on Fuchs dynamic correction model used in Global Precipitation Climatology Centre monitoring is preferred.

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2018

Alka Singh, Behrangi, A., Fisher, J. B., and Reager, J. T., “On the desiccation of the South Aral Sea observed from spaceborne missions”, Remote Sensing, vol. 10, no. 5, p. 793, 2018.[Abstract]


The South Aral Sea has been massively affected by the implementation of a mega-irrigation project in the region, but ground-based observations have monitored the Sea poorly. This study is a comprehensive analysis of the mass balance of the South Aral Sea and its basin, using multiple instruments from ground and space. We estimate lake volume, evaporation from the lake, and the Amu Darya streamflow into the lake using strengths offered by various remote-sensing data. We also diagnose the attribution behind the shrinking of the lake and its possible future fate. Terrestrial water storage (TWS) variations observed by the Gravity Recovery and Climate Experiment (GRACE) mission from the Aral Sea region can approximate water level of the East Aral Sea with good accuracy (1.8% normalized root mean square error (RMSE), and 0.9 correlation) against altimetry observations. Evaporation from the lake is back-calculated by integrating altimetry-based lake volume, in situ streamflow, and Global Precipitation Climatology Project (GPCP) precipitation. Different evapotranspiration (ET) products (Global Land Data Assimilation System (GLDAS), the Water Gap Hydrological Model (WGHM)), and Moderate-Resolution Imaging Spectroradiometer (MODIS) Global Evapotranspiration Project (MOD16) significantly underestimate the evaporation from the lake. However, another MODIS based Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) ET estimate shows remarkably high consistency (0.76 correlation) with our estimate (based on the water-budget equation). Further, streamflow is approximated by integrating lake volume variation, PT-JPL ET, and GPCP datasets. In another approach, the deseasonalized GRACE signal from the Amu Darya basin was also found to approximate streamflow and predict extreme flow into the lake by one or two months. They can be used for water resource management in the Amu Darya delta. The spatiotemporal pattern in the Amu Darya basin shows that terrestrial water storage (TWS) in the central region (predominantly in the primary irrigation belt other than delta) has increased. This increase can be attributed to enhanced infiltration, as ET and vegetation index (i.e., normalized difference vegetation index (NDVI)) from the area has decreased. The additional infiltration might be an indication of worsening of the canal structures and leakage in the area. The study shows how altimetry, optical images, gravimetric and other ancillary observations can collectively help to study the desiccating Aral Sea and its basin. A similar method can be used to explore other desiccating lakes. View Full-Text

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2016

Alka Singh, Seitz, F., Eicker, A., and Güntner, A., “Water budget analysis within the surrounding of prominent lakes and reservoirs from multi-sensor earth observation data and hydrological models: case studies of the Aral Sea and Lake Mead”, Remote sensing, vol. 8, no. 11, p. 953, 2016.[Abstract]


The hydrological budget of a region is determined based on the horizontal and vertical water fluxes acting in both inward and outward directions. These integrated water fluxes vary, altering the total water storage and consequently the gravitational force of the region. The time-dependent gravitational field can be observed through the Gravity Recovery and Climate Experiment (GRACE) gravimetric satellite mission, provided that the mass variation is above the sensitivity of GRACE. This study evaluates mass changes in prominent reservoir regions through three independent approaches viz. fluxes, storages, and gravity, by combining remote sensing products, in-situ data and hydrological model outputs using WaterGAP Global Hydrological Model (WGHM) and Global Land Data Assimilation System (GLDAS). The results show that the dynamics revealed by the GRACE signal can be better explored by a hybrid method, which combines remote sensing-based reservoir volume estimates with hydrological model outputs, than by exclusive model-based storage estimates. For the given arid/semi-arid regions, GLDAS based storage estimations perform better than WGHM.

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2015

Alka Singh, Kumar, U., and Seitz, F., “Remote sensing of storage fluctuations of poorly gauged reservoirs and state space model (ssm)-based estimation”, Remote Sensing, vol. 7, pp. 17113–17134, 2015.[Abstract]


To reduce hydrological uncertainties in the regular monitoring of poorly gauged lakes and reservoirs, multi-dimensional remote sensing data have emerged as an excellent alternative. In this paper, we propose three methods to delineate the volume of such equipotential water bodies through a combination of altimetry (1D), Landsat (2D) and bathymetry (2D) data, namely an altimetry-bathymetry-volume method (ABV), a Landsat-bathymetry-volume method (LBV) and an altimetry-Landsat-volume-variation method (ALVV). The first two data products are further merged by a Kalman-filter-based state space model (SSM) to obtain a combined estimate (CSSME) time series and near future prediction. To validate our methods, we tested them on the well-measured Lake Mead and further applied them on the poorly gauged Aral Sea, which has inaccurate bathymetry and very limited ground observation data. We updated the lake bathymetry of the Aral Sea, which was more than half a century old. The resultant remote sensing products have a very good long-term agreement among each other. The Lake Mead volume estimations are very highly coherent with the ground observations for all cases (R2 > 0.96 and NRMSE < 2.1%), except for the forecast (R2 = 0.75 and NRMSE = 3.7%). Due to lack of in situ data for the Aral Sea, the estimated volumes are compared, and the entire Aral Sea LBV and ABV have R2 = 0.91 and NRMSE = 5.5%, and the forecast compared to CSSME has R2 = 0.60 and NRMSE = 2.4%.

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2013

Alka Singh, Seitz, F., and Schwatke, C., “Application of Multi-Sensor Satellite Data to Observe Water Storage Variations”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 6, no. 3, pp. 1502-1508, 2013.[Abstract]


In this study we apply geometric and gravimetric observations from various Earth observation satellites in order to estimate the variability in a lake with respect to its geometrical extent and water storage. Our test case is the Aral Sea, located in the arid zone of central Asia. Due to the diversion of its primary inlet rivers for irrigation purposes the lake suffered a devastating decline until its south eastern part had almost dried out in 2009. The study is focused on the period of the satellite gravity field mission GRACE from 2002 onwards. We present the change of the lake's surface extent based on optical remote sensing data from Landsat images that were analyzed for spring and autumn each year. Height variations of the lake surface were computed from multi-mission satellite altimetry. Both the surface extent and the water stage of the lake reached an absolute minimum in autumn 2009. However in 2010 a clear reversal of the negative trend of the previous years is visible. A geometrical intersection of the water level with a digital elevation model allows for estimating water volume changes. The resulting volume changes are subsequently analyzed with respect to satellite-based estimates of mass variations observed by GRACE. The results reveal that water storage variations in the Aral Sea are indeed the principal contributor to the GRACE signal of mass variations in this region. The different observations from all missions agree very well with respect to their temporal behavior.

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2012

Alka Singh, Dutta, R., Stein, A., and Bhagat, R. M., “A wavelet-based approach for monitoring plantation crops (tea: Camellia sinensis) in North East India”, International journal of remote sensing, vol. 33, no. 16, pp. 4982–5008, 2012.[Abstract]


This study analysed the monitoring of tea replantation using Linear Imaging Self‐Scanning Sensor (LISS-III) and Cartosat-1 images and identified patterns based on wavelet approaches. Monitoring identifies four phases of replantation and rejuvenation, starting at the time of uprooting and finishing when new plants are planted. The study was conducted within the Dooars region of North East India. The perpendicular vegetation index and perpendicular soil index were derived to measure changes from bare soil reflectances caused by vegetation, whereas the soil index was designed to enhance brightness. Being a multi-resolution study, wavelets such as Haar, Daubechies and Symlet were compared at different levels of decomposition, and information was extracted at different scales. Using topographic and hydrological parameters, informative patterns for each stage of replantation were selected at individual sections within the estate on the basis of spatial correlation. The study showed that levels 3 and 4 gave superior information compared with the other levels. Anisotropic autocorrelation gave constant spatial variation at different scales and in different directions. The selected patterns were weakly correlated with slope, flow accumulation and the compound topographic index, whereas management activities and a small variation in elevation proved less efficient in explaining the extracted patterns. It also showed that hydrological processes could be evaluated using cross-correlations. From the study, it was observed that the asymmetric Daubechies-4 wavelet gave the best results for extraction of fine features, whereas the symmetric Symlet-8 wavelet best represented the extraction of smooth features. Although a strong quantitative linear relationship between the extracted patterns and topographic parameters could not be established, we conclude that wavelets are useful to extract patterns and interpret spatial variations observed at different phases of tea replantation.

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2012

Alka Singh, Seitz, F., and Schwatke, C., “Inter-annual water storage changes in the Aral Sea from multi-mission satellite altimetry, optical remote sensing, and GRACE satellite gravimetry”, Remote Sensing of Environment , vol. 123, pp. 187 - 195, 2012.[Abstract]


The estimation of water storage variations in lakes is essential for water resource management activities in a region. In areas of ungauged or poorly gauged water bodies, satellite altimetry acts as a powerful tool to measure changes in surface water level. Remote sensing provides images of temporal coastline variations, and a combination of both measurement techniques can indicate a change in water volume. In this study variations of the water level of the Aral Sea were computed for the period 2002–2011 from the combination of radar and laser satellite altimetry data sets over the lake. The estimated water levels were analyzed in combination with coastline changes from Landsat images in order to obtain a comprehensive picture of the lake water changes. In addition to these geometrical observations temporal changes of water storage in the lake and its surrounding were computed from GRACE satellite gravimetry. With respect to its temporal evolution the GRACE results agree very well with the geometrical changes determined from altimetry and Landsat. The advancing desiccation until the beginning of 2009 and a subsequent abrupt gain of water in 2009–2010 due to exceptional discharge from Amu Darya can clearly be identified in all data sets.

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Publication Type: Conference Paper

Year of Publication Title

2020

Z. Z., A., C., L., O., Alka Singh, and A., P., “Assimilating SMAP soil moisture dynamics into LPJ Land surface model in the ABoVE domain: Implications for the carbon cycle research”, in 2020 ABoVE Science Team Meeting (ASTM-6) Virtual Meeting (Poster Presentation), 2020.

2018

Alka Singh, Reager, J. T., and Behrangi, A., “Probabilistic drought recovery analysis using GRACE and precipitation data”, in The American Geophysical Union (AGU) (Oral Presentation), Washington DC, USA, 2018.

2018

Alka Singh, Chatterjee, A., Poulter, B., and Zhang, Z., “Improved understanding of terrestrial water-carbon linkages using satellite soil moisture and a dynamic global vegetation model”, in The American Geophysical Union (AGU) (Oral Presentation), San Francisco, California, USA, 2018.[Abstract]


A suite of recent remote-sensing missions provides promising opportunities for improving our understanding of the processes governing the carbon and the water cycle. The study involves inter-comparison of different but complementary satellite observations that are being integrated into the Lund-Potsdam-Jena (LPJ-wsl) dynamic global vegetation model. In this presentation, we will show the results from comparison of different soil moisture products, specifically the Level-3 and Level-4 product from NASA's Soil Moisture Active-Passive (SMAP) mission and the soil moisture product from the European Space Agency's Climate Change Initiative (ESA CCI v04.2). The ESA CCI soil moisture product is a merged product of all existing active and passive Level-2 satellite soil moisture datasets. Preliminary results show significant spatial and temporal differences between soil moisture from the two products in the southeastern US, La Plata basin and Northern high latitudes. Differences in the Northern high latitudes are prominent during ice/snow thaw period, which indicates potential for the exploration of freeze/thaw mechanism based on differences in sensors. Finally, we will highlight our ongoing work to assimilate these soil moisture products into the LPJ-wsl model to better parameterize the feedback mechanism in water, carbon, and energy exchange between land surface and atmosphere. We will be specifically focusing on drought effected regions to explore the impact of soil moisture on the modeled vegetation response, assess the spatiotemporal scales at which vegetation responds and the difference in the response based on the soil moisture product used to drive the model.

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2017

Alka Singh and Behrangi, A., “A Utilizing a suite of NASA satellite missions for analysis of the Aral Sea desiccation”, in Postdoc Research day at the Jet Propulsion Lab (Poster Presentation), Caltech, Pasadena, USA, 2017.

2017

Alka Singh, Behrangi, A., Fisher, J., Reager, J. T., and Gardner, A. S., “Utilizing a suite of satellite missions to address poorly constrained hydrological fluxes”, in The American Geophysical Union (AGU) (Oral Presentation), New Orleans, Louisiana, USA, 2017.

2015

Alka Singh, Seitz, F., and Kumar, U., “Estimation and prediction of the ungauged basins using satellite remote sensing and state space model”, in The International Union of Geodesy and Geophysics (IUGG) (Oral Presentation), Prague, Czech Republic, 2015.

2014

S. Abelen, Schnitzer, S., Alka Singh, Seitz, F., R del Rio, A., and Güntner, A., “How heavy are extreme weather events?”, in WCRP-ICTP Summer School on Attribution and Prediction of Extreme Events (Poster Presentation), Trieste, Italy, 2014.

2013

Alka Singh, Seitz, F., Schwatke, C., and Guentner, A., “Hydrological storage variations in a lake water balance, observed from multi-sensor satellite data and hydrological models.”, in European Geosciences Union General Assembly 2013 (Oral Presentation), Vienna, Austria , 2013.

2012

Alka Singh, Seitz, F., Schwatke, C., and Bosch, W., “Application of the Satellite Altimetry over Terrestrial Water Body: A Case Study on Aral Sea”, in 20 Years of Progress in Radar Altimetry (Poster Presentation), Venice, Italy, 2012.

2012

Alka Singh, Seitz, F., and Güntner, A., “Volumetric and Gravimetric variations in the Aral Sea observed from multi-sensor Satellite data, GRACE and hydrological models”, in ESA EO Summer School 2012 (Poster Presentation), Frascati, Italy, 2012.

2012

Alka Singh, Seitz, F., Schwatke, C., and Güntner, A., “Geometrical and gravimetrical observations of the Aral Sea and its tributaries along with hydrological models”, in European Geosciences Union General Assembly (Poster Presentation), Vienna, Austria, 2012.[Abstract]


Satellite altimetry is capable of measuring surface water level changes of large water bodies. This is especially interesting for regions where in-situ gauges are sparse or not available. Temporal variations of coastline and horizontal extent of a water body can be derived from optical remote sensing data. A joint analysis of both data types together with a digital elevation model allows for the estimation of water volume changes. Related variations of water mass map into the observations of the satellite gravity field mission GRACE. In this presentation, we demonstrate the application of heterogeneuous remote sensing methods for studying chages of water volume and mass of the Aral Sea and compare the results with respect to their consistency. Our analysis covers the period 2002-2011. In particular we deal with data from multi-mission radar and laser satellite altimetry that are analyzed in combination with coastlines from Landsat images. The resultant vertical and horizontal variations of the lake surface are geometrically intersected with the bathymetry of the Aral Sea in order to compute volumetric changes. These are transformed into variations of water mass that are subsequently compared with storage changes derived from GRACE satellite gravimetry. Hence we obtain a comprehensive picture of the hydrological changes in the region. Observations from all datasets correspond quite well with each other with respect to their temporal development. However, geometrically determined volume changes and mass changes observed by GRACE agree less well during years of heavy water inflow in to the Aral Sea from its southern tributary 'Amu Darya' since the GRACE signals are contaminated by the large mass of water stored in the river delta and prearalie region On the other hand, GRACE observations of the river basins of Syr Darya and Amu Dayra correspond very well with hydrological models and mass changes computed from the balance of precipitation, evaporation and runoff determined from the atmospheric-terrestrial water balance.

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2012

F. Seitz, Abelen, S., Alka Singh, and Schnitzer, S., “Compartmental water storage changes from multi-sensor data and their signatures in GRACE observations”, in SPP 1257 Workshop on GRACE-Hydrology (Oral Presentation), Bonn, Germany, 2012.

2012

G. Misra, Kumar, A., Patel, N. R., Zurita-Milla, R., and Alka Singh, “Mapping Specific Crop- A Multi-Sensor Temporal Approach”, in IGARSS (IEEE International Geoscience and Remote Sensing Symposium) (Oral Presentation), München, Germany, 2012.

2012

Alka Singh, Seitz, F., and Schwatke, C., “Observations of Water Storage Variations in the Aral Sea from Multi-sensor Satellite data”, in 2nd IAHR Europe Congress (Oral Presentation), Munich, Germany, 2012.

2012

Alka Singh and Seitz, F., “Water storage variations in the Aral Sea from multi-sensor satellite data in comparison with results from GRACE gravimetry”, in (IGARSS) IEEE International Geoscience and Remote Sensing Symposium (Oral Presentation), München, Germany, 2012.

2011

Alka Singh, Seitz, F., Schwatke, C., Schmidt, M., and Güntner, A., “Changing hydrology of the Aral Sea: Results from satellite altimetry, GRACE satellite gravimetry and hydrological modeling”, in European Geosciences Union General Assembly 2011 (Poster Presentation), Vienna, Austria, 2011.

2011

Alka Singh, Seitz, F., and Schwatke, C., “Inter-annual water storage changes in the Aral Sea from multi-mission satellite altimetry, remote sensing, and GRACE satellite gravimetry”, in Geodätische Woche 2011 (Oral Presentation), Nürnberg, Germany, 2011.

2007

R. Dutta, Bhagat, R. M., and Alka Singh, “Monitoring tea plantations in India using remote sensing approaches”, in 7th FIG Regional Conference. Spatial Data Serving People: Land Governance and the Environment–Building the Capacity (Oral Presentation), Hanoi, Vietnam, 2007.

2007

Alka Singh, “Spatio-Temporal Land Transformation and demographic analysis of Delhi”, in In National Conference on Environmental Development and Health (Oral Presentation), Aligarh Muslim University, Aligarh, India, 2007.

Publication Type: Book Chapter

Year of Publication Title

2017

Alka Singh, “Dynamics of water mass variations in lake/reservoir dominated regions from multi-sensor Earth observation data and hydrological model outputs”, in Ph.D. Dissertation, Munich, Germany: Online in the Library of the Technical University of Munich (TUM), 2017.[Abstract]


This study focuses on deriving methods for a remote sensing based lake / reservoir volume estimation by applying heterogeneous multisensor Earth observation data and global hydrological models. The study estimates gravimetric and geometric variations ongoing in the Aral Sea and the Lake Mead regions. The results demonstrate that lakes / reservoirs can be effectively monitored using remote sensing data. More »»

2009

A. Rahman, Netzband, M., Alka Singh, and Javed, M., “An assessment of urban environmental issues using remote sensing and GIS techniques an integrated approach: A case study: Delhi, India”, in Urban Population-Environment Dynamics in the Developing World: Case Studies an Lessons Learned, International Cooperation in National Research in Demography (CICRED), Paris, Committee for International Cooperation in National Research in Demography (CICRED), 2009, pp. 181–211.

2009

Alka Singh, “Analyzing Tea replantation pattern by wavelet and geospatial technique”, in M.Sc. Dissertation , Library of Faculty of Geo-Information Science and Earth Observation, ITC , 2009.

Publication Type: Conference Proceedings

Year of Publication Title

2012

G. Misra, Kumar, A., Patel, N. R., Zurita-Milla, R., and Alka Singh, “Mapping specific crop- A multi sensor temporal approach”, 2012 IEEE International Geoscience and Remote Sensing Symposium, vol. 6. pp. 3034–3037, 2012.[Abstract]


This study explores the applicability of temporal and multi sensor data for specific crop mapping. For this, temporal data from a single sensor (LISS III from IRS- P6 satellite) was used and classified after selecting the best dates for mapping. In the second case a Landsat- 5 TM image (other sensor/ multi sensor approach) is added to the selected best LISS III temporal dates combination and classified again for evaluating the effect of the addition of a another sensor data (i.e. Landsat- 5 TM) on the overall accuracy of classification. A Possibilistic c-Means (PCM) classification technique has been used for extracting single class of interest (Sugarcane-ratoon) and for including the mixed pixels occurring in the heterogeneous landscape of the study area. In the absence of reference data, evaluation of the soft (fuzzy) classified outputs was done as an entropy measurement, where entropy provides an indirect absolute measurement of the classification accuracy in the form of an uncertainty measure.

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2012

Alka Singh, Seitz, F., and Schwatke, C., “Observations of Water Storage Variations in the Aral Sea from Multi-sensor Satellite data”, 2nd IAHR Europe Congress, Water Infinitely Deformable but Still Limited. Bavarian State Library, Munich & German National Library, Frankfurt, 2012.

2012

Alka Singh and Seitz, F., “Water storage variations in the Aral Sea from multi-sensor satellite data in comparison with results from GRACE gravimetry”, 2012 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), , vol. 6. IEEE International, pp. 3042 - 3045 , 2012.

2007

R. Dutta, Bhagat, R. M., and Alka Singh, “Monitoring tea plantations in India using remote sensing approaches”, 7th FIG Regional Conference. Spatial Data Serving People: Land Governance and the Environment–Building the Capacity FIG Surveyor's Reference Library (Commission 6: Engg. Surveys). Hanoi, Vietnam, 2007.

Faculty Research Interest: