RESEARCH/FUNDED PROJECTS
1. ET/Irrigation monitoring in Peru (Funded by UNSA, Co-PI) - Global Change and Human Health Institute (GCHHI)
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Some farms in Peru
Mapping ET in Arequipa, Peru using a two source energy balance model in Google Earth Engine
2. Monitoring tillage practices and water use efficienct across winter wheat farms in Oklahoma (Funded by USDA NIFA, PI)
3. E-RISE: Center for Grassland Ecosystem Forecasting (Funded by NSF, Co-PI)
4. A novel vegetation water use and efficiency monitoring tool to support land and water management decision making and sustainable agroecosystem production (Funded by USGS, PI)
The tool aims to provide real-time data on plant water consumption and efficiency using high-resolution satellite data and advanced computer models.
5. CropMap (Funded by NSF SBIR/STTR, co-PI)
CropMAP (Crop Ecosystem Monitoring, Analysis, and Prediction) is an integrated tool for crop ecosystem monitoring, analysis, and prediction. The project aims to to identify the economic impact of using CropMap information in the production of sesame and sunflowers for oilseed production.
6. NASA EPSCoR - Thermal Remote Sensing and ET model uncertainties in Oklahoma (Funded by NSASA EPSCoR, PI)
We are advancing the surface energy balance algorithms to map ET.
7. USGS 104b Water Center- High-resolution ET modeling (Funded by USGS, PI)
8. Mapping ET across rice farms in Bangladesh (Funded by AidData)
Other ongoing but Unfunded Projects
Improving near real time crop water stress monitoring across the US (multi sensor fusion combining Sentinel-2 and Landsat datasets)
ET from new sensors: Hydrosat, Planet, NISAR, etc.
ET/crop water demand forecasting
Deep learning/ML to design ensemble ET modeling framework (multiple ET/hydrological and machine learning models)
Impact of environmental changes on farming decisions and crop production
Environmental and socioeconomic Impact Assessments combining geospatial, field, survey, and climate datasets
Bhattarai, N., Lobell, D.B., Balwinder-Singh, Fishman, R., Kustas, W.P., Pokhrel, Y. and Jain, M., 2023. Warming temperatures exacerbate groundwater depletion rates in India. Science Advances, 9(35), p.eadi1401.
Media: NYTimes, The Hindu, The Times of India, The Economic Times, The Weather Channel
Groundwater depletion reduces agricultural production and cropping intensity in India
Bhattarai, N., Pollack, A., Lobell, D. B., Fishman, R., Singh, B., Dar, A., & Jain, M. (2021). The impact of groundwater depletion on agricultural production in India. Environmental Research Letters, 16 (8), 085003. ) [Link] Jain M., Fishman, R., Mondal, P., Galford, G.L., Bhattarai, N., Naeem, S., Lall, U., Singh, B., & DeFries, R.S. 2021. Groundwater depletion will reduce cropping intensity in India. Science Advances, 7: eabd2849 [Link]. Media: CNN, AAAS, NPR, Earther.Groundwater depletion reduces agricultural production and cropping intensity in India
Spatial distribution of district level crop production and groundwater table depth in India. Maps display (A) 1997–2014 mean district level crop production in tons for each of five staple crops (winter wheat, winter and monsoon rice, winter and monsoon maize, winter and monsoon sorghum, and monsoon pearl millet) and (B) 2004–2013 mean district level preseason groundwater table (m). Polygons in red in panel (B) show the states of Punjab (top) and Haryana (bottom).
A new multimodal based ensemble ET model
Bhattarai, N., Mallick, K., Stuart, J.**, Vishwakarma, B.D., Niraula, R., Sen, S., & Jain, M. 2019. An automated multi-model evapotranspiration mapping framework using remotely sensed and reanalysis data. Remote Sensing of Environment, 229: 69-92. [Link]Yun, B., Sha, Z., Bhattarai, N., Mallick, K., Qi, L, Tang, L., Im, J., Guo, L., & Jiahua, Z. 2021. On the use of machine learning algorithms to improve cropland evapotranspiration across a wide environmental gradient. Agricultural and Forest Meteorology, 288-289: 208308 [Link].A new multimodal based ensemble ET model
The Surface temperature initiated closure (STIC) model
Bhattarai, N., Mallick, K., Brunsell, N. A., Sun, G., & Jain, M. 2018. Regional evapotranspiration from an image-based implementation of the Surface Temperature Initiated Closure (STIC1.2) model and its validation across an aridity gradient in the conterminous United States, Hydrology and Earth System Sciences, 22: 2311-2341. [Link]Trebs, I, Mallick, K., Bhattarai, N., Sulis, M., Cleverly J, Woodgate W, Silberstein, R., Najera, Hinko-Najera, N., Beringer J, Su Z., & Boulet G. 2021. The role of aerodynamic resistance in thermal remote sensing-based evapotranspiration models. Remote Sensing of Environment, 264: 112602 [Link].Bai, Y., Bhattarai, N., Mallick K., Zhang, S., & Zhang, J. 2022. Thermally derived evapotranspiration from the Surface Temperature Initiated Closure (STIC) model improves cropland GPP estimates under dry conditions. Remote Sensing of Environment 271: 112901 [Link].The Surface temperature initiated closure (STIC) model
Full Automation of the SEBAL and METRIC ET models
Bhattarai, N., Quackenbush, L.J., Im, Jungho, & Shaw, S.B., 2017. A new optimized algorithm for automating endmember pixel selection in the SEBAL and METRIC models. Remote Sensing of Environment, 196:178-192. [Link]Bhattarai, N. & Liu, T. 2019. LandMOD ET Mapper: a new Matlab-based graphical user interface (GUI) for automated implementation of SEBAL and METRIC models in thermal imagery. Environmental Modelling and Software, 118: 76-82. [Link]Full Automation of the SEBAL and METRIC ET models
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