Project: 10m E, T, and water efficiency across all agroecosystems in the states of OK and TX

Funding: USGS

Other Research Projects

Improving near real time crop water stress monitoring across the US (multi sensor fusion combining Sentinel-2 and Landsat datasets)
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 (causal regression models)
Environmental and socioeconomic Impact Assessments combining geospatial, field, survey, and climate datasets (causal regression models)

Warming temperature amplifies groundwater depletion

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.

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].

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].

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]