Jitendra Kumar

Arendt, Carli A., et al. “Increased Arctic NO3− Availability As a Hydrogeomorphic Consequence of Permafrost Degradation and Landscape Drying”. Nitrogen, vol. 3, no. 2, 2022, pp. 314-32, https://doi.org/10.3390/nitrogen3020021.

Pallandt, Martijn, et al. “Representativeness Assessment of the Pan-Arctic Eddy Covariance Site Network and Optimized Future Enhancements”. Biogeosciences, vol. 19, no. 3, 2022, pp. 559-83, https://doi.org/10.5194/bg-19-559-2022.

Bennett, Katrina E., et al. “Spatial Patterns of Snow Distribution for Improved Earth System Modelling in the Arctic”. The Cryosphere, 2022, https://doi.org/https://doi.org/10.5194/tc-2021-341.

Salmon, Verity G., et al. “Alder Distribution and Expansion across a Tundra Hillslope: Implications for Local N Cycling”. Frontiers in Plant Science, vol. 10, 2019, https://doi.org/10.3389/fpls.2019.01099.

Langford, Zachary L., et al. “Arctic Vegetation Mapping Using Unsupervised Training Datasets and Convolutional Neural Networks”. Remote Sensing, vol. 11, no. 1, 2019, p. 69, https://doi.org/10.3390/rs11010069.

Wang, Yihui, et al. “Mechanistic Modeling of Microtopographic Impacts on Carbon Dioxide and Methane Fluxes in an Alaskan Tundra Ecosystem Using the CLM‐Microbe Model”. Journal of Advances in Modeling Earth Systems, vol. 11, 2019, p. 17, https://doi.org/10.1029/2019MS001771.

Langford, Zachary L., et al. “Convolutional Neural Network Approach for Mapping Arctic Vegetation Using Multi-Sensor Remote Sensing Fusion”. 2017 IEEE International Conference on Data Mining Workshops (ICDMW)2017 IEEE International Conference on Data Mining Workshops (ICDMW), IEEE, 2017, https://doi.org/10.1109/ICDMW.2017.48.

Langford, Zachary L., et al. “Convolutional Neural Network Approach for Mapping Arctic Vegetation Using Multi-Sensor Remote Sensing Fusion”. 2017 IEEE International Conference on Data Mining Workshops (ICDMW), 2017, pp. 770-8, https://doi.org/10.1109/ICDMW.2017.48.

Tang, Guoping, et al. “Addressing Numerical Challenges in Introducing a Reactive Transport Code into a Land Surface Model: A Biogeochemical Modeling Proof-of-Concept With CLM–PFLOTRAN 1.0”. Geoscientific Model Development, vol. 9, no. 3, 2016, pp. 927-46, https://doi.org/10.5194/gmd-9-927-2016.

Langford, Zachary L., et al. “Mapping Arctic Plant Functional Type Distributions in the Barrow Environmental Observatory Using WorldView-2 and LiDAR Datasets”. Remote Sensing, vol. 8, no. 9, 2016, p. 733, https://doi.org/10.3390/rs8090733.

Kumar, Jitendra, et al. “Modeling the Spatiotemporal Variability in Subsurface Thermal Regimes across a Low-Relief Polygonal Tundra Landscape”. The Cryosphere, vol. 10, no. 5, 2016, pp. 2241-74, https://doi.org/10.5194/tc-10-2241-2016.

Warren, Jeffery M., et al. “Root Structural and Functional Dynamics in Terrestrial Biosphere Models - Evaluation and Recommendations”. New Phytologist, vol. 205, no. 1, 2015, pp. 59-78, https://doi.org/10.1111/nph.13034.

Hoffman, Forrest M., et al. “Representativeness-Based Sampling Network Design for the State of Alaska”. Landscape Ecology, vol. 28, no. 8, 2013, pp. 1567-86, https://doi.org/10.1007/s10980-013-9902-0.