Christopher M. U. Neale

7.8k total citations
217 papers, 5.2k citations indexed

About

Christopher M. U. Neale is a scholar working on Global and Planetary Change, Ecology and Environmental Engineering. According to data from OpenAlex, Christopher M. U. Neale has authored 217 papers receiving a total of 5.2k indexed citations (citations by other indexed papers that have themselves been cited), including 126 papers in Global and Planetary Change, 70 papers in Ecology and 69 papers in Environmental Engineering. Recurrent topics in Christopher M. U. Neale's work include Plant Water Relations and Carbon Dynamics (104 papers), Remote Sensing in Agriculture (53 papers) and Irrigation Practices and Water Management (48 papers). Christopher M. U. Neale is often cited by papers focused on Plant Water Relations and Carbon Dynamics (104 papers), Remote Sensing in Agriculture (53 papers) and Irrigation Practices and Water Management (48 papers). Christopher M. U. Neale collaborates with scholars based in United States, Brazil and Spain. Christopher M. U. Neale's co-authors include William P. Kustas, John H. Prueger, Martha C. Anderson, Fuqin Li, Isidro Campos, James L. Wright, Nurit Agam, Marshall J. McFarland, José O. Payero and Harikishan Jayanthi and has published in prestigious journals such as SHILAP Revista de lepidopterología, Environmental Science & Technology and Remote Sensing of Environment.

In The Last Decade

Christopher M. U. Neale

201 papers receiving 5.0k citations

Peers

Christopher M. U. Neale
Albert Olioso France
Salah Er‐Raki Morocco
Prasanna H. Gowda United States
Richard L. Snyder United States
Paul V. Bolstad United States
Pamela L. Nagler United States
Paul D. Colaizzi United States
Gilles Boulet France
Wei Shangguan China
Abdelghani Chehbouni Morocco
Albert Olioso France
Christopher M. U. Neale
Citations per year, relative to Christopher M. U. Neale Christopher M. U. Neale (= 1×) peers Albert Olioso

Countries citing papers authored by Christopher M. U. Neale

Since Specialization
Citations

This map shows the geographic impact of Christopher M. U. Neale's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Christopher M. U. Neale with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Christopher M. U. Neale more than expected).

Fields of papers citing papers by Christopher M. U. Neale

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Christopher M. U. Neale. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Christopher M. U. Neale. The network helps show where Christopher M. U. Neale may publish in the future.

Co-authorship network of co-authors of Christopher M. U. Neale

This figure shows the co-authorship network connecting the top 25 collaborators of Christopher M. U. Neale. A scholar is included among the top collaborators of Christopher M. U. Neale based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Christopher M. U. Neale. Christopher M. U. Neale is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
2.
Sehgal, Vinay Kumar, Rajkumar Dhakar, Christopher M. U. Neale, et al.. (2024). Estimation of ET and Crop Water Productivity in a Semi-Arid Region Using a Large Aperture Scintillometer and Remote Sensing-Based SETMI Model. Water. 16(3). 422–422. 7 indexed citations
3.
Rezaie, Fatemeh, Mahdi Panahi, Sayed M. Bateni, et al.. (2023). Development of novel optimized deep learning algorithms for wildfire modeling: A case study of Maui, Hawai‘i. Engineering Applications of Artificial Intelligence. 125. 106699–106699. 11 indexed citations
4.
Bateni, Sayed M., Fatemeh Rezaie, Mahdi Panahi, et al.. (2023). Enhancing predictive ability of optimized group method of data handling (GMDH) method for wildfire susceptibility mapping. Agricultural and Forest Meteorology. 339. 109587–109587. 8 indexed citations
5.
Heeren, Derek M., et al.. (2023). Toward automated irrigation management with integrated crop water stress index and spatial soil water balance. Precision Agriculture. 24(6). 2223–2247. 10 indexed citations
6.
Althoff, Daniel, Helizani Couto Bazame, Christopher M. U. Neale, et al.. (2021). Evaluating the Latest IMERG Products in a Subtropical Climate: The Case of Paraná State, Brazil. Remote Sensing. 13(5). 906–906. 21 indexed citations
7.
Xu, Tongren, Sayed M. Bateni, Christopher M. U. Neale, et al.. (2019). Mapping Regional Turbulent Heat Fluxes via Assimilation of MODIS Land Surface Temperature Data into an Ensemble Kalman Smoother Framework. Earth and Space Science. 6(12). 2423–2442. 13 indexed citations
8.
Woldt, Wayne, et al.. (2019). Calibration of a common shortwave multispectral camera system for quantitative agricultural applications. Precision Agriculture. 21(4). 922–935. 10 indexed citations
9.
Hain, Christopher, Martha C. Anderson, Mitch Schull, & Christopher M. U. Neale. (2017). A Framework for Mapping Global Evapotranspiration using 375-m VIIRS LST. NASA STI Repository (National Aeronautics and Space Administration). 2017. 3 indexed citations
10.
Foster, Timothy, Nicholas Brozović, Adrian P. Butler, et al.. (2017). AquaCrop-OS: A tool for resilient management of land and water resources in agriculture. Lirias (KU Leuven). 2842. 1 indexed citations
11.
Xia, Ting, William P. Kustas, Martha C. Anderson, et al.. (2016). Mapping evapotranspiration with high-resolution aircraft imagery over vineyards using one- and two-source modeling schemes. Hydrology and earth system sciences. 20(4). 1523–1545. 79 indexed citations
12.
Kettenring, Karin M., et al.. (2010). Paradoxes in Adapting to Droughts: The Rationality of Locality. Digital Commons - USU (Utah State University).
13.
Neale, Christopher M. U., et al.. (2007). Modeling The Water Table In The Middle Rio Grande River Riparian Corridor. AGU Fall Meeting Abstracts. 2007.
14.
Neale, Christopher M. U., et al.. (2004). An Assessment Of Meso-Scale Hydraulic And Vegetation Characteristics Of The Middle Rio Grande River Using High Resolution Multispectral Airborne Imagery. AGU Fall Meeting Abstracts. 2004.
15.
Prueger, John H., Lawrence E. Hipps, William P. Kustas, et al.. (2001). Feasibility of évapotranspiration monitoring of riparian vegetation with remote sensing. IAHS-AISH publication. 246–251. 3 indexed citations
16.
Jayanthi, Harikishan, Christopher M. U. Neale, & James L. Wright. (2001). Seasonal evapotranspiration estimation using canopy reflectance: a case study involving pink beans.. IAHS-AISH publication. 302–305. 12 indexed citations
17.
Neale, Christopher M. U., Lawrence E. Hipps, John H. Prueger, et al.. (2001). Spatial mapping of evapotranspiration and energy balance components over riparian vegetation using airborne remote sensing. IAHS-AISH publication. 311–315. 10 indexed citations
18.
Tarboton, David G., et al.. (1995). A Grid Based Distributed Hydrologic Model: Testing Against Data from Reynolds Creek Experimental Watershed. Science Robotics. 6(59). 79–eabf8136. 5 indexed citations
19.
Neale, Christopher M. U., et al.. (1990). An Assessment of Input Variables to the Penman-Monteith Equation Using Canopy Reflectance Measurements. Irrigation and Drainage. 118–128. 1 indexed citations
20.
Neale, Christopher M. U. & Walter C. Bausch. (1983). CROP COEFFICIENTS DERIVED FROM REFLECTED CANOPY RADIATION.. Paper - American Society of Agricultural Engineers. 4 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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