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Publications Found: 3

Coupling Remote Sensing With A Process Model For The Simulation Of Rangeland Carbon Dynamics
Xia, Y., Sanderman, J., Watts, J. D., Machmuller, M. B., Mullen, A. L., Rivard, C., Endsley, A., Hernandez, H., Kimball, J., Ewing, S. A., Litvak, M., Duman, T., Krishnan, P., Meyers, T., Brunsell, N. A., Mohanty, B., Liu, H., Gao, Z., Chen, J., Abraha, M., Scott, R. L., Flerchinger, G. N., Clark, P. E., Stoy, P. C., Khan, A. M., Brookshire, E. N., Zhang, Q., Cook, D. R., Thienelt, T., Mitra, B., Mauritz‐Tozer, M., Tweedie, C. E., Torn, M. S., Billesbach, D.

Rangelands provide significant environmental benefits through many ecosystem services, which may include soil organic carbon (SOC) sequestration. However, quantifying SOC stocks and monitoring carbon (C) fluxes in rangelands are challenging due to the considerable spatial and temporal variability tied to rangeland C dynamics as well …


Journal: Journal Of Advances In Modeling Earth Systems, Volume 17 (3): (2025). DOI: 10.1029/2024MS004342 Sites: US-A32, US-AR1, US-AR2, US-ARb, US-ARc, US-Aud, US-Bkg, US-BMM, US-BRG, US-Cop, US-Ctn, US-CZ1, US-Dia, US-FPe, US-Fwf, US-Hn1, US-Hn2, US-IB2, US-Jo1, US-KFS, US-KLS, US-KM2, US-KM3, US-KM4, US-Kon, US-LS1, US-LS2, US-Mpj, US-RFW, US-Rls, US-Rms, US-Ro4, US-Rwe, US-Rwf, US-Rws, US-SCg, US-SdH, US-Seg, US-Ses, US-Snd, US-SRM, US-Ton, US-Tx1, US-Tx2, US-Var, US-Wdn, US-Wjs, US-Wkg, US-xAE, US-xCL, US-xCP, US-xDC, US-xJR, US-xKA, US-xKZ, US-xMB, US-xNG, US-xNQ, US-xSJ, US-xWD, US-xYE

Representativeness Of Eddy-Covariance Flux Footprints For Areas Surrounding Ameriflux Sites
Chu, H., Luo, X., Ouyang, Z., Chan, W. S., Dengel, S., Biraud, S. C., Torn, M. S., Metzger, S., Kumar, J., Arain, M. A., Arkebauer, T. J., Baldocchi, D., Bernacchi, C., Billesbach, D., Black, T. A., Blanken, P. D., Bohrer, G., Bracho, R., Brown, S., Brunsell, N. A., Chen, J., Chen, X., Clark, K., Desai, A. R., Duman, T., Durden, D., Fares, S., Forbrich, I., Gamon, J. A., Gough, C. M., Griffis, T., Helbig, M., Hollinger, D., Humphreys, E., Ikawa, H., Iwata, H., Ju, Y., Knowles, J. F., Knox, S. H., Kobayashi, H., Kolb, T., Law, B., Lee, X., Litvak, M., Liu, H., Munger, J. W., Noormets, A., Novick, K., Oberbauer, S. F., Oechel, W., Oikawa, P., Papuga, S. A., Pendall, E., Prajapati, P., Prueger, J., Quinton, W. L., Richardson, A. D., Russell, E. S., Scott, R. L., Starr, G., Staebler, R., Stoy, P. C., Stuart-Haëntjens, E., Sonnentag, O., Sullivan, R. C., Suyker, A., Ueyama, M., Vargas, R., Wood, J. D., Zona, D.

Large datasets of greenhouse gas and energy surface-atmosphere fluxes measured with the eddy-covariance technique (e.g., FLUXNET2015, AmeriFlux BASE) are widely used to benchmark models and remote-sensing products. This study addresses one of the major challenges facing model-data integration: To what spatial extent do flux measurements …


Journal: Agricultural And Forest Meteorology, Volume 301-302: 108350 (2021). DOI: 10.1016/j.agrformet.2021.108350 Sites: CA-ARB, CA-ARF, CA-Ca1, CA-Ca2, CA-Ca3, CA-Cbo, CA-DBB, CA-ER1, CA-Gro, CA-Let, CA-Man, CA-MR3, CA-MR5, CA-Na1, CA-NS1, CA-NS2, CA-NS3, CA-NS4, CA-NS5, CA-NS6, CA-NS7, CA-Oas, CA-Obs, CA-Ojp, CA-Qc2, CA-Qcu, CA-Qfo, CA-SCC, CA-SF1, CA-SF2, CA-SF3, CA-SJ2, CA-SJ3, CA-TP1, CA-TP3, CA-TP4, CA-TPD, CA-WP1, US-A03, US-A10, US-A32, US-A74, US-ADR, US-AR1, US-AR2, US-ARb, US-ARc, US-ARM, US-Aud, US-Bar, US-Bi1, US-Bi2, US-Bkg, US-Blk, US-Blo, US-Bn1, US-Bn2, US-Bn3, US-Bo1, US-Bo2, US-Br3, US-CaV, US-Ced, US-CF1, US-CF2, US-CF3, US-CF4, US-ChR, US-Cop, US-CPk, US-CRT, US-Ctn, US-Dia, US-Dix, US-Dk1, US-Dk2, US-Dk3, US-EDN, US-Elm, US-EML, US-Fmf, US-FPe, US-FR2, US-FR3, US-Fuf, US-Fwf, US-GLE, US-GMF, US-Goo, US-Ha1, US-Ha2, US-Hn2, US-Hn3, US-Ho1, US-Ho2, US-Ho3, US-IB1, US-IB2, US-Ivo, US-KFS, US-KLS, US-Kon, US-KS1, US-KS2, US-KUT, US-Lin, US-Los, US-LPH, US-LWW, US-Me1, US-Me2, US-Me3, US-Me4, US-Me5, US-Me6, US-MMS, US-MOz, US-Mpj, US-MRf, US-MtB, US-Myb, US-NC1, US-NC2, US-NC3, US-NC4, US-Ne1, US-Ne2, US-Ne3, US-NGB, US-NR1, US-Oho, US-ORv, US-PHM, US-Pon, US-Prr, US-RC1, US-RC2, US-RC3, US-RC4, US-RC5, US-Rls, US-Rms, US-Ro1, US-Ro2, US-Ro5, US-Ro6, US-Rpf, US-Rws, US-SdH, US-Seg, US-Ses, US-SFP, US-Shd, US-Skr, US-Slt, US-Snd, US-Sne, US-Snf, US-SO2, US-SO3, US-SO4, US-SP1, US-SP2, US-SP3, US-SRC, US-SRG, US-SRM, US-Srr, US-Sta, US-StJ, US-Syv, US-Ton, US-Tw1, US-Tw2, US-Tw3, US-Tw4, US-Tw5, US-Twt, US-Uaf, US-UMB, US-UMd, US-Var, US-Vcm, US-Vcp, US-Vcs, US-WBW, US-WCr, US-Wdn, US-Wgr, US-Whs, US-Wi0, US-Wi1, US-Wi3, US-Wi4, US-Wi5, US-Wi6, US-Wi7, US-Wi8, US-Wi9, US-Wjs, US-Wkg, US-Wlr, US-Wpp, US-WPT, US-Wrc, US-xBR, US-xCP, US-xDL, US-xHA, US-xKA, US-xKZ, US-xRM, US-xSR, US-xWD

Intercomparison Of In Situ Sensors For Ground-Based Land Surface Temperature Measurements
Krishnan, P., Meyers, T. P., Hook, S. J., Heuer, M., Senn, D., Dumas, E. J.

Land surface temperature (LST) is a key variable in the determination of land surface energy exchange processes from local to global scales. Accurate ground measurements of LST are necessary for a number of applications including validation of satellite LST products or improvement of both climate and numerical weather prediction …


Journal: Sensors, Volume 20 (18): 5268 (2020). DOI: https://doi.org/10.3390/s20185268 Sites: US-Aud, US-Bkg, US-CaV, US-FPe