Results 21 to 30 of about 7,186,066 (138)

Land surface phenology from SPOT VEGETATION time series [PDF]

Revista de Teledetección, 2016
Land surface phenology from time series of satellite data are expected to contribute to improve the representation of vegetation phenology in earth system models.
A. Verger, I. Filella, F. Baret, J. Peñuelas   +3 more
doaj   +2 more sources

Comparing land surface phenology of major European crops as derived from SAR and multispectral data of Sentinel-1 and -2.

Remote Sens Environ, 2021
Meroni M, d'Andrimont R, Vrieling A, Fasbender D, Lemoine G, Rembold F, Seguini L, Verhegghen A.   +7 more
europepmc   +2 more sources

What is driving the phenology across the Amazon? A benchmark for land-surface models [PDF]

, 2010
We present a study which has used a Fourier-based time-series analysis method applied to 8 years of EO-derived observations of phenology (vegetation indices) and their potential drivers (downward shortwave radiation and precipitation).
Anderson, Liana, Barbier, Nicolas, Zelazowski, Przemyslaw, Huntingford, Christopher, de Aragao, Luiz E., Arain, Egidio, Weedon, Graham P., Gerard, France F., Bradley, Andrew V.   +8 more
core   +3 more sources

Implementation of a new crop phenology and irrigation scheme in the ISBA land surface model using SURFEX_v8.1 [PDF]

Geoscientific Model Development, 2022
With an increase in the number of natural processes represented, global land surface models (LSMs) have become more and more accurate in representing natural terrestrial ecosystems.
A. Druel, A. Druel, S. Munier, A. Mucia, C. Albergel, C. Albergel, J.-C. Calvet   +6 more
doaj   +1 more source

Comparisons of numerical phenology models and machine learning methods on predicting the spring onset of natural vegetation across the Northern Hemisphere

Ecological Indicators, 2021
The timing of vegetation spring onset is largely influenced by climate factors, making it sensitive to climate variation. Robust models that predict vegetation spring onset via the climate forcing data are needed in the land surface models for ...
Wanjing Li, Qinchuan Xin, Xuewen Zhou, Zhicheng Zhang, Yongjian Ruan   +4 more
doaj   +1 more source

Impact of Snowpack on the Land Surface Phenology in the Tianshan Mountains, Central Asia

Remote Sensing, 2022
The accumulation and ablation processes of seasonal snow significantly affect the land surface phenology in a mountainous ecosystem. However, the ability of snow to regulate the alpine land surface phenology in the arid regions is not well described in ...
Tao Yang, Qian Li, Qiang Zou, Rafiq Hamdi, Fengqi Cui, Lanhai Li   +5 more
doaj   +1 more source

Global-Scale Associations of Vegetation Phenology with Rainfall and Temperature at a High Spatio-Temporal Resolution

Remote Sensing, 2014
Phenology response to climatic variables is a vital indicator for understanding changes in biosphere processes as related to possible climate change. We investigated global phenology relationships to precipitation and land surface temperature (LST) at ...
Nicholas Clinton, Le Yu, Haohuan Fu, Conghui He, Peng Gong   +4 more
doaj   +1 more source

Land surface phenological response to decadal climate variability across Australia using satellite remote sensing [PDF]

Biogeosciences, 2014
Land surface phenological cycles of vegetation greening and browning are influenced by variability in climatic forcing. Quantitative spatial information on phenological cycles and their variability is important for agricultural applications, wildfire ...
M. Broich, A. Huete, M. G. Tulbure, X. Ma, Q. Xin, M. Paget, N. Restrepo-Coupe, K. Davies, R. Devadas, A. Held   +9 more
doaj   +1 more source

Uncertainties in climate responses to past land cover change: First results from the LUCID intercomparison study [PDF]

, 2009
Seven climate models were used to explore the biogeophysical impacts of human-induced land cover change (LCC) at regional and global scales. The imposed LCC led to statistically significant decreases in the northern hemisphere summer latent heat flux in ...
Lawrence, P.J., Reick, C. H., van den Hurk, B., Molen, M.K. van der, Reick, C., Brovkin, V., Strengers, B.J., Bonan, G., Lawrence, P., van den Hurk, B.J.J.M., Gayler, V., Seneviratne, S.I., Davin, E.L., Cruz, F.T., Lawrence, P. J., Mueller, C., Cruz, F. T., Seneviratne, S., Strengers, B., Brovkin, V. ; https://orcid.org/, van der Molen, M.K., Seneviratne, S. I., Davin, E., Bonan, G. B., Pitman, A., Gayler, V. ; https://orcid.org/, Claussen, M. ; https://orcid.org/, Delire, C., Senevirantne, S.I., van der Molen, M., Reick, C.H., Voldoire, A., Davin, E. L., Pitman, A.J., Bonan, G.B., Claussen, M., van den Hurk, B. J. J. M., Ganzeveld, L.N., Strengers, B. J., Ganzeveld, L., van der Molen, M. K., Müller, C., Ganzefeld, L., Hurk, B.J.J.M. van den, Cruz, F., Pitman, A. J., Delire, Christine, Noblet-Ducoudré, N., de, Davin, Edouard Léopold; id_orcid, Seneviratne, Sonia I.; id_orcid, de Noblet-Ducoudré, Nathalie, Molen, M.K., van der, Pitman, Andrew J., Hurk, B.J.J.M., van den, de Noblet-Ducoudré, N.   +54 more
core   +2 more sources

The blsp R package with a Bayesian land surface phenology model

, 2022
This software is an R package providing the Bayesian land surface phenology (BLSP) model that quantifies long-term annual land surface phenology from temporally sparse optical remote sensing time series (originally developed for 30 m Landsat time series).
Xiaojie Gao, Isabella Hinks, Owen Smith, Matt Shisler, Ian R. McGregor   +4 more
core   +1 more source