@article{alemohammad_global_2018,
	title = {Global downscaling of remotely sensed soil moisture using neural networks},
	volume = {22},
	issn = {1607-7938},
	url = {https://hess.copernicus.org/articles/22/5341/2018/},
	doi = {10.5194/hess-22-5341-2018},
	abstract = {Abstract. Characterizing soil moisture at spatiotemporal scales relevant to land surface processes (i.e.,
of the order of 1 km) is necessary in order to quantify its role in regional
feedbacks between the land surface and the atmospheric boundary layer.
Moreover, several applications such as agricultural management can benefit
from soil moisture information at fine spatial scales. Soil moisture
estimates from current satellite missions have a reasonably good temporal
revisit over the globe (2–3-day repeat time); however, their finest spatial
resolution is 9 km. NASA's Soil Moisture Active Passive (SMAP) satellite has
estimated soil moisture at two different spatial scales of 36 and 9 km since
April 2015. In this study, we develop a neural-network-based downscaling
algorithm using SMAP observations and disaggregate soil moisture to 2.25 km
spatial resolution. Our approach uses the mean monthly Normalized Differenced
Vegetation Index (NDVI) as ancillary data to quantify the subpixel
heterogeneity of soil moisture. Evaluation of the downscaled soil moisture
estimates against in situ observations shows that their accuracy is better
than or equal to the SMAP 9 km soil moisture estimates.},
	language = {en},
	number = {10},
	urldate = {2022-11-04},
	journal = {Hydrology and Earth System Sciences},
	author = {Alemohammad, Seyed Hamed and Kolassa, Jana and Prigent, Catherine and Aires, Filipe and Gentine, Pierre},
	month = oct,
	year = {2018},
	pages = {5341--5356},
}

{"_id":"Gh6mg8jJYE5v255dq","bibbaseid":"alemohammad-kolassa-prigent-aires-gentine-globaldownscalingofremotelysensedsoilmoistureusingneuralnetworks-2018","author_short":["Alemohammad, S. H.","Kolassa, J.","Prigent, C.","Aires, F.","Gentine, P."],"bibdata":{"bibtype":"article","type":"article","title":"Global downscaling of remotely sensed soil moisture using neural networks","volume":"22","issn":"1607-7938","url":"https://hess.copernicus.org/articles/22/5341/2018/","doi":"10.5194/hess-22-5341-2018","abstract":"Abstract. Characterizing soil moisture at spatiotemporal scales relevant to land surface processes (i.e., of the order of 1 km) is necessary in order to quantify its role in regional feedbacks between the land surface and the atmospheric boundary layer. Moreover, several applications such as agricultural management can benefit from soil moisture information at fine spatial scales. Soil moisture estimates from current satellite missions have a reasonably good temporal revisit over the globe (2–3-day repeat time); however, their finest spatial resolution is 9 km. NASA's Soil Moisture Active Passive (SMAP) satellite has estimated soil moisture at two different spatial scales of 36 and 9 km since April 2015. In this study, we develop a neural-network-based downscaling algorithm using SMAP observations and disaggregate soil moisture to 2.25 km spatial resolution. Our approach uses the mean monthly Normalized Differenced Vegetation Index (NDVI) as ancillary data to quantify the subpixel heterogeneity of soil moisture. Evaluation of the downscaled soil moisture estimates against in situ observations shows that their accuracy is better than or equal to the SMAP 9 km soil moisture estimates.","language":"en","number":"10","urldate":"2022-11-04","journal":"Hydrology and Earth System Sciences","author":[{"propositions":[],"lastnames":["Alemohammad"],"firstnames":["Seyed","Hamed"],"suffixes":[]},{"propositions":[],"lastnames":["Kolassa"],"firstnames":["Jana"],"suffixes":[]},{"propositions":[],"lastnames":["Prigent"],"firstnames":["Catherine"],"suffixes":[]},{"propositions":[],"lastnames":["Aires"],"firstnames":["Filipe"],"suffixes":[]},{"propositions":[],"lastnames":["Gentine"],"firstnames":["Pierre"],"suffixes":[]}],"month":"October","year":"2018","pages":"5341–5356","bibtex":"@article{alemohammad_global_2018,\n\ttitle = {Global downscaling of remotely sensed soil moisture using neural networks},\n\tvolume = {22},\n\tissn = {1607-7938},\n\turl = {https://hess.copernicus.org/articles/22/5341/2018/},\n\tdoi = {10.5194/hess-22-5341-2018},\n\tabstract = {Abstract. Characterizing soil moisture at spatiotemporal scales relevant to land surface processes (i.e.,\nof the order of 1 km) is necessary in order to quantify its role in regional\nfeedbacks between the land surface and the atmospheric boundary layer.\nMoreover, several applications such as agricultural management can benefit\nfrom soil moisture information at fine spatial scales. Soil moisture\nestimates from current satellite missions have a reasonably good temporal\nrevisit over the globe (2–3-day repeat time); however, their finest spatial\nresolution is 9 km. NASA's Soil Moisture Active Passive (SMAP) satellite has\nestimated soil moisture at two different spatial scales of 36 and 9 km since\nApril 2015. In this study, we develop a neural-network-based downscaling\nalgorithm using SMAP observations and disaggregate soil moisture to 2.25 km\nspatial resolution. Our approach uses the mean monthly Normalized Differenced\nVegetation Index (NDVI) as ancillary data to quantify the subpixel\nheterogeneity of soil moisture. Evaluation of the downscaled soil moisture\nestimates against in situ observations shows that their accuracy is better\nthan or equal to the SMAP 9 km soil moisture estimates.},\n\tlanguage = {en},\n\tnumber = {10},\n\turldate = {2022-11-04},\n\tjournal = {Hydrology and Earth System Sciences},\n\tauthor = {Alemohammad, Seyed Hamed and Kolassa, Jana and Prigent, Catherine and Aires, Filipe and Gentine, Pierre},\n\tmonth = oct,\n\tyear = {2018},\n\tpages = {5341--5356},\n}\n\n\n\n","author_short":["Alemohammad, S. H.","Kolassa, J.","Prigent, C.","Aires, F.","Gentine, P."],"key":"alemohammad_global_2018","id":"alemohammad_global_2018","bibbaseid":"alemohammad-kolassa-prigent-aires-gentine-globaldownscalingofremotelysensedsoilmoistureusingneuralnetworks-2018","role":"author","urls":{"Paper":"https://hess.copernicus.org/articles/22/5341/2018/"},"metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"https://bibbase.org/zotero/tereno","dataSources":["cq3J5xX6zmBvc2TQC"],"keywords":[],"search_terms":["global","downscaling","remotely","sensed","soil","moisture","using","neural","networks","alemohammad","kolassa","prigent","aires","gentine"],"title":"Global downscaling of remotely sensed soil moisture using neural networks","year":2018}