文献
J-GLOBAL ID:202202282504170881
整理番号:22A1057446
SMAP土壌水分製品を非集計するための表面特性と組み合わせた高分解能レーダ後方散乱の利点を得るためのアンサンブル学習の利用【JST・京大機械翻訳】
Using ensemble learning to take advantage of high-resolution radar backscatter in conjunction with surface features to disaggregate SMAP soil moisture product
著者 (7件):
Karami Ayoob
(Department of Watershed Management Engineering, Faculty of Natural Resources, Tarbiat Modares University, Tehran, Iran)
,
Karami Ayoob
(Fenner School of Environment & Society, Australian National University, Canberra, Australia)
,
Moradi Hamid Reza
(Department of Watershed Management Engineering, Faculty of Natural Resources, Tarbiat Modares University, Tehran, Iran)
,
Mousivand Alijafar
(Department of GIS and Remote Sensing (GRS), Tarbiat Modares University, Tehran, Iran)
,
Mousivand Alijafar
(Department of Environmental Sciences, Wageningen University & Research, Wageningen, The Netherlands)
,
van Dijk Albert I. J. M.
(Fenner School of Environment & Society, Australian National University, Canberra, Australia)
,
Renzullo Luigi
(Fenner School of Environment & Society, Australian National University, Canberra, Australia)
資料名:
International Journal of Remote Sensing
(International Journal of Remote Sensing)
巻:
43
号:
3
ページ:
894-914
発行年:
2022年
JST資料番号:
B0645B
ISSN:
0143-1161
資料種別:
逐次刊行物 (A)
記事区分:
原著論文
発行国:
イギリス (GBR)
言語:
英語 (EN)