25 related articles for article (PubMed ID: 29868265)
1. Prediction of Crop Yield Using Phenological Information Extracted from Remote Sensing Vegetation Index.
Ji Z; Pan Y; Zhu X; Wang J; Li Q
Sensors (Basel); 2021 Feb; 21(4):. PubMed ID: 33671356
[TBL] [Abstract][Full Text] [Related]
2. Intercomparison of Same-Day Remote Sensing Data for Measuring Winter Cover Crop Biophysical Traits.
Thieme A; Prabhakara K; Jennewein J; Lamb BT; McCarty GW; Hively WD
Sensors (Basel); 2024 Apr; 24(7):. PubMed ID: 38610550
[TBL] [Abstract][Full Text] [Related]
3. Combined use of Landsat-8 and Sentinel-2A images for winter crop mapping and winter wheat yield assessment at regional scale.
Skakun S; Vermote E; Roger JC; Franch B
AIMS Geosci; 2017; 3(2):163-186. PubMed ID: 29888751
[TBL] [Abstract][Full Text] [Related]
4. The 10-m crop type maps in Northeast China during 2017-2019.
You N; Dong J; Huang J; Du G; Zhang G; He Y; Yang T; Di Y; Xiao X
Sci Data; 2021 Feb; 8(1):41. PubMed ID: 33531510
[TBL] [Abstract][Full Text] [Related]
5. A daily gap-free normalized difference vegetation index dataset from 1981 to 2023 in China.
Li H; Cao Y; Xiao J; Yuan Z; Hao Z; Bai X; Wu Y; Liu Y
Sci Data; 2024 May; 11(1):527. PubMed ID: 38778028
[TBL] [Abstract][Full Text] [Related]
6. Joint measurements of NDVI and crop production data-set related to combination of management zones delineation and nitrogen fertilisation levels.
Cordero E; Longchamps L; Khosla R; Sacco D
Data Brief; 2020 Feb; 28():104968. PubMed ID: 31970270
[TBL] [Abstract][Full Text] [Related]
7. Crop Classification Based on Red Edge Features Analysis of GF-6 WFV Data.
Kang Y; Meng Q; Liu M; Zou Y; Wang X
Sensors (Basel); 2021 Jun; 21(13):. PubMed ID: 34202705
[TBL] [Abstract][Full Text] [Related]
8. Estimation of different data compositions for early-season crop type classification.
Hao P; Wu M; Niu Z; Wang L; Zhan Y
PeerJ; 2018; 6():e4834. PubMed ID: 29868265
[TBL] [Abstract][Full Text] [Related]
9. Early-season crop mapping using improved artificial immune network (IAIN) and Sentinel data.
Hao P; Tang H; Chen Z; Liu Z
PeerJ; 2018; 6():e5431. PubMed ID: 30186678
[TBL] [Abstract][Full Text] [Related]
10. Multi-Year Mapping of Major Crop Yields in an Irrigation District from High Spatial and Temporal Resolution Vegetation Index.
Yu B; Shang S
Sensors (Basel); 2018 Nov; 18(11):. PubMed ID: 30404139
[TBL] [Abstract][Full Text] [Related]
11. Transfer Learning for Crop classification with Cropland Data Layer data (CDL) as training samples.
Hao P; Di L; Zhang C; Guo L
Sci Total Environ; 2020 Sep; 733():138869. PubMed ID: 32450376
[TBL] [Abstract][Full Text] [Related]
12. [Comparison of GIMMS and MODIS normalized vegetation index composite data for Qing-Hai-Tibet Plateau].
Du JQ; Shu JM; Wang YH; Li YC; Zhang LB; Guo Y
Ying Yong Sheng Tai Xue Bao; 2014 Feb; 25(2):533-44. PubMed ID: 24830255
[TBL] [Abstract][Full Text] [Related]
13. Using of Multi-Source and Multi-Temporal Remote Sensing Data Improves Crop-Type Mapping in the Subtropical Agriculture Region.
Sun C; Bian Y; Zhou T; Pan J
Sensors (Basel); 2019 May; 19(10):. PubMed ID: 31130689
[TBL] [Abstract][Full Text] [Related]
14.
; ; . PubMed ID:
[No Abstract] [Full Text] [Related]
15.
; ; . PubMed ID:
[No Abstract] [Full Text] [Related]
16.
; ; . PubMed ID:
[No Abstract] [Full Text] [Related]
17.
; ; . PubMed ID:
[No Abstract] [Full Text] [Related]
18.
; ; . PubMed ID:
[No Abstract] [Full Text] [Related]
19.
; ; . PubMed ID:
[No Abstract] [Full Text] [Related]
20.
; ; . PubMed ID:
[No Abstract] [Full Text] [Related]
[Next] [New Search]
