165 related articles for article (PubMed ID: 36405847)
1. Downscaling Global Gridded Crop Yield Data Products and Crop Water Productivity Mapping Using Remote Sensing Derived Variables in the South Asia.
Mohanasundaram S; Kasiviswanathan KS; Purnanjali C; Santikayasa IP; Singh S
Int J Plant Prod; 2023; 17(1):1-16. PubMed ID: 36405847
[TBL] [Abstract][Full Text] [Related]
2. Predicting rice productivity for ground data-sparse regions: A transferable framework and its application to North Korea.
Shi Y; Li L; Wu B; Zhang Y; Wang B; Niu W; He L; Jin N; Pan S; Tian H; Yu Q
Sci Total Environ; 2024 Jun; 946():174227. PubMed ID: 38936710
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. Reconstruction and application of the temperature-vegetation-precipitation drought index in mainland China based on remote sensing datasets and a spatial distance model.
Wei W; Zhang H; Ma L; Wang X; Guo Z; Xie B; Zhou J; Wang J
J Environ Manage; 2022 Dec; 323():116208. PubMed ID: 36261977
[TBL] [Abstract][Full Text] [Related]
5. Improved agricultural Water management in data-scarce semi-arid watersheds: Value of integrating remotely sensed leaf area index in hydrological modeling.
Paul M; Rajib A; Negahban-Azar M; Shirmohammadi A; Srivastava P
Sci Total Environ; 2021 Oct; 791():148177. PubMed ID: 34118663
[TBL] [Abstract][Full Text] [Related]
6. Water productivity of rainfed maize and wheat: A local to global perspective.
Rattalino Edreira JI; Guilpart N; Sadras V; Cassman KG; van Ittersum MK; Schils RLM; Grassini P
Agric For Meteorol; 2018 Sep; 259():364-373. PubMed ID: 30224833
[TBL] [Abstract][Full Text] [Related]
7. Synergistic integration of optical and microwave satellite data for crop yield estimation.
Mateo-Sanchis A; Piles M; Muñoz-Marí J; Adsuara JE; Pérez-Suay A; Camps-Valls G
Remote Sens Environ; 2019 Dec; 234():111460. PubMed ID: 31798192
[TBL] [Abstract][Full Text] [Related]
8. Reduced tillage and crop diversification can improve productivity and profitability of rice-based rotations of the Eastern Gangetic Plains.
Hoque MA; Gathala MK; Timsina J; Ziauddin MATM; Hossain M; Krupnik TJ
Field Crops Res; 2023 Feb; 291():108791. PubMed ID: 36742349
[TBL] [Abstract][Full Text] [Related]
9. Forecasting wheat and barley crop production in arid and semi-arid regions using remotely sensed primary productivity and crop phenology: A case study in Iraq.
Qader SH; Dash J; Atkinson PM
Sci Total Environ; 2018 Feb; 613-614():250-262. PubMed ID: 28915461
[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. Improved gross primary production estimation in rice fields through integrated multi-scale methodologies.
Lee B; Kwon H; Zhao P; Tenhunen J
Plant Environ Interact; 2023 Jun; 4(3):163-174. PubMed ID: 37362422
[TBL] [Abstract][Full Text] [Related]
12. Implication of climate change on crop water requirement in the semi-arid region of Western Maharashtra, India.
Gade SA; Khedkar DD
Environ Monit Assess; 2023 Jun; 195(7):829. PubMed ID: 37294360
[TBL] [Abstract][Full Text] [Related]
13. Irrigated Crop Types Mapping in Tashkent Province of Uzbekistan with Remote Sensing-Based Classification Methods.
Erdanaev E; Kappas M; Wyss D
Sensors (Basel); 2022 Jul; 22(15):. PubMed ID: 35957240
[TBL] [Abstract][Full Text] [Related]
14. Water Productivity Mapping (WPM) Using Landsat ETM+ Data for the Irrigated Croplands of the Syrdarya River Basin in Central Asia.
Platonov A; Thenkabail PS; Biradar CM; Cai X; Gumma M; Dheeravath V; Cohen Y; Alchanatis V; Goldshlager N; Ben-Dor E; Vithanage J; Manthrithilake H; Kendjabaev S; Isaev S
Sensors (Basel); 2008 Dec; 8(12):8156-8180. PubMed ID: 27873981
[TBL] [Abstract][Full Text] [Related]
15. Sensitivity of crop cover to climate variability: insights from two Indian agro-ecoregions.
Mondal P; Jain M; DeFries RS; Galford GL; Small C
J Environ Manage; 2015 Jan; 148():21-30. PubMed ID: 24680541
[TBL] [Abstract][Full Text] [Related]
16. Combining machine learning and remote sensing-integrated crop modeling for rice and soybean crop simulation.
Ko J; Shin T; Kang J; Baek J; Sang WG
Front Plant Sci; 2024; 15():1320969. PubMed ID: 38410726
[TBL] [Abstract][Full Text] [Related]
17. [Evaluating the performance of the UCLA method for spatially downscaling soil moisture products using three Ts/VI indices].
Ling ZW; He LB; Zeng H
Ying Yong Sheng Tai Xue Bao; 2014 Feb; 25(2):545-52. PubMed ID: 24830256
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. How Universal Is the Relationship between Remotely Sensed Vegetation Indices and Crop Leaf Area Index? A Global Assessment.
Kang Y; Özdoğan M; Zipper SC; Román MO; Walker J; Hong SY; Marshall M; Magliulo V; Moreno J; Alonso L; Miyata A; Kimball B; Loheide SP
Remote Sens (Basel); 2016; 8(7):597. PubMed ID: 30002923
[TBL] [Abstract][Full Text] [Related]
20. Biophysical and biochemical features' feedback associated with a flood episode in a tropical river basin model.
Bellanthudawa BKA; Nawalage NMSK; Halwatura D; Ahmed SH; Kendaragama KMN; Neththipola MMTD
Environ Monit Assess; 2023 Mar; 195(4):504. PubMed ID: 36952040
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]