Rice yield estimation using synthetic aperture radar (SAR) and the ORYZA crop growth model: development and application of the system in South and South-east Asian countries-Reference-Cited by-同舟云学术

Rice yield estimation using synthetic aperture radar (SAR) and the ORYZA crop growth model: development and application of the system in South and South-east Asian countries

Published:2018-12-11 Issue:21 Volume:40 Page:8093-8124
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Setiyono T. D.¹,Quicho E. D.¹,Holecz F. H.²,Khan N. I.¹,Romuga G.¹,Maunahan A.¹,Garcia C.¹,Rala A.¹,Raviz J.¹,Collivignarelli F.²,Gatti L.²,Barbieri M.²,Phuong D. M.³,Minh V. Q.⁴,Vo Q. T.⁴,Intrman A.⁵,Rakwatin P.⁶,Sothy M.⁷,Veasna T.⁸,Pazhanivelan S.⁹,Mabalay M. R. O.¹⁰

Affiliation:

1. Sustainable Impact Platform, International Rice Research Institute (IRRI), Metro Manila, Philippines

2. sarmap, Purasca, Switzerland

3. Ministry of Agriculture and Rural Development (MARD), National Institute of Agricultural Planning and Projection (NIAPP), Hanoi, Vietnam

4. Department of Land Resources, Can Tho University (CTU), Can Tho, Vietnam

5. Thailand Rice Department (TRD), Ministry of Agriculture and Cooperatives (MOAC), Bangkok, Thailand

6. Research and Development Group, Geo-Informatics and Space Technology Development Agency (GISTDA), Bangkok, Thailand

7. Department of Planning and Statistics (DPS), Ministry of Agriculture, Forestry and Fisheries (MAFF), Phnom Penh, Cambodia

8. Soil and Water Sciences Division, Cambodian Agricultural Research and Development Institute (CARDI), Phnom Penh, Cambodia

9. Department of Remote Sensing and Geographic Information System, Tamil Nadu Agricultural University (TNAU), Coimbatore, India

10. Agronomy Soils and Plant Physiology Division, Philippine Rice Research Institute (PhilRice), Muñoz, Philippines

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2018.1547457

Reference50 articles.

1. Application of a rice growth and water balance model in an irrigated semi-arid subtropical environment

2. Vegetation modeled as a water cloud

3. Neural network estimation of LAI, fAPAR, fCover and LAI×Cab, from top of canopy MERIS reflectance data: Principles and validation

4. Modelling the effect of groundwater depth on yield-increasing interventions in rainfed lowland rice in Central Java, Indonesia

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