Satellite-Based Crop Monitoring and Yield Estimation—A Review
- Olipa N. Lungu
- Lydia M. Chabala
- Chizumba Shepande
Abstract
To sustain food security and crop condition monitoring, yield estimation must improve at local and global scales. The aim of this review was to give a background of satellite-based crop monitoring and crop yield estimation, including the use of crop models. Recently, most advances in remote sensing techniques, aimed at complimenting the traditional crop harvest surveys, have focused on high-production and information-rich areas. However, there is limited research in dynamic landscapes using these techniques at local scales in most Southern African countries. Models such as the Decision Support System Agro-Technology’s (DSSAT) CERES-model, and Agricultural Production Simulator (APSIM) have been used to simulate maize biophysical parameters and yield variability in a changing climate. Despite the successes, there is still need to consider yield prediction using simplified models that decision-makers can use to plan for food support and sales. The application of freely-available satellite data with focus on maize crop as a staple for Southern Africa, highlights some challenges such as heavy reliance on agro-meteorological estimations and regional estimations of crop yield. It also raises questions of predicting across large growing belts without consideration of diverse cropping patterns. Conversely, future opportunities in crop monitoring and yield estimation using remotely sensed-data still shed a light of hope. For instance, employing multi-model configurations or multi-model ensembles is one of the major missing gaps needing consideration by crop modeling research. Other simpler, but versatile opportunities are the use of crop –monitoring applications on smart phones by small holder farmers to provide phenological data to decision makers throughout a growing season.
- Full Text:
PDF
- DOI:10.5539/jas.v13n1p180
Journal Metrics
(The data was calculated based on Google Scholar Citations)
- Google-based Impact Factor (2018): 2.28
- h-index (December 2018): 31
- i10-index (December 2018): 304
- h5-index (December 2018): 22
- h5-median (December 2018): 27
Index
- ACNP
- AGRICOLA
- AGRIS
- BASE (Bielefeld Academic Search Engine)
- CAB Abstracts
- CiteFactor
- CNKI Scholar
- CrossRef
- DESY Publication Database
- DTU Findit (DTU Library)
- Elektronische Zeitschriftenbibliothek (EZB)
- Excellence in Research for Australia (ERA)
- FindIt@Bham (UoB Library)
- Genamics JournalSeek
- Google Scholar
- HOLLIS (Harvard Library)
- Index Copernicus
- Infotrieve (Copyright Clearance Center)
- Jisc Library Hub Discover
- JournalTOCs
- Library of Congress
- LIVIVO (ZB MED)
- LOCKSS
- LSE Library
- Max Planck Institutes
- MIAR
- Norwegian Centre for Research Data (NSD)
- OAJI
- Open J-Gate
- OskiCat (UCB Library)
- PKP Open Archives Harvester
- Publons
- Qualis/CAPES
- ROAD
- Scilit
- SHERPA/RoMEO
- Southwest-German Union Catalogue
- Standard Periodical Directory
- Stanford Libraries
- StarPlus (UoS Library)
- Technische Informationsbibliothek (TIB)
- Trove
- UCR Library
- Ulrich's
- UniCat
- Universe Digital Library
- University Library (USask)
- WorldCat
- WorldWideScience
- WRLC Catalog
Contact
- Anne BrownEditorial Assistant
- jas@ccsenet.org