+ Site Statistics
+ Search Articles
+ PDF Full Text Service
How our service works
Request PDF Full Text
+ Follow Us
Follow on Facebook
Follow on Twitter
Follow on LinkedIn
+ Subscribe to Site Feeds
Most Shared
PDF Full Text
+ Translate
+ Recently Requested

Estimating crop yields and production by integrating the FAO Crop Specific Water Balance model with real-time satellite data and ground-based ancillary data



Estimating crop yields and production by integrating the FAO Crop Specific Water Balance model with real-time satellite data and ground-based ancillary data



International Journal of Remote Sensing 21(18): 3487-3508, 15 December



An operational crop yield model was developed by introducing real-time satellite imagery into a Geographical Information System (GIS) and the Crop Specific Water Balance (CSWB) model of the Food and Agriculture Organization (FAO). Input databases were developed with three different resolutions; agro-ecological zone (AEZ) polygons, 7.6 km and 1.1 km pixels; from archived satellite data commonly used by Early Warning Systems (EWS) to simulate maize yield and production in Kenya from 1989 to 1997. Simulated production results from the GIS-based CSWB model were compared to historical maize production reports from two Government of Kenya (GoK) agencies. The coefficients of determination (r2) between the model and GoK district reports ranged from 0.86 to 0.89. The results indicated the 7.6 km pixel-by-pixel analysis was the most favorable method due to the Rainfall Estimate (RFE) input data having the same resolution. The GIS-based CSWB model developed by this study could also be easily expanded for use in other countries, extended for other crops, and improved in the future as satellite technologies improve.

Please choose payment method:






(PDF emailed within 0-6 h: $19.90)

Accession: 010604959

Download citation: RISBibTeXText

DOI: 10.1080/014311600750037516


Related references

Estimating millet production for famine early warning: An application of crop simulation modelling using satellite and ground-based data in Burkina Faso. Agricultural & Forest Meteorology 83(1-2): 95-112, 1997

Crop production and water-use: I. A model for estimating crop water-use with limited data. Journal of Agricultural Science 123(1): 9-13, 1994

A summary of observations concerning the information in the spectral-temporal-ancillary data available for estimating ground cover crop proportions. 1981

Use of climatic data with a crop water balance model and crop and pest response models in northern New South Wales. Technical Memorandum Division of Water Resources, Institute of Natural Resources and Environment, CSIRO ( 89/5): 221-241, 1989

Estimating crop acreage in small irrigated districts via ground-gathered and satellite data. Agronomie 12(9): 661-668, 1992

Estimating crop water requirements by the combined use of advanced numerical models and remote sensing satellite data. Annali della Facolta di Agraria della Universita degli Studi di Napoli Federico II, Portici 1: 261-278, 2004

Geo-Parcel Based Crop Identification by Integrating High Spatial-Temporal Resolution Imagery from Multi-Source Satellite Data. Remote Sensing 9(12): 1298, 2017

Deriving Maximum Light Use Efficiency From Crop Growth Model and Satellite Data to Improve Crop Biomass Estimation. Ieee Journal of Selected Topics in Applied Earth Observations and Remote 10(1): 104-117, 2017

Near-Real-Time Ocean Color Data Processing Using Ancillary Data From the Global Forecast System Model. IEEE Transactions on Geoscience and Remote Sensing 49(4): 1485-1495, 2011

Estimating age from recapture data: integrating incremental growth measures with ancillary data to infer age-at-length. Ecological Applications 21(7): 2487-2497, 2011

Estimating near future regional corn yields by integrating multi-source observations into a crop growth model. European Journal of Agronomy 49: 126-140, 2013

Estimating crop residue from ground and satellite-based spectral reflectance measurements. Dissertation Abstracts International B, Sciences and Engineering 53(2): 620B, 1992

Estimating crop yields from simulated daily weather data. Applied Engineering in Agriculture 3(2): 290-294, 1987

Model-Based Forecasting of Agricultural Crop Disease Risk at the Regional Scale, Integrating Airborne Inoculum, Environmental, and Satellit-Based Monitoring Data. Frontiers in Environmental Science 6: 63, 2018

Contribution to Real-Time Estimation of Crop Phenological States in a Dynamical Framework Based on NDVI Time Series: Data Fusion With SAR and Temperature. Ieee Journal of Selected Topics in Applied Earth Observations and Remote 9(8): 3512-3523, 2016