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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

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
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
Crop production and yield estimation using remotely sensed data have been studied widely, but such information is generally scarce in arid and semi-arid regions. In these regions, inter-annual variation in climatic factors (such as rainfall) combined with anthropogenic factors (such as civil war) pose major risks to food security. Thus, an operational crop production estimation and forecasting system is required to help decision-makers to make early estimates of potential food availability. Data from NASA's MODIS with official crop statistics were combined to develop an empirical regression-based model to forecast winter wheat and barley production in Iraq. The study explores remotely sensed indices representing crop productivity over the crop growing season to find the optimal correlation with crop production. The potential of three different remotely sensed indices, and information related to the phenology of crops, for forecasting crop production at the governorate level was tested and their results were validated using the leave-one-year-out approach. Despite testing several methodological approaches, and extensive spatio-temporal analysis, this paper depicts the difficulty in estimating crop yield on an annual base using current satellite low-resolution data. However, more precise estimates of crop production were possible. The result of the current research implies that the date of the maximum vegetation index (VI) offered the most accurate forecast of crop production with an average R2=0.70 compared to the date of MODIS EVI (Avg R2=0.68) and a NPP (Avg R2=0.66). When winter wheat and barley production were forecasted using NDVI, EVI and NPP and compared to official statistics, the relative error ranged from -20 to 20%, -45 to 28% and -48 to 22%, respectively. The research indicated that remotely sensed indices could characterize and forecast crop production more accurately than simple cropping area, which was treated as a null model against which to evaluate the proposed approach.
Vegetation phenology, EVI, NDVI, MODIS, Crop yield/production forecasting
0048-9697
250-262
Qader, Sarchil
b1afb647-aeff-4bb8-84f2-56865c4eb9e4
Dash, Jadunandan
51468afb-3d56-4d3a-aace-736b63e9fac8
Atkinson, Peter
96e96579-56fe-424d-a21c-17b6eed13b0b
Qader, Sarchil
b1afb647-aeff-4bb8-84f2-56865c4eb9e4
Dash, Jadunandan
51468afb-3d56-4d3a-aace-736b63e9fac8
Atkinson, Peter
96e96579-56fe-424d-a21c-17b6eed13b0b

Qader, Sarchil, Dash, Jadunandan and Atkinson, Peter (2018) 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 Science of Total Environment, 613-614, pp. 250-262.

Record type: Article

Abstract

Crop production and yield estimation using remotely sensed data have been studied widely, but such information is generally scarce in arid and semi-arid regions. In these regions, inter-annual variation in climatic factors (such as rainfall) combined with anthropogenic factors (such as civil war) pose major risks to food security. Thus, an operational crop production estimation and forecasting system is required to help decision-makers to make early estimates of potential food availability. Data from NASA's MODIS with official crop statistics were combined to develop an empirical regression-based model to forecast winter wheat and barley production in Iraq. The study explores remotely sensed indices representing crop productivity over the crop growing season to find the optimal correlation with crop production. The potential of three different remotely sensed indices, and information related to the phenology of crops, for forecasting crop production at the governorate level was tested and their results were validated using the leave-one-year-out approach. Despite testing several methodological approaches, and extensive spatio-temporal analysis, this paper depicts the difficulty in estimating crop yield on an annual base using current satellite low-resolution data. However, more precise estimates of crop production were possible. The result of the current research implies that the date of the maximum vegetation index (VI) offered the most accurate forecast of crop production with an average R2=0.70 compared to the date of MODIS EVI (Avg R2=0.68) and a NPP (Avg R2=0.66). When winter wheat and barley production were forecasted using NDVI, EVI and NPP and compared to official statistics, the relative error ranged from -20 to 20%, -45 to 28% and -48 to 22%, respectively. The research indicated that remotely sensed indices could characterize and forecast crop production more accurately than simple cropping area, which was treated as a null model against which to evaluate the proposed approach.

Text Manuscript_SQ2017 - Accepted Manuscript
Restricted to Repository staff only until 7 September 2018.

More information

Accepted/In Press date: 7 September 2017
e-pub ahead of print date: 12 September 2017
Published date: 1 February 2018
Keywords: Vegetation phenology, EVI, NDVI, MODIS, Crop yield/production forecasting

Identifiers

Local EPrints ID: 413821
URI: http://eprints.soton.ac.uk/id/eprint/413821
ISSN: 0048-9697
PURE UUID: 99e976b5-60e8-4766-b694-99d28d5bbfde
ORCID for Jadunandan Dash: ORCID iD orcid.org/0000-0002-5444-2109

Catalogue record

Date deposited: 02 Oct 2017 16:30
Last modified: 07 Oct 2017 18:34

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