Crop yield forecasting on the Canadian Prairies using MODIS NDVI data

Mkhabela, M.S.; Bullock, Paul; Raj, Shammi; Wang, Shusen; Yang, Yongchun

doi:10.1016/j.agrformet.2010.11.012

Cited by 414 publications

(278 citation statements)

References 47 publications

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“…The PBIAS between the predicted yield showed a variation of less than ±10 per cent in 5 out of the 10 districts under study. This disagreements between the actual and predicted rice yield estimates could be attributed by other factors, such as atmospheric interference such as cloud (Mkhabela et al, 2011), micro level weather variation and water availability (Son et al, 2013) and uncertaininty associated with ground based estimates (Mosleh and Hassan, 2014).…”

Section: Resultsmentioning

confidence: 99%

“…The NDVI data derived from the AVHHR of the National Oceanic and Atmospheric Administration (NOAA) have been used extensively to monitor crop condition and forecasting yield and subsequently production in many countries as reported by Mkhabela (2011). He also studied the potential of using MODIS-NDVI to forecast crop yield on the Canadian Prairies and also to identify the suitable time for making a reliable crop yield forecast.…”

Section: Issn: 2319-7706 Volume 6 Number 12 (2017) Pp 2277-2293mentioning

confidence: 99%

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Rice Yield Prediction Using MODIS - NDVI (MOD13Q1) and Land Based Observations

Ajith¹,

Geethalakshmi²,

Ragunath³

et al. 2017

Int.J.Curr.Microbiol.App.Sci

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Section: Resultsmentioning

confidence: 99%

Section: Issn: 2319-7706 Volume 6 Number 12 (2017) Pp 2277-2293mentioning

confidence: 99%

Rice Yield Prediction Using MODIS - NDVI (MOD13Q1) and Land Based Observations

Ajith¹,

Geethalakshmi²,

Ragunath³

et al. 2017

Int.J.Curr.Microbiol.App.Sci

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“…The timely availability of the spatial distribution of crop types is required for statistical and economic purposes as well as agrarian policy actions related to subsidy payments or implementation of agro-environmental measurements [1,2]. Understanding the dynamic progress of the composition and spatial structure of mosaicking crops is critical for a diversity of agricultural monitoring activities (e.g., crop acreage estimation, yield modeling, harvest operations schedules and greenhouse gas mitigation) [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

In-Season Crop Mapping with GF-1/WFV Data by Combining Object-Based Image Analysis and Random Forest

Song

Zhou

et al. 2017

Remote Sensing

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Producing accurate crop maps during the current growing season is essential for effective agricultural monitoring. Substantial efforts have been made to study regional crop distribution from year to year, but less attention is paid to the dynamics of composition and spatial extent of crops within a season. Understanding how crops are distributed at the early developing stages allows for the timely adjustment of crop planting structure as well as agricultural decision making and management. To address this knowledge gap, this study presents an approach integrating object-based image analysis with random forest (RF) for mapping in-season crop types based on multi-temporal GaoFen satellite data with a spatial resolution of 16 meters. A multiresolution local variance strategy was used to create crop objects, and then object-based spectral/textural features and vegetation indices were extracted from those objects. The RF classifier was employed to identify different crop types at four crop growth seasons by integrating available features. The crop classification performance of different seasons was assessed by calculating F-score values. Results show that crop maps derived using seasonal features achieved an overall accuracy of more than 87%. Compared to the use of spectral features, a feature combination of in-season textures and multi-temporal spectral and vegetation indices performs best when classifying crop types. Spectral and temporal information is more important than texture features for crop mapping. However, texture can be essential information when there is insufficient spectral and temporal information (e.g., crop identification in the early spring). These results indicate that an object-based image analysis combined with random forest has considerable potential for in-season crop mapping using high spatial resolution imagery.

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“…Previous research has reported that TINDVI can be a good indicator for crop production [20] [44] [55]. However, the sensitivity of TINDVI for crop production depends on rainfall seasonality; it can be a good indicator for crop production when water is the limiting factor for crop growth [55].…”

Section: Relationship Between Management Zone and Temporal Variabilitmentioning

confidence: 99%

Remote Sensing Derived Phenological Metrics to Assess the Spatio-Temporal Growth Variability in Cropping Fields

Araya¹,

Ostendorf²,

Lyle³

et al. 2017

ARS

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Precision Agriculture (PA) recognizes and manages intra-field spatial variability to increase profitability and reduced environmental impact. Site Specific Crop Management (SSCM), a form of PA, subdivides a cropping field into uniformly manageable zones, based on quantitative measurement of yield limiting factors. In Mediterranean environments, the spatial and temporal yield variability of rain-fed cropping system is strongly influenced by the spatial variability of Plant Available Water-holding Capacity (PAWC) and its strong interaction with temporally variable seasonal rainfall. The successful adoption of SSCM depends on the understanding of both spatial and temporal variabilities in cropping fields. Remote sensing phenological metrics provide information about the biophysical growth conditions of crops across fields. In this paper, we examine the potential of phenological metrics to assess the spatial and temporal crop growth variability across a cropping field. The study was conducted at a wheat cropping field in Minnipa, South Australia. The field was classified into three management zones using prolonged observations including soil assessment and multiple year yield data. The main analytical steps followed in this study were: calculation of the phenological metrics using time series NDVI data from Moderate Resolution Imaging Spectroradiometer (MODIS) for 15 years (2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015); producing spatial trend and temporal variability maps of phenological metrics; and finally, assessment of association between the spatial patterns and temporal variability of the metrics with management zones of the cropping field. The spatial trend of the seasonal peak NDVI metric showed significant association with the management zone pattern. In terms of temporal variability, Time-integrated NDVI (TINDVI) showed higher variability in the "good" zone compared with the "poor" zone. This indicates that the magnitude of the seasonal peak is more sensitive to soil related factors across the field, whereas TINDVI is more sensitive to seasonal variability. The interpretation of the association between phenological metrics and the management zone site conditions was discussed in relation to soil-climate interaction. The results demonstrate the potential of the phenological metrics to assess the spatial and temporal growth variability across cropping fields and to understand the soil-climate interaction. The approach presented in this paper provides a pathway to utilize phenological metrics for precision agricultural management application.

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Crop yield forecasting on the Canadian Prairies using MODIS NDVI data

Cited by 414 publications

References 47 publications

Rice Yield Prediction Using MODIS - NDVI (MOD13Q1) and Land Based Observations

Rice Yield Prediction Using MODIS - NDVI (MOD13Q1) and Land Based Observations

In-Season Crop Mapping with GF-1/WFV Data by Combining Object-Based Image Analysis and Random Forest

Remote Sensing Derived Phenological Metrics to Assess the Spatio-Temporal Growth Variability in Cropping Fields

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