Predicting maize yield in Zimbabwe using dry dekads derived from remotely sensed Vegetation Condition Index

Kuri, Farai; Murwira, Amon; Murwira, Karin S.; Masocha, Mhosisi

doi:10.1016/j.jag.2014.04.021

Cited by 67 publications

(34 citation statements)

References 29 publications

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“…In locations with a limited number of sampling gauges, remote sensing data may be the only available information source for drought monitoring [6,7]. Satellite-based drought indices such as the normalized difference vegetation index (NDVI)-based vegetation condition index (VCI) [8] have been widely used for detecting the onset of drought and measuring the intensity, duration, and impact of drought globally [8][9][10][11][12][13][14][15]. The obvious advantage of VCI is that it can be easily computed owing to the fact that it does not require station observation data, and as a satellite-based drought product it can provide near real-time data over the globe at a relatively high spatial resolution [16].…”

Section: Introductionmentioning

confidence: 99%

Evaluating an Enhanced Vegetation Condition Index (VCI) Based on VIUPD for Drought Monitoring in the Continental United States

et al. 2016

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Drought is a complex hazard, and it has an impact on agricultural, ecological, and socio-economic systems. The vegetation condition index (VCI), which is derived from remote-sensing data, has been widely used for drought monitoring. However, VCI based on the normalized difference vegetation index (NDVI) does not perform well in certain circumstances. In this study, we examined the utility of the vegetation index based on the universal pattern decomposition method (VIUPD) based VCI for drought monitoring in various climate divisions across the continental United States (CONUS). We compared the VIUPD-derived VCI with the NDVI-derived VCI in various climate divisions and during different sub-periods of the growing season. It was also compared with other remote-sensing-based drought indices, such as the temperature condition index (TCI), precipitation condition index (PCI) and the soil moisture condition index (SMCI). The VIUPD-derived VCI had stronger correlations with long-term in situ drought indices, such as the Palmer Drought Severity Index (PDSI) and the standardized precipitation index (SPI-3, SPI-6, SPI-9, and SPI-12) than did the NDVI-derived VCI, and other indices, such as TCI, PCI and SMCI. The VIUPD has considerable potential for drought monitoring. As VIUPD can make use of the information from all the observation bands, the VIUPD-derived VCI can be regarded as an enhanced VCI.

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Section: Introductionmentioning

confidence: 99%

Evaluating an Enhanced Vegetation Condition Index (VCI) Based on VIUPD for Drought Monitoring in the Continental United States

et al. 2016

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“…Satellite remote sensing enables assessment of agricultural crop growth and yield across large territories [3][4][5][6][7]. Various Biomass Proxies (hereafter BPs) have been developed from remote sensing imagery to be used in empirical regressive models that monitor agricultural crop growth and estimate crop yield [8][9][10]. NDVI, fAPAR, LAI (Leaf Area Index), GAI (Green Area Index) and EVI (Enhanced Vegetation Index) are examples of BPs that have been derived from remote sensing and that are used in vegetation monitoring and crop yield forecasting [5,7,[11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Testing the Contribution of Stress Factors to Improve Wheat and Maize Yield Estimations Derived from Remotely-Sensed Dry Matter Productivity

et al. 2016

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Abstract:According to Monteith's theory, crop biomass is linearly correlated with the amount of absorbed photosynthetically active radiation (APAR) and a constant radiation use efficiency (RUE) down-regulated by stress factors such as CO 2 fertilisation, temperature and water stress. The objective was to investigate the relative importance of these stress factors in relation to regional biomass production and yield. The production efficiency model Copernicus Global Land Service-Dry Matter Productivity (CGLS-DMP), which follows Monteith's theory, was modified and evaluated for common wheat and silage maize in France, Belgium and Morocco using SPOT VEGETATION for the period 1999-2012. For each study site the stress factor that has the highest correlation with crop yield was retained. The correlation between crop yield data and cumulative modified DMP, CGLS-DMP, fAPAR, and NDVI values were analysed for different crop growth stages. A leave-one-year-out cross validation was used to test the robustness of the model. On average, R 2 values increased from 0.49 for CGLS-DMP to 0.68 for modified DMP, RMSE (t/ha) decreased from 0.84-0.61, RRMSE (%) reduced from 13.1-8.9, MBE (t/ha) decreased from 0.05-0.03 and the index of model performance (E 1 ) increased from 0.08-0.28 for the selected sites and crops. The best results were obtained by including combinations of the most appropriate stress factors for each selected region and cumulating the modified DMP during part of the growing season that includes the reproductive stage. Though no single solution to the improvement of a global product could be demonstrated, our findings encourage an extension of the methodology to other regions of the world.

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“…The approach was extended to an area of about 900 km 2 (study area in Figure 1). To understand whether the color anomalies identified from SPOT images (Figure 2a) are related to vegetation state or soil humidity, we processed different combinations of spectral bands from Landsat satellite images: (i) near infrared-red-green, 432 (Figure 2b), useful for vegetation studies [45], drainage monitoring, soil patterns and various stages of crop growth; (ii) near infrared-medium infrared-red, 453 (Figure 2c) (this combination highlights moisture differences and is useful for the analysis of soil and vegetation conditions [46]; generally, the wetter the soil, the darker it appears, because of its infrared absorption by water); and (iii) shortwave infrared, near infrared, green, 742 (Figure 2d), a combination used in fire studies [47], allows locating burned areas. For each of the described combinations, we used band 8 (15 m spatial resolution) to generate panchromatic image sharpening and to increase the geometric resolution of the RGB composites.…”

Section: Data and Methodsologymentioning

confidence: 99%

Identification of Leveled Archeological Mounds (Höyük) in the Alluvial Plain of the Ceyhan River (Southern Turkey) by Satellite Remote-Sensing Analyses

et al. 2018

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Abstract:The alluvial plain of the Ceyhan River (SE Turkey) has been populated since the Neolithic. In 1954, Marjory Veronica Seton-Williams described for this area several archeological mounds (höyük), which are the remains of ancient settlements. Today, according to the archeological research carried out in the area, some of these mounds result to have been leveled by agricultural activities. In this work, we identified many color anomalies by low-cost remote-sensing analyses of satellite images. We checked the nature of these anomalies in a dedicated survey and we found a good correspondence between color anomalies and archeological remains consistent with leveled höyük. We compared the grain size and chemical characteristics of the soil collected inside the color anomalies with the soil collected in other areas of the alluvial plain. We found irrelevant differences in grain-size characteristics, but a higher content of CaCO 3 in soils collected inside the anomalies with respect to those collected outside. Therefore, the content of CaCO 3 could be considered the feature that makes the color anomalies visible. The reason for this higher content of CaCO 3 is related to the anthropogenic material used in the different phases of höyük growth. This work suggests a low-cost analysis useful for rapid identification and preservation of archeological information on the history of Mediterranean settlement.

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Predicting maize yield in Zimbabwe using dry dekads derived from remotely sensed Vegetation Condition Index

Cited by 67 publications

References 29 publications

Evaluating an Enhanced Vegetation Condition Index (VCI) Based on VIUPD for Drought Monitoring in the Continental United States

Evaluating an Enhanced Vegetation Condition Index (VCI) Based on VIUPD for Drought Monitoring in the Continental United States

Testing the Contribution of Stress Factors to Improve Wheat and Maize Yield Estimations Derived from Remotely-Sensed Dry Matter Productivity

Identification of Leveled Archeological Mounds (Höyük) in the Alluvial Plain of the Ceyhan River (Southern Turkey) by Satellite Remote-Sensing Analyses

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