Forecasting crop production using satellite-based vegetation health indices in Kansas, USA

Kogan, Felix; Salazar, Luis Alonso; Roytman, Leonid

doi:10.1080/01431161.2011.621464

Cited by 114 publications

(79 citation statements)

References 21 publications

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“…It is released by the NOAA Center online [51]. The VHI dataset has been widely applied for early drought warning, monitoring of crop yield and production and assessment of irrigated areas and excessive wetness [52][53][54][55][56]. VHI is a weighted average of two sub-indices: the Vegetation Condition Index (VCI) calculated from Normalized Difference Vegetation Index (NDVI) and the Temperature Condition Index (TCI) computed from brightness temperature (TB) data.…”

Section: Avhrr Vegetation Health Productmentioning

confidence: 99%

Meteorological Drought Analysis in the Lower Mekong Basin Using Satellite-Based Long-Term CHIRPS Product

et al. 2017

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Lower Mekong Basin (LMB) experiences a recurrent drought phenomenon. However, few studies have focused on drought monitoring in this region due to lack of ground observations. The newly released Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) with a long-term record and high resolution has a great potential for drought monitoring. Based on the assessment of CHIRPS for capturing precipitation and monitoring drought, this study aims to evaluate the drought condition in LMB by using satellite-based CHIRPS from January 1981 to July 2016. The Standardized Precipitation Index (SPI) at various time scales (1-12-month) is computed to identify and describe drought events. Results suggest that CHIRPS can properly capture the drought characteristics at various time scales with the best performance at three-month time scale. Based on high-resolution long-term CHIRPS, it is found that LMB experienced four severe droughts during the last three decades with the longest one in 1991-1994 for 38 months and the driest one in 2015-2016 with drought affected area up to 75.6%. Droughts tend to occur over the north and south part of LMB with higher frequency, and Mekong Delta seems to experience more long-term and extreme drought events. Severe droughts have significant impacts on vegetation condition.

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Section: Avhrr Vegetation Health Productmentioning

confidence: 99%

Meteorological Drought Analysis in the Lower Mekong Basin Using Satellite-Based Long-Term CHIRPS Product

et al. 2017

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“…The method assumes that the "greenness" attained at a given time of the year is a predictor of the final grain yield. This specific timing is selected by finding the NDVI (or FAPAR) dekad that provides the highest correlation with yearly yield records (e.g., [21,22]). FAPAR is considered in the analysis in order to evaluate if it provides any improvement with respect to the vegetation index.…”

Section: Rs-derived Biomass Proxiesmentioning

confidence: 99%

Remote Sensing Based Yield Estimation in a Stochastic Framework — Case Study of Durum Wheat in Tunisia

Meroni

Marinho

Sghaier³

et al. 2013

Remote Sensing

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Multitemporal optical remote sensing constitutes a useful, cost efficient method for crop status monitoring over large areas. Modelers interested in yield monitoring can rely on past and recent observations of crop reflectance to estimate aboveground biomass and infer the likely yield. Therefore, in a framework constrained by information availability, remote sensing data to yield conversion parameters are to be estimated. Statistical models are suitable for this purpose, given their ability to deal with statistical errors. This paper explores the performance in yield estimation of various remote sensing indicators based on varying degrees of bio-physical insight, in interaction with statistical methods (linear regressions) that rely on different hypotheses. Performances in estimating the temporal and spatial variability of yield, and implications of data scarcity in both dimensions are investigated. Jackknifed results (leave one year out) are presented for the case of wheat yield regional estimation in Tunisia using the SPOT-VEGETATION instrument. Best performances, up to 0.8 of R 2 , are achieved using the most physiologically sound remote sensing indicator, in conjunction with statistical specifications allowing for parsimonious spatial adjustment of the parameters. OPEN ACCESSRemote Sens. 2013, 5 540

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“…Application of these indices in some countries showed that they correlate strongly with productivity of crops and pastures and can be used as satellite-based numerical weather-related indicators of crop yield in advance of harvest. 12,13,[31][32][33][34][35] Further discussion is focused on the indices applied to Australia wheat.…”

Section: 1034mentioning

confidence: 99%

“…9,10 Additionally, such data-aided drought detection and monitoring in USA, China, Greece, Mongolia, Brazil, Poland, Argentina, Morocco, Kazakhstan, Mexico, and other countries. 12,13,[30][31][32][33][34][35][36][37][38] In the strategy of yield modeling was to: (a) derive weather-impact component in crop yield; (b) derive VHI; (c) correlate weather-related components of yield and VHI; (d) assess if the time of the strongest correlation coincides with wheat critical period in Australia; (e) select predictors; (f) construct models; (g) predict wheat yield; and (h) validate the results in an independent test. One of the most important problems to match wheat area with the corresponding VHI is to calculate wheat area mask in order to collect satellite data for that area.…”

Section: Satellite Datamentioning

confidence: 99%

Space-based vegetation health for wheat yield modeling and prediction in Australia

Kogan

Guo²,

Yang³

et al. 2018

J. Appl. Rem. Sens.

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, "Space-based vegetation health for wheat yield modeling and prediction in Australia," J. Appl. Remote Sens. 12(2), 026002 (2018), doi: 10.1117/1.JRS.12.026002. Abstract. An early warning of crop losses in response to weather fluctuations helps farmers, governments, traders, and policy makers better monitor global food supply and demand and identifies nations in need of aid. This paper discusses the utility of vegetation health (VH) indices, derived from the advance very high-resolution radiometer (AVHRR) and visible infrared imaging radiometer suite (VIIRS), as a proxy for modeling Australian wheat from the National Oceanic and Atmospheric Administration (NOAA) operational afternoon polar-orbiting satellites. These models are used to assess wheat production and to provide an early warning of drought-related losses. The NOAA AVHRR-and VIIRS-based VH indices were used to model wheat yield in Australia. A strong correlation (≥0.7) between wheat yield and VH indices was found during the critical reproductive stage of development (enhanced crops sensitivity to weather), which starts 2 to 3 weeks before and ends 2 to 3 weeks after wheat heading. The results of modeling and independent testing proved that the VH indices (especially those estimating thermal and health conditions) are a good proxy providing 1 to 2 months before harvest yield prediction (with 3% to 6% error). With the new generation of NOAA-20 operational polar-orbiting satellites, launched in November 2017, the VH method will be improved considerably both in an advanced crop/pasture prediction, spatial resolution, and accuracy. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Forecasting crop production using satellite-based vegetation health indices in Kansas, USA

Cited by 114 publications

References 21 publications

Meteorological Drought Analysis in the Lower Mekong Basin Using Satellite-Based Long-Term CHIRPS Product

Meteorological Drought Analysis in the Lower Mekong Basin Using Satellite-Based Long-Term CHIRPS Product

Remote Sensing Based Yield Estimation in a Stochastic Framework — Case Study of Durum Wheat in Tunisia

Space-based vegetation health for wheat yield modeling and prediction in Australia

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