Regional yield estimation for winter wheat with MODIS-NDVI data in Shandong, China

Ren, Jianqiang; Chen, Zhongxin; Zhou, Qingbo; Tang, Huajun

doi:10.1016/j.jag.2007.11.003

Cited by 256 publications

(166 citation statements)

References 40 publications

(43 reference statements)

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“…Through data assimilation, the MODIS LAI product and extracted vegetation indices of NDVI and EVI forecast crop yield, using only a partial year of data, with relative deviations from reference data less than 3.5% (Fang et al 2011). Passive MODIS, AVHRR, and Medium Resolution Imaging Spectrometer (MERIS), and active ASAR data, have been used to estimate wheat or maize yield with relative differences less than 11% (Moriondo et al 2007;Ren et al 2008;Yan et al 2009;Dente et al 2008). Furthermore, LiDAR data, either airborne (Jaskierniak et al 2011;Tonolli et al 2011;Latifi et al 2010) or spaceborne ), have been the primary sources to estimate timber volume.…”

Section: Provisioning Servicesmentioning

confidence: 99%

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

2015

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Recognizing the imperative need for biodiversity protection, the Convention on Biological Diversity (CBD) has recently established new targets towards 2020, the so-called Aichi targets, and updated proposed sets of indicators to quantitatively monitor the progress towards these targets. Remote sensing has been increasingly contributing to timely, accurate, and cost-effective assessment of biodiversity-related characteristics and functions during the last years. However, most relevant studies constitute individual research efforts, rarely related with the extraction of widely adopted CBD biodiversity indicators. Furthermore, systematic operational use of remote sensing data by managing authorities has still been limited. In this study, the Aichi targets and the related CBD indicators whose monitoring can be facilitated by remote sensing are identified. For each headline indicator a number of recent remote sensing approaches able for the extraction of related properties are reviewed. Methods cover a wide range of fields, including: habitat extent and condition monitoring; species distribution; pressures from unsustainable management, pollution and climate change; ecosystem service monitoring; and conservation status assessment of protected areas. The advantages and limitations of different remote sensing data and algorithms are discussed. Sorting of the methods based on their reported accuracies is attempted, when possible. The extensive literature survey aims at reviewing highly performing methods that can be used for large-area, effective, and timely biodiversity assessment, to encourage the more systematic use of remote sensing solutions in monitoring progress towards the Aichi targets, and to decrease the gaps between the remote sensing and management communities.

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Section: Provisioning Servicesmentioning

confidence: 99%

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

2015

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“…Prasad et al (2006) conducted multivariate regression analyses to estimate corn and soybean yields in Iowa using MODIS (Moderate Resolution Imaging Spectroradiometer) NDVI (Normalized Difference Vegetation Index), climate factors and soil moisture. Ren et al (2008) presented regression models for the estimation of winter wheat yields using MODIS NDVI and weather data in Shandong, China. Kim et al (2014) estimated corn and soybean yields using several MODIS products and climatic variables for Midwestern United States (US) and represented prediction errors of about 10 %.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approaches to Corn Yield Estimation Using Satellite Images and Climate Data: A Case of Iowa State

Kim¹,

Lee²

2016

Journal of the Korean Society of Surveying, Geodesy, Photogramm

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Remote sensing data has been widely used in the estimation of crop yields by employing statistical methods such as regression model. Machine learning, which is an efficient empirical method for classification and prediction, is another approach to crop yield estimation. This paper described the corn yield estimation in Iowa State using four machine learning approaches such as SVM (Support Vector Machine), RF (Random Forest), ERT (Extremely Randomized Trees) and DL (Deep Learning). Also, comparisons of the validation statistics among them were presented. To examine the seasonal sensitivities of the corn yields, three period groups were set up: (1) MJJAS (May to September), (2) JA (July and August) and (3) OC (optimal combination of month). In overall, the DL method showed the highest accuracies in terms of the correlation coefficient for the three period groups. The accuracies were relatively favorable in the OC group, which indicates the optimal combination of month can be significant in statistical modeling of crop yields. The differences between our predictions and USDA (United States Department of Agriculture) statistics were about 6-8 %, which shows the machine learning approaches can be a viable option for crop yield modeling. In particular, the DL showed more stable results by overcoming the overfitting problem of generic machine learning methods. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http:// creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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“…These values are in agreement with Kastens et al (2005) that used a yield-correlation mask to estimate soybean yield in Iowa and Illinois (USA) and obtained an RMSE 0.15 t ha −1 and 0.16 t ha −1 , respectively. In contrast, using the crop specific mask approach, Ren et al (2008) obtained RMSE = 0.21 t ha −1 , Mkhabela et al (2011) reported RMSE values below 0.65 t ha −1 to predict cereal grain in Canada. MAE values showed that the correlation masks approach had magnitude error lower (0.14 t ha −1 to 0.02 t ha −1 ) than the crop specific mask (0.3 t ha −1 to 0.18 t ha −1 ).…”

Section: Estimated Yield: Correlation Maps Versus Crop Specific Masksmentioning

confidence: 95%

“…The methods are often based on monthly vegetation indices values (Maselli and Rembold, 2001) or on accumulation over determined periods of the crop phenological stage (Tucker et al, 1980;Rasmussen, 1992;Genovese et al, 2001;Kastens et al, 2005;Ren et al, 2008).…”

Section: Correlation Mapsmentioning

confidence: 99%

“…The Moderate Resolution Imaging Spectroradiometer (MODIS) data have great potential to monitor biophysical parameters (Huete et al, 2002) and improve accuracy in crop yield assessment (Ren et al, 2008;Funk and Budde, 2009). The coarse spatial resolution is a limiting factor to the use of MODIS data, which results in mixed pixels that may not be suitable for crop yield models (Shao et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Correlation maps to assess soybean yield from EVI data in Paraná State, Brazil

Figueiredo

Brunsell

Higa

et al. 2016

Sci. agric. (Piracicaba, Braz.)

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Vegetation indices are widely used to monitor crop development and generally used as input data in models to forecast yield. The first step of this study consisted of using monthly . We compared the ability of forecasting crop yield based on correlation maps and crop specific masks. We ran a preliminary regression model to test its ability on yield estimation for four municipalities during the soybean growing season. A regression model was developed for both methodologies to forecast soybean crop yield using leave-one-out cross validation.The Root Mean Squared Error (RMSE) values in the implementation of the model ranged from 0.037 t ha −1 to 0.19 t ha −1 using correlation maps, while for crop specific masks, it varied from 0.21 t ha −1 to 0.35 t ha −1 . The model was able to explain 96 % to 98 % of the variance in estimated yield from correlation maps, while it was able to explain only 2 % to 67 % for crop specific mask approach. The results showed that the correlation maps could be used to predict crop yield more effectively than crop specific masks. In addition, this method can provide an indication of soybean yield prior to harvesting.

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Regional yield estimation for winter wheat with MODIS-NDVI data in Shandong, China

Cited by 256 publications

References 40 publications

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

Machine Learning Approaches to Corn Yield Estimation Using Satellite Images and Climate Data: A Case of Iowa State

Correlation maps to assess soybean yield from EVI data in Paraná State, Brazil

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