Estimated rice cultivation date using an extended Kalman filter on MODIS NDVI time-series data

Chumkesornkulkit, Kitti; Kasetkasem, Teerasit; Rakwatin, Preesan; Eiumnoh, Apisit; Kumazawa, Itsuo; Buddhaboon, Chitnucha

doi:10.1109/ecticon.2013.6559573

Cited by 14 publications

(6 citation statements)

References 9 publications

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“…Boschetti et al (2017) synthesized literature on time series analysis for regional to continental rice monitoring and revealed that only 6 out of 18 studies [e.g. Sakamoto et al, 2005;Boschetti et al, 2009;Chumkesornkulkit et al, 2013;Nguyen et al, 2012;Manfron et al, 2012;Suwannachatkul et al, 2014] addressed the quantitative estimation of occurrence dates of agricultural practices/ phenological stages. Only one study addressed estimating the length of the cropping season (Sakamoto et al, 2005).…”

Section: From Area Mapping To Multiyear Seasonal Crop Monitoringmentioning

confidence: 99%

Analysing spatial–temporal changes in rice cultivation practices in the Senegal River Valley using MODIS time-series and the PhenoRice algorithm

Busetto

Zwart

Boschetti

2019

International Journal of Applied Earth Observation and Geoinfor

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Section: From Area Mapping To Multiyear Seasonal Crop Monitoringmentioning

confidence: 99%

Analysing spatial–temporal changes in rice cultivation practices in the Senegal River Valley using MODIS time-series and the PhenoRice algorithm

Busetto

Zwart

Boschetti

2019

International Journal of Applied Earth Observation and Geoinfor

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“…Methodologies applied for phenological metrics determination vary from the use of threshold values to the identification of maximum and minimum values. These studies focus on determining one or more phenometrics from one index or from a combination of indices (Sakamoto et al, 2005;Leinenkugel et al, 2013;Chumkesornkulkit et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Assessment of MODIS spectral indices for determining rice paddy agricultural practices and hydroperiod

Tornos

Huesca

Domínguez

et al. 2015

ISPRS Journal of Photogrammetry and Remote Sensing

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a b s t r a c tRice agricultural practices and hydroperiod dates must be determined to obtain information on water management practices and their environmental effects. Spectral indices derived from an 8-day MODIS composite allows to identify rice phenometrics at varying degrees of success. The aims of this study were (1) to assess the dynamics of the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI(1) and NDWI(2)) and Shortwave Angle Slope Index (SASI) in relation to rice agricultural practices and hydroperiod, and (2) to assess the capability for these indices to detect phenometrics in rice under different flooding regimes. Two rice farming areas in Spain that are governed under different water management practices, the Ebro Delta and Orellana, were studied over a 12-year period (2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012). The index time series autocorrelation function was calculated to determine index dynamics in both areas. Secondly, average indices were calculated to identify significant points close to key agricultural and flooding dates, and index behaviors and capacities to identify phenometrics were assessed on a pixel level. The index autocorrelation function produced a regular pattern in both zones, being remarkably homogeneous in the Ebro Delta. It was concluded that a combination of NDVI, NDWI(1), NDWI (2) and SASI may improve the results obtained through each index. NDVI was more effective at detecting the heading date and flooding trends in the Ebro Delta. NDWI(1), NDWI(2) and SASI identified the harvest and the end of environmental flooding in the Delta, and the flooding in Orellana, more effectively. These results may set strong foundations for the development of new strategies in rice monitoring systems, providing useful information to policy makers and environmental studies.

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“…Furthermore, time-series MODIS images show superiority in land-use type classification and crop monitoring, as they can extract vegetation information at different growing stages [28]- [34]. Among the time-series MODIS data, spectral [35]- [37], land surface water index (LSWI) [17], [38], and vegetation index [e.g., normalized different vegetation index (NDVI) and enhanced vegetation index (EVI)] [39]- [41] have been widely used in monitoring and mapping rice cultivated areas, because of their high temporal resolutions. EVI is more consistent with the in situ phenology data than NDVI [42].…”

mentioning

confidence: 99%

Classification of Paddy Rice Using a Stacked Generalization Approach and the Spectral Mixture Method Based on MODIS Time Series

Zhang

Liu

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Paddy rice is a major stable food, accounting for about 20% world's food supply. And the rice paddy, an important artificial wetland type, plays an important role in the regional ecological environment. This study proposes a stacked generalization and spectral mixture approach to map paddy rice using coarse spatial resolution images [Moderate Resolution Imaging Spectralradiometer, (MODIS)]. By this method, the time series MODIS enhanced vegetation index images, phenological variables, land surface water index, elevation, and slope images are all employed to produce the optimal feature combination, which is then used to map paddy rice by the stacking algorithm. The validation experiment using the data of the Dongting Lake area showed that the proposed method can improve the overall accuracy of single classifiers, including the support vector machine, random forest, k-nearestneigbor (kNN), extreme gradient boosting (XGB), and decision tree. Stacking (XGB) achieves the highest overall accuracy (90.3%) and Kappa coefficient (0.86), which are 2.8% and 0.03 higher than that of using the single kNN classifier. Furthermore, its user accuracies for distinguishing double-cropping rice and single-season rice are 92.5% and 90.0%, respectively. In terms of the paddy rice classification accuracy, the stacking model is also superior to single classifiers. Moreover, the MODIS-derived rice map obtained by the stacked generalization approach and the spectral mixture method area has a large determination coefficient (R 2 = 0.9975) with the government statistic data. The results demonstrate the potential of the proposed method in using coarse spatial resolution images for large-scale paddy rice mapping. Index Terms-Moderate Resolution Imaging Spectralradiometer (MODIS) time series, paddy rice, phonological variables, spectral mixture, stack generalization. I. INTRODUCTION P ADDY rice feeds 1/5 of the world's people. The demand for paddy rice is increasing due to the fast-growing global population and cause cultivate land and water shortage and biodiversity damage [1]-[5]. As an important cultivated wetland, rice paddy consumes a large amount of fresh water and Manuscript

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Estimated rice cultivation date using an extended Kalman filter on MODIS NDVI time-series data

Cited by 14 publications

References 9 publications

Analysing spatial–temporal changes in rice cultivation practices in the Senegal River Valley using MODIS time-series and the PhenoRice algorithm

Analysing spatial–temporal changes in rice cultivation practices in the Senegal River Valley using MODIS time-series and the PhenoRice algorithm

Assessment of MODIS spectral indices for determining rice paddy agricultural practices and hydroperiod

Classification of Paddy Rice Using a Stacked Generalization Approach and the Spectral Mixture Method Based on MODIS Time Series

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