Winter wheat area estimation from MODIS-EVI time series data using the Crop Proportion Phenology Index

Pan, Yaozhong; Li, Le; Zhang, Jinshui; Liang, Shunlin; Zhu, Xiufang; Sulla‐Menashe, Damien

doi:10.1016/j.rse.2011.10.011

Cited by 222 publications

(124 citation statements)

References 29 publications

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“…MOD13A1 is processed from the MODIS level-2 daily surface reflectance product (MOD09 series), which provides red and near-infrared surface reflectance corrected for the effect of atmospheric gases, thin cirrus clouds, and aerosols. MODIS NDVI data are widely used for the global monitoring of vegetation conditions and are used in products displaying land cover and land cover changes [42][43][44].…”

Section: Modis Ndvi Datamentioning

confidence: 99%

Distribution and Variation of Forests in China from 2001 to 2011: A Study Based on Remotely Sensed Data

Zhao

Zhou

et al. 2013

Forests

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Abstract:Forests are one of the most important components of the global biosphere and have critical influences on the Earth's ecological balance. Regularly updated forest cover information is necessary for various forest management applications as well as climate modeling studies. However, map products are not widely updated at continental or national scales because the current land cover products have overly coarse spatial resolution or insufficiently large training data sets. This study presents the results of forests distribution and variation information over China using Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time series data with the first layer of MODIS Land Cover Type product (MODIS LC-1). The NDVI time series histogram characteristic curves for forestland were estimated from MODIS LC-1 and MODIS NDVI time series data.

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Section: Modis Ndvi Datamentioning

confidence: 99%

Distribution and Variation of Forests in China from 2001 to 2011: A Study Based on Remotely Sensed Data

Zhao

Zhou

et al. 2013

Forests

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“…Since traditional agricultural statistics on crop acreages are usually provided by the end of the season or later, in-season agricultural production managers lack necessary information about the current year's crops [9,10]. Alternatively, remote sensing satellites, owing to their synoptic and repetitive nature, have proven to be an effective means for mapping and monitoring crop extent [11][12][13].…”

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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“…Compared with traditional ground-based methods, such as visual examination and survey sampling, remote sensing that provides synoptic and repetitive observations of the land surface is well suited for agricultural mapping [4][5][6] and monitoring [7,8] large geographic areas. In particular, satellite-derived vegetation indices, as measures of plant chlorophyll abundance and vegetation radiation absorption [9], have proven to be closely related to crop growth in field studies and theoretical models [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

A Production Efficiency Model-Based Method for Satellite Estimates of Corn and Soybean Yields in the Midwestern US

Xin

Gong

et al. 2013

Remote Sensing

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Remote sensing techniques that provide synoptic and repetitive observations over large geographic areas have become increasingly important in studying the role of agriculture in global carbon cycles. However, it is still challenging to model crop yields based on remotely sensed data due to the variation in radiation use efficiency (RUE) across crop types and the effects of spatial heterogeneity. In this paper, we propose a production efficiency model-based method to estimate corn and soybean yields with MODerate Resolution Imaging Spectroradiometer (MODIS) data by explicitly handling the following two issues: (1) field-measured RUE values for corn and soybean are applied to relatively pure pixels instead of the biome-wide RUE value prescribed in the MODIS vegetation productivity product (MOD17); and (2) contributions to productivity from vegetation other than crops in mixed pixels are deducted at the level of MODIS resolution. Our estimated yields statistically correlate with the national survey data for rainfed counties in the require any retrospective analysis that constructs empirical relationships between the reported yields and remotely sensed data, it could monitor crop yields over large areas.

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Winter wheat area estimation from MODIS-EVI time series data using the Crop Proportion Phenology Index

Cited by 222 publications

References 29 publications

Distribution and Variation of Forests in China from 2001 to 2011: A Study Based on Remotely Sensed Data

Distribution and Variation of Forests in China from 2001 to 2011: A Study Based on Remotely Sensed Data

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

A Production Efficiency Model-Based Method for Satellite Estimates of Corn and Soybean Yields in the Midwestern US

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