Improved maize cultivated area estimation over a large scale combining MODIS–EVI time series data and crop phenological information

Zhang, Jiahua; Feng, Lili; Yao, Fengmei

doi:10.1016/j.isprsjprs.2014.04.023

Cited by 146 publications

(88 citation statements)

References 41 publications

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“…This result was a little lower than our result of classification. Using the support vector machine algorithm as well, Zhang et al [72] obtained the classification for corn, based on the MODIS EVI data and HJ-1 images over the whole of Northeast China, and the overall accuracy was 79%, which was also a little lower than our result. Our classification result was available for a distribution map of the winter wheat yield.…”

Section: Discussioncontrasting

confidence: 80%

Winter Wheat Production Estimation Based on Environmental Stress Factors from Satellite Observations

et al. 2018

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Abstract:The rapid and accurate estimation of wheat production at a regional scale is crucial for national food security and sustainable agricultural development. This study developed a new gross primary productivity (GPP) estimation model (denoted as the [ACPM]), based on the effects of light, heat, soil moisture, and nitrogen content (N) on the light-use efficiency of winter wheat. The ACPM model used the quantic additivity of the environmental factors to improve the minimum form or multiple multiplication form in the previous model and thus characterized the joint effects of heat, soil moisture, and N on crop photosynthesis performance. The key parameters (i.e., light) were determined from the photosynthetically active radiation product of the Himawari-8 sensor and the fraction of photosynthetically active radiation product of Moderate Resolution Imaging Spectroradiometer (MODIS). The heat was determined from the land temperature products of MODIS. The soil moisture was obtained from the inversion using a visible and shortwave infrared drought index (VSDI), whereas the N stress of winter wheat was detected using the newly developed modified ratio vegetation index (MRVI), which could accurately obtain the spatiotemporal distribution of the leaf chlorophyll content of winter wheat. The ACPM and two other previous models (named the GPP1 and GPP2 models) were applied on the Himawari-8 and MODIS images in Hengshui City. The evaluation results, based on the ground measurement, indicated that the ACPM models exhibited the best estimate of dry aboveground biomass (DAM) and the wheat yield in Hengshui City, with errors of <10% and <12% for the DAM and yield, respectively. Considering the easy operation of the ACPM model and the accessibility of the corresponding satellite images, the Agriculture Crop Photosynthesis Model (ACPM) can be expected to provide information on the winter wheat shortfalls and surplus ahead of the availability of official statistical data.

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Section: Discussioncontrasting

confidence: 80%

Winter Wheat Production Estimation Based on Environmental Stress Factors from Satellite Observations

et al. 2018

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“…It exploits the complementary aspects of data collected by the MODIS and Landsat sensors to produce fused data with Landsat resolution from MODIS imagery [17]. It has been applied and has performed well in several studies of subjects as diverse as public health [18], forest disturbance and regrowth monitoring [19,20], vegetation phenology analysis [21], efforts to improve land cover classification accuracy [22], and estimation of biophysical parameters such as evapotranspiration [23], leaf area index [24], plant biomass [25], and gross primary productivity [26]. STARFM is not restricted to MODIS and Landsat data, but use of other data sources appears to have been rarely reported [27].…”

Section: Introductionmentioning

confidence: 99%

Land Cover Classification Based on Fused Data from GF-1 and MODIS NDVI Time Series

Fanjie

Wang

et al. 2016

Remote Sensing

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Accurate regional and global information on land cover and its changes over time is crucial for environmental monitoring, land management, and planning. In this study, we selected Fengning County, in China's Hebei Province, as a case study area. Using satellite data, we generated fused normalized-difference vegetation index (NDVI) data with high spatial and temporal resolution by utilizing the STARFM algorithm to produce a fused GF-1 and MODIS NDVI dataset. We extracted seven phenological parameters (including the start, end, and length of the growing season, base value, mid-season date, maximum NDVI, seasonal NDVI amplitude) from a fused NDVI time-series after reconstruction using the TIMESAT software. We developed four classification scenarios based on different combinations of GF-1 spectral features, the fused NDVI time-series, and the phenological parameters. We then classified the land cover using a support vector machine and analyzed the classification accuracies. We found that the proposed method achieved satisfactory classification results, and that the combination of the fused NDVI data with the extracted phenological parameters significantly improved classification accuracy. The classification accuracy based on the composited GF-1 multi-spectral bands combined with the phenological parameters was the highest among the four scenarios, with an overall classification accuracy of 88.8% and a Kappa coefficient of 0.8714, which represent increases of 9.3 percentage points and 0.1073, respectively, compared with GF-1 spectral data alone. The producer's and user's accuracy for different land cover types improved, with a few exceptions, and cropland and broadleaf forest had the largest increase.

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“…Moreover, the crop maps are the foundation for crop modeling, irrigation water distributions and land water management, which are important for decision makers [11][12][13][14][15]. However, most of the previous studies relied on the field reference data in the mapping year to train the classifiers [16,17]. When the cropland map need to be provided on yearly basis, the ground-reference data will be collected at annual frequency, which leads to considerable financial, time and labor costs [18].…”

Section: Introductionmentioning

confidence: 99%

Using Moderate-Resolution Temporal NDVI Profiles for High-Resolution Crop Mapping in Years of Absent Ground Reference Data: A Case Study of Bole and Manas Counties in Xinjiang, China

Hao

Wang

Zhan

et al. 2016

IJGI

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Most methods used for crop classification rely on the ground-reference data of the same year, which leads to considerable financial and labor cost. In this study, we presented a method that can avoid the requirements of a large number of ground-reference data in the classification year. Firstly, we extracted the Normalized Difference Vegetation Index (NDVI) time series profiles of the dominant crops from MODIS data using the historical ground-reference data in multiple years (2006, 2007, 2009 and 2010). Artificial Antibody Network (ABNet) was then employed to build reference NDVI time series for each crop based on the historical NDVI profiles. Afterwards, images of Landsat and HJ were combined to obtain 30 m image time series with 15-day acquisition frequency in 2011. Next, the reference NDVI time series were transformed to Landsat/HJ NDVI time series using their linear model. Finally, the transformed reference NDVI profiles were used to identify the crop types in 2011 at 30 m spatial resolution. The result showed that the dominant crops could be identified with overall accuracy of 87.13% and 83.48% in Bole and Manas, respectively. In addition, the reference NDVI profiles generated from multiple years could achieve better classification accuracy than that from single year (such as only 2007). This is mainly because the reference knowledge from multiple years contains more growing conditions of the same crop. Generally, this approach showed potential to identify crops without using large number of ground-reference data at 30 m resolution.

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Improved maize cultivated area estimation over a large scale combining MODIS–EVI time series data and crop phenological information

Cited by 146 publications

References 41 publications

Winter Wheat Production Estimation Based on Environmental Stress Factors from Satellite Observations

Winter Wheat Production Estimation Based on Environmental Stress Factors from Satellite Observations

Land Cover Classification Based on Fused Data from GF-1 and MODIS NDVI Time Series

Using Moderate-Resolution Temporal NDVI Profiles for High-Resolution Crop Mapping in Years of Absent Ground Reference Data: A Case Study of Bole and Manas Counties in Xinjiang, China

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