Mapping Crop Cycles in China Using MODIS-EVI Time Series

@article{Li2014MappingCC,
  title={Mapping Crop Cycles in China Using MODIS-EVI Time Series},
  author={Le Li and Mark A. Friedl and Qinchuan Xin and Josh M. Gray and Yaozhong Pan and Steve Frolking},
  journal={Remote. Sens.},
  year={2014},
  volume={6},
  pages={2473-2493}
}
Abstract: As the Earth’s population continues to grow and demand for food increases, the need for improved and timely information related to the properties and dynamics of global agricultural systems is becoming increasingly important. Global land cover maps derived from satellite data provide indispensable information regarding the geographic distribution and areal extent of global croplands. However, land use information, such as cropping intensity (defined here as the number of cropping… 
View PDF
106 Citations
Highly Influential Citations
4
Background Citations
23
Methods Citations
15
Results Citations
1

Figures and Tables from this paper

106 Citations

Citation Type

Citation Type

Research on cropping intensity mapping of the Huai River Basin (China) based on multi-source remote sensing data fusion
TLDR
An algorithm to extract CI based on per-pixel physiognomy is developed based on the Google Earth Engine platform and constructs a high temporal spatial resolution dataset with the fusion of Landsat 7/8 and Sentinel-2 A/B image data and extracts CI information using a time series of peak discovery method, threshold method and phenological period feature extraction.
Mapping cropping intensity in China using time series Landsat and Sentinel-2 images and Google Earth Engine
High Resolution Mapping of Cropping Cycles by Fusion of Landsat and MODIS Data
TLDR
This paper generates dense time series of satellite data at 30 m resolution by fusion of Landsat and MODIS data, and derives the cropping cycles from the fused time series data and enables improved depiction of the spatial distribution of multiple cropping.
Framework for agricultural performance assessment based on MODIS multitemporal data
Abstract. We present a hierarchical classification framework for automated detection and mapping of spatial patterns of agricultural performance using satellite-based Earth observation data…
High resolution crop intensity mapping using harmonized Landsat-8 and Sentinel-2 data
Mapping cropland-use intensity across Europe using MODIS NDVI time series
Global agricultural production will likely need to increase in the future due to population growth, changing diets, and the rising importance of bioenergy. Intensifying already existing cropland is…
Mapping the Northern Limit of Double Cropping Using a Phenology-Based Algorithm and Google Earth Engine
Double cropping is an important cropping system in China, with more than half of China’s cropland adopting the practice. Under the background of global climate change, agricultural policies, and…
Mapping cropping intensity trends in China during 1982–2013
A new framework to map fine resolution cropping intensity across the globe: Algorithm, validation, and implication
Crop-specific phenomapping by fusing Landsat and Sentinel data with MODIS time series
ABSTRACT Agricultural production and food security highly depend on crop growth and condition throughout the growing season. Timely and spatially explicit information on crop phenology can assist in…
...
...

References

SHOWING 1-10 OF 83 REFERENCES
Large-area crop mapping using time-series MODIS 250 m NDVI data: An assessment for the U.S. Central Great Plains
Cropland distributions from temporal unmixing of MODIS data
Winter wheat area estimation from MODIS-EVI time series data using the Crop Proportion Phenology Index
Remote Sensing Based Detection of Crop Phenology for Agricultural Zones in China Using a New Threshold Method
TLDR
A new method was used to define thresholds for identifying the start and end of the growing season (SOS/EOS) for 43 different agricultural zones in China, showing that the developed NDS threshold method had a significantly higher accuracy compared with other methods.
Estimating Global Cropland Extent with Multi-year MODIS Data
TLDR
Overall results indicate that the MODIS layer best depicts regions of intensive broadleaf crop production (corn and soybean), both in correspondence with existing maps and in associated high probability matching thresholds.
The spatial distribution of crop types from MODIS data: Temporal unmixing using Independent Component Analysis
Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images
Wavelet analysis of MODIS time series to detect expansion and intensification of row-crop agriculture in Brazil
Monitoring vegetation phenology using MODIS
Mapping Irrigated Lands at 250-m Scale by Merging MODIS Data and National Agricultural Statistics
TLDR
A method in a geospatial modeling framework that assimilates irrigation statistics with remotely sensed parameters describing vegetation growth conditions in areas with agricultural land cover to spatially identify irrigated lands at 250-m cell size across the conterminous United States for 2002 is developed.
...
...