Important Variables of a RapidEye Time Series for Modelling Biophysical Parameters of Winter Wheat

2016 ◽  
Vol 2016 (5) ◽  
pp. 285-299 ◽  
Author(s):  
Thorsten Dahms ◽  
Sylvia Seissiger ◽  
Erik Borg ◽  
Hermann Vajen ◽  
Bernd Fichtelmann ◽  
...  
Keyword(s):  
Time Series ◽  
Winter Wheat ◽  
Biophysical Parameters

An automated early-season method to map winter wheat using time-series Sentinel-2 data: A case study of Shandong, China

2021 ◽  
Vol 182 ◽  
pp. 105962
Author(s):  
Hongyan Zhang ◽  
Hongyu Du ◽  
Chengkang Zhang ◽  
Liangpei Zhang
Keyword(s):  
Time Series ◽  
Winter Wheat ◽  
Early Season ◽  
Sentinel 2

scholarly journals Comparison of LAI Estimates from High Resolution Satellite Observations Using Different Biophysical Processors

2021 ◽  
Vol 3 (1) ◽  
pp. 5
Author(s):  
Federico Filipponi
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Agricultural Land ◽  
Spectral Characteristics ◽  
Earth Observation ◽  
Estimation Accuracy ◽  
Landsat 8 ◽  
Biophysical Parameters ◽  
Area Index ◽  
Satellite Sensors

Earth observation provides timely and spatially explicit information about crop phenology and vegetation dynamics that can support decision making and sustainable agricultural land management. Vegetation spectral indices calculated from optical multispectral satellite sensors have been largely used to monitor vegetation status. In addition, techniques to retrieve biophysical parameters from satellite acquisitions, such as the Leaf Area Index (LAI), have allowed to assimilate Earth observation time series in numerical modeling for the analysis of several land surface processes related to agroecosystem dynamics. More recently, biophysical processors used to estimate biophysical parameters from satellite acquisitions have been calibrated for retrieval from sensors with different high spatial resolution and spectral characteristics. Virtual constellations of satellite sensors allow the generation of denser LAI time series, contributing to improve vegetation phenology estimation accuracy and, consequently, enhancing agroecosystems monitoring capacity. This research study compares LAI estimates over croplands using different biophysical processors from Sentinel-2 MSI and Landsat-8 OLI satellite sensors. The results are used to demonstrate the capacity of virtual satellite constellation to strengthen LAI time series to derive important cropland use information over large areas.

scholarly journals An Improved CASA Model for Estimating Winter Wheat Yield from Remote Sensing Images

2019 ◽  
Vol 11 (9) ◽  
pp. 1088 ◽  
Author(s):  
Yulong Wang ◽  
Xingang Xu ◽  
Linsheng Huang ◽  
Guijun Yang ◽  
Lingling Fan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Winter Wheat ◽  
Crop Yield ◽  
Satellite Remote Sensing ◽  
Wheat Yield ◽  
Growing Season ◽  
Remote Sensing Images ◽  
Casa Model ◽  
Winter Wheat Yield

The accurate and timely monitoring and evaluation of the regional grain crop yield is more significant for formulating import and export plans of agricultural products, regulating grain markets and adjusting the planting structure. In this study, an improved Carnegie–Ames–Stanford approach (CASA) model was coupled with time-series satellite remote sensing images to estimate winter wheat yield. Firstly, in 2009 the entire growing season of winter wheat in the two districts of Tongzhou and Shunyi of Beijing was divided into 54 stages at five-day intervals. Net Primary Production (NPP) of winter wheat was estimated by the improved CASA model with HJ-1A/B satellite images from 39 transits. For the 15 stages without HJ-1A/B transit, MOD17A2H data products were interpolated to obtain the spatial distribution of winter wheat NPP at 5-day intervals over the entire growing season of winter wheat. Then, an NPP-yield conversion model was utilized to estimate winter wheat yield in the study area. Finally, the accuracy of the method to estimate winter wheat yield with remote sensing images was verified by comparing its results to the ground-measured yield. The results showed that the estimated yield of winter wheat based on remote sensing images is consistent with the ground-measured yield, with R2 of 0.56, RMSE of 1.22 t ha−1, and an average relative error of −6.01%. Based on time-series satellite remote sensing images, the improved CASA model can be used to estimate the NPP and thereby the yield of regional winter wheat. This approach satisfies the accuracy requirements for estimating regional winter wheat yield and thus may be used in actual applications. It also provides a technical reference for estimating large-scale crop yield.

Monitoring winter-wheat phenology in North China using time-series MODIS EVI

2009 ◽  
Author(s):  
Mingwei Zhang ◽  
Jinlong Fan ◽  
Xiaoxiang Zhu ◽  
Guicai Li ◽  
Yeping Zhang
Keyword(s):  
Time Series ◽  
Winter Wheat ◽  
North China ◽  
Modis Evi

Estimation of winter wheat crop growth parameters using time series Sentinel-1A SAR data

2017 ◽  
Vol 33 (9) ◽  
pp. 942-956 ◽  
Author(s):  
P. Kumar ◽  
R. Prasad ◽  
D. K. Gupta ◽  
V. N. Mishra ◽  
A. K. Vishwakarma ◽  
...  
Keyword(s):  
Time Series ◽  
Winter Wheat ◽  
Growth Parameters ◽  
Crop Growth ◽  
Wheat Crop ◽  
Winter Wheat Crop ◽  
Sar Data

scholarly journals Early-season crop mapping using improved artificial immune network (IAIN) and Sentinel data

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5431 ◽  
Author(s):  
Pengyu Hao ◽  
Huajun Tang ◽  
Zhongxin Chen ◽  
Zhengjia Liu
Keyword(s):  
Time Series ◽  
Winter Wheat ◽  
Vegetation Index ◽  
Immune Network ◽  
Artificial Immune ◽  
Study Region ◽  
Early Season ◽  
Spring Maize ◽  
Artificial Immune Network ◽  
Sentinel 2

Substantial efforts have been made to identify crop types by region, but few studies have been able to classify crops in early season, particularly in regions with heterogeneous cropping patterns. This is because image time series with both high spatial and temporal resolution contain a number of irregular time series, which cannot be identified by most existing classifiers. In this study, we firstly proposed an improved artificial immune network (IAIN), and tried to identify major crops in Hengshui, China at early season using IAIN classifier and short image time series. A time series of 15-day composited images was generated from 10 m spatial resolution Sentinel-1 and Sentinel-2 data. Near-infrared (NIR) band and normalized difference vegetation index (NDVI) were selected as optimal bands by pair-wise Jeffries–Matusita distances and Gini importance scores calculated from the random forest algorithm. When using IAIN to identify irregular time series, overall accuracy of winter wheat and summer crops were 99% and 98.55%, respectively. We then used the IAIN classifier and NIR and NDVI time series to identify major crops in the study region. Results showed that winter wheat could be identified 20 days before harvest, as both the producer’s accuracy (PA) and user’s accuracy (UA) values were higher than 95% when an April 1–May 15 time series was used. The PA and UA of cotton and spring maize were higher than 95% with image time series longer than April 1–August 15. As spring maize and cotton mature in late August and September–October, respectively, these two crops can be accurately mapped 4–6 weeks before harvest. In addition, summer maize could be accurately identified after August 15, more than one month before harvest. This study shows the potential of IAIN classifier for dealing with irregular time series and Sentinel-1 and Sentinel-2 image time series at early-season crop type mapping, which is useful for crop management.

scholarly journals Mapping Winter Wheat Biomass and Yield Using Time Series Data Blended from PROBA-V 100- and 300-m S1 Products

2016 ◽  
Vol 8 (10) ◽  
pp. 824 ◽  
Author(s):  
Yang Zheng ◽  
Miao Zhang ◽  
Xin Zhang ◽  
Hongwei Zeng ◽  
Bingfang Wu
Keyword(s):  
Time Series ◽  
Winter Wheat ◽  
Time Series Data ◽  
Series Data
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