scholarly journals A Precision Agriculture Approach for Durum Wheat Yield Assessment Using Remote Sensing Data and Yield Mapping

Agronomy ◽  
2019 ◽  
Vol 9 (8) ◽  
pp. 437 ◽  
Author(s):  
Piero Toscano ◽  
Annamaria Castrignanò ◽  
Salvatore Filippo Di Gennaro ◽  
Alessandro Vittorio Vonella ◽  
Domenico Ventrella ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Durum Wheat ◽  
Precision Agriculture ◽  
Wheat Yield ◽  
Remote Sensing Data ◽  
Farming Practices ◽  
Site Specific ◽  
Sensing Data ◽  
Yield Monitoring ◽  
Specific Management

The availability of big data in agriculture, enhanced by free remote sensing data and on-board sensor-based data, provides an opportunity to understand within-field and year-to-year variability and promote precision farming practices for site-specific management. This paper explores the performance in durum wheat yield estimation using different technologies and data processing methods. A state-of-the-art data cleaning technique has been applied to data from a yield monitoring system, giving a good agreement between yield monitoring data and hand sampled data. The potential use of Sentinel-2 and Landsat-8 images in precision agriculture for within-field production variability is then assessed, and the optimal time for remote sensing to relate to durum wheat yield is also explored. Comparison of the Normalized Difference Vegetation Index(NDVI) with yield monitoring data reveals significant and highly positive linear relationships (r ranging from 0.54 to 0.74) explaining most within-field variability for all the images acquired between March and April. Remote sensing data analyzed with these methods could be used to assess durum wheat yield and above all to depict spatial variability in order to adopt site-specific management and improve productivity, save time and provide a potential alternative to traditional farming practices.

scholarly journals Automatic delineation algorithm for site-specific management zones based on satellite remote sensing data

2017 ◽  
Vol 19 (4) ◽  
pp. 684-707 ◽  
Author(s):  
Claudia Georgi ◽  
Daniel Spengler ◽  
Sibylle Itzerott ◽  
Birgit Kleinschmit
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Remote Sensing Data ◽  
Site Specific ◽  
Management Zones ◽  
Sensing Data ◽  
Specific Management

Estimation of winter wheat yield by using remote sensing data and crop model

2012 ◽  
Author(s):  
Jianmao Guo ◽  
Tengfei Zheng ◽  
Qi Wang ◽  
Jia Yang ◽  
Junyi Shi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Winter Wheat ◽  
Wheat Yield ◽  
Remote Sensing Data ◽  
Crop Model ◽  
Sensing Data ◽  
Winter Wheat Yield

MANIPULATION OF HIGH SPATIAL RESOLUTION AIRCRAFT REMOTE SENSING DATA FOR USE IN SITE-SPECIFIC FARMING

1998 ◽  
Vol 41 (2) ◽  
pp. 489-495 ◽  
Author(s):  
G. B. Senay ◽  
A. D. Ward ◽  
J. G. Lyon ◽  
N. R. Fausey ◽  
S. E. Nokes
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Remote Sensing Data ◽  
Site Specific ◽  
Sensing Data ◽  
Site Specific Farming

scholarly journals Assimilating Soil Moisture Retrieved from Sentinel-1 and Sentinel-2 Data into WOFOST Model to Improve Winter Wheat Yield Estimation

2019 ◽  
Vol 11 (13) ◽  
pp. 1618 ◽  
Author(s):  
Wen Zhuo ◽  
Jianxi Huang ◽  
Li Li ◽  
Xiaodong Zhang ◽  
Hongyuan Ma ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Winter Wheat ◽  
Cloud Model ◽  
Wheat Yield ◽  
Remote Sensing Data ◽  
Yield Estimation ◽  
Sensing Data ◽  
Winter Wheat Yield ◽  
Sentinel 2

Crop yield estimation at a regional scale over a long period of time is of great significance to food security. In past decades, the integration of remote sensing observations and crop growth models has been recognized as a promising approach for crop growth monitoring and yield estimation. Optical remote sensing data are susceptible to cloud and rain, while synthetic aperture radar (SAR) can penetrate through clouds and has all-weather capabilities. This allows for more reliable and consistent crop monitoring and yield estimation in terms of radar sensor data. The aim of this study is to improve the accuracy for winter wheat yield estimation by assimilating time series soil moisture images, which are retrieved by a water cloud model using SAR and optical data as input, into the crop model. In this study, SAR images were acquired by C-band SAR sensors boarded on Sentinel-1 satellites and optical images were obtained from a Sentinel-2 multi-spectral instrument (MSI) for Hengshui city of Hebei province in China. Remote sensing data and ground data were all collected during the main growing season of winter wheat. Both the normalized difference vegetation index (NDVI), derived from Sentinel-2, and backscattering coefficients and polarimetric indicators, computed from Sentinel-1, were used in the water cloud model to derive time series soil moisture (SM) images. To improve the prediction of crop yields at the field scale, we incorporated remotely sensed soil moisture into the World Food Studies (WOFOST) model using the Ensemble Kalman Filter (EnKF) algorithm. In general, the trend of soil moisture inversion was consistent with the ground measurements, with the coefficient of determination (R2) equal to 0.45, 0.53, and 0.49, respectively, and RMSE was 9.16%, 7.43%, and 8.53%, respectively, for three observation dates. The winter wheat yield estimation results showed that the assimilation of remotely sensed soil moisture improved the correlation of observed and simulated yields (R2 = 0.35; RMSE =934 kg/ha) compared to the situation without data assimilation (R2 = 0.21; RMSE = 1330 kg/ha). Consequently, the results of this study demonstrated the potential and usefulness of assimilating SM retrieved from both Sentinel-1 C-band SAR and Sentinel-2 MSI optical remote sensing data into WOFOST model for winter wheat yield estimation and could also provide a reference for crop yield estimation with data assimilation for other crop types.

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