scholarly journals A Geostatistical Approach for Modeling Soybean Crop Area and Yield Based on Census and Remote Sensing Data

2018 ◽  
Vol 10 (5) ◽  
pp. 680 ◽  
Author(s):  
Michel Chaves ◽  
Marcelo de Carvalho Alves ◽  
Marcelo de Oliveira ◽  
Thelma Sáfadi
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Soybean Crop ◽  
Sensing Data ◽  
Geostatistical Approach ◽  
Crop Area

scholarly journals REMOTE SENSING DATA FUSION TO DETECT ILLICIT CROPS AND UNAUTHORIZED AIRSTRIPS

2018 ◽  
Vol XLII-3 ◽  
pp. 1363-1368
Author(s):  
J. A. Pena ◽  
T. Yumin ◽  
H. Liu ◽  
B. Zhao ◽  
J. A. Garcia ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Fusion ◽  
Information Acquisition ◽  
Spatial Information ◽  
Remote Sensing Data ◽  
Identification Accuracy ◽  
Sensing Data ◽  
Multi Temporal ◽  
Crop Area ◽  
Area Monitoring

Remote sensing data fusion has been playing a more and more important role in crop planting area monitoring, especially for crop area information acquisition. Multi-temporal data and multi-spectral time series are two major aspects for improving crop identification accuracy. Remote sensing fusion provides high quality multi-spectral and panchromatic images in terms of spectral and spatial information, respectively. In this paper, we take one step further and prove the application of remote sensing data fusion in detecting illicit crop through LSMM, GOBIA, and MCE analyzing of strategic information. This methodology emerges as a complementary and effective strategy to control and eradicate illicit crops.

scholarly journals Construction of Comprehensive Drought Monitoring Model in Jing-Jin-Ji Region Based on Multisource Remote Sensing Data

Water ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 1077 ◽  
Author(s):  
Haozhe Yu ◽  
Lijuan Li ◽  
Yang Liu ◽  
Jiuyi Li
Keyword(s):  
Remote Sensing ◽  
Correlation Coefficient ◽  
Remote Sensing Data ◽  
Condition Index ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
Sensing Data ◽  
Monitoring Model ◽  
Crop Area ◽  
Unit Yield

Drought is a complex hazard that has more adverse effects on agricultural production and economic development. Studying drought monitoring techniques and assessment methods can improve our ability to respond to natural disasters. Numerous drought indices deriving from meteorological or remote sensing data are focused mainly on monitoring single drought response factors such as soil or vegetation, and the ability to reflect comprehensive information on drought was poor. This study constructed a comprehensive drought-monitoring model considering the drought factors including precipitation, vegetation growth status, and soil moisture balance during the drought process for the Jing-Jin-Ji region, China. The comprehensive drought index of remote sensing (CDIR), a drought indicator deduced by the model, was composed of the vegetation condition index (VCI), the temperature condition index (TCI), and the precipitation condition index (PCI). The PCI was obtained from the Tropical Rainfall Measuring Mission (TRMM) satellite. The VCI and TCI were obtained from a moderate-resolution imaging spectroradiometer (MODIS). In this study, a heavy drought process was accurately explored using the CDIR in the Jing-Jin-Ji region in 2016. Finally, a three-month scales standardized precipitation index (SPI-3), drought affected crop area, and standardized unit yield of wheat were used as validation to evaluate the accuracy of this model. The results showed that the CDIR is closely related to the SPI-3, as well as variations in the drought-affected crop area and standardized unit yield of crop. The correlation coefficient of the CDIR with SPI-3 was between 0.45 and 0.85. The correlation coefficient between the CDIR and drought affected crop was between −0.81 and −0.86. Moreover, the CDIR was positively correlated with the standardized unit yield of crop. It showed that the CDIR index is a decent indicator that can be used for integrated drought monitoring and that it can synthetically reflect meteorological and agricultural drought information.

Assessment of the national market demand for high-resolution remote sensing data

2002 ◽  
Vol 8 (1) ◽  
pp. 15-22
Author(s):  
V.N. Astapenko ◽  
◽  
Ye.I. Bushuev ◽  
V.P. Zubko ◽  
V.I. Ivanov ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Remote Sensing Data ◽  
Market Demand ◽  
Sensing Data ◽  
National Market
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