The use of remote sensing data to extract information from agricultural land with emphasis on soil salinity

Soil Research ◽  
2003 ◽  
Vol 41 (7) ◽  
pp. 1243 ◽  
Author(s):  
F. M. Howari
Keyword(s):  
Remote Sensing ◽  
Soil Salinity ◽  
Large Scale ◽  
Agricultural Land ◽  
Rio Grande Valley ◽  
Remote Sensing Data ◽  
Rio Grande ◽  
The United States ◽  
Sensing Data ◽  
Matching Techniques

The rapid growth of information technologies has provided exciting new sources of data, interpretation tools, and modelling techniques to soil research and education communities at all levels. This paper presents some examples of the capability of remote sensing data such as Landsat ETM+, airborne visible/infrared imaging spectrometer (AVIRIS), colour infrared aerial photos (CIR), and high-resolution field spectroradiometer (GER 3700) to extract surface information about soil salinity. The study used image processing techniques such as supervised classification, spectral extraction, and matching techniques to investigate types and occurrences of salts in the Rio Grande Valley on the United States–Mexico border. Soil salinity groups were established using soil physico-chemical properties and image elements (absorption-reflectivity profiles, band combinations, grey tones of the investigated images, and textures of soil and vegetation covers as they appear in images). The lack of vegetation or scattered vegetation on salt-affected soil (SAS) surfaces makes it possible to detect salt in several locations of the investigated area. The presented remote sensing datasets reveal the presence of gypsum and halite as the dominant salt crusts in the Rio Grande Valley. This information can help agricultural scientists and engineers to produce large-scale maps of salt-affected lands, which will help improve salinity management in watersheds and ecosystems.

scholarly journals Soil Salinity Inversion in Coastal Corn Planting Areas by the Satellite-UAV-Ground Integration Approach

2021 ◽  
Vol 13 (16) ◽  
pp. 3100
Author(s):  
Guanghui Qi ◽  
Chunyan Chang ◽  
Wei Yang ◽  
Peng Gao ◽  
Gengxing Zhao
Keyword(s):  
Remote Sensing ◽  
Soil Salinity ◽  
Large Scale ◽  
Yellow River ◽  
Remote Sensing Data ◽  
Soil Salinization ◽  
Large Area ◽  
Sensing Data ◽  
Inversion Model ◽  
Uav Image

Soil salinization is a significant factor affecting corn growth in coastal areas. How to use multi-source remote sensing data to achieve the target of rapid, efficient and accurate soil salinity monitoring in a large area is worth further study. In this research, using Kenli District of the Yellow River Delta as study area, the inversion of soil salinity in a corn planting area was carried out based on the integration of ground imaging hyperspectral, unmanned aerial vehicles (UAV) multispectral and Sentinel-2A satellite multispectral images. The UAV and ground images were fused, and the partial least squares inversion model was constructed by the fused UAV image. Then, inversion model was scaled up to the satellite by the TsHARP method, and finally, the accuracy of the satellite-UAV-ground inversion model and results was verified. The results show that the band fusion of UAV and ground images effectively enrich the spectral information of the UAV image. The accuracy of the inversion model constructed based on the fused UAV images was improved. The inversion results of soil salinity based on the integration of satellite-UAV-ground were highly consistent with the measured soil salinity (R2 = 0.716 and RMSE = 0.727), and the inversion model had excellent universal applicability. This research integrated the advantages of multi-source data to establish a unified satellite-UAV-ground model, which improved the ability of large-scale remote sensing data to finely indicate soil salinity.

scholarly journals Simultaneous corn and soybean yield prediction from remote sensing data using deep transfer learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Saeed Khaki ◽  
Hieu Pham ◽  
Lizhi Wang
Keyword(s):  
Remote Sensing ◽  
Transfer Learning ◽  
Large Scale ◽  
Crop Yields ◽  
Remote Sensing Data ◽  
The United States ◽  
Growth Cycle ◽  
Yield Potential ◽  
Soybean Yield ◽  
Sensing Data

AbstractLarge-scale crop yield estimation is, in part, made possible due to the availability of remote sensing data allowing for the continuous monitoring of crops throughout their growth cycle. Having this information allows stakeholders the ability to make real-time decisions to maximize yield potential. Although various models exist that predict yield from remote sensing data, there currently does not exist an approach that can estimate yield for multiple crops simultaneously, and thus leads to more accurate predictions. A model that predicts the yield of multiple crops and concurrently considers the interaction between multiple crop yields. We propose a new convolutional neural network model called YieldNet which utilizes a novel deep learning framework that uses transfer learning between corn and soybean yield predictions by sharing the weights of the backbone feature extractor. Additionally, to consider the multi-target response variable, we propose a new loss function. We conduct our experiment using data from 1132 counties for corn and 1076 counties for soybean across the United States. Numerical results demonstrate that our proposed method accurately predicts corn and soybean yield from one to four months before the harvest with an MAE being 8.74% and 8.70% of the average yield, respectively, and is competitive to other state-of-the-art approaches.

LESS: LargE-Scale remote sensing data and image simulation framework over heterogeneous 3D scenes

2019 ◽  
Vol 221 ◽  
pp. 695-706 ◽  
Author(s):  
Jianbo Qi ◽  
Donghui Xie ◽  
Tiangang Yin ◽  
Guangjian Yan ◽  
Jean-Philippe Gastellu-Etchegorry ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Remote Sensing Data ◽  
Image Simulation ◽  
Simulation Framework ◽  
Sensing Data

scholarly journals Quantifying large‐scale ecosystem stability with remote sensing data

2020 ◽  
Vol 6 (3) ◽  
pp. 354-365
Author(s):  
Hannah J. White ◽  
Willson Gaul ◽  
Dinara Sadykova ◽  
Lupe León‐Sánchez ◽  
Paul Caplat ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Remote Sensing Data ◽  
Ecosystem Stability ◽  
Sensing Data

Time-series processing of large scale remote sensing data with extreme learning machine

2014 ◽  
Vol 128 ◽  
pp. 199-206 ◽  
Author(s):  
Jiaoyan Chen ◽  
Guozhou Zheng ◽  
Cong Fang ◽  
Ningyu Zhang ◽  
Huajun Chen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Extreme Learning Machine ◽  
Large Scale ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Learning Machine
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