Soil monitoring for precision farming using hyperspectral remote sensing and soil sensors

2021 ◽  
Vol 69 (4) ◽  
pp. 325-335
Author(s):  
Simon Schreiner ◽  
Dubravko Culibrk ◽  
Michele Bandecchi ◽  
Wolfgang Gross ◽  
Wolfgang Middelmann

Abstract This work describes an approach to calculate pedological parameter maps using hyperspectral remote sensing and soil sensors. These maps serve as information basis for automated and precise agricultural treatments by tractors and field robots. Soil samples are recorded by a handheld hyperspectral sensor and analyzed in the laboratory for pedological parameters. The transfer of the correlation between these two data sets to aerial hyperspectral images leads to 2D-parameter maps of the soil surface. Additionally, rod-like soil sensors provide local 3D-information of pedological parameters under the soil surface. The goal is to combine the area-covering 2D-parameter maps with the local 3D-information to extrapolate large-scale 3D-parameter maps using AI approaches.

2021 ◽  
Author(s):  
Florian Betz ◽  
Magdalena Lauermann ◽  
Bernd Cyffka

<p>In fluvial geomorphology as well as in freshwater ecology, rivers are commonly seen as nested hierarchical systems functioning over a range of spatial and temporal scales. Thus, for a comprehensive assessment, information on various scales is required. Over the past decade, remote sensing based approaches have become increasingly popular in river science to increase the spatial scale of analysis. However, data-scarce areas have been mostly ignored so far despite the fact that most remaining free flowing – and thus ecologically valuable – rivers worldwide are located in regions characterized by a lack of data sources like LiDAR or even aerial imagery. High resolution satellite data would be able to fill this data gap, but tends to be too costly for large scale applications what limits the ability for comprehensive studies on river systems in such remote areas. This in turn is a limitation for management and conservation of these rivers.</p><p>In this contribution, we suggest an approach for river corridor mapping based on open access data only in order to foster large scale geomorphological mapping of river corridors in data-scarce areas. For this aim, we combine advanced terrain analysis with multispectral remote sensing using the SRTM-1 DEM along with Landsat OLI imagery. We take the Naryn River in Kyrgyzstan as an example to demonstrate the potential of these open access data sets to derive a comprehensive set of parameters for characterizing this river corridor. The methods are adapted to the specific characteristics of medium resolution open access data sets and include an innovative, fuzzy logic based approach for riparian zone delineation, longitudinal profile smoothing based on constrained quantile regression and a delineation of the active channel width as needed for specific stream power computation. In addition, an indicator for river dynamics based on Landsat time series is developed. For each derived river corridor parameter, a rigor validation is performed. The results demonstrate, that our open access approach for geomorphological mapping of river corridors is capable to provide results sufficiently accurate to derive reach averaged information. Thus, it is well suited for large scale river characterization in data-scarce regions where otherwise the river corridors would remain largely unexplored from an up-to-date riverscape perspective. Such a characterization might be an entry point for further, more detailed research in selected study reaches and can deliver the required comprehensive background information for a range of topics in river science.</p>


Author(s):  
Alpana Shukla ◽  
Rajsi Kot

<div><p><em>Recent advances in remote sensing and geographic information has opened new directions for the development of hyperspectral sensors. Hyperspectral remote sensing, also known as imaging spectroscopy is a new technology. Hyperspectral imaging is currently being investigated by researchers and scientists for the detection and identification of vegetation, minerals, different objects and background.</em><em> Hyperspectral remote sensing combines imaging and spectroscopy in a single system which often includes large data sets and requires new processing methods. Hyperspectral data sets are generally made of about 100 to 200 spectral bands of relatively narrow bandwidths (5-10 nm), whereas, multispectral data sets are usually composed of about 5 to 10 bands of relatively large bandwidths (70-400 nm). Hyperspectral imagery is collected as a data cube with spatial information collected in the X-Y plane, and spectral information represented in the Z-direction. </em><em>Hyperspectral remote sensing is applicable in many different disciplines. It was originally developed for mining and geology; it has now spread into fields such as agriculture and forestry, ecology, coastal zone management, geology and mineral exploration. This paper presents an overview of hyperspectral imaging, data exploration and analysis, applications in various disciplines, advantages and disadvantages and future aspects of the technique.</em></p></div>


2019 ◽  
Vol 11 (17) ◽  
pp. 2011 ◽  
Author(s):  
Lifei Wei ◽  
Ming Yu ◽  
Yajing Liang ◽  
Ziran Yuan ◽  
Can Huang ◽  
...  

The precise classification of crop types is an important basis of agricultural monitoring and crop protection. With the rapid development of unmanned aerial vehicle (UAV) technology, UAV-borne hyperspectral remote sensing imagery with high spatial resolution has become the ideal data source for the precise classification of crops. For precise classification of crops with a wide variety of classes and varied spectra, the traditional spectral-based classification method has difficulty in mining large-scale spatial information and maintaining the detailed features of the classes. Therefore, a precise crop classification method using spectral-spatial-location fusion based on conditional random fields (SSLF-CRF) for UAV-borne hyperspectral remote sensing imagery is proposed in this paper. The proposed method integrates the spectral information, the spatial context, the spatial features, and the spatial location information in the conditional random field model by the probabilistic potentials, providing complementary information for the crop discrimination from different perspectives. The experimental results obtained with two UAV-borne high spatial resolution hyperspectral images confirm that the proposed method can solve the problems of large-scale spatial information modeling and spectral variability, improving the classification accuracy for each crop type. This method has important significance for the precise classification of crops in hyperspectral remote sensing imagery.


1997 ◽  
Author(s):  
Tom Wilson ◽  
Rebecca Baugh ◽  
Ron Contillo ◽  
Tom Wilson ◽  
Rebecca Baugh ◽  
...  

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