scholarly journals Clustering Tools for Integration of Satellite Remote Sensing Imagery and Proximal Soil Sensing Data

2019 ◽  
Vol 11 (9) ◽  
pp. 1036 ◽  
Author(s):  
Md Saifuzzaman ◽  
Viacheslav Adamchuk ◽  
Roberto Buelvas ◽  
Asim Biswas ◽  
Shiv Prasher ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Data Clustering ◽  
Bare Soil ◽  
Neighborhood Search ◽  
Soil Variability ◽  
Agricultural Fields ◽  
Essential Information ◽  
Spatial Continuity ◽  
Proximal Soil Sensing

Remote sensing (RS) and proximal soil sensing (PSS) technologies offer an advanced array of methods for obtaining soil property information and determining soil variability for precision agriculture. A large amount of data collected by these sensors may provide essential information for precision or site-specific management in a production field. Data clustering techniques are crucial for data mining, and high-density data analysis is important for field management. A new clustering technique was introduced and compared with existing clustering tools to determine the relatively homogeneous parts of agricultural fields. A DUALEM-21S sensor, along with high-accuracy topography data, was used to characterize soil variability in three agricultural fields situated in Ontario, Canada. Sentinel-2 data assisted in quantifying bare soil and vegetation indices (VIs). The custom Neighborhood Search Analyst (NSA) data clustering tool was implemented using Python scripts. In this algorithm, part of the variance of each data layer is accounted for by subdividing the field into smaller, relatively homogeneous, areas. The algorithm’s attributes were illustrated using field elevation, shallow and deep apparent electrical conductivity (ECa), and several VIs. The unique feature of this proposed protocol was the successful development of user-friendly and open source options for defining the spatial continuity of each group and for use in the zone delineation process.

Landscape Change in the Steppe of Algeria South-West Using Remote Sensing

2018 ◽  
Vol 18 (1) ◽  
pp. 41-52
Author(s):  
Tayeb Sitayeb ◽  
Ishak Belabbes
Keyword(s):  
Remote Sensing ◽  
Social Systems ◽  
Bare Soil ◽  
Climatic Conditions ◽  
Landscape Dynamics ◽  
Landsat 8 ◽  
Natural Ecosystems ◽  
Stipa Tenacissima ◽  
Steppe Species ◽  
Sparse Vegetation

Abstract Landscape dynamics is the result of interactions between social systems and the environment, these systems evolving significantly over time. climatic conditions and biophysical phenomena are the main factors of landscape dynamics. Also, currently man is responsible for most changes affecting natural ecosystems. The objective of this work is to study the dynamics of a typical landscape of western Algeria in time and space, and to map the distribution of vegetation groups constitute the vegetation cover of this ecosystem. as well as using a method of monitoring the state of a fragile ecosystem by remote sensing to understand the processes of changes in this area. The steppe constitutes a large arid area, with little relief, covered with low and sparse vegetation. it lies between the annual isohyets of 100 to 400 mm, subjected to a very old human exploitation with an activity of extensive breeding of sheep, goats, and camels. Landsat satellite data were used to mapping vegetation groups in the Mecheria Steppe at a scale of 1: 300,000. Then, a comparison was made between the two maps obtained by a classification of Landsat-8 sensor Operational Land Imager (OLI) acquired on March 18, 2014, and Landsat-5 sensor Thematic Mapper (TM) acquired on April 25, 1987. The results obtained show the main changes affecting the natural distribution of steppe species, a strong change in land occupied by the Stipa tenacissima steppe with 65% of change, this steppe is replaced by Thymelaea microphylla, Salsola vermiculata, lygeum spartum and Peganum harmala steppe. an absence from the steppe Artemisia herba-alba that has also been replaced by the same previous steppes species. The groups with Quercus ilex and Juniperus phoenicea are characterized by a strong regression that was lost 60% of its global surface and transformed by steppe to stipa tenacissima and bare soil.

Application of Remote Sensing in Agriculture

2014 ◽  
Vol 13 (1) ◽  
Author(s):  
Jan Piekarczyk
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Crop Production ◽  
Satellite Images ◽  
Crop Yields ◽  
Spatial Scales ◽  
Strong Relationship ◽  
Physical Parameters ◽  
Agricultural Crop ◽  
Remote Sensing Methods

AbstractWith increasing intensity of agricultural crop production increases the need to obtain information about environmental conditions in which this production takes place. Remote sensing methods, including satellite images, airborne photographs and ground-based spectral measurements can greatly simplify the monitoring of crop development and decision-making to optimize inputs on agricultural production and reduce its harmful effects on the environment. One of the earliest uses of remote sensing in agriculture is crop identification and their acreage estimation. Satellite data acquired for this purpose are necessary to ensure food security and the proper functioning of agricultural markets at national and global scales. Due to strong relationship between plant bio-physical parameters and the amount of electromagnetic radiation reflected (in certain ranges of the spectrum) from plants and then registered by sensors it is possible to predict crop yields. Other applications of remote sensing are intensively developed in the framework of so-called precision agriculture, in small spatial scales including individual fields. Data from ground-based measurements as well as from airborne or satellite images are used to develop yield and soil maps which can be used to determine the doses of irrigation and fertilization and to take decisions on the use of pesticides.

scholarly journals A Modified Bare Soil Index to Identify Bare Land Features during Agricultural Fallow-Period in Southeast Asia Using Landsat 8

Land ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 231
Author(s):  
Can Trong Nguyen ◽  
Amnat Chidthaisong ◽  
Phan Kieu Diem ◽  
Lian-Zhi Huo
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Urban Areas ◽  
Near Infrared ◽  
Urban Landscape ◽  
Bare Soil ◽  
Critical Element ◽  
Landsat 8 ◽  
Climate Conditions ◽  
Fallow Period

Bare soil is a critical element in the urban landscape and plays an essential role in urban environments. Yet, the separation of bare soil and other land cover types using remote sensing techniques remains a significant challenge. There are several remote sensing-based spectral indices for barren detection, but their effectiveness varies depending on land cover patterns and climate conditions. Within this research, we introduced a modified bare soil index (MBI) using shortwave infrared (SWIR) and near-infrared (NIR) wavelengths derived from Landsat 8 (OLI—Operational Land Imager). The proposed bare soil index was tested in two different bare soil patterns in Thailand and Vietnam, where there are large areas of bare soil during the agricultural fallow period, obstructing the separation between bare soil and urban areas. Bare soil extracted from the MBI achieved higher overall accuracy of about 98% and a kappa coefficient over 0.96, compared to bare soil index (BSI), normalized different bare soil index (NDBaI), and dry bare soil index (DBSI). The results also revealed that MBI considerably contributes to the accuracy of land cover classification. We suggest using the MBI for bare soil detection in tropical climatic regions.

scholarly journals Biodiversity decline with increasing crop productivity in agricultural fields revealed by satellite remote sensing

2021 ◽  
Vol 130 ◽  
pp. 108098
Author(s):  
Abdulhakim M. Abdi ◽  
Romain Carrié ◽  
William Sidemo-Holm ◽  
Zhanzhang Cai ◽  
Niklas Boke-Olén ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Crop Productivity ◽  
Agricultural Fields ◽  
Biodiversity Decline

scholarly journals Remote Sensing and Machine Learning in Crop Phenotyping and Management, with an Emphasis on Applications in Strawberry Farming

2021 ◽  
Vol 13 (3) ◽  
pp. 531
Author(s):  
Caiwang Zheng ◽  
Amr Abd-Elrahman ◽  
Vance Whitaker
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Precision Agriculture ◽  
Image Data ◽  
Fruit Shape ◽  
Biological Information ◽  
Genetic Traits ◽  
Multiple Sensors ◽  
Fruit Maturity ◽  
High Throughput Phenotyping

Measurement of plant characteristics is still the primary bottleneck in both plant breeding and crop management. Rapid and accurate acquisition of information about large plant populations is critical for monitoring plant health and dissecting the underlying genetic traits. In recent years, high-throughput phenotyping technology has benefitted immensely from both remote sensing and machine learning. Simultaneous use of multiple sensors (e.g., high-resolution RGB, multispectral, hyperspectral, chlorophyll fluorescence, and light detection and ranging (LiDAR)) allows a range of spatial and spectral resolutions depending on the trait in question. Meanwhile, computer vision and machine learning methodology have emerged as powerful tools for extracting useful biological information from image data. Together, these tools allow the evaluation of various morphological, structural, biophysical, and biochemical traits. In this review, we focus on the recent development of phenomics approaches in strawberry farming, particularly those utilizing remote sensing and machine learning, with an eye toward future prospects for strawberries in precision agriculture. The research discussed is broadly categorized according to strawberry traits related to (1) fruit/flower detection, fruit maturity, fruit quality, internal fruit attributes, fruit shape, and yield prediction; (2) leaf and canopy attributes; (3) water stress; and (4) pest and disease detection. Finally, we present a synthesis of the potential research opportunities and directions that could further promote the use of remote sensing and machine learning in strawberry farming.

scholarly journals CubeSats Enable High Spatiotemporal Retrievals of Crop-Water Use for Precision Agriculture

2018 ◽  
Vol 10 (12) ◽  
pp. 1867 ◽  
Author(s):  
Bruno Aragon ◽  
Rasmus Houborg ◽  
Kevin Tu ◽  
Joshua B. Fisher ◽  
Matthew McCabe
Keyword(s):  
Remote Sensing ◽  
Eddy Covariance ◽  
Water Use ◽  
Spatial Resolution ◽  
Precision Agriculture ◽  
High Spatial Resolution ◽  
Assessment Tools ◽  
Optical Data ◽  
Crop Water ◽  
Crop Water Use

Remote sensing based estimation of evapotranspiration (ET) provides a direct accounting of the crop water use. However, the use of satellite data has generally required that a compromise between spatial and temporal resolution is made, i.e., one could obtain low spatial resolution data regularly, or high spatial resolution occasionally. As a consequence, this spatiotemporal trade-off has tended to limit the impact of remote sensing for precision agricultural applications. With the recent emergence of constellations of small CubeSat-based satellite systems, these constraints are rapidly being removed, such that daily 3 m resolution optical data are now a reality for earth observation. Such advances provide an opportunity to develop new earth system monitoring and assessment tools. In this manuscript we evaluate the capacity of CubeSats to advance the estimation of ET via application of the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) retrieval model. To take advantage of the high-spatiotemporal resolution afforded by these systems, we have integrated a CubeSat derived leaf area index as a forcing variable into PT-JPL, as well as modified key biophysical model parameters. We evaluate model performance over an irrigated farmland in Saudi Arabia using observations from an eddy covariance tower. Crop water use retrievals were also compared against measured irrigation from an in-line flow meter installed within a center-pivot system. To leverage the high spatial resolution of the CubeSat imagery, PT-JPL retrievals were integrated over the source area of the eddy covariance footprint, to allow an equivalent intercomparison. Apart from offering new precision agricultural insights into farm operations and management, the 3 m resolution ET retrievals were shown to explain 86% of the observed variability and provide a relative RMSE of 32.9% for irrigated maize, comparable to previously reported satellite-based retrievals. An observed underestimation was diagnosed as a possible misrepresentation of the local surface moisture status, highlighting the challenge of high-resolution modeling applications for precision agriculture and informing future research directions. .

Sign in / Sign up

Export Citation Format

Share Document