scholarly journals Potential of hyperspectral remote sensing for field scale soil mapping and precision agriculture applications

2012 ◽  
Vol 7 (4) ◽  
pp. 43 ◽  
Author(s):  
Raffaele Casa ◽  
Fabio Castaldi ◽  
Simone Pascucci ◽  
Stefano Pignatti
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Hyperspectral Remote Sensing ◽  
Soil Mapping ◽  
Field Scale

scholarly journals Improving Field-Scale Wheat LAI Retrieval Based on UAV Remote-Sensing Observations and Optimized VI-LUTs

2019 ◽  
Vol 11 (20) ◽  
pp. 2456 ◽  
Author(s):  
Wanxue Zhu ◽  
Zhigang Sun ◽  
Yaohuan Huang ◽  
Jianbin Lai ◽  
Jing Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Management Practices ◽  
Near Infrared ◽  
Lookup Table ◽  
Field Scale ◽  
Area Index ◽  
Biophysical Parameter ◽  
Retrieval Accuracy

Leaf area index (LAI) is a key biophysical parameter for monitoring crop growth status, predicting crop yield, and quantifying crop variability in agronomic applications. Mapping the LAI at the field scale using multispectral cameras onboard unmanned aerial vehicles (UAVs) is a promising precision-agriculture application with specific requirements: The LAI retrieval method should be (1) robust so that crop LAI can be estimated with similar accuracy and (2) easy to use so that it can be applied to the adjustment of field management practices. In this study, three UAV remote-sensing missions (UAVs with Micasense RedEdge-M and Cubert S185 cameras) were carried out over six experimental plots from 2018 to 2019 to investigate the performance of reflectance-based lookup tables (LUTs) and vegetation index (VI)-based LUTs generated from the PROSAIL model for wheat LAI retrieval. The effects of the central wavelengths and bandwidths for the VI calculations on the LAI retrieval were further examined. We found that the VI-LUT strategy was more robust and accurate than the reflectance-LUT strategy. The differences in the LAI retrieval accuracy among the four VI-LUTs were small, although the improved modified chlorophyll absorption ratio index-lookup table (MCARI2-LUT) and normalized difference vegetation index-lookup table (NDVI-LUT) performed slightly better. We also found that both of the central wavelengths and bandwidths of the VIs had effects on the LAI retrieval. The VI-LUTs with optimized central wavelengths (red = 612 nm, near-infrared (NIR) = 756 nm) and narrow bandwidths (~4 nm) improved the wheat LAI retrieval accuracy (R2 ≥ 0.75). The results of this study provide an alternative method for retrieving crop LAI, which is robust and easy use for precision-agriculture applications and may be helpful for designing UAV multispectral cameras for agricultural monitoring.

Hyperspectral remote sensing in precision agriculture: present status, challenges, and future trends

2020 ◽  
pp. 121-146 ◽  
Author(s):  
Prachi Singh ◽  
Prem Chandra Pandey ◽  
George P. Petropoulos ◽  
Andrew Pavlides ◽  
Prashant K. Srivastava ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Present Status ◽  
Hyperspectral Remote Sensing ◽  
Future Trends

Covariates selection assessment for field scale digital soil mapping in the context of precision fertilization management

2020 ◽  
Author(s):  
Nada Mzid ◽  
Stefano Pignatti ◽  
Irina Veretelnikova ◽  
Raffaele Casa
Keyword(s):  
Soil Properties ◽  
Precision Agriculture ◽  
Prediction Models ◽  
Central Italy ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Soil Maps ◽  
Field Scale ◽  
Umbria Region ◽  
Digital Soil Maps

<p>The application of digital soil mapping in precision agriculture is extremely important, since an assessment of the spatial variability of soil properties within cultivated fields is essential in order to optimize agronomic practices such as fertilization, sowing, irrigation and tillage. In this context, it is necessary to develop methods which rely on information that can be obtained rapidly and at low cost. In the present work, an assessment is carried out of what are the most useful covariates to include in the digital soil mapping of field-scale properties of agronomic interest such as texture (clay, sand, silt), soil organic matter and pH in different farms of the Umbria Region in Central Italy. In each farm a proximal sensing-based mapping of the apparent soil electrical resistivity was carried out using the EMAS (Electro-Magnetic Agro Scanner) sensor. Soil sampling and subsequent analysis in the laboratory were carried out in each field. Different covariates were then used in the development of digital soil maps: apparent resistivity, high resolution Digital Elevation Model (DEM) from Lidar data, and bare soil and/or vegetation indices derived from Sentinel-2 images of the experimental fields. The approach followed two steps: (i) estimation of the variables using a Multiple Linear Regression (MLR) model, (ii) spatial interpolation via prediction models (including regression kriging and block kriging). The validity of the digital soil maps results was assessed both in terms of the accuracy in the estimation of soil properties and in terms of their impact on the fertilization prescription maps for nitrogen (N), phosphorus (P) and potassium (K).</p>

scholarly journals Special Issue “Hyperspectral Remote Sensing of Agriculture and Vegetation”

2020 ◽  
Vol 12 (21) ◽  
pp. 3665
Author(s):  
Simone Pascucci ◽  
Stefano Pignatti ◽  
Raffaele Casa ◽  
Roshanak Darvishzadeh ◽  
Wenjiang Huang
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Hyperspectral Remote Sensing ◽  
Estimation Methods ◽  
Support Vector ◽  
Classification Methods ◽  
Special Issue ◽  
Biophysical Parameters ◽  
Vegetation Monitoring ◽  
Practical Applications

The advent of up-to-date hyperspectral technologies, and their increasing performance both spectrally and spatially, allows for new and exciting studies and practical applications in agriculture (soils and crops) and vegetation mapping and monitoring atregional (satellite platforms) andwithin-field (airplanes, drones and ground-based platforms) scales. Within this context, the special issue has included eleven international research studies using different hyperspectral datasets (from the Visible to the Shortwave Infrared spectral region) for agricultural soil, crop and vegetation modelling, mapping, and monitoring. Different classification methods (Support Vector Machine, Random Forest, Artificial Neural Network, Decision Tree) and crop canopy/leaf biophysical parameters (e.g., chlorophyll content) estimation methods (partial least squares and multiple linear regressions) have been evaluated. Further, drone-based hyperspectral mapping by combining bidirectional reflectance distribution function (BRDF) model for multi-angle remote sensing and object-oriented classification methods are also examined. A review article on the recent advances of hyperspectral imaging technology and applications in agriculture is also included in this issue. The special issue is intended to help researchers and farmers involved in precision agriculture technology and practices to a better comprehension of strengths and limitations of the application of hyperspectral measurements for agriculture and vegetation monitoring. The studies published herein can be used by the agriculture and vegetation research and management communities to improve the characterization and evaluation of biophysical variables and processes, as well as for a more accurate prediction of plant nutrient using existing and forthcoming hyperspectral remote sensing technologies.

scholarly journals A Tool for Analysis of Spectral Indices for Remote Sensing of Vegetation and Crops Using Hyperspectral Images

2019 ◽  
pp. 51-58
Author(s):  
David Ruiz Hidalgo ◽  
Bladimir Bacca Cortés ◽  
Eduardo Caicedo Bravo
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Infrared Imaging ◽  
Software Tool ◽  
Hyperspectral Remote Sensing ◽  
Hyperspectral Images ◽  
Spectral Indices ◽  
Imaging Spectrometer ◽  
Software Application ◽  
Erdas Imagine

Food requirements in the world have increased, evidencing the necessity to improve standard techniques of agricultural production. To do so, one option is through technological elements like hyperspectral remote sensing of vegetation and crops. Remote sensing and hyperspectral imagery are not invasive methods. They allow covering large land space in a reduced amount of time. These features have done the hyper-spectral remote sensing a powerful tool used in precision agriculture. This paper presents a software application to process hyperspectral images and generating pseudo-color images computed using spectral indices. This work uses the hyperspectral images were taken by Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) sensor, which was designed by the NASA. The software application aims to show different elements associated with the hyperspectral remote sensing of vegetation and crops. Functional tests are presented to verify the software requirements. Finally, quantitative results are reported comparing the results of the software proposes in this work with the ERDAS Imagine software tool.

scholarly journals Remote Sensing-Guided Sampling Design with Both Good Spatial Coverage and Feature Space Coverage for Accurate Farm Field-Level Soil Mapping

2019 ◽  
Vol 11 (16) ◽  
pp. 1946 ◽  
Author(s):  
Yongji Wang ◽  
Lili Jiang ◽  
Qingwen Qi ◽  
Ying Liu ◽  
Jun Wang
Keyword(s):  
Remote Sensing ◽  
Spatial Variability ◽  
Precision Agriculture ◽  
Sampling Design ◽  
Feature Space ◽  
Soil Mapping ◽  
Mapping Accuracy ◽  
Field Level ◽  
Good Feature ◽  
Spatial Coverage

With the increasing requirements of precision agriculture for massive and various kinds of data, remote sensing technology has become indispensable in acquiring the necessary data for precision agriculture. Understanding the spatial variability of a target soil variable (i.e., soil mapping) is a critical issue in solving many agricultural problems. Field sampling is one of the most commonly used technologies for soil mapping, but sample sizes are restricted by resources, such as field labor, soil physicochemical analysis, and funding. In this paper, we proposed a sampling design method with both good spatial coverage and feature space coverage to achieve more precise spatial variability of farm field-level target soil variables for limited sample sizes. The proposed method used the super-grid to achieve good spatial coverage, and it took advantage of remote sensing products that were highly correlated with the target soil property (SOM content) to achieve good feature space coverage. For the experiments, we employed the ordinary kriging (OK) method to map the soil organic matter (SOM) content. The different sized super-grid comparison experiments showed that the 400 × 400 m2 super-grid had the highest SOM content mapping accuracy. Then, we compared the proposed method to regular grid sampling (good spatial coverage) and k-means sampling (good feature space coverage), and the experimental results indicated that the proposed method had greater potential in the selection of representative samples that could improve the SOM content mapping accuracy.

Remote sensing in soil mapping — A review

2014 ◽  
Vol 63 (2) ◽  
pp. 353-370 ◽  
Author(s):  
László Pásztor ◽  
Katalin Takács
Keyword(s):  
Remote Sensing ◽  
Soil Mapping

A távérzékeléses eljárásokkal gyűjtött információk számos módon hasznosultak, hasznosulnak a talajtérképezésben. A légi fotók és műholdképek megfelelő alaptérképet és térbeli keretet nyújtanak a talajtérképezéshez. A távérzékelt adatok közvetlen, illetve közvetett információt szolgáltatnak az egyes talajtulajdonságokról. Ezáltal mindkét módon támogatni tudják a talajok térbeli változékonyságát leíró két alapvető koncepciót: az objektum alapút, illetve a talajtulajdonságok folytonos térbeli változását hangsúlyozót. Cikkünkben ezen kategorizálás mentén, többé-kevésbé időrendi sorrendet is megtartva, tekintettük át röviden és példákkal illusztrálva, hogy konkrétan milyen formában képes a távérzékelés segítséget nyújtani a talajtérképezés kérdéseinek megoldásában, illetve kivitelezésének gyakorlatában. A távérzékelés által szolgáltatott térben folytonos és kvantitatív adatok megfelelő támogatást nyújtanak a pontszerűnek tekinthető talajtani mérések térbeli kiterjesztéséhez. A távérzékelés sokrétű lehetőséget biztosít a talaj-táj kapcsolatának modellezésére, illetve az ezen alapuló térképezési koncepció számára. A távérzékelésből származó több időpontú spektrális, illetve egyes speciális kiértékelések során előállított felszínborítási, földhasználati térképek, valamint domborzati modellek a digitális talajtérképezés számára a legalapvetőbb környezeti segédváltozókat szolgáltatják. Mindezeken túl a távérzékelt képek hatékonyan támogatják a térképezéseket megelőző mintavétel tervezést és megújították a digitális formában publikált térképek alaptérképi környezetét is. A távérzékelés a közeljövőben is dinamikusan növekvő mennyiségű adatot fog szolgáltatni a földfelszínről. Az ebben rejlő lehetőséget semmiképpen sem szabad elszalasztani a talajtérképezésben érdekelteknek, főképpen a távérzékeléssel nyert adatmennyiséggel összehasonlítva igen csak szűkösen keletkező és elérhető talajtani információ tükrében. A támogatás tehát adott, annak kihasználása azonban már rajtunk múlik.

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