Item 5: Introduction to the Global Spectral Calibration LibrarySoils FAO-GSP
First plenary meeting on spectroscopy
Virtual | 23 - 25 September 2020
Lucrezia Caon, FAO
Richard Ferguson, USDA, United States of America
Fenny van Egmond, ISRIC, Netherlands
Item 5: Introduction to the Global Spectral Calibration LibrarySoils FAO-GSP
First plenary meeting on spectroscopy
Virtual | 23 - 25 September 2020
Lucrezia Caon, FAO
Richard Ferguson, USDA, United States of America
Fenny van Egmond, ISRIC, Netherlands
Estimation of phosphorus loss from agricultural land in the southern region o...LPE Learning Center
Full Proceedings is available at: http://www.extension.org/72817
The purpose of our work was to determine, within the southern region (AL, AR, FL, GA, KY, LA, MS, NC, OK, SC, TN, and TX), the feasibility of using different models to determine potential phosphorus loss from agricultural fields in lieu of phosphorus indices.
Modeling phosphorus runoff in the chesapeake bay region to test the phosphoru...LPE Learning Center
Full Proceedings available at: http://www.extension.org/72795
The revision of USDA-NRCS’s standard for nutrient management coincided with significant assessment of the performance of Phosphorus (P) Indices in the six states that are tied to the Chesapeake Bay watershed. The 64,000 square mile watershed is the focus of unprecedented activity around nutrient management as a result of a 2011 Total Maximum Daily Load for P, nitrogen (N), and sediment under the Clean Water Act. In addition, the state of Maryland had required updates to it’s original P Index, resulting in broad scrutiny by various interest groups. Within this setting, USDA-NRCS funded a multi-state project to help advance the testing and harmonization of P-based management in the Chesapeake region.
The Development of a Catchment Management Modelling System for the Googong Re...GavanThomas
A scenario assessment model to assist the end-user in determining priorities for a series of agreed management prescriptions that can be enacted through controls on existing landuse
Research poster - 2018 Battelle Conference on Remediation of Chlorinated Comp...Nick Jenshak
Co-Authored abstract and research poster that was accepted to the 2018 Battelle Conference on Remediation of Chlorinated and Recalcitrant Compounds under the title “Using Soil Gas Concentration Mapping to Predict Soil Vapor Extraction Radius of Influence Variances and Optimize Remedial System Design.”
Estimation of phosphorus loss from agricultural land in the heartland region ...LPE Learning Center
Full Proceedings is available at: http://www.extension.org/72813
Phosphorus (P) indices are a key tool to minimize P loss from agricultural fields but there is insufficient water quality data to fully test them. Our goal is to use the Agricultural Policy/Environmental eXtender Model (APEX), calibrated with existing edge-of-field runoff data, to refine P indices and demonstrate their utility as a field assessment tool capable of protecting water quality. In this phase of the project our goal is to use existing small-watershed data from the Heartland Region (IA, KS, MO and NE) to determine the level of calibration needed for APEX before using the model to generate estimates of P loads appropriate for evaluating a P Index.
Scale-dependency and Sensitivity of Hydrological Estimations to Land Use and ...Beniamino Murgante
Scale-dependency and Sensitivity of Hydrological Estimations to Land Use and Topography for a Coastal Watershed in Mississippi - Vladimir J. Alarcon and Charles G. O’Hara
Full proceedings available at: http://www.extension.org/72818
Phosphorus indices provide relative loss ratings that then have a corresponding management response. Because most state Phosphorus Indices are qualitative it is not clear how the relative loss rating corresponds to actual phosphorus inputs into the receiving water and how the receiving water would react to these additions. Even with qualitative Phosphorus Indices, unless the water resource has a specific Total Maximum Daily Load, it is not clear how losses correspond to water quality outcomes. These issues will be discussed in the context of the 590 Natural Resources Conservation Standard for nutrient management.
Using Infrared Spectroscopy for Detection of Changes in Soil Properties in Se...ExternalEvents
This presentation was presented during the 3 Parallel session on Theme 1, Monitoring, mapping, measuring, reporting and verification (MRV) of SOC, of the Global Symposium on Soil Organic Carbon that took place in Rome 21-23 March 2017. The presentation was made by Ms. Caroline Ouko, from CETRAD - Kenya, in FAO Hq, Rome
Estimation of phosphorus loss from agricultural land in the southern region o...LPE Learning Center
Full Proceedings is available at: http://www.extension.org/72817
The purpose of our work was to determine, within the southern region (AL, AR, FL, GA, KY, LA, MS, NC, OK, SC, TN, and TX), the feasibility of using different models to determine potential phosphorus loss from agricultural fields in lieu of phosphorus indices.
Modeling phosphorus runoff in the chesapeake bay region to test the phosphoru...LPE Learning Center
Full Proceedings available at: http://www.extension.org/72795
The revision of USDA-NRCS’s standard for nutrient management coincided with significant assessment of the performance of Phosphorus (P) Indices in the six states that are tied to the Chesapeake Bay watershed. The 64,000 square mile watershed is the focus of unprecedented activity around nutrient management as a result of a 2011 Total Maximum Daily Load for P, nitrogen (N), and sediment under the Clean Water Act. In addition, the state of Maryland had required updates to it’s original P Index, resulting in broad scrutiny by various interest groups. Within this setting, USDA-NRCS funded a multi-state project to help advance the testing and harmonization of P-based management in the Chesapeake region.
The Development of a Catchment Management Modelling System for the Googong Re...GavanThomas
A scenario assessment model to assist the end-user in determining priorities for a series of agreed management prescriptions that can be enacted through controls on existing landuse
Research poster - 2018 Battelle Conference on Remediation of Chlorinated Comp...Nick Jenshak
Co-Authored abstract and research poster that was accepted to the 2018 Battelle Conference on Remediation of Chlorinated and Recalcitrant Compounds under the title “Using Soil Gas Concentration Mapping to Predict Soil Vapor Extraction Radius of Influence Variances and Optimize Remedial System Design.”
Estimation of phosphorus loss from agricultural land in the heartland region ...LPE Learning Center
Full Proceedings is available at: http://www.extension.org/72813
Phosphorus (P) indices are a key tool to minimize P loss from agricultural fields but there is insufficient water quality data to fully test them. Our goal is to use the Agricultural Policy/Environmental eXtender Model (APEX), calibrated with existing edge-of-field runoff data, to refine P indices and demonstrate their utility as a field assessment tool capable of protecting water quality. In this phase of the project our goal is to use existing small-watershed data from the Heartland Region (IA, KS, MO and NE) to determine the level of calibration needed for APEX before using the model to generate estimates of P loads appropriate for evaluating a P Index.
Scale-dependency and Sensitivity of Hydrological Estimations to Land Use and ...Beniamino Murgante
Scale-dependency and Sensitivity of Hydrological Estimations to Land Use and Topography for a Coastal Watershed in Mississippi - Vladimir J. Alarcon and Charles G. O’Hara
Full proceedings available at: http://www.extension.org/72818
Phosphorus indices provide relative loss ratings that then have a corresponding management response. Because most state Phosphorus Indices are qualitative it is not clear how the relative loss rating corresponds to actual phosphorus inputs into the receiving water and how the receiving water would react to these additions. Even with qualitative Phosphorus Indices, unless the water resource has a specific Total Maximum Daily Load, it is not clear how losses correspond to water quality outcomes. These issues will be discussed in the context of the 590 Natural Resources Conservation Standard for nutrient management.
Using Infrared Spectroscopy for Detection of Changes in Soil Properties in Se...ExternalEvents
This presentation was presented during the 3 Parallel session on Theme 1, Monitoring, mapping, measuring, reporting and verification (MRV) of SOC, of the Global Symposium on Soil Organic Carbon that took place in Rome 21-23 March 2017. The presentation was made by Ms. Caroline Ouko, from CETRAD - Kenya, in FAO Hq, Rome
Prediction of Soil Total Nitrogen Content Using Spectraradiometer and GIS in ...Agriculture Journal IJOEAR
— In this study, soil samples were collected from two locations: Samawa and Rumetha in southern Iraq. The samples from each location were split into two datasets: calibration set and validation set. VNIR reflectance (350-2500 nm) and GIS-Kriging were used in combination with Partial Least Square (PLS) to predict total N. only two regions reported higher determination coefficient R 2 and lower Root Mean Square Error (RMSE) than the other wavelength regions. PLS calibration models yielded an R 2 of 0.96 and 0.97 for Rumetha and 0.87 and 0.94 for Samawa location in bands at 500-600 and 800-1000 nm, respectively. The potential of VNIR-based and GIS-Kriging models to predict new unknown soil samples were assessed by using validation datasets from both studied locations. The cross-validation of GIS-Kriging models were unsatisfactory predicted with an Q 2 of 0.28 between laboratory-measured and predicted total N values for Rumetha and 0.43 for Samawa location. While VNIR-based validation models achieved highly predictive power with an R 2 v of 0.84 between laboratory-measured and predicted total N values for Rumetha and 0.85 for Samawa location. These results reveal extremely decreasing in model predictive ability when shifting from VNIR Spectroscopy method to GIS-Kriging.
Diffuse reflectance spectroscopy to estimate soil attributes of Brazilian wet...Agriculture Journal IJOEAR
Abstract— The study of soils and characterization of its attributes are continually evolving, however, for the condition of wetlands, such information is still scarce and poorly distributed. Thus, the objective of this work was to characterize spectrally the soils of a wetland area. On the study area were collected georeferenced soil samples and sent for chemical and physical analysis routine and then subjected to spectral evaluation. Were identified seven soil classes with hydromorphic characteristics in their spectral curves? The information contained in these curves then led the development of equations for soil attributes. Sand was the physical attribute of a better correlation with laboratory data and Cationic Exchange Capacity (CEC), the chemical attributes that showed better results.
EVALUATING REFLECTIVE SPECTROSCOPY FOR PREDICTING SOIL PROPERTIES IN GAJAPATI...indexPub
Visible near-infrared spectroscopy, renowned for its non-destructive nature, rapidity, cost-efficiency, and minimal sample preparation requirements, holds promise as a substitute for in vitro techniques. This ongoing study aims to evaluate the viability of reflective spectroscopy for predicting soil properties in ion farming plains across Gajapati district Odisha. A meticulous collection of 110 soil samples from these regions formed the basis, with their core attributes established using conventional in vitro methods. Employing a land spectroscopic device, the soil samples underwent spectral analysis within the wavelength band of 240 to 400 nm. Following spectrum recording, diverse pre-processing approaches were assessed, paving the way for the application of PCA (Principal Component Analysis) and PLSR (Partial Least Squares Regression) models to decipher pivotal soil properties. The superior model choice was subsequently employed to formulate regressive functions, facilitating the prediction of targeted parameters through linear regression. Findings spotlight the precision of both PCA and PLSR models in elucidating soil properties, with the latter displaying heightened accuracy. Evaluated using the RPD (Ratio of Performance to Deviation) metric, the most accurate estimations were achieved for minerals (RPD=9.34), pH (RPD=4.45), and nitrogen (RPD>2), all classified within category A. In contrast, accuracy proved lower for variables like clay, silt, gravel, phosphorus, potassium, calcium, magnesium, and gypsum, where RPD values ranged between 0.01 and 0.28. These values collectively affirm the satisfactory precision of spectral regressive functions in forecasting the targeted foundational properties. In summary, outcomes of this study underscore the commendable precision of both PCA and PLSR models in determining crucial soil parameters. Moreover, soil spectral data emerges as an effective indirect means to estimate the physical and chemical attributes of soil. Compared to conventional laboratory methods, this technique emerges as a more cost-effective alternative, enhancing efficiency in terms of both time and cost while maintaining heightened precision.
Spectroscopy - A new paradigm for Evidence-based Land &Soil Management recomm...Stankovic G
AfriLAB: Regional Soil Laboratory Network for Africa | First meeting. 21 - 24 May 2019 | Nairobi, Kenya
Ermias Betemariam, Erick Towett & Andrew Sila World Agroforestry (ICRAF), Kenya
Use of remote sensing techniques for nutrient studies in soil and plant _Knig...KnightNthebere
Remote Sensing Techniques used in nutrient studies in plant and soil . Prospects as far as space technology is concerned in agriculture. The usefulness of Hyperspectral and multispectral remote sensing in agriculture sector. How can remote sensing protect the soil from degradation and increase food production with sustainable management practice of agricultural land?
Spatial-temporal variation of biomass production by shrubs in the succulent k...Innspub Net
Forage production in arid and semi-arid rangelands is not uniform but varies with seasons and in various landscapes. The aim of this study was to investigate the spatial and temporal variation in forage production in RNP. Plants sampling was carried out in 225 plots distributed in each of the five vegetation types. In each vegetation strata, sampling points was based on proximity to an occupied stock post, a rain gauge, a foothill and flat plains. A total of were measured in the 5 study sites. Line Intercept Method in combination with harvest method were used in ground measurement of biomass production. To assess biomass production using remote sensing technique, par values were obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) imageries which consisted of 8 days composite images at spatial resolution of 1km² pixel size. There was positive correlation between line intercepts and biomass production Biomass production was higher in succulent Karoo biome than in desert biome. There was a strong relationship between biomass production with rainfall and with fpar values. Since leaf and stem succulents’ plants were found to contribute the highest amount of forage production in RNP, they should be given conservation priority.
Standard Soil Testing Laboratory
time consuming, Laborious, use of chemical and reagents which effect human health and environment, costly, do not consider spatial variation in the field.
Electrochemical Sensing
Ion Selective Electrodes
Ion Sensitive Field Effect Transistor
Optical Spectroscopy
NIR Spectroscopy
Class Project
Mapping of Crop Residues Using Hyperspectral Data:
· Techniques and indexes for quantification,
· Data sources and
· Unsupervised classification for tillage systems
Table of contents / INDEX
Topic
Page
1.
Problem / application
3
2.
Working hypothesis
3
3.
Project outcomes
4
4.
Literature review
4
5.
Data sources
8
6.
Methods
10
7.
Results
16
8
Issues and learning
20
9
Conclusions and future works
21
10.
Annex 1. Corrected bands and columns
22
11
Annex 2. Copy of in-running matlab code for de-striping
23
12
References
25
2
1. PROBLEM / APPLICATION
Agriculture is a widespread, basic activity around the world, which main purpose is to harvest food, fiber or/and energy. After every growing season residues are left in fields. It is important to quantify the amount and cover of agricultural residues for enhancing the understanding in global biogeochemical cycles, and for applications such as their role for preventing soil erosion and their contribution in carbon sequestration. However, it is not completely understood yet how to estimate crop residues cover, their discrimination under tillage or no tillage cropping systems, and its seasonal variability as well as their temporal changes. This class project proposes to explore the estimation and mapping of crop residues by remote sensing techniques using hyperspectral image data.
2. WORKING HYPOTHESIS
Crop residues cover and amount can be accurately estimated by remote sensing techniques. A wide range of crop species and their residues can be studied in the near future and they might be even differentiated by spectral classification. Future work might include description of temporal patterns upon analyzing hyperspectral data (EO-1 Hyperion) in complement with multispectral data (Landsat 7 ETM+ and EO-1 ALI).
3
3. PROJECT OUTCOMES
This class project will generate an estimation of crop residues cover in agricultural fields in Central Indiana in Tipton County. In addition, the amount of crop residues will be approximately calculated based upon yield/residues ratio assumptions. Also, unsupervised classifications for different tillage management (two classes: tilled areas and no-tilled areas) in agricultural fields in Tipton County. Finally, by this study we expect to integrate/use three different data sources (Landsat 7 ETM+, EO-1 ALI and EO-1 Hyperion) and to calculate Cellulose Absorption Index on hyperspectral data.
4. LITERATURE REVIEW
Crop residues are any portion of crop plants that is left in the field after harvest. Crop residues cover is a relevant topic to be studied because of three main reasons: they are widespread in the landscape of agriculture in the Midwest, they represent one of the most important organic inputs for soil carbon sequestration estimating input, and also they relate to soil conservation and reduction of soil erosion (Lal, 2002 & 2004). Remote sensing techniques are also a potential fo ...
Similar to Item 1: Soil infrared spectroscopy (20)
Global Soil Partnership efforts to promote soil governance from the global to...Soils FAO-GSP
Webinar on soil governance and launch of SoiLEX
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Webinar on soil governance and launch of SoiLEX
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http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
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Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
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It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
4. 1 = Fingerprint region e.g Si-O-Si stretching/bending
2 = Double-bond region (e.g. C=O, C=C, C=N)
3 = Triple bond (e.g. C≡C, C≡N)
4 = X–H stretching (e.g. O–H stretching)
NIR = Overtones; key features clay lattice and water OH; SOM affects overall
shape
Soil IR fundamentals
K.D. Shepherd and M.G. Walsh, J. Near
Infrared Spectrosc. 15, 1–19 (2007)
5. Spectral Shape Relates to Basic Soil
Properties:
• Mineral composition
• Iron oxides
• Organic matter
• Carbonates
• Soluble salts
• Particle size distribution
(NIR provides the overtones of MIR)
These properties are the determinants of most functions!
MIR spectral fingerprints
6. Field spectroscopy
World Agroforestry Centre, 2006. Improved Land Management in the Lake Victoria Basin: Final Report on the
TransVic project. ICRAF Occasional Paper No. 7. Nairobi. World Agroforestry Centre.
7. Soil VNIR spectra Lake Victoria basin
(a) sediment samples
(b) sheet and rill eroded soils
(c) hardset soils
(d) gully eroded soils
Each horizontal line is a single spectrum, with
wavelength increasing from left to right
Bright colours indicate high reflectance values,
whereas dark colours indicate low reflectance
values.
Spectral Soil-Erosion-Deposition index
(SEDI):
a measure of the distance in spectral
data space of a soil from the
population of sediment spectra
World Agroforestry Centre, 2006. Improved Land Management in
the Lake Victoria Basin: Final Report on the TransVic project. ICRAF
Occasional Paper No. 7. Nairobi. World Agroforestry Centre.
8. Lessons
1. Conisder using spectra to directly predict soil functional
attributes, not only conventional soil properties
9. From field to lab spectroscopy
Shepherd KD and Walsh MG. (2002).
Development of reflectance spectral
libraries for characterization of soil
properties.
Soil Science Society of America Journal
66:988-998.
10. Dispersive VNIR FT-NIR FT-MIR Robotic FT-MIR Portable
Handheld MIR Mobile soil colourHandheld NIR
Instrument developments
16. Spectral calibration libraries
Africa Soil Information Service (AfSIS)
https://data.worldagroforestry.org/dataset.xhtml?persistentId=doi%3A10.34725%2FD
VN%2FQXCWP1
Spectral Libraries
•Shepherd KD, Walsh MG 2002.
•ICRAF-ISRIC 2005
•Brown D, Shepherd KD, Walsh
MG 2006.
•Terhoeven-Urselmans et al
2010.
•ViscarraRossel & Webster
2012.
•Stevens et al 2013
•Viscarra Rossel RA et al 2016.
•Dematte et al 2019.
•Dangal et al 2019.
17. Lessons
5. Sample the diversity of conditions for which you wish to
predict
6. Use appropriate spectral transforms and calibration algorithms
7. Beware of over-fitting models – use independent validation
19. MIR
Consistently good:
Plant N
Soil total and organic C
Total N
pH
Texture
Extractable Al, Ca, Mg
ECEC
P sorption
Total P
Water holding capacity
Engineering properties
Inconsistent:
Extractable S, Na, P, K,
micronutrients
pXRF
Consistently good:
All essential macro and
micro nutrients in plants
Total element analysis of
soils
Heavy metals in soils
and plants
What soil MIR and XRF predict
20. Lessons
8. When selecting tools, be clear on objectives & decision
problem; required accuracy & precision
21. MIR prediction of soil
organic carbon fractions
Kenya and Australian soils
Janik LJ, Skjemstad JO, Shepherd KD and
Spouncer LR (2007).
The prediction of soil carbon fractions using
mid-infrared-partial least square analysis.
Journal of Australian Soil Research 45(2): 73–81
TOC
POC
Char-C
22. Spectral prediction of C mineralization rates in SOC
fractions
Mutuo PK, Shepherd KD, Albrecht A, and Cadisch G (2006) Prediction of Carbon Mineralization
Rates from Different Soil Physical Fractions Using Diffuse Reflectance Spectroscopy. Soil Biology
& Biochemistry 38:1658–1664.
23. Spectral signatures respond to management-
induced changes in soil functional properties
KALRO-NARL long-term experiment, Kenya
Shepherd KD and Walsh MG. 2000. Sensing soil quality: the evidence from Africa. Working Paper. World
Agroforestry Centre (ICRAF), Nairobi.
25. Soil properties maps of Africa
Vagen et al 2016. Mapping of
soil properties and land
degradation risk in Africa using
MODIS reflectance. Geoderma
263: 216–225
Hengl T et al 2017. Soil nutrient
maps of Sub-Saharan Africa:
assessment of soil nutrient
content at 250 m spatial
resolution using machine
learning. Nutrient Cycling in
Agroecosystems 109:77–102.
AfSIS: Vagen et al 2016
26. Total N content
(g/kg) of farm fields
Range 0.5 – 2.6 g/kg
Tittonell P, Vanlauwe B, Leffelaar PA , Shepherd KD, and
Giller KE. 2005. Exploring diversity in soil fertility
management of smallholder farms in western Kenya II.
Within-farm variability in resource allocation, nutrient
flows and soil fertility status. Agriculture, Ecosystems and
Environment 110 166–184.
2.1 ha
Handheld on-farm applications
• Farm soil testing – Site specific nutrient management
• UAV calibration
• High resolution digital maps
• On-the-go
27. Source: Dangal et al. 2019,
Sanderman et al. 2020
USDA-NSSC-KSSL
GLOSOLAN-GSP
ICRAF, iSDA, ISRIC,
Woodwell Climate
Research Center, Univ
Nebraska, Univ Sydney
• Provide a freely available and easy-to-use
soil property prediction service based on
the global spectral library.
• Support countries to contribute to the
global spectral calibration library and use
the soil property prediction service.
Global Soil Spectral Library & Estimation
Service
29. Remaining challenges
• Standardisation – both spectral and reference
• Calibration transfer (within/across instruments)
• Global vs local calibration
• Data management and modelling toolboxes
• Data sharing (eg Blockchain)
• Diagnostic chains
• Communicating spectral predictions
• Global spectral library and estimation service
http://www.worldagroforestry.org/sd/landhealth/soil-plant-spectral-diagnostics-laboratory
30. Thanks for your attention
The pictures in the covers of this presentation are a courtesy of Dr.
Fenny van Egmond, ISCRI