114 research outputs found
Artificial Neural Networks in Agriculture
Modern agriculture needs to have high production efficiency combined with a high quality of obtained products. This applies to both crop and livestock production. To meet these requirements, advanced methods of data analysis are more and more frequently used, including those derived from artificial intelligence methods. Artificial neural networks (ANNs) are one of the most popular tools of this kind. They are widely used in solving various classification and prediction tasks, for some time also in the broadly defined field of agriculture. They can form part of precision farming and decision support systems. Artificial neural networks can replace the classical methods of modelling many issues, and are one of the main alternatives to classical mathematical models. The spectrum of applications of artificial neural networks is very wide. For a long time now, researchers from all over the world have been using these tools to support agricultural production, making it more efficient and providing the highest-quality products possible
ALOS-2/PALSAR-2 Calibration, Validation, Science and Applications
Twelve edited original papers on the latest and state-of-art results of topics ranging from calibration, validation, and science to a wide range of applications using ALOS-2/PALSAR-2. We hope you will find them useful for your future research
The application of deep learning for remote sensing of soil organic carbon stocks distribution in South Africa.
Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.Soil organic carbon (SOC) is a vital measure for ecosystem health and offers opportunities to
understand carbon fluxes and associated implications. However, unprecedented anthropogenic
disturbances have significantly altered SOC distribution across the globe, leading to
considerable carbon losses. In addition, reliable SOC estimates, particularly over large spatial
extents remain a major challenge due to among others limited sample points, quality of
simulation data and suitable algorithms. Remote sensing (RS) approaches have emerged as a
suitable alternative to field and laboratory SOC determination, especially at large spatial extent.
Nevertheless, reliable determination of SOC distribution using RS data requires robust
analytical approaches. Compared to linear and classical machine learning (ML) models, deep
learning (DL) models offer a considerable improvement in data analysis due to their ability to
extract more representative features and identify complex spatial patterns associated with big
data. Hence, advancements in remote sensing, proliferation of big data, and deep learning
architecture offer great potential for large-scale SOC mapping. However, there is paucity in
literature on the application of DL-based remote sensing approaches for SOC prediction. To
this end, this study is aimed at exploring DL-based approaches for the remote sensing of SOC
stocks distribution across South Africa. The first objective sought to provide a synopsis of the
use of traditional neural network (TNN) and DL-based remote sensing of SOC with emphasis
on basic concepts, differences, similarities and limitations, while the second objective provided
an in-depth review of the history, utility, challenges, and prospects of DL-based remote sensing
approaches for mapping SOC. A quantitative evaluation between the use of TNN and DL
frameworks was also conducted. Findings show that majority of published literature were
conducted in the Northern Hemisphere while Africa have only four publications. Results also
reveal that most studies adopted hyperspectral data, particularly spectrometers as compared to
multispectral data. In comparison to DL (10%), TNN (90%) models were more commonly
utilized in the literature; yet, DL models produced higher median accuracy (93%) than TNN
(85%) models. The review concludes by highlighting future opportunities for retrieving SOC
from remotely sensed data using DL frameworks.
The third objective compared the accuracy of DL—deep neural network (DNN) model and a
TNN—artificial neural network (ANN), as well as other popular classical ML models that
include random forest (RF) and support vector machine (SVM), for national scale SOC
mapping using Sentinel-3 data. With a root mean square error (RMSE) of 10.35 t/ha, the DNN
model produced the best results, followed by RF (11.2 t/ha), ANN (11.6 t/ha), and SVM (13.6 t/ha). The DNN's analytical abilities, combined with its capacity to handle large amounts of
data is a key advantage over other classical ML models. Having established the superiority of
DL models over TNN and other classical models, the fourth objective focused on investigating
SOC stocks distribution across South Africa’s major land uses, using Deep Neural Networks
(DNN) and Sentinel-3 satellite data. Findings show that grasslands contributed the most to
overall SOC stocks (31.36 %), while urban vegetation contributed the least (0.04%). Results
also show that commercial (46.06 t/h) and natural (44.34 t/h) forests had better carbon
sequestration capacity than other classes. These findings provide an important guideline for
managing SOC stocks in South Africa, useful in climate change mitigation by promoting
sustainable land-use practices.
The fifth objective sought to determine the distribution of SOC within South Africa’s major
biomes using remotely sensed-topo-climatic data and Concrete Autoencoder-Deep Neural
Networks (CAE-DNN). Findings show that the CAE-DNN model (built from 26 selected
variables) had the best accuracy of the DNNs examined, with an RMSE of 7.91 t/h. Soil organic
carbon stock was also shown to be related to biome coverage, with the grassland (32.38%) and
savanna (31.28%) biomes contributing the most to the overall SOC pool in South Africa.
forests (44.12 t/h) and the Indian ocean coastal belt (43.05 t/h) biomes, despite having smaller
footprints, have the highest SOC sequestration capacity. To increase SOC storage, it is
recommended that degraded biomes be restored; however, a balance must be maintained
between carbon sequestration capability, biodiversity health, and adequate provision of
ecosystem services. The sixth objective sought to project the present SOC stocks in South
Africa into the future (i.e. 2050). Soil organic carbon variations generated by projected climate
change and land cover were mapped and analysed using a digital soil mapping (DSM)
technique combined with space-for-time substitution (SFTS) procedures over South Africa
through 2050. The potential SOC stocks variations across South Africa's major land uses were
also assessed from current (2021) to future (2050). The first part of the study uses a Deep
Neural Network (DNN) to estimate current SOC content (2021), while the second phase uses
an average of five WorldClim General Circulation Models to project SOC to the future (2050)
under four Shared Socio-economic Pathways (SSPs). Results show a general decline in
projected future SOC stocks by 2050, ranging from 4.97 to 5.38 Pg, compared to estimated
current stocks of 5.64 Pg. The findings are critical for government and policymakers in
assessing the efficacy of current management systems in South Africa. Overall, this study provides a cost-effective framework for national scale mapping of SOC
stocks, which is the largest terrestrial carbon pool using advanced DL-based remote sensing
approach. These findings are valuable for designing appropriate management strategies to
promote carbon uptake, soil quality, and measuring terrestrial ecosystem responses and
feedbacks to climate change. This study is also the first DL-based remote sensing of SOC
stocks distribution in South Africa
Sustainable Agriculture and Advances of Remote Sensing (Volume 1)
Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publishing the results, among others
Sentinel2GlobalLULC: A Sentinel-2 RGB image tile dataset for global land use/cover mapping with deep learning
Land-Use and Land-Cover (LULC) mapping is relevant for many applications, from Earth system and climate modelling to territorial and urban planning. Global LULC products are continuously developing as remote sensing data and methods grow. However, there still exists low consistency among LULC products due to low accuracy in some regions and LULC types. Here, we introduce Sentinel2GlobalLULC, a Sentinel-2 RGB image dataset, built from the spatial-temporal consensus of up to 15 global LULC maps available in Google Earth Engine. Sentinel2GlobalLULC v2.1 contains 194877 single-class RGB image tiles organized into 29 LULC classes. Each image is a 224 × 224 pixels tile at 10 × 10 m resolution built as a cloud-free composite from Sentinel-2 images acquired between June 2015 and October 2020. Metadata includes a unique LULC annotation per image, together with level of consensus, reverse geo-referencing, global human modification index, and number of dates used in the composite. Sentinel2GlobalLULC is designed for training deep learning models aiming to build precise and robust global or regional LULC maps.This work is part of the project “Thematic Center on Mountain Ecosystem & Remote sensing, Deep learning-AI e-Services University of Granada-Sierra Nevada” (LifeWatch-2019-10-UGR-01), which has been co-funded by the Ministry of Science and Innovation through the FEDER funds from the Spanish Pluriregional Operational Program 2014-2020 (POPE), LifeWatch-ERIC action line, within the Workpackages LifeWatch-2019-10-UGR-01 WP-8, LifeWatch-2019-10-UGR-01 WP-7 and LifeWatch-2019-10-UGR-01 WP-4. This work was also supported by projects A-RNM-256-UGR18, A-TIC-458-UGR18, PID2020-119478GB-I00 and P18-FR-4961. E.G. was supported by the European Research Council grant agreement n° 647038 (BIODESERT) and the Generalitat Valenciana, and the European Social Fund (APOSTD/2021/188). We thank the “Programa de Unidades de Excelencia del Plan Propio” of the University of Granada for partially covering the article processing charge
Sentinel2GlobalLULC: A Sentinel-2 RGB image tile dataset for global land use/cover mapping with deep learning
Land-Use and Land-Cover (LULC) mapping is relevant for many applications, from Earth system
and climate modelling to territorial and urban planning. Global LULC products are continuously
developing as remote sensing data and methods grow. However, there still exists low consistency
among LULC products due to low accuracy in some regions and LULC types. Here, we introduce
Sentinel2GlobalLULC, a Sentinel-2 RGB image dataset, built from the spatial-temporal consensus of up
to 15 global LULC maps available in Google Earth Engine. Sentinel2GlobalLULC v2.1 contains 194877
single-class RGB image tiles organized into 29 LULC classes. Each image is a 224 × 224 pixels tile at
10 × 10 m resolution built as a cloud-free composite from Sentinel-2 images acquired between June
2015 and October 2020. Metadata includes a unique LULC annotation per image, together with level of
consensus, reverse geo-referencing, global human modification index, and number of dates used in the
composite. Sentinel2GlobalLULC is designed for training deep learning models aiming to build precise
and robust global or regional LULC maps.Ministry of Science and Innovation through the FEDER funds from the Spanish Pluriregional Operational Program LifeWatch-2019-10-UGR-01LifeWatch-ERIC action line, within the Workpackages LifeWatch-2019-10-UGR-01 WP-8
LifeWatch-2019-10-UGR-01 WP-7
LifeWatch-2019-10-UGR-01 WP-4European Research Council (ERC)European Commission 647038Center for Forestry Research & Experimentation (CIEF) APOSTD/2021/188
A-RNM-256-UGR18
A-TIC-458-UGR18
PID2020-119478GB-I00
P18-FR-496
Investigating the feasibility of using remote sensing in index-based crop insurance for South Africa’s smallholder farming systems
Crop farming in Sub-Saharan Africa (SSA) is largely practiced by resource-poor farmers under rain-fed and unpredictable weather conditions. Since agriculture is the mainstay of SSA’s economy, the lack of improved and adapted agricultural technologies in this region sets back economic development and the fight against poverty. Overcoming this constraint and achieving the sustainable development goal to end poverty, requires innovative tools that can be used for weather risk management. One tool that has been gaining momentum recently is index-based crop insurance (IBCI). Since the launch of the first IBCI program in Africa around 2005, the number of IBCI programs has increased. Unfortunately, these programs are constrained by poor product design, basis risk, and low uptake of contracts. When these issues were first pointed-out in the earliest IBCI programs, many reports suggested satellite remote sensing (RS) as a viable solution. Hence, the first objective of this study was to assess how RS has been used in IBCI, the challenges RS faces, and potential contributions of RS that have not yet been meaningfully exploited. The literature shows that IBCI programs are increasingly adopting RS. RS has improved demarcation of unit areas of insurance and enabled IBCI to reach inaccessible areas that do not have sufficient meteorological infrastructure. However, the literature also shows that IBCI is still tainted by basis risk, which emanates from poor contract designs, the influence of non-weather factors on crop yields, imperfect correlations between satellite-based indices and crop yields, and the lack of historical data for calibration. Although IBCI reports cover vegetation and crop health monitoring, few to none cover crop type and crop area mapping. Furthermore, areas including high-resolution mapping, data fusion, microwave RS, machine learning, and computer vision have not been sufficiently tested in IBCI. The second objective of this study was to assess how RS and machine learning techniques can be used to enhance the mapping of smallholder crop farming landscapes. The findings show that machine learning ensembles and the combination of optical and microwave data can map a smallholder farming landscape with a maximum accuracy of 97.71 percent. The third objective was to identify factors that influence crop yields and crop losses in order to improve IBCI design. Results demonstrated that the pervasive notion that low yields in smallholder farms are related to rainfall is an oversimplification. Factors including fertilizer use, seed variety, soil properties, soil moisture, growing degree-days, management, and socioeconomic conditions are some of the most important factors influencing crop yields and crop losses in smallholder farming systems. This shows why IBCI needs to be part of a comprehensive risk management system that understands and approaches smallholder crop farming as complex by linking insurance with advisories and input supplies. Improved inputs and good farming practices could reduce the influence of non-weather factors on crop losses, and thereby reduce basis risk in weather-based index insurance (WII) contracts. The fourth objective of this study was to assess how well the combination of synthetic aperture radar (SAR) and optical indices estimate soil moisture. As stated earlier, soil moisture was found to be one of the most important factors affecting crop yields. Although this method better estimated soil moisture over the first half of the growing season, estimation accuracies were comparable to those found in studies that had used similar datasets (RMSE = 0.043 m3 m-3, MAE = 0.034 m3 m- 3). Further interrogation of interaction effects between the variables used in this study and consideration of other factors that affect SAR backscatter could improve the method. More importantly, incorporating high-resolution satellite-based monitoring of soil moisture into IBCI could potentially reduce basis risk. The fifth objective of this study was to develop an IBCI for smallholder crop farming systems. The proposed IBCI scheme covers maize and derives index thresholds from crop water requirements and satellite-based rainfall estimates. It covers rainfall deficits over the vegetative, mid-season, and late-season stages of maize growth. The key contribution of this system is the derivation of index thresholds from CWR and site-specific rainfall conditions. The widely used approach, which calibrates IBCI by correlating yields and rainfall, exposes contracts to basis risk because, by simply correlating yield and rainfall data, it overlooks the influence of non-weather factors on crop yields and losses. The proposed system must be linked or bundled with non-weather variables that affect crop yields. Effectively, this means that the insurance must be linked or bundled with advisories and input supplies to address the influence of non-weather factors on crop losses. This system also incorporates a crop area-mapping component, which was found to be lacking in many IBCI systems. In conclusion, an IBCI that is based on crop water requirements, which incorporates crop area mapping and links insurance with non-weather crop yield-determining factors, is potentially capable of improving crop insurance for smallholder farming systems.Thesis (PhD) -- Faculty of Science and Agriculture, 202
Investigating the feasibility of using remote sensing in index-based crop insurance for South Africa’s smallholder farming systems
Crop farming in Sub-Saharan Africa (SSA) is largely practiced by resource-poor farmers under rain-fed and unpredictable weather conditions. Since agriculture is the mainstay of SSA’s economy, the lack of improved and adapted agricultural technologies in this region sets back economic development and the fight against poverty. Overcoming this constraint and achieving the sustainable development goal to end poverty, requires innovative tools that can be used for weather risk management. One tool that has been gaining momentum recently is index-based crop insurance (IBCI). Since the launch of the first IBCI program in Africa around 2005, the number of IBCI programs has increased. Unfortunately, these programs are constrained by poor product design, basis risk, and low uptake of contracts. When these issues were first pointed-out in the earliest IBCI programs, many reports suggested satellite remote sensing (RS) as a viable solution. Hence, the first objective of this study was to assess how RS has been used in IBCI, the challenges RS faces, and potential contributions of RS that have not yet been meaningfully exploited. The literature shows that IBCI programs are increasingly adopting RS. RS has improved demarcation of unit areas of insurance and enabled IBCI to reach inaccessible areas that do not have sufficient meteorological infrastructure. However, the literature also shows that IBCI is still tainted by basis risk, which emanates from poor contract designs, the influence of non-weather factors on crop yields, imperfect correlations between satellite-based indices and crop yields, and the lack of historical data for calibration. Although IBCI reports cover vegetation and crop health monitoring, few to none cover crop type and crop area mapping. Furthermore, areas including high-resolution mapping, data fusion, microwave RS, machine learning, and computer vision have not been sufficiently tested in IBCI. The second objective of this study was to assess how RS and machine learning techniques can be used to enhance the mapping of smallholder crop farming landscapes. The findings show that machine learning ensembles and the combination of optical and microwave data can map a smallholder farming landscape with a maximum accuracy of 97.71 percent. The third objective was to identify factors that influence crop yields and crop losses in order to improve IBCI design. Results demonstrated that the pervasive notion that low yields in smallholder farms are related to rainfall is an oversimplification. Factors including fertilizer use, seed variety, soil properties, soil moisture, growing degree-days, management, and socioeconomic conditions are some of the most important factors influencing crop yields and crop losses in smallholder farming systems. This shows why IBCI needs to be part of a comprehensive risk management system that understands and approaches smallholder crop farming as complex by linking insurance with advisories and input supplies. Improved inputs and good farming practices could reduce the influence of non-weather factors on crop losses, and thereby reduce basis risk in weather-based index insurance (WII) contracts. The fourth objective of this study was to assess how well the combination of synthetic aperture radar (SAR) and optical indices estimate soil moisture. As stated earlier, soil moisture was found to be one of the most important factors affecting crop yields. Although this method better estimated soil moisture over the first half of the growing season, estimation accuracies were comparable to those found in studies that had used similar datasets (RMSE = 0.043 m3 m-3, MAE = 0.034 m3 m- 3). Further interrogation of interaction effects between the variables used in this study and consideration of other factors that affect SAR backscatter could improve the method. More importantly, incorporating high-resolution satellite-based monitoring of soil moisture into IBCI could potentially reduce basis risk. The fifth objective of this study was to develop an IBCI for smallholder crop farming systems. The proposed IBCI scheme covers maize and derives index thresholds from crop water requirements and satellite-based rainfall estimates. It covers rainfall deficits over the vegetative, mid-season, and late-season stages of maize growth. The key contribution of this system is the derivation of index thresholds from CWR and site-specific rainfall conditions. The widely used approach, which calibrates IBCI by correlating yields and rainfall, exposes contracts to basis risk because, by simply correlating yield and rainfall data, it overlooks the influence of non-weather factors on crop yields and losses. The proposed system must be linked or bundled with non-weather variables that affect crop yields. Effectively, this means that the insurance must be linked or bundled with advisories and input supplies to address the influence of non-weather factors on crop losses. This system also incorporates a crop area-mapping component, which was found to be lacking in many IBCI systems. In conclusion, an IBCI that is based on crop water requirements, which incorporates crop area mapping and links insurance with non-weather crop yield-determining factors, is potentially capable of improving crop insurance for smallholder farming systems.Thesis (PhD) -- Faculty of Science and Agriculture, 202
Book of short Abstracts of the 11th International Symposium on Digital Earth
The Booklet is a collection of accepted short abstracts of the ISDE11 Symposium
- …