scholarly journals Spatial Prediction of Agrochemical Properties on the Scale of a Single Field Using Machine Learning Methods Based on Remote Sensing Data

Agronomy ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 2266
Author(s):  
Ilnas Sahabiev ◽  
Elena Smirnova ◽  
Kamil Giniyatullin
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Remote Sensing Data ◽  
Spatial Prediction ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Agrochemical Properties ◽  
Sentinel 2

Creating accurate digital maps of the agrochemical properties of soils on a field scale with a limited data set is a problem that slows down the introduction of precision farming. The use of machine learning methods based on the use of direct and indirect predictors of spatial changes in the agrochemical properties of soils is promising. Spectral indicators of open soil based on remote sensing data, as well as soil properties, were used to create digital maps of available forms of nitrogen, phosphorus, and potassium. It was shown that machine learning methods based on support vectors (SVMr) and random forest (RF) using spectral reflectance data are similarly accurate at spatial prediction. An acceptable prediction was obtained for available nitrogen and available potassium; the variability of available phosphorus was modeled less accurately. The coefficient of determination (R2) of the best model for nitrogen is R2SVMr = 0.90 (Landsat 8 OLI) and R2SVMr = 0.79 (Sentinel 2), for potassium—R2SVMr = 0.82 (Landsat 8 OLI) and R2SVMr = 0.77 (Sentinel 2), for phosphorus—R2SVMr = 0.68 (Landsat 8 OLI), R2SVMr = 0.64 (Sentinel 2). The models based on remote sensing data were refined when soil organic matter (SOC) and fractions of texture (Silt, Clay) were included as predictors. The SVMr models were the most accurate. For Landsat 8 OLI, the SVMr model has a R2 value: nitrogen—R2 = 0.95, potassium—R2 = 0.89 and phosphorus—R2 = 0.65. Based on Sentinel 2, nitrogen—R2 = 0.92, potassium—R2 = 0.88, phosphorus—R2 = 0.72. The spatial prediction of nitrogen content is influenced by SOC, potassium—by SOC and texture, phosphorus—by texture. The validation of the final models was carried out on an independent sample on soils from a chernozem zone. For nitrogen based on Landsat 8 OLI R2 = 0.88, for potassium R2 = 0.65, and for phosphorus R2 = 0.31. Based on Sentinel 2, for nitrogen R2 = 0.85, for potassium R2 = 0.62, and for phosphorus R2 = 0.71. The inclusion of SOC and texture in remote sensing-based machine learning models makes it possible to improve the spatial prediction of nitrogen, phosphorus and potassium availability of soils in chernozem zones and can potentially be widely used to create digital agrochemical maps on the scale of a single field.

scholarly journals Improving Unmanned Aerial Vehicle Remote Sensing-Based Rice Nitrogen Nutrition Index Prediction with Machine Learning

2020 ◽  
Vol 12 (2) ◽  
pp. 215 ◽  
Author(s):  
Hainie Zha ◽  
Yuxin Miao ◽  
Tiantian Wang ◽  
Yue Li ◽  
Jing Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
Spectral Reflectance ◽  
Remote Sensing Data ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Aerial Vehicle ◽  
N Management ◽  
N Status

Optimizing nitrogen (N) management in rice is crucial for China’s food security and sustainable agricultural development. Nondestructive crop growth monitoring based on remote sensing technologies can accurately assess crop N status, which may be used to guide the in-season site-specific N recommendations. The fixed-wing unmanned aerial vehicle (UAV)-based remote sensing is a low-cost, easy-to-operate technology for collecting spectral reflectance imagery, an important data source for precision N management. The relationships between many vegetation indices (VIs) derived from spectral reflectance data and crop parameters are known to be nonlinear. As a result, nonlinear machine learning methods have the potential to improve the estimation accuracy. The objective of this study was to evaluate five different approaches for estimating rice (Oryza sativa L.) aboveground biomass (AGB), plant N uptake (PNU), and N nutrition index (NNI) at stem elongation (SE) and heading (HD) stages in Northeast China: (1) single VI (SVI); (2) stepwise multiple linear regression (SMLR); (3) random forest (RF); (4) support vector machine (SVM); and (5) artificial neural networks (ANN) regression. The results indicated that machine learning methods improved the NNI estimation compared to VI-SLR and SMLR methods. The RF algorithm performed the best for estimating NNI (R2 = 0.94 (SE) and 0.96 (HD) for calibration and 0.61 (SE) and 0.79 (HD) for validation). The root mean square errors (RMSEs) were 0.09, and the relative errors were <10% in all the models. It is concluded that the RF machine learning regression can significantly improve the estimation of rice N status using UAV remote sensing. The application machine learning methods offers a new opportunity to better use remote sensing data for monitoring crop growth conditions and guiding precision crop management. More studies are needed to further improve these machine learning-based models by combining both remote sensing data and other related soil, weather, and management information for applications in precision N and crop management.

scholarly journals A RADIOMETRIC NORMALIZATION METHOD OF CONTROLLING NO-CHANGED SET (CNCS) FOR DIVERSE LANDCOVER USING MULTI-SENSOR DATA

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 863-870
Author(s):  
L. T. Huang ◽  
W. L. Jiao ◽  
T. F. Long ◽  
C. L. Kang
Keyword(s):  
Remote Sensing ◽  
Linear Relationship ◽  
Land Surface ◽  
Remote Sensing Data ◽  
Normalization Method ◽  
Sensor Data ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Sensing Data ◽  
Sentinel 2

Abstract. The accurate acquisition of land surface reflectance (SR) data determines the accuracy of ground objects recognition, classification and land surface parameter inversion using remote sensing data, which is the basis of remote sensing data application. In this study, a Control No-Changed Set (CNCS) radiometric normalization method is proposed to realize spectral information transformation of multi-sensor data, which is based on the Iteratively Reweighted Multivariate Alteration Detection (IR-MAD), and includes automatic selection and step-by-step optimization of no-change pixels. The No-Changed set (NC) is obtained by selecting the original no-change pixels between the target image and the reference image according to the linear relationship. In the obtained original no-change regions, IR-MAD rules with iterative control are used to fix the final no-change pixels, after regression modeling and calculation, the normalized images are obtained. The method is tested on multi-images from multi-sensors in three groups of experiments (GF-1 WFV and Landsat-8 OLI, GF-1 PMS and Sentinel-2 MSI, and Landsat-8 OLI and Sentinel-2 MSI) with different landcover areas. The results of radiometric normalization are evaluated qualitatively and quantitatively. The data of the three groups of experiments have a high correlation (correlation coefficient r values > 0.85), indicating that they can be used together as complementary data. The Root Mean Squared Error (RMSE) values calculate from the NC between the reference and normalized target images are much smaller than those between the reference and original target images. The radiometric colour composition effects, and the typical ground objects spectral reflective curves of the reference and normalized target images are very similar after radiometric normalization. These results indicate that the CNCS method considers the linear relationship of the no-change pixels and is effective, stable, and can be used to improve the consistency of SR of multi-images from multi-sensors.

scholarly journals Statistical Machine Learning Methods and Remote Sensing for Sustainable Development Goals: A Review

2018 ◽  
Vol 10 (9) ◽  
pp. 1365 ◽  
Author(s):  
Jacinta Holloway ◽  
Kerrie Mengersen
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Sustainable Development ◽  
Sustainable Development Goals ◽  
Remote Sensing Data ◽  
Statistical Machine Learning ◽  
Learning Methods ◽  
Sensing Data ◽  
Machine Learning Methods ◽  
Development Goals

Interest in statistical analysis of remote sensing data to produce measurements of environment, agriculture, and sustainable development is established and continues to increase, and this is leading to a growing interaction between the earth science and statistical domains. With this in mind, we reviewed the literature on statistical machine learning methods commonly applied to remote sensing data. We focus particularly on applications related to the United Nations World Bank Sustainable Development Goals, including agriculture (food security), forests (life on land), and water (water quality). We provide a review of useful statistical machine learning methods, how they work in a remote sensing context, and examples of their application to these types of data in the literature. Rather than prescribing particular methods for specific applications, we provide guidance, examples, and case studies from the literature for the remote sensing practitioner and applied statistician. In the supplementary material, we also describe the necessary steps pre and post analysis for remote sensing data; the pre-processing and evaluation steps.

scholarly journals Forecasting Rainfed Agricultural Production in Arid and Semi-Arid Lands Using Learning Machine Methods: A Case Study

2021 ◽  
Vol 13 (9) ◽  
pp. 4607
Author(s):  
Shahram Rezapour ◽  
Erfan Jooyandeh ◽  
Mohsen Ramezanzade ◽  
Ali Mostafaeipour ◽  
Mehdi Jahangiri ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Remote Sensing Data ◽  
Weather Data ◽  
Support Vector ◽  
Learning Methods ◽  
K Nearest Neighbors ◽  
Sensing Data ◽  
Machine Learning Methods ◽  
Rainfed Farming

With the rising demand for food products and the direct impact of climate change on food production in many parts of the world, recent years have seen growing interest in the subject of food security and the role of rainfed farming in this area. Machine learning methods can be used to predict crop yield based on a combination of remote sensing data and data collected by ground weather stations. This paper argues that forecasting drylands farming yield can be reliable for management purpose under uncertain conditions using machine learning methods and remote sensing data and determines which indicators are most important in predicting the yield of chickpea. In this study, the yield of rainfed chickpea farms in 11 top chickpea producing counties in Kermanshah province, Iran, was predicted using three machine learning methods, namely support vector regression (SVR), random forest (RF), and K-nearest neighbors (KNN). To improve prediction accuracy, for each county, remote sensing data were overlaid by the satellite images of rainfed farms with a suitable slope and altitude for rainfed farming. An integrated database was created by combining weather data, remote sensing data, and chickpea yield statistics. The methods were evaluated using the leave-one-out cross-validation (LOOCV) technique and compared in terms of multiple measures. Given the sensitivity of rainfed chickpea yield to the time of data, the predictions were made in two scenarios: (1) using the averages of the data of all growing months, and (2) using the data of a combination of months. The results showed that RF provides more accurate yield predictions than other methods. The predictions of this method were 7–8% different from the statistics reported by the Statistical Center and the Ministry of Agriculture of Iran. It was found that for pre-harvest prediction of rainfed chickpea yield, using the data of the March–April period (the averages of two months) offers the best result in terms of the correlation coefficient for the relationship between the yield and the predictor indices.

scholarly journals Machine Learning Algorithms for Automatic Lithological Mapping Using Remote Sensing Data: A Case Study from Souk Arbaa Sahel, Sidi Ifni Inlier, Western Anti-Atlas, Morocco

2019 ◽  
Vol 8 (6) ◽  
pp. 248 ◽  
Author(s):  
Imane Bachri ◽  
Mustapha Hakdaoui ◽  
Mohammed Raji ◽  
Ana Cláudia Teodoro ◽  
Abdelmajid Benbouziane
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Learning Algorithms ◽  
Remote Sensing Data ◽  
Machine Learning Algorithms ◽  
High Dimensional ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Lithological Mapping ◽  
Sensing Data

Remote sensing data proved to be a valuable resource in a variety of earth science applications. Using high-dimensional data with advanced methods such as machine learning algorithms (MLAs), a sub-domain of artificial intelligence, enhances lithological mapping by spectral classification. Support vector machines (SVM) are one of the most popular MLAs with the ability to define non-linear decision boundaries in high-dimensional feature space by solving a quadratic optimization problem. This paper describes a supervised classification method considering SVM for lithological mapping in the region of Souk Arbaa Sahel belonging to the Sidi Ifni inlier, located in southern Morocco (Western Anti-Atlas). The aims of this study were (1) to refine the existing lithological map of this region, and (2) to evaluate and study the performance of the SVM approach by using combined spectral features of Landsat 8 OLI with digital elevation model (DEM) geomorphometric attributes of ALOS/PALSAR data. We performed an SVM classification method to allow the joint use of geomorphometric features and multispectral data of Landsat 8 OLI. The results indicated an overall classification accuracy of 85%. From the results obtained, we can conclude that the classification approach produced an image containing lithological units which easily identified formations such as silt, alluvium, limestone, dolomite, conglomerate, sandstone, rhyolite, andesite, granodiorite, quartzite, lutite, and ignimbrite, coinciding with those already existing on the published geological map. This result confirms the ability of SVM as a supervised learning algorithm for lithological mapping purposes.

scholarly journals Estimating crop primary productivity with Sentinel-2 and Landsat 8 using machine learning methods trained with radiative transfer simulations

2019 ◽  
Vol 225 ◽  
pp. 441-457 ◽  
Author(s):  
Aleksandra Wolanin ◽  
Gustau Camps-Valls ◽  
Luis Gómez-Chova ◽  
Gonzalo Mateo-García ◽  
Christiaan van der Tol ◽  
...  
Keyword(s):  
Machine Learning ◽  
Radiative Transfer ◽  
Primary Productivity ◽  
Landsat 8 ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Sentinel 2

scholarly journals Mapping Allochemical Limestone Formations in Hazara, Pakistan Using Google Cloud Architecture: Application of Machine-Learning Algorithms on Multispectral Data

2021 ◽  
Vol 10 (2) ◽  
pp. 58
Author(s):  
Muhammad Fawad Akbar Khan ◽  
Khan Muhammad ◽  
Shahid Bashir ◽  
Shahab Ud Din ◽  
Muhammad Hanif
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Learning Algorithms ◽  
Remote Sensing Data ◽  
Kappa Coefficient ◽  
Machine Learning Algorithms ◽  
Landsat 8 ◽  
Sensing Data ◽  
Fossiliferous Limestone

Low-resolution Geological Survey of Pakistan (GSP) maps surrounding the region of interest show oolitic and fossiliferous limestone occurrences correspondingly in Samanasuk, Lockhart, and Margalla hill formations in the Hazara division, Pakistan. Machine-learning algorithms (MLAs) have been rarely applied to multispectral remote sensing data for differentiating between limestone formations formed due to different depositional environments, such as oolitic or fossiliferous. Unlike the previous studies that mostly report lithological classification of rock types having different chemical compositions by the MLAs, this paper aimed to investigate MLAs’ potential for mapping subclasses within the same lithology, i.e., limestone. Additionally, selecting appropriate data labels, training algorithms, hyperparameters, and remote sensing data sources were also investigated while applying these MLAs. In this paper, first, oolitic (Samanasuk), fossiliferous (Lockhart and Margalla) limestone-bearing formations along with the adjoining Hazara formation were mapped using random forest (RF), support vector machine (SVM), classification and regression tree (CART), and naïve Bayes (NB) MLAs. The RF algorithm reported the best accuracy of 83.28% and a Kappa coefficient of 0.78. To further improve the targeted allochemical limestone formation map, annotation labels were generated by the fusion of maps obtained from principal component analysis (PCA), decorrelation stretching (DS), X-means clustering applied to ASTER-L1T, Landsat-8, and Sentinel-2 datasets. These labels were used to train and validate SVM, CART, NB, and RF MLAs to obtain a binary classification map of limestone occurrences in the Hazara division, Pakistan using the Google Earth Engine (GEE) platform. The classification of Landsat-8 data by CART reported 99.63% accuracy, with a Kappa coefficient of 0.99, and was in good agreement with the field validation. This binary limestone map was further classified into oolitic (Samanasuk) and fossiliferous (Lockhart and Margalla) formations by all the four MLAs; in this case, RF surpassed all the other algorithms with an improved accuracy of 96.36%. This improvement can be attributed to better annotation, resulting in a binary limestone classification map, which formed a mask for improved classification of oolitic and fossiliferous limestone in the area.

Application of optical and radar data for lineaments mapping in Kerdous inlier of the Anti Atlas belt, Morocco

2021 ◽  
Author(s):  
Amine Jellouli ◽  
Abderrazak El Harti ◽  
Zakaria Adiri ◽  
Mohcine Chakouri ◽  
Jaouad El Hachimi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Radar Data ◽  
Ore Deposits ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Alos Palsar ◽  
Geological Map ◽  
Color Composite ◽  
Manual Extraction

&lt;p&gt;Lineament mapping is an important step for lithological and hydrothermal alterations mapping. It is considered as an efficient research task which can be a part of structural investigation and mineral ore deposits identification. The availability of optical as well as radar remote sensing data, such as Landsat 8 OLI, Terra ASTER and ALOS PALSAR data, allows lineaments mapping at regional and national scale. The accuracy of the obtained results depends strongly on the spatial and spectral resolution of the data. The aim of this study was to compare Landsat 8 OLI, Terra ASTER, and radar ALOS PALSAR satellite data for automatic and manual lineaments extraction. The module Line of PCI Geomatica software was applied on PC1 OLI, PC3 ASTER and HH and HV polarization images to automatically extract geological lineaments. However, the manual extraction was achieved using the RGB color composite of the directional filtered images N - S (0&amp;#176;), NE - SW (45&amp;#176;) and E - W (90&amp;#176;) of the OLI panchromatic band 8. The obtained lineaments from automatic and manual extraction were compared against the faults and photo-geological lineaments digitized from the existing geological map of the study area. The extracted lineaments from PC1 OLI and ALOS PALSAR polarizations images showed the best correlation with faults and photo-geological lineaments. The results indicate that the lineaments extracted from HH and HV polarizations of ALOS PALSAR radar data used in this study, with 1499 and 1507 extracted lineaments, were more efficient for structural lineament mapping, as well as the PC1 OLI image with 1057 lineaments.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Keywords&lt;/strong&gt; Remote Sensing . OLI. ALOS PALSAR . ASTER . Kerdous Inlier . Anti Atlas&lt;/p&gt;

Detecting a keystone species European aspen in boreal forests with airborne hyperspectral, LiDAR and UAV data with machine learning methods

2021 ◽  
Author(s):  
Timo Kumpula ◽  
Janne Mäyrä ◽  
Anton Kuzmin ◽  
Arto Viinikka ◽  
Sonja Kivinen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Boreal Forests ◽  
Tree Level ◽  
Support Vector ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
European Aspen

&lt;p&gt;Sustainable forest management increasingly highlights the maintenance of biological diversity and requires up-to-date information on the occurrence and distribution of key ecological features in forest environments. Different proxy variables indicating species richness and quality of the sites are essential for efficient detecting and monitoring forest biodiversity. European aspen (Populus tremula L.) is a minor deciduous tree species with a high importance in maintaining biodiversity in boreal forests. Large aspen trees host hundreds of species, many of them classified as threatened. However, accurate fine-scale spatial data on aspen occurrence remains scarce and incomprehensive.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;We studied detection of aspen using different remote sensing techniques in Evo, southern Finland. Our study area of 83 km&lt;sup&gt;2&lt;/sup&gt; contains both managed and protected southern boreal forests characterized by Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) Karst), and birch (Betula pendula and pubescens L.), whereas European aspen has a relatively sparse and scattered occurrence in the area. We collected high-resolution airborne hyperspectral and airborne laser scanning data covering the whole study area and ultra-high resolution unmanned aerial vehicle (UAV) data with RGB and multispectral sensors from selected parts of the area. We tested the discrimination of aspen from other species at tree level using different machine learning methods (Support Vector Machines, Random Forest, Gradient Boosting Machine) and deep learning methods (3D convolutional neural networks).&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;Airborne hyperspectral and lidar data gave excellent results with machine learning and deep learning classification methods The highest classification accuracies for aspen varied between 91-92% (F1-score). The most important wavelengths for discriminating aspen from other species included reflectance bands of red edge range (724&amp;#8211;727 nm) and shortwave infrared (1520&amp;#8211;1564 nm and 1684&amp;#8211;1706 nm) (Viinikka et al. 2020; M&amp;#228;yr&amp;#228; et al 2021). Aspen detection using RGB and multispectral data also gave good results (highest F1-score of aspen = 87%) (Kuzmin et al 2021). Different remote sensing data enabled production of a spatially explicit map of aspen occurrence in the study area. Information on aspen occurrence and abundance can significantly contribute to biodiversity management and conservation efforts in boreal forests. Our results can be further utilized in upscaling efforts aiming at aspen detection over larger geographical areas using satellite images.&lt;/p&gt;

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