Landcover and crop type classification with intra-annual times series of sentinel-2 and machine learning at central Portugal

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesLand cover and crop type mapping have benefited from a daily revisiting period of sensors such as MODIS, SPOT-VGT, NOAA-AVHRR that contains long time-series archive. However, they have low accuracy in an Area of Interest (ROI) due to their coarse spatial resolution (i.e., pixel size > 250m). The Copernicus Sentinel-2 mission from the European Spatial Agency (ESA) provides free data access for Sentinel 2-A(S2a) and B (S2b). This satellite constellation guarantees a high temporal (5-day revisit cycle) and high spatial resolution (10m), allowing frequent updates on land cover products through supervised classification. Nevertheless, this requires training samples that are traditionally collected manually via fieldwork or image interpretation. This thesis aims to implement an automatic workflow to classify land cover and crop types at 10m resolution in central Portugal using existing databases, intra-annual time series of S2a and S2b, and Random Forest, a supervised machine learning algorithm. The agricultural classes such as temporary and permanent crops as well as agricultural grasslands were extracted from the Portuguese Land Parcel Identification System (LPIS) of the Instituto de Financiamento da Agricultura e Pescas (IFAP); land cover classes like urban, forest and water were trained from the Carta de Ocupação do Solo (COS) that is the national Land Use and Land Cover (LULC) map of Portugal; and lastly, the burned areas are identified from the corresponding national map of the Instituto da Conservação da Natureza e das Florestas (ICNF). Also, a set of preprocessing steps were defined based on the implementation of ancillary data allowing to avoid the inclusion of mislabeled pixels to the classifier. Mislabeling of pixels can occur due to errors in digitalization, generalization, and differences in the Minimum Mapping Unit (MMU) between datasets. An inner buffer was applied to all datasets to reduce border overlap among classes; the mask from the ICNF was applied to remove burned areas, and NDVI rule based on Landsat 8 allowed to erase recent clear-cuts in the forest. Also, the Copernicus High-Resolution Layers (HRL) datasets from 2015 (latest available), namely Dominant Leaf Type (DLT) and Tree Cover Density (TCD) are used to distinguish between forest with more than 60% coverage (coniferous and broadleaf) such as Holm Oak and Stone Pine and between 10 and 60% (coniferous) for instance Open Maritime Pine. Next, temporal gap-filled monthly composites were created for the agricultural period in Portugal, ranging from October 2017 till September 2018. The composites provided data free of missing values in opposition to single date acquisition images. Finally, a pixel-based approach classification was carried out in the “Tejo and Sado” region of Portugal using Random Forest (RF). The resulting map achieves a 76% overall accuracy for 31 classes (17 land cover and 14 crop types). The RF algorithm captured the most relevant features for the classification from the cloud-free composites, mainly during the spring and summer and in the bands on the Red Edge, NIR and SWIR. Overall, the classification was more successful on the irrigated temporary crops whereas the grasslands presented the most complexity to classify as they were confused with other rainfed crops and burned areas

A systematic review of the use of Deep Learning in Satellite Imagery for Agriculture

Agricultural research is essential for increasing food production to meet the requirements of an increasing population in the coming decades. Recently, satellite technology has been improving rapidly and deep learning has seen much success in generic computer vision tasks and many application areas which presents an important opportunity to improve analysis of agricultural land. Here we present a systematic review of 150 studies to find the current uses of deep learning on satellite imagery for agricultural research. Although we identify 5 categories of agricultural monitoring tasks, the majority of the research interest is in crop segmentation and yield prediction. We found that, when used, modern deep learning methods consistently outperformed traditional machine learning across most tasks; the only exception was that Long Short-Term Memory (LSTM) Recurrent Neural Networks did not consistently outperform Random Forests (RF) for yield prediction. The reviewed studies have largely adopted methodologies from generic computer vision, except for one major omission: benchmark datasets are not utilised to evaluate models across studies, making it difficult to compare results. Additionally, some studies have specifically utilised the extra spectral resolution available in satellite imagery, but other divergent properties of satellite images - such as the hugely different scales of spatial patterns - are not being taken advantage of in the reviewed studies.Comment: 25 pages, 2 figures and lots of large tables. Supplementary materials section included here in main pd

Crop type identification and spatial mapping using Sentinel-2 satellite data with focus on field-level information

Accurate monitoring of croplands helps in making decisions (for insurance claims, crop management and contingency plans) at the macro-level, especially in drylands where variability in cropping is very high owing to erratic weather conditions. Dryland cereals and grain legumes are key to ensuring the food and nutritional security of a large number of vulnerable populations living in the drylands. Reliable information on area cultivated to such crops forms part of the national accounting of food production and supply in many Asian countries, many of which are employing remote sensing tools to improve the accuracy of assessments of cultivated areas. This paper assesses the capabilities and limitations of mapping cultivated areas in the Rabi (winter) season and corresponding cropping patterns in three districts characterized by small-plot agriculture. The study used Sentinel-2 Normalized Difference Vegetation Index (NDVI) 15-day time-series at 10m resolution by employing a Spectral Matching Technique (SMT) approach. The use of SMT is based on the well-studied relationship between temporal NDVI signatures and crop phenology. The rabi season in India, dominated by non-rainy days, is best suited for the application of this method, as persistent cloud cover will hamper the availability of images necessary to generate clearly differentiating temporal signatures. Our study showed that the temporal signatures of wheat, chickpea and mustard are easily distinguishable, enabling an overall accuracy of 84%, with wheat and mustard achieving 86% and 94% accuracies, respectively. The most significant misclassifications were in irrigated areas for mustard and wheat, in small-plot mustard fields covered by trees and in fragmented chickpea areas. A comparison of district-wise national crop statistics and those obtained from this study revealed a correlation of 96%

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

Village level identification of sugarcane in Sangali, Maharashtra using open source data

Agriculture and crop monitoring are very important for an agrarian country like India. This study is done in June Khed village in the Sangli district of Maharashtra, India to assessing the efficiency of an open source cloud-based remote sensing platform Google Earth Engine (GEE), in the village-scale identification of sugarcane. The ground-truth data was collected and the efficiency of Landsat-8 and Sentinel-2 satellite data was assessed in driving GEE’s inbuilt Machine Learning classifiers: Classification and Regression Tree (CART), Support Vector Machine (SVM) and Random Forest (RF). Results were validated with the ground truth data and the official data. Of the methods used, SVM outperformed RF and CART with the lowest relative deviation (+9.2%), highest F1-score (0.8) and overall accuracy (78%), using the Sentinel-2 data. Results also indicated the in-situ use of observation data with high spatio-temporal resolution data. The validated model was then up-scaled for the Walwa Taluka level, to map sugarcane area that can be used for further agriculture tasks such as crop monitoring and yield prediction, leading to better management of crop and better formulating of sugarcane farmer policy

Analyzing the Adoption, Cropping Rotation, and Impact of Winter Cover Crops in the Mississippi Alluvial Plain (MAP) Region through Remote Sensing Technologies

This dissertation explores the application of remote sensing technologies in conservation agriculture, specifically focusing on identifying and mapping winter cover crops and assessing voluntary cover crop adoption and cropping patterns in the Arkansas portion of the Mississippi Alluvial Plain (MAP). In the first chapter, a systematic review using the PRISMA methodology examines the last 30 years of thematic research, development, and trends in remote sensing applied to conservation agriculture from a global perspective. The review uncovers a growing interest in remote sensing-based research in conservation agriculture and emphasizes the necessity for further studies dedicated to conservation practices. Among the 68 articles examined, 94% of studies utilized a pixel-based classification method, while only 6% employed an object-based approach. The analysis also revealed a thematic shift over time, with tillage practices being extensively studied before 2005, followed by a focus on crop residue from 2004 to 2012. From 2012 to 2020, there was a renewed emphasis on cover crops research. These findings highlight the evolving research landscape and provide insights into the trends within remote sensing-based conservation agriculture studies. The second chapter presents a methodological framework for identifying and mapping winter cover crops. The framework utilizes the Google Earth Engine (GEE) and a Random Forest (RF) classifier with time series data from Landsat 8 satellite. Results demonstrate a high classification accuracy (97.7%) and a significant increase (34%) in model-predicted cover crop adoption over the study period between 2013 and 2019. Additionally, the study showcases the use of multi-year datasets to efficiently map the growing season\u27s length and cover crops\u27 phenological characteristics. The third chapter assesses the voluntary adoption of winter cover crops and cropping patterns in the MAP region. Remote sensing technologies, USDA-NRCS government cover crop data sources, and the USDA Cropland Data Layer (CDL) are employed to identify cover crop locations, analyze county-wide voluntary adoption, and cropping rotations. The result showed a 5.33% increase in the overall voluntary adoption of cover crops in the study region between 2013 and 2019. The findings also indicate a growing trend in cover crop adoption, with soybean-cover crop rotations being prominent. This dissertation enhances our understanding of the role of remote sensing in conservation agriculture with a particular focus on winter cover crops. These insights are valuable for policymakers, stakeholders, and researchers seeking to promote sustainable agricultural practices and increased cover crop adoption. The study also underscores the significance of integrating remote sensing technologies into agricultural decision-making processes and highlights the importance of collaboration among policymakers, researchers, and producers. By leveraging the capabilities of remote sensing, it will enhance conservation agriculture contribution to long-term environmental sustainability and agricultural resilience. Keywords: Remote sensing technologies, Conservation agriculture, Winter cover crops, Voluntary adoption, Cropping patterns, Sustainable agricultural practice