Search results for "Satellite remote sensing"
showing 6 items of 6 documents
Latent heat flux variability and response to drought stress of black poplar: A multi-platform multi-sensor remote and proximal sensing approach to re…
2022
Abstract High-throughput mapping of latent heat flux (λET) is critical to efforts to optimize water resources management and to accelerate forest tree breeding for improved drought tolerance. Ideally, investigation of the energy response at the tree level may promote tailored irrigation strategies and, thus, maximize crop biomass productivity. However, data availability is limited and planning experimental campaigns in the field can be highly operationally complex. To this end, a multi-platform multi-sensor observational approach is herein developed to dissect the λET signature of a black poplar (Populus nigra) breeding population (“POP6”) at the canopy level. POP6 comprised more than 4600 …
FPGA/LST-SW Encryption Module Implementation for Satellite Remote Sensing Secure Systems
2020
The need for security of data transmitted from satellites to the ground has increased. Therefore, the need for secure onboard systems is in great demand, particularly in satellite remote sensing missions. This paper describes an approach for a secure Field Programmable Gate Arrays (FPGA) implementation of the Land Surface Temperature Split Window (LST-SW) algorithm, with objective to meat real-time requirements, area optimization and achieved higher Throughput goals to be sufficient for a secure remote sensing satellite applications and missions. The system is designed using VHDL (VHSIC Hardware Description Language) in a Highlevel design method. The experimental results demonstrate that th…
Classifying Major Explosions and Paroxysms at Stromboli Volcano (Italy) from Space
2021
Stromboli volcano has a persistent activity that is almost exclusively explosive. Predominated by low intensity events, this activity is occasionally interspersed with more powerful episodes, known as major explosions and paroxysms, which represent the main hazards for the inhabitants of the island. Here, we propose a machine learning approach to distinguish between paroxysms and major explosions by using satellite-derived measurements. We investigated the high energy explosive events occurring in the period January 2018–April 2021. Three distinguishing features are taken into account, namely (i) the temporal variations of surface temperature over the summit area, (ii) the magnitude of the …
MODIS probabilistic cloud masking over the Amazonian evergreen tropical forests: a comparison of machine learning-based methods
2019
Amazonian tropical forests play a significant role in global water, carbon and energy cycles. Satellite remote sensing is presented as a feasible means in order to monitor these forests. In particu...
Chlorophyll and Suspended Solids Estimation in Portuguese Reservoirs (Aguieira and Alqueva) from Sentinel-2 Imagery
2021
Reservoirs have been subject to anthropogenic stressors, becoming increasingly degraded. The evaluation of ecological potential in reservoirs is remarkably challenging, and consistent and regular monitoring using the traditional in situ methods defined in the WFD is often time- and money-consuming. Alternatively, remote sensing offers a low-cost, high frequency, and practical complement to these methods. This paper proposes a novel approach, using a C2RCC processor to analyze Sentinel-2 imagery data to retrieve information on water quality in two reservoirs of Portugal, Aguieira and Alqueva. We evaluate the temporal and spatial evolution of Chl a and total suspended solids (TSS), between 20…
Farm-Scale Crop Yield Prediction from Multi-Temporal Data Using Deep Hybrid Neural Networks
2021
Farm-scale crop yield prediction is a natural development of sustainable agriculture, producing a rich amount of food without depleting and polluting environmental resources. Recent studies on crop yield production are limited to regional-scale predictions. The regional-scale crop yield predictions usually face challenges in capturing local yield variations based on farm management decisions and the condition of the field. For this research, we identified the need to create a large and reusable farm-scale crop yield production dataset, which could provide precise farm-scale ground-truth prediction targets. Therefore, we utilise multi-temporal data, such as Sentinel-2 satellite images, weath…