Estimation of Key Dates and Stages in Rice Crops Using Dual-Polarization SAR Time Series and a Particle Filtering Approach

Information of crop phenology is essential for evaluating crop productivity. In a previous work, we determined phenological stages with remote sensing data using a dynamic system framework and an extended Kalman filter (EKF) approach. In this paper, we demonstrate that the particle filter is a more reliable method to infer any phenological stage compared to the EKF. The improvements achieved with this approach are discussed. In addition, this methodology enables the estimation of key cultivation dates, thus providing a practical product for many applications. The dates of some important stages, as the sowing date and the day when the crop reaches the panicle initiation stage, have been chosen to show the potential of this technique.

Contribution to Real-Time Estimation of Crop Phenological States in a Dynamical Framework Based on NDVI Time Series: Data Fusion With SAR and Temperature

In this study, a methodology based in a dynamical framework is proposed to incorporate additional sources of information to normalized difference vegetation index (NDVI) time series of agricultural observations for a phenological state estimation application. The proposed implementation is based on the particle filter (PF) scheme that is able to integrate multiple sources of data. Moreover, the dynamics-led design is able to conduct real-time (online) estimations, i.e., without requiring to wait until the end of the campaign. The evaluation of the algorithm is performed by estimating the phenological states over a set of rice fields in Seville (SW, Spain). A Landsat-5/7 NDVI series of images is complemented with two distinct sources of information: SAR images from the TerraSAR-X satellite and air temperature information from a ground-based station. An improvement in the overall estimation accuracy is obtained, especially when the time series of NDVI data is incomplete. Evaluations on the sensitivity to different development intervals and on the mitigation of discontinuities of the time series are also addressed in this work, demonstrating the benefits of this data fusion approach based on the dynamic systems.

Electrocatalytic reduction of CO2 to formate using particulate Sn electrodes: Effect of metal loading and particle size

The development of electrochemical processes for the conversion of CO2 into value-added products allows innovative carbon capture & utilization (CCU) instead of carbon capture & storage (CCS). In addition, coupling this conversion with renewable energy sources would make it possible to chemically store electricity from these intermittent renewable sources. The electroreduction of CO2 to formate in aqueous solution has been performed using Sn particles deposited over a carbon support. The effect of the particle size and Sn metal loading has been evaluated using cyclic voltammetry and chronoamperometry. The selected electrode has been tested on an experimental filter-press type cell system for continuous and single pass CO2 electroreduction to obtain formate as main product at ambient pressure and temperature. Experimental results show that using electrodes with 0.75 mg Sn cm−2, 150 nm Sn particles, and working at a current density of 90 mA cm−2, it is possible to achieve rates of formate production over 3.2 mmol m−2 s−1 and faradaic efficiencies around 70% for 90 min of continuous operation. These experimental conditions allow formate concentrations of about 1.5 g L−1 to be obtained on a continuous mode and with a single pass of catholyte through the cell.