Optimisation of analytical methods for the characterisation of oranges, clementines and citrus hybrids cultivated in Spain on the basis of their composition in ascorbic acid, citric acid and major sugars

Three HPLC methods were optimised for the determination of citric acid, succinic acid and ascorbic acid using a photodiode array detector and fructose, glucose and sucrose using a refractive index in twenty eight citrus juices. The analysis was completed in <16 min. Two different harvests were taken into account for this study. For the season 2011, ascorbic acid content was comprised between 19.4 and 59 mg vitamin C/100 mL; meanwhile for the season 2012, the content was slightly higher for most of the samples ranging from 33.5 to 85.3 mg vitamin C/100 mL. Moreover, the citric acid content in orange juices ranged between 9.7 and 15.1 g L−1, while for clementines the content was clearly lower (i.e. from 3.5 to 8.4 g L−1). However, clementines showed the highest sucrose content with values near to 6 g/100 mL. Finally, a cluster analysis was applied to establish a classification of the citrus species.

Crop Phenology Estimation Using a Multitemporal Model and a Kalman Filtering Strategy

In this letter, a new approach for crop phenology estimation with remote sensing is presented. The proposed methodology is aimed to exploit tools from a dynamical system context. From a temporal sequence of images, a geometrical model is derived, which allows us to translate this temporal domain into the estimation problem. The evolution model in state space is obtained through dimensional reduction by a principal component analysis, defining the state variables, of the observations. Then, estimation is achieved by combining the generated model with actual samples in an optimal way using a Kalman filter. As a proof of concept, an example with results obtained with this approach over rice fields by exploiting stacks of TerraSAR-X dual polarization images is shown.

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.