Assimilation of Multisensor Optical and Multiorbital SAR Satellite Data in a Simplified Agrometeorological Model for Rapeseed Crops Monitoring

Abstract

This paper investigates the potential of the assimilation of both Synthetic Aperture Radar (SAR) -derived Dry Mass (DM) and optically-derived Green Area Index (GAI) in an agro-meteorological model (i.e. SAFY) for a better rapeseed crops modeling. The GAI was derived from both 566 Sentinel-2 and 149 Landsat-8 images, whereas DM was derived from 884 Sentinel-1 images acquired from six different orbits. The ground data were collected during 3 agricultural years on 43 rapeseed fields located in three study areas in France with contrasted pedoclimatic conditions. Results show that the temporal evolutions of both DM and GAI can be accurately simulated over the 43 monitored rapeseed fields (R 2 = 0.84 and 0.92, respectively and relative Root Mean Square Error, RMSEr = 41 % and 28 %, respectively) for the best satellite configuration. Tested assimilation scenarios reveal that the concomitant assimilation of SAR and optical data allows a significant better control of the model than the assimilation of SAR or optical data alone. Small differences in the simulations of the model are observed when it is controlled by either multisensor optical and/or multiorbital SAR data or monosensor optical and/or mono-orbital SAR data. The discrepancies of performance between fields push towards the strengthening of this study by considering other rapeseed fields with ground observations acquired worldwide for the calibration of SARderived DM. These results are however promising in view of the development of a near-real time assimilation scheme as a decision support tool for farmers and decision makers.

Can Yield Prediction Be Fully Digitilized? A Systematic Review

“…The results demonstrated that the assimilation of both SAR-derived dry matter (DM) and the optically derived Green Area Index (GAI) allowed for better control of the model compared to using SAR or optical data alone. This integration notably improved the optimization of parameters governing dry matter partitioning into leaves and effective light-use efficiency [94]. Another crucial aspect in satellite-based crop yield estimation is the spatial and temporal resolution of the deployed satellites.…”

Ship Target Detection Method in Synthetic Aperture Radar Images Based on Block Thumbnail Particle Swarm Optimization Clustering

“…The minimum value of the objective function can be obtained using the Lagrangian multiplication method, and the definitions of the membership u ij and the cluster center v j are shown in Equation (2) and Equation (3), respectively. The specific steps of the FCM algorithm for image segmentation can be found in references [27,28].…”

“…Synthetic aperture radar (SAR) is an active microwave imaging radar, which is not limited by weather and light conditions. This important detection technology has seen widespread use in many fields [1][2][3][4][5][6]. Target detection is an important aspect of SAR image processing and interpretation applications.…”

“…At the same time, it can reduce the dimension (the size of the image) of the segmented image, subsequently reducing the computation and runtime. (2) The PSO theory is used to optimize the clustering center of FCM, which can overcome the defect of the local minima in the FCM algorithm and achieve a global optimal image segmentation. (3) The idea of using the number of similar pixel groups as the initial clustering center of FCM is proposed, which makes the algorithm adaptability and adjustability, so it has broad application prospects.…”