STICS crop model and Sentinel-2 images for monitoring rice growth and yield in the Camargue region

Abstract

The assessment of rice yield at territory level is important for strategic economic decisions. Assessing spatial and temporal yield variability at regional scale is difficult because of the numerous factors involved, including agricultural practices, phenological calendars, and environmental contexts. New remote sensing data acquired at decametric resolution (Sentinel missions) can provide information on this spatial variability. The study objective was thus to evaluate the potential of Sentinel-2 images for monitoring rice cropping systems and yield from farm to region scales. The approach considered both observations and modeling. Indepth farmers surveys were carried out in the Camargue region, Southeastern France. The novelty was to use operational tools (BVNET and PHENOTB) to compute leaf area index, to daily interpolate this biophysical variable from 44 images acquired in 2016 and 2017 for each rice field, and to derive key phenological parameters from the analysis of the temporal profiles. The STICS crop model was spatially used, considering the biophysical variables derived from remote sensing. We tested four simulation strategies, differing in the integration intensity of remote sensing information into the model. Results have shown that (1) Sentinel-2 data allowed distinguishing early and late rice varieties. (2) The phenological stages mapped at the regional level allowed to better understand the agricultural practices of farmers. (3) The assimilation of remote sensing data to the STICS crop model significantly improved yield estimation and provided useful information on the spatial variability observed at regional scale. It was the first time that Sentinel-2 data are used with STICS crop model to assess rice yield at both farm and regional scale in the Camargue area. The proposed method is based on free open data and free access model, easily reproducible in other environmental contexts.

Identification of Paddy Croplands and Its Stages Using Remote Sensors: A Systematic Review

“…Others combined several vegetation indices by looking for correlations and peak or bottom measures of each to define different steps. To determine the nursery with high BSI values following the vegetative phase, a combination of a fast drop in BSI and a rapid increase in LSWI [ 24 ] at the initial days of the stage, followed by a rapid rise in NDVI [ 88 , 91 , 115 , 116 , 127 ], EVI [ 16 , 25 , 81 , 83 , 86 , 117 , 135 ] and Leaf Area Index (LAI) [ 136 ] are assessed. To identify the reproductive phase, NDVI reaches its peak [ 24 , 49 , 88 , 91 , 108 , 115 , 116 , 127 ] along with EVI [ 24 , 49 , 81 , 83 , 88 , 91 , 108 , 115 , 116 , 117 , 127 ] to identify the harvest phase, and there is a marked rise in Plant Senescence Reflectance Index (PRSI) index combined with a severe decrease in NDVI and EVI due to loose of chlorophyll content in paddy [ 24 ] and with LSWI at its lowest value [ 25 , 124 ].…”

“…Therefore, their use will increase in the coming years. However, some constraints, such as cloud contamination [ 5 , 8 , 43 , 57 , 59 , 60 , 65 , 81 , 85 , 89 , 108 , 130 , 136 ], are all common. Cloud contamination could be easily avoided using radar data.…”

A Global Systematic Review of Improving Crop Model Estimations by Assimilating Remote Sensing Data: Implications for Small-Scale Agricultural Systems

“…Other studies used optical RS datasets with high spatial and temporal resolution, including Sentinel-2 [65,66], Huanjing-1 [67,68], RapidEye [69,70], SPOT-6 [71], and GeoFan-1 [72]. However, the optical satellite sensors only work during the daytime and are limited by weather conditions and vegetation density.…”

Breast Ultrasound Image Segmentation Algorithm Using Adaptive Region Growing and Variation Level Sets

“…In the nonlinear partial differential equation (PDE) composed of Euler-Lagrange, to address the extreme value of the minimum energy, it is necessary to refer to the time variable, which also transforms the static nonlinear PDE problem into a dynamic PDE problem and realizes the steady evolution of the solution [44].…”