Monitoring rice agropractices in North Africa: a comparison of MODIS and Sentinel-1 results

Agro-monitoring systems need up-to-date information on where, when and how much a crop is cultivated, in particular in developing countries and for food security reasons. Such information can be derived from remote sensing imagery with fast revisiting cycles. In the past, only time series of optical moderate resolution data such as HVRR, SPOT-Vegetation and MODIS provided the necessary high temporal resolution for this kind of applications. These datasets have been successfully used for agro-monitoring activities and to perform retrospective and trend analysis. Due to their moderate to coarse spatial resolution (~ 250 – 1000 m) their applications are limited however to regional to continental scales. In this context, the advent of the Sentinel sensors opens new opportunities, since they provide time series of satellite imagery with decametric spatial resolution and revisit times of 5 days. Studies that fully exploit Sentinel imagery for crop monitoring are therefore needed to assess their potential contribution for i) performing high resolution crop-monitoring activities and, ii) extending time series of information derived from archive coarse resolution imagery with the aim of performing analyses of temporal trends over a reasonably long time span. This contribution presents a comparison of MODIS or Sentinel1 time series for detection (cultivated area and number of seasons) and seasonal dynamics’ analysis (sowing, harvesting and flowering dates) for irrigated rice cultivation in the Senegal River Valley (SRV)for the 2016 dry and wet rice seasons. MODIS time series analysis exploited the PhenoRice algorithm (Boschetti et al., 2017), a rule-based algorithm specifically designed for rice detection and seasonal dynamics monitoring and based on the use of time series of TERRA and AQUA 250 m resolution 16-day Composite Vegetation Indexes (MODIS products MOD13Q1 and MYD13Q1). The SAR data analysis was instead based on analysis of Sentinel-1A time series acquired over the study area from January to December 2016. In particular, the RICEscape software was used for analysing the SAR backscatter (0) temporal profiles both in the VV and in the VH polarization, to define a set of rules allowing to properly identify rice cultivated areas. The algorithm mostly exploits SAR data, although cloud free Landsat-8 Optical images were used to crosscheck and complement the information derived from SAR. This approach was applied to generate rice crop area and Start of Season (SOS) maps for both the dry (sowing in February – April) and the wet (sowing in September – November) rice seasons. Results showed a strong consistency between the thematic maps derived from the two data sources. We observed that, although the rice-classified area is rather different due to the large difference in spatial resolution, the main spatial patterns of estimated sowing dates and crop intensity are quite similar. A comparison between the average values of MODIS and SAR estimated dates after aggregation on a 2x2 km regular grid shows a strong correlation between the sowing dates derived from Sentinel-1 and MODIS data, for both the dry and the wet season of 2016. The comparability of MODIS and Sentinel results is encouraging for the development of innovative services for characterization and monitoring of crop systems. Such systems could in fact exploit both the sufficiently long MODIS time series to characterize the main characteristics of crop systems and their recent evolution, as well as the innovative Sentinel-1 time series for monitoring of present-day and future conditions

A European map of living forest biomass and carbon stock - Executive report

Forest ecosystems have a key role in the global carbon cycle and are considered large and persistent carbon sinks. The CO2 fixed by photosynthesis is one of the most important components of the carbon cycle, and forests play a determinant role in this process. Therefore, spatially explicit data and assessments of forest biomass and carbon is of paramount importance for the design and implementation of effective sustainable forest management options and forest related policies at the European level. The aim of this report is to present a summary of early results of the FOREST Action activities on forest biomass and carbon stock in Europe. In this report we present European-wide maps of forest biomass and carbon stock at IPCC Tier 1 level. Maps of forest biomass and carbon stock are relevant for quantifying terrestrial carbon storage and carbon sinks as well as for estimating potential emissions from land cover changes (afforestation, deforestation, reforestation), forest fragmentation and biotic (pests) and abiotic (e.g. forest fires, windstorms) disturbances. We describe the input data and approach, then present a summary examining the potential of the approach and further work as well as data needs in this field. The maps presented, implemented following the IPCC methodology, represent spatially explicit biomass and carbon stock on forested land disaggregated at 1 km x 1 km grid cells. The resulting maps represent the biomass and carbon at continental level, accounting for around 90% of the total continental amounts of biomass and carbon reported in the FAO’s Global Forest Resource Assessment (FRA) and State of Europe’s Forest report from the Ministerial Conference on the Protection of Forest in Europe (MCPFE). To account for regional discrepancies the maps were then adjusted to match FRA amounts of biomass and carbon at the country level. This report will be followed by an extended report including methodological details of the approach implemented.JRC.H.3-Forest Resources and Climat

PhenoRice:A method for automatic extraction of spatio-temporal information on rice crops using satellite data time series

Agricultural monitoring systems require spatio-temporal information on widely cultivated staple crops like rice. More emphasis has been made on area estimation and crop detection than on the temporal aspects of crop cultivation, but seasonal and temporal information such as i) crop duration, ii) date of crop establishment and iii) cropping intensity are as important as area for understanding crop production. Rice cropping systems are diverse because genetic, environmental and management factors (G × E × M combinations) influence the spatio-temporal patterns of cultivation. We present a rule based algorithm called PhenoRice for automatic extraction of temporal information on the rice crop using moderate resolution hypertemporal optical imagery from MODIS. Performance of PhenoRice against spatially and temporally explicit reference information was tested in three diverse sites: rice-fallow (Italy), rice-other crop (India) and rice-rice (Philippines) systems. Regional product accuracy assessments showed that PhenoRice made a conservative, spatially representative and robust detection of rice cultivation in all sites (r2 between 0.75 and 0.92) and crop establishment dates were in close agreement with the reference data (r2 = 0.98, Mean Error = 4.07 days, Mean Absolute Error = 9.95 days, p < 0.01). Variability in algorithm performance in different conditions in each site (irrigated vs rainfed, direct seeding vs transplanting, fragmented vs clustered rice landscapes and the impact of cloud contamination) was analysed and discussed. Analysis of the maps revealed that cropping intensity and season length per site matched well with local information on agro-practices and cultivated varieties. The results show that PhenoRice is robust for deriving essential temporal descriptions of rice systems in both temperate and tropical regions at a level of spatial and temporal detail that is suitable for regional crop monitoring on a seasonal basis

Modeling Gross Primary Production of Agro-Forestry Ecosystems by Assimilation of Satellite-Derived Information in a Process-Based Model

In this paper we present results obtained in the framework of a regional-scale analysis of the carbon budget of poplar plantations in Northern Italy. We explored the ability of the process-based model BIOME-BGC to estimate the gross primary production (GPP) using an inverse modeling approach exploiting eddy covariance and satellite data. We firstly present a version of BIOME-BGC coupled with the radiative transfer models PROSPECT and SAILH (named PROSAILH-BGC) with the aims of i) improving the BIOME-BGC description of the radiative transfer regime within the canopy and ii) allowing the assimilation of remotely-sensed vegetation index time series, such as MODIS NDVI, into the model. Secondly, we present a two-step model inversion for optimization of model parameters. In the first step, some key ecophysiological parameters were optimized against data collected by an eddy covariance flux tower. In the second step, important information about phenological dates and about standing biomass were optimized against MODIS NDVI. Results obtained showed that the PROSAILH-BGC allowed simulation of MODIS NDVI with good accuracy and that we described better the canopy radiation regime. The inverse modeling approach was demonstrated to be useful for the optimization of ecophysiological model parameters, phenological dates and parameters related to the standing biomass, allowing good accuracy of daily and annual GPP predictions. In summary, this study showed that assimilation of eddy covariance and remote sensing data in a process model may provide important information for modeling gross primary production at regional scale

Nitrogen status assessment for variable rate fertilization in maize through hyperspectral imagery

This paper presents a method for mapping the nitrogen (N) status in a maize field using hyperspectral remote sensing imagery. An airborne survey was conducted with an AISA Eagle hyperspectral sensor over an experimental farm where maize (Zea mays L.) was grown with two N fertilization levels (0 and 100 kg N ha-1) in four replicates. Leaf and canopy field data were collected during the flight. The nitrogen (N) status has been estimated in this work based on the Nitrogen Nutrition Index (NNI) defined as the ratio between the leaf actual N concentration (%Na) of the crop and the minimum N content required for the maximum biomass production (critical N concentration (%Nc)) calculated through the dry mass at the time of the flight (Wflight). The inputs required to calculate the NNI (i.e. %Na and Wflight) have been estimated through regression analyses between field data and remotely sensed vegetation indices. MCARI/MTVI2 (Modified Chlorophyll Absorption Ratio Index / Modified Triangular Vegetation Index 2) showed the best performances in estimating the %Na (R2 = 0.59) and MTVI2 in estimating the Wflight (R2 = 0.80). The %Na and the Wflight were then mapped and used to compute the NNI map over the entire field. The NNI map agreed with the NNI estimated using field data through traditional destructive measurements (R2 = 0.70) confirming the potential of using remotely sensed indices to assess the crop N condition. Finally, a method to derive a pixel based variable rate N fertilization map was proposed as the difference between the actual N content and the optimal N content. We think that the proposed operational methodology is promising for precision farming since it represents an innovative attempt to derive from an aerial hyperspectral image a variable rate N fertilization map based on the actual crop N status.JRC.H.4-Monitoring Agricultural Resource

El proyecto ERMES: Servicios de seguimento del cultivo de arroz basados en una IDE

Ponènica presentada a les V Jornadas Ibéricas de Infraestructura de Datos Espaciales (JIIDE 2014), celebrat a Lisboa els dies 5-7 de novembre de 2014En los tiempos actuales, ante el aumento de la demanda de alimentos, el incremento del precio de ellos y la adopción de nuevas técnicas para la agricultura, es más importante la optimización de las explotaciones agrícolas. Ante est e escenario , nace el proyecto Europeo ER MES (An Earth Observation Model based Rice Information Service, http://www. ERMES - fp7space.eu/ ) , dentro del programa FP7, el cual tiene como objetivo principal proporcionar un conjunto de herramientas y servici os que ayuden a cooperativas y agricultores en las tareas de seguimiento del cultivo de arroz y acumular paulatinamente evidencias basadas en datos científicos, geográficos, de la observación de la tierra, e incluso datos proporcionados por los propios agr icultores (usuarios) para facilitar la toma de decisiones . Esta tarea implica necesariamente la utilización e integración de diversas fuentes de datos así como también de múltiples técnicas de observación de la tierra, modelos de cultivos y herramientas TI C para el desarrollo Web y en entornos móvil . EL proyecto ERMES se dirige a dos tipos de usuarios bien diferenciados que realizarán usos distintos de los resultados del proyecto: un uso local dirigido a los agricultores ( productores ) y un uso regional dir igido a las autoridades encargadas de la supervisión y gestión del cultivo del arroz en una región determinada (por ejemplo cooperativas) . El ámbito de aplicación del proyecto corresponde a los principales productores de arroz a nivel Europeo — Italia, Espa ña y Grecia (en ese orden), d e donde son los principales socios del proyecto. A parte de la diversidad de datos y técnicas a emplear, el proyecto se enfrenta a las necesidades particulares de escala/resolución (necesidades del usuario local frente a l usuar io regional) y de escalado de la solución (misma aplicación para tres casos de uso distintos en tres países). Todos estos requisitos hacen de ERMES un proyecto especialmente atractivo para validar las capacidades de las Infraestructuras de Datos Espaciales . D urante el inicio del proyecto se han definido tareas de consulta de requisitos con los usuarios del proyecto (tanto regionales como locales), que den pie a la captura y definición pormenorizada de los requisitos funcionales de la solución final del proy ecto, los cuales servirán para el diseño de los servicios finales para el seguimiento de cultivo s del arroz. Dichos servicios seguirán los fundamentos INSPIRE y se estructurarán en base a los servicios básicos y estándares claves de una IDE .Proyecto FP7-Space ERMES (606983

Nitrogen status assessment for variable rate fertilization in maize through hyperspectral imagery

JRC.H.4-Monitoring Agricultural Resource

Conceptual Architecture and Service-oriented Implementation of a Regional Geoportal for Rice Monitoring

Agricultural monitoring has greatly benefited from the increased availability of a wide variety of remote-sensed satellite imagery, ground-sensed data (e.g., weather station networks) and crop models, delivering a wealth of actionable information to stakeholders to better streamline and improve agricultural practices. Nevertheless, as the degree of sophistication of agriculture monitoring systems increases, significant challenges arise due to the handling and integration of multi-scale data sources to present information to decision-makers in a way which is useful, understandable and user friendly. To address these issues, in this article we present the conceptual architecture and service-oriented implementation of a regional geoportal, specifically focused on rice crop monitoring in order to perform unified monitoring with a supporting system at regional scale. It is capable of storing, processing, managing, serving and visualizing monitoring and generated data products with different granularity and originating from different data sources. Specifically, we focus on data sources and data flow, and their importance for and in relation to different stakeholders. In the context of an EU-funded research project, we present an implementation of the regional geoportal for rice monitoring, which is currently in use in Europe’s three largest rice-producing countries, Italy, Greece and Spain