Image time series processing for agriculture monitoring

AbstractGiven strong year-to-year variability, increasing competition for natural resources, and climate change impacts on agriculture, monitoring global crop and natural vegetation conditions is highly relevant, particularly in food insecure areas. Data from remote sensing image series at high temporal and low spatial resolution can help to assist in this monitoring as they provide key information in near-real time over large areas. The SPIRITS software, presented in this paper, is a stand-alone toolbox developed for environmental monitoring, particularly to produce clear and evidence-based information for crop production analysts and decision makers. It includes a large number of tools with the main aim of extracting vegetation indicators from image time series, estimating the potential impact of anomalies on crop production and sharing this information with different audiences. SPIRITS offers an integrated and flexible analysis environment with a user-friendly graphical interface, which allows sequential tasking and a high level of automation of processing chains. It is freely distributed for non-commercial use and extensively documented

Sustained data access and tools as key ingredients to strengthening EO capacities : examples from land application perspective + powerpoint

Sustainably managing agriculture and forests is key for development, in particular in Africa, and for facing global challenges such as climate change or food security, but requires reliable information. As Earth Observation (EO) satellite data can contribute to these information needs, more and more institutes integrate this technology into their daily work. Facing ever-growing and evolving EO data sources (e.g. new satellites and sensors) and access technology (both online and via EUMETCast satellite broadcast), their applications require software tools to particularly facilitate (i) the exchange of data between the analysis tools, so users can take advantage of each tool’s strengths, and (ii) the processing and analysis of time series. A first example is the Land Surface Analysis Satellite Application Facility (LSA-SAF), that entered the second part of the Continuous Development and Operations Phase (CDOP-2), under the lead of the Portuguese Institute for Sea and Atmosphere (IPMA), in 2011. VITO, joining the LSA-SAF network for the first time and building on previous experiences (e.g. http://www.metops10.vito.be), aims to contribute by producing and delivering operational, 10-daily vegetation indicators based on MetOp-AVHRR. Furthermore, a software tool is developed to aid exploitation of LSA-SAF products, provisionally called “MSG Toolbox”. A second example is the AGRICAB project, that receives funding from the European Union’s 7th Framework Programme for Research (FP7) and aims to build a comprehensive framework for strengthening capacities in the use of EO for agriculture and forestry management in Africa. This framework starts from sustained access to relevant satellite data (e.g. CBERS-3, DEIMOS) and derived products, such as those from the European Copernicus Global Land service, the 15 year time series of SPOT-VEGETATION (and its transition to PROBA-V) and Meteosat Second Generation (e.g. rainfall estimates). It combines local and EO data with tools and training into applications on crop monitoring, area statistics and yield forecasting, livestock insurance and modelling, forest and fire management, all fitted to the needs of stakeholders in the African focus countries

A survey of image processing techniques for agriculture

Computer technologies have been shown to improve agricultural productivity in a number of ways. One technique which is emerging as a useful tool is image processing. This paper presents a short survey on using image processing techniques to assist researchers and farmers to improve agricultural practices. Image processing has been used to assist with precision agriculture practices, weed and herbicide technologies, monitoring plant growth and plant nutrition management. This paper highlights the future potential for image processing for different agricultural industry contexts

Land surface phenology from VEGETATION and PROBA-V data. Assessment over deciduous forests

Land surface phenology has been widely retrieved although no consensus exists on the optimal satellite dataset and the method to extract phenology metrics. This study is the first comprehensive comparison of vegetation variables and methods to retrieve land surface phenology for 1999-2017 time series of Copernicus Global Land products derived from SPOT-VEGETATION and PROBA-V data. We investigated the sensitivity of phenology to (I) the input vegetation variable: normalized difference vegetation index (NDVI), leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and fraction of vegetation cover (FCOVER); (II) the smoothing and gap filling method for deriving seasonal trajectories; and (III) the method to extract phenological metrics: thresholds based on a percentile of the annual amplitude of the vegetation variable, autoregressive moving averages, logistic function fitting, and first derivative methods. We validated the derived satellite phenological metrics (start of the season (SoS) and end of the season (EoS)) using available ground observations of Betula pendula, B. alleghaniensis, Acer rubrum, Fagus grandifolia, and Quercus rubra in Europe (Pan-European PEP725 network) and the USA (National Phenology Network, USA-NPN). The threshold-based method applied to the smoothed and gap-filled LAI V2 time series agreed best with the ground phenology, with root mean square errors of ˜10 d and ˜25 d for the timing of SoS and EoS respectively. This research is expected to contribute for the operational retrieval of land surface phenology within the Copernicus Global Land Servic

ASAP Water Satisfaction Index

This technical report describes the Water Satisfaction Index model that used in the ASAP (Anomaly hotSpots of Agricultural Production) early warning system.JRC.D.5-Food Securit

Mapping crop phenology using NDVI time-series derived from HJ-1 A/B data

With the availability of high frequent satellite data, crop phenology could be accurately mapped using time-series remote sensing data. Vegetation index time-series data derived from AVHRR, MODIS, and SPOT-VEGETATION images usually have coarse spatial resolution. Mapping crop phenology parameters using higher spatial resolution images (e.g., Landsat TM-like) is unprecedented. Recently launched HJ-1 A/B CCD sensors boarded on China Environment Satellite provided a feasible and ideal data source for the construction of high spatio-temporal resolution vegetation index time-series. This paper presented a comprehensive method to construct NDVI time-series dataset derived from HJ-1 A/B CCD and demonstrated its application in cropland areas. The procedures of time-series data construction included image preprocessing, signal filtering, and interpolation for daily NDVI images then the NDVI time-series could present a smooth and complete phenological cycle. To demonstrate its application, TIMESAT program was employed to extract phenology parameters of crop lands located in Guanzhong Plain, China. The small-scale test showed that the crop season start/end derived from HJ-1 A/B NDVI time-series was comparable with local agro-metrological observation. The methodology for reconstructing time-series remote sensing data had been proved feasible, though forgoing researches will improve this a lot in mapping crop phenology. Last but not least, further studies should be focused on field-data collection, smoothing method and phenology definitions using time-series remote sensing data

Early season weed mapping in rice crops using multi-spectral UAV data

In this article, we propose an automatic procedure for classification of UAV imagery to map weed presence in rice paddies at early stages of the growing cycle. The objective was to produce a weed map (common weeds and cover crop remnants) to support variable rate technologies for site-specific weed management. A multi-spectral ortho-mosaic, derived from images acquired by a Parrot Sequoia sensor mounted on a quadcopter, was classified through an unsupervised clustering algorithm; cluster labelling into â weed/no weed classes was achieved using geo-referenced observations. We tested the best set of input features among spectral bands, spectral indices and textural metrics. Weed mapping performance was assessed by calculating overall accuracy (OA) and, for the weed class, omission (OE) and commission errors (CE). Classification results were assessed under an alarmist approach in order to minimise the chance of overestimating weed coverage. Under this condition, we found that best results are provided by a set of spectral indices (OA= 96.5%, weed CE = 2.0%). The output weed map was aggregated to a grid layer of 5 x 5 m to simulate variable rate management units; a weed threshold was applied to identify the portion of the field to be subject to treatment with herbicides. Ancillary information on weed and crop conditions were derived over the grid cells to support precision agronomic management of rice crops at the early stage of growth