FutureWater’s partner HiView has completed the prototype of their latest innovation, the Rapid Eye XS. This ultra light is in the first place designed for use at a small scale in agriculture, but has many other potential applications. It can process NDVI imagery on-board, and is very easy to use. FutureWater played a prominent in the development of this revolutionary system, partly thanks to funding of the TWIGA project, by the European Commission’s Horizon 2020 programme. 

Features

The prototype of the Rapid Eye XS is equipped with a Raspberry Pi near-infrared camera that can be used to monitor crop performance. It can be launched very quickly using a single button on the remote control. It has a return-to-home function to make sure the drone will always find its way back.

Once airborne the camera can be triggered through a radio connection, with a range up to 400 meters. Right after capturing the image at a typical height of 120 meters, an NDVI map is produced on the fly in as little as 12 seconds.

A global premier: NDVI processing on board

Shortly after landing, the NDVI map can be downloaded in-field on any mobile phone or tablet through Wi-Fi or Bluetooth and can be viewed instantly in the field, without needing complex time-demanding processing skills that are usually required.

Easy-to-understand colors on the map indicate healthy and problematic zones, which the agronomic pilot assesses in the field right away, together with the farmer. In this way the map is used to provide real time advisory to farmers on how to improve their crop growth.

Real time in- field advisory

As these farmers usually have limited access to resources and are often hindered in their access to information, this real time advisory will help improve their farming practices and increase their yield and water productivity.

Agronomists and farmers from all over the world are already responding very positively to the first prototype of this innovative small scale drone. By downgrading the performance requirements and with a cost price of just a few hundred euros, the Rapid Eye XS is going to be a real game-changer for smallholder farmers in developing countries.

Field demonstration with the farmers
Farmers and operators

 

The beneficiaries of this training, provided by FutureWater together with Solidaridad, belong to the Zambia Agricultural Research Institute (ZARI).
ZARI is a department within the Ministry of Agriculture of Zambia with the overall objective to provide a high quality, appropriate and cost-effective service to farmers, generating and adapting crop, soil and plant protection technologies. This department comprises a number of sections, one of which, for the purpose of this training request is the Soil and Water Management (SWM) division. ZARI and the SWM carry out demand-driven research, trying to find solutions to the problems faced by Zambian small-scale farmers, especially considering the near- and long-term impacts of climate change.
The training programme consists of a hybrid approach of e-learning and in-person training sessions and is structured around the following modules:
  1. Remote sensing-based analysis using Google Earth Engine to assess trends in land use, management, degradation and hotspots for intervention.
  2. Data collection and database management.
  3. GIS and remote sensing to assess suitability for SWC.
  4. Effectiveness and prioritization of SWC using open-source tools.
  5. Independent working on case study.
At the end of the training, it is expected that participants have achieved several objectives such as acquisition of technical skills for extracting relevant data from open access remote sensing products and improved knowledge of data collection and database management.

Last week, first module of the Nuffic TMT training course on Geo-spatial data skills development was kicked-off at Zambia Agricultural Research Institute (ZARI). 

Solidaridad is partnering with FutureWater to conduct a tailor-made training on ‘Geo-spatial data skills development for improved soil water
management and enhanced crop productivity at the national level in Zambia’.

The training project for ZARI is a Tailor-Made Training (TMT), as part of the Orange Knowledge Programme, funded by Nuffic and will enhance capacity in accessing and using innovative data and tools in the public domain, to analyse crop performance and improve soil water management.

This first module was focused on the use of Google Earth Engine (GEE).

The objective of this module was to build capacity of the participants in using the basic functionalities of GEE, by working on applications specifically relevant for land use management and identification of degradation hotspots. This objective has been achieved by participants by obtaining hands-on experience in script-building and interpretation of outcomes.

The first module has been conducted by:

And the rest of modules will take place during the incoming months, following the training project.

Introduction in classroom
Trainers and participants

InfoSequia is the Drought Early Warning and Forecasting System developed by FutureWater to support the decision-making and risk management of drought impacts. InfoSequia rests on an advanced cloud computing and geoprocessing architecture able to effectively integrate large volume of data from satellite, reanalysis and ground-observation networks, with machine learning techniques to generate local-tailored seasonal outlooks of drought risk failures at the river basin and agricultural district levels.

InfoSequia has been recently and effectively integrated into the TWIGA geoportal, a platform that offers to African users the possibility for accessing data from ground-observation networks, and climate or agro- services with monitoring and forecasting capabilities.

The new and enhanced InfoSequia indices and products are operationally delivered for the Inkomati River Basin, a transboundary basin which extends through South Africa, Eswatini and Mozambique.

This lite service includes a full suite of 3 dekad-based (10-days) meteorological drought indices and 3 dekad-based vegetative health indices, all of them timescale aggregated at 1, 3, 6 and 12 months, and updated every month. The service is easily scalable and user-tailored to other regions of Africa upon request and agreement with FutureWater. Thanks to the advanced front-end capabilities of the TWIGA portal, users can easily access InfoSequia data and incorporate them into dashboards specifically set up according to their information needs.

A video was made to highlight how InfoSequía has been implemented, watch it here:

More information about the FutureWater’s approach on Water Scarcity and Drought can be found here.

 

One-pager brochure of InfoSequia-TWIGA for Africa. Downloadable as PDF here.

 

FutureWater recently submitted the InfoSequia solution to the 2nd Dinapsis Challenge, an innovation call launched by Dinapsis, the network of digital hubs of the Agbar group.

This second challenge focused on innovative solutions and apps developed to cope with “Extreme weather events, optimization of water consumption, and management of wastewater”. 48 proposals were submitted to the challenge, of which 5 finally passed to the final phase of the call which was held in the Dinapsis hub located in Cartagena. InfoSequia was introduced through a short video after which FutureWater staff, represented by Sergio Contreras and Amelia Fernández, answered the jury’s questions.

InfoSequia is the Drought Early Warning and Forecasting System developed by FutureWater to support the decision-making and risk management of drought impacts. InfoSequia rests on a cloud computing and geoprocessing architecture which allows the integration of large volume of data retrieved from satellite, reanalysis and ground-observation datasets, and machine learning techniques to generate local-tailored seasonal outlooks of drought risk failures at the river basin and agricultural district levels.

More information on the Dinapsis event can be found on YouTube. FutureWater’s approach on Water Scarcity and Drought can be found here.

InfoSequia video at 2nd Dinapsis Open Challenge

FutureWater and finalists at the 2nd Dinapsis Challenge.

Water and food security are at risk in many places in the world: now and most likely even more in the future, having large economic and humanitarian consequences. Risk managers and decision-makers, such as water management authorities and humanitarian-aid agencies/NGOs, can prevent harmful consequences more efficiently if information is available on-time on (1) the impact on the system, economy or society, and also (2) the probabilities for a failure in the system. EO information has proven to be extremely useful for (1). For looking into the future, considering the uncertainties, novel machine learning techniques are becoming available.

The proposed development is incorporated into an existing solution for providing Drought and Early Warning Systems (DEWS), called InfoSequia. InfoSequia is a modular and flexible toolbox for the operational assessment of drought patterns and drought severity. Currently, the InfoSequia toolbox provides a comprehensive picture of current drought status, based mainly on EO data, through its InfoSequia-MONITOR module. The proposed additional module, called InfoSequia-4CAST, is a major extension of current InfoSequia capabilities, responding to needs that have been assessed in several previous experiences.

InfoSequia-4CAST provides the user with timely, future outlooks of drought impacts on crop yield and water supply. These forecasts are provided on the seasonal scale, i.e. 3-6 months ahead. Seasonal outlooks are computed by a novel state-of-the-art Machine Learning technique. This technique has already been tested for applications related to crop production forecasting and agricultural drought risk financing. The FFTrees algorithm uses predictor datasets (in this case, a range of climate variability indices alongside other climatic and vegetative indices) to generate FFTs predicting a binary outcome – crop yields or water supply-demand balance above or below a given threshold (failure: yes/no).

The activity includes intensive collaboration with stakeholders in Spain, Colombia and Mozambique, in order to establish user requirements, inform system design, and achieve pilot implementation of the system in the second project year. Generic machine learning procedures for training the required FFTs will be developed, and configured for these pilot areas. An intuitive user interface is developed for disseminating the output information to the end users. In addition to development of the forecasting functionality, InfoSequia-MONITOR will be upgraded by integrating state-of-the art ESA satellite data and creating multi-sensor blended drought indices.

Twiga’ is the Swahili word for ‘giraffe’, a keen observer of the African landscape. TWIGA aims to provide actionable geo-information on weather, water, and climate in Africa through innovative combinations of new in situ sensors and satellite-based geo-data. With the foreseen new services, TWIGA expects to reach twelve million people within the four years of the project, based on sustainable business models.

Africa needs reliable geo-information to develop its human and natural resources. Sixty percent of all uncultivated arable land lies in Africa. At the same time Africa is extremely vulnerable to climate change. Unfortunately, the in situ observation networks for weather, water, and climate have been declining since the 1970s. As a result, rainfall predictions in Africa for tomorrow have the same accuracy as predictions in Europe, ten days ahead. To realize the tremendous potential of Africa while safeguarding the population against impacts of climate change, Earth observation must be enhanced and actionable geoinformation services must be developed for policy makers, businesses, and citizens. New in situ observations need to be developed that leverage the satellite information provided through GEOSS and Copernicus (Open data/information systems).

TWIGA covers the complete value chain, from sensor observation, to GEOSS data and actionable geoinformation services for the African market. The logic followed throughout is that in situ observation, combined with satellite observations and mathematical models, will result in products consisting of maps and time series of basic variables, such as atmospheric water vapour, soil moisture, or crop stage. These products are either produced within TWIGA, or are already available with the GEOSS and Copernicus information systems. These products of basic variables are then combined and processed to derive actionable geo-information, such as flash flood warnings, sowing dates, or infra-structural maintenance scheduling.

The TWIGA consortium comprises seven research organisations, nine SMEs and two government organisations. In addition it uses a network of 500 ground weather stations in Africa, providing ready-to-use technical infrastructure.

FutureWater’s main role in TWIGA is centered around the use of flying sensors to map crop conditons, flood extent, and energy fluxes, complementing and improving data from in situ sensors and satellites. Furthermore, FutureWater is involved in innovative app development.