ASAP: A new global early warning system to detect anomaly hot spots of agricultural production for food security analysis

Abstract

Monitoring crop and rangeland conditions is highly relevant for early warning and response planning in food insecure areas of the world. Satellite remote sensing can obtain relevant and timely information in such areas where ground data are scattered, non-homogenous, or frequently unavailable. Rainfall estimates provide an outlook of the drivers of vegetation growth, whereas time series of satellite-based biophysical indicators at high temporal resolution provide key information about vegetation status in near real-time and over large areas. The new early warning decision support system ASAP (Anomaly hot Spots of Agricultural Production) builds on the experience of the MARS crop monitoring activities for food insecure areas, that have started in the early 2000's and aims at providing timely information about possible crop production anomalies. The information made available on the website (https://mars.jrc.ec.europa.eu/asap/) directly supports multi-agency early warning initiatives such as for example the GEOGLAM Crop Monitor for Early Warning and provides inputs to more detailed food security assessments that are the basis for the annual Global Report on Food Crises. ASAP is a two-step analysis framework, with a first fully automated step classifying the first sub-national level administrative units into four agricultural production deficit warning categories. Warnings are based on rainfall and vegetation index anomalies computed over crop and rangeland areas and are updated every 10 days. They take into account the timing during the crop season at which they occur, using remote sensing derived phenology per-pixel. The second step involves the monthly analysis at country level by JRC crop monitoring experts of all the information available, including the automatic warnings, crop production and food security-tailored media analysis, high-resolution imagery (e.g. Landsat 8, Sentinel 1 and 2) processed in Google Earth Engine and ancillary maps, graphs and statistics derived from a set of indicators. Countries with potentially critical conditions are marked as minor or major hotspots and a global overview is provided together with short national level narratives.

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

“…GEE hosted extensive publicly available RS datasets that can be effectively utilized for productivity, quality, profitability, and sustainability studies of agriculture production. Researchers have applied GEE to plantation mapping and monitoring [75], [76], phenology-based classification [77], [78], cropland mapping [79], [80], crop condition monitoring [81], [82], crop yield estimation [83], [84], irrigation mapping [85], [86], and other agricultural studies [87], [88]. For example, seasonal median composites of Sentinel-1 and Sentinel-2 were calculated in GEE to predict the Maize yield in Kenya and Tanzania [83].…”

Exploring the Spatio-Temporal Dynamics of Winter Rape on the Middle Reaches of Yangtze River Valley Using Time-Series MODIS Data

“…Remote sensing has been proven to be a valuable technique to provide large-scale multi-temporal data, and has been widely used to map individual crops or cropping structure owing to its low cost and high efficiency. Remote sensing can also significantly contribute to agricultural systems monitoring such as anomaly hot spots of agricultural production over large areas [14]. The literature shows that there are many studies to map grain crops in China such as rice, wheat, and soybean [15][16][17][18][19].…”

Integrating multiple land cover maps through a multi-criteria analysis to improve agricultural monitoring in Africa

“…The mask displays some spatial inconsistencies arising from the border effect, as occurs in north-western Nigeria. The mask has been generated on a per-country framework that is appropriate in national-level early warning systems such as ASAP ( Rembold et al, 2019 ), so the border effect cannot be completely avoided. To overcome this issue, some modifications to the proposed method could be considered, such as applying MCA on a per pixel basis ( Pérez-Hoyos et al, 2012a , b ).…”