Using GIS and Remote Sensing to build Master Sampling Frames for Agricultural Statistics

This report is a JRC contribution to the FAO Global Strategy to improve Agricultural and Rural Statistics (GSARS). Its aim is providing guidelines on the suitable ways to use satellite images and geographic information tools to build master sampling frames that can be used both for agricultural and environmental statistics. The main readers to which the report is addressed are agricultural and environmental statisticians in developing countries. We consider separately the use of technological tools for area sampling frames and for list sampling frames. The use of Global Navigation Satellite Systems (GNSS), better known as GPS, is also discussed, although its use is more connected to carrying out field surveys rather than to the design of sampling frames.JRC.H.4-Monitoring Agricultural Resource

Summary of climate variability and extremes and their main impacts on agricultural production in 2019

This yearly summary reviews the main climate extremes described by the WMO state of the climate preliminary report for 2019 that had an impact on agricultural production according to JRC’s agricultural early warning system ASAP (Anomaly hotspots of Agricultural Production). Such a summary can be used as a starting point for more detailed analysis of agricultural production problems and their impact on food security as it is done for example by the multi-agency Global Report on Food Crises and the Food Security and Nutrition State in the World. The year 2019 was warmer than 2018 (second warmest on record) and saw major heat waves in several parts of the world. Droughts affected crop and rangeland productivity mainly in Europe, Southern and Eastern Africa, South East Asia and in Australia. Tropical storms and cyclones caused fatalities and major damage to infrastructure and agriculture in the Bahamas and along East Africas coast. High intensity rainfall lead to floods in all continents. The final part of the report includes an oveview of climate extremes affecting crop seasons ongoing in early 2020 as well as a short summary of seasonal forecasts until April 2020.JRC.D.5-Food Securit

GNSS Utilization in the Framework of the EU Common Agricultural Policy

The UE Member States are obliged by Article 30(1) of Commission Regulation (EC) No 796/2004 to provide proof of quality of the tools and method used in the annual control process of the area based subsidies. The measurement methods and modes with GNSS are the choice of each Member State and the GNSS devices are more or less often used for different goals: On The Spot controls, for follow up inspection of rejected claims, etc. For all purposes the GNSS devices have to be certified or validated for area measurement following the protocol based on the ISO 5725 norm.JRC.H.4-Monitoring Agricultural Resource

Assessment of Parcel Area Measurement based on VHR SAR Images

In the frame of the Common Agriculture Policy, Member States have to measure parcels claimed for subsidies with a recommended precision. This is usually done using Very High Resolution (VHR) optical images with ground sampling distance of around 1m or better. However acquisition of such imagery may fail due to cloud cover. It is therefore worth examining the potential of almost weather independent VHR radar data for replacing VHR optical imagery: during this study, the identification of agricultural parcels and the assessment of the measurement accuracy on VHR SAR images were tested. Airborne VHR X band SAR data were provided over 4 agricultural test sites in France. Three of these sites were covered with 1m monopolarized (“B&W”) data from 2002-2004 whereas the remaining one was covered with 2m multipolarized (“colour”) data from 2002. Orthophotos (1m B&W and 50 cm colour ADS 40) acquired over 2001-2004 were used as reference. All parcels falling on the frame of the VHR SAR images were digitized on the orthophotos and examined on the VHR SAR data. Two sets of around 40 parcels each were selected on the two types of VHR SAR images (2m “colour” and 1m “B&W”). Each parcel was measured randomly 3 times by 3 operators on both the SAR imagery and the orthophoto. The errors on the parcel area were translated into buffer widths around the parcel perimeter. After the elimination of outlier measurements, the buffer variations were analyzed and a tolerance interval around the buffer estimated. The results indicate that (1) about 30% of the parcels were not visible on the X-band SAR data; (2) the estimated tolerance intervals of the buffer values were of 4.14 m and 4.81 m on 2 m colour composition and 1 m black-and-white SAR data respectively, which is larger than requested by the EU Regulation.JRC.G.3-Agricultur

Assessment of cropland area on sloping land in DPRK

Following the famines of the mid 1990s, the government of the Democratic People’s Republic of Korea (DPRK) authorized cultivation on sloping land before deciding, in the years 2000, to limit this practice on slopes above 15 degrees in order to reduce erosion. There are still many cultivated fields on slopes and their total estimated area ranges from 300,000 ha to more than 2 million ha. This study aims at assessing cropland areas on slopes above 10 and 15 degrees by using high to very high resolution remote sensing satellite imagery. For this purpose, a grid of points was superimposed over the DPRK territory and stratified according to slope, as derived from two DEMs, the 30 m ASTER GDEM V2 and the 3 arc second (~90 m) SRTM Dem V4. A sample of about 2100 points was drawn using an optimal allocation sampling plan, based on a preliminary assessment of the variance of the estimated cropland percentage per class of slope. These 2100 points were interpreted into cropland, no cropland and doubt using mostly Google Earth imagery acquired after 2004. For slopes above 10 degrees, the area cropped was estimated to be around 1,000,000 ha (5.6% CV) and 742,000 ha (8.1% CV) according to the ASTER and SRTM DEM respectively. Above 15 degrees, the estimated cropland area ranges from 360,000 ha (9.7% CV) with SRTM to 540,000 ha (6.6.% CV) with ASTER. To decide between these two estimations, a validation of the two DEMs should be carried out on a region with similar relief. Alternatively, a higher accuracy DEM such as the one to be derived from the TanDEM-X mission in 2014 should provide more accurate estimates of the cropland area on sloping land.JRC.H.4-Monitoring Agricultural Resource

Assessment of the 2019 main cropping season in the Democratic People’s Republic of Korea

This report provides an early assessment of the 2019 main crop season by analysing the meteorological (Temperature and Rainfall) and vegetation conditions until end-September 2019. Overall, the start of the season was delayed and poor due to long dry spells until June that negatively affected the establishment of early crops planted in spring as well as main season rainfed crops (Annex 1). Despite some rain improvements in June in the north of the rice bowl area, persistent dry spells in July and August negatively affected crop conditions in southwestern regions (i.e. Hwanghae Bukto, Hwanghae Namdo and Pyongyang-si). Heavy rain in September brought by Typhoon Lingling reduced water deficits and resulted in improvement of vegetation conditions but did not significantly improve crop prospects due to their late occurrence during the growing season. According to the weather and Earth Observation data analysed, irregular rainfall distribution coupled with irrigation water shortages, negatively affected the 2019 crop production, placing the country in a critical food security situation, if we also consider the poor harvest of 2018. Nonetheless, a more detailed analysis based on field observation would be necessary to confirm this diagnosis.JRC.D.5-Food Securit

Geomatics in support of the Common Agricultural Policy- Proceedings of the 14th GeoCAP Annual Conference, 2008

The 2008 Annual Conference, jointly organised by the GeoCAP (former MARS PAC) action of the Joint Research Centre (Ispra, Italy) and the Ministry of Agriculture, Forestry and Food of the Republic of Slovenia, covered the Control with Remote sensing Activities as well as technical aspects of Land Parcel Identification Systems (LPIS) and ortho-imagery use in Common Agricultural Policy (CAP) management and control procedures. The conference was the 14th organised by GeoCAP to review this important and still growing area of technical activity, in support of the CAP implementation. The program was structured into two days of plenary sessions (Wednesday 3rd and Friday 5th December) and one day (Thursday 4th December) with parallel sessions, including a restricted session for national and regional administrations. More than 350 participants from 36 countries attended. The presentations delivered during the conference were made available on line within some days of the event, and this publication represents the best presentations judged worthy of inclusion in a conference proceedings aimed at recording the state of the art of technology and practice of that time.JRC.G.3-Monitoring agricultural resource

Use of high resolution imagery and ground survey data for estimating crop areas in Mengcheng county, China

The use of remote sensing images in combination with ground survey data was assessed for deriving crop areas over Mengcheng County in 2011 in the North China Plain. First, a stratification of the county into arable land, permanent crops and non agricultural land was carried out by photo-interpreting a grid of points on Google Earth and a 2.5m Spot5 image from 2011. Then a sample of 83 segments was randomly selected in the arable stratum and surveyed with GPS. Two high resolution images (TM 30m and Spot5 10m) were acquired over the 2011 summer crop season and classified using maximum likelihood. The regression estimator was then applied using the surveyed segments and the classification and compared to the direct expansion estimate derived from the segments only; the calibration estimator was also tested using the same classification and the 83 arable points that served as seeds for the segments and compared to the estimate derived from the 83 points alone. The regression estimator proved to be the most efficient one in the North China Plain landscape. To reach the same variance of estimate as the regression estimator, the number of points to be surveyed for the calibration estimator should be multiplied by seven. Last pixel counting tested on the whole county and on the arable points of the grid resulted in biased estimates, in contrast to estimates based on ground data, in combination with remote sensing or not.JRC.H.4-Monitoring Agricultural Resource

JRC MARS Bulletin - Global outlook series Impact of El Niño on agriculture in Southern Africa for the 2015/2016 main season

Impact of El Niño on agriculture in Southern Africa for the 2015/2016 main seasonJRC.H.4-Monitoring Agricultural Resource

Comparison of Global Land Cover Datasets for Cropland Monitoring

Accurate and reliable information on the spatial distribution of major crops is needed for detecting possible production deficits with the aim of preventing food security crises and anticipating response planning. In this paper, we compared some of the most widely used global land cover datasets to examine their comparative advantages for cropland monitoring. Cropland class areas are compared for the following datasets: FAO-GLCshare (FAO Global Land Cover Network), Geowiki IIASA-Hybrid (Hybrid global land cover map from the International Institute of Applied System Analysis), GLC2000 (Global Land Cover 2000), GLCNMO2008 (Global Land Cover by National Mapping Organizations), GlobCover, Globeland30, LC-CCI (Land Cover Climate Change Initiative) 2010 and 2015, and MODISLC (MODIS Land Cover product). The methodology involves: (1) highlighting discrepancies in the extent and spatial distribution of cropland, (2) comparing the areas with FAO agricultural statistics at the country level, and (3) providing accuracy assessment through freely available reference datasets. Recommendations for crop monitoring at the country level are based on a priority ranking derived from the results obtained from analyses 2 and 3. Our results revealed that cropland information varies substantially among the analyzed land cover datasets. FAO-GLCshare and Globeland30 generally provided adequate results to monitor cropland areas, whereas LC-CCI2010 and GLC2000 are less unsuitable due to large overestimations in the former and out of date information and low accuracy in the latter. The recently launched LC-CCI datasets (i.e., LC-CCI2015) show a higher potential for cropland monitoring uses than the previous version (i.e., LC-CCI2010)JRC.D.5-Food Securit