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Annex B. Quick reference to CropWatch indicators, spatial units and methodologies

The following sections give a brief overview of CropWatch indicators and spatial units, along with a description of the CropWatch production estimation methodology. For more information about CropWatch methodologies, visit CropWatch online at cloud.cropwatch.com.cn.

Agroecological zones for 49 key countries

Overview

231 agroecological zones for the 49 key countries across the globe

Description

49 key agricultural countries are divided into 231 agro-ecological zones based on cropping systems, climatic zones, and topographic conditions. Each country is considered separately. A limited number of regions (e.g., region 001, region 027, and region 127) are not relevant for the crops currently monitored by CropWatch but are included to allow for more complete coverage of the 49 key countries. Some regions are more relevant for rangeland and livestock monitoring, which is also essential for food security.

CropWatch indicators

The CropWatch indicators are designed to assess the condition of crops and the environment in which they grow and develop; the indicators—RAIN (for rainfall), TEMP (temperature), and RADPAR (photosynthetically active radiation, PAR)—are not identical to the weather variables, but instead are value-added indicators computed only over crop growing areas (thus for example excluding deserts and rangelands) and spatially weighted according to the agricultural production potential, with marginal areas receiving less weight than productive ones. The indicators are expressed using the usual physical units (e.g., mm for rainfall) and were thoroughly tested for their coherence over space and time. CWSU are the CropWatch Spatial Units, including MRUs, MPZ, and countries (including first-level administrative districts in select large countries). For all indicators, high values indicate "good" or "positive."

INDICATOR
BIOMSS
Biomass accumulation potential
Crop/ satellite	Grams dry matter/m², pixel or CWSU	An estimate of biomass that could potentially be accumulated over the reference period given the prevailing rainfall and temperature conditions.	Biomass is presented as maps by pixels, maps showing average pixels values over CropWatch spatial units (CWSU), or tables giving average values for the CWSU. Values are compared to the average value for the recent fifteen years, with departures expressed in percentage.
CALF
Cropped arable land and cropped arable land fraction
Crop/ Satellite	[0,1] number, pixel or CWSU average	The area of cropped arable land as fraction of total (cropped and uncropped) arable land. Whether a pixel is cropped or not is decided based on NDVI twice a month. (For each four-month reporting period, each pixel thus has 8 cropped/ uncropped values).	The value shown in tables is the maximum value of the 8 values available for each pixel; maps show an area as cropped if at least one of the 8 observations is categorized as "cropped." Uncropped means that no crops were detected over the whole reporting period. Values are compared to the average value for the last five years, with departures expressed in percentage.
CROPPING INTENSITY
Cropping intensity Index
Crop/ Satellite	0, 1, 2, or 3; Number of crops growing over a year for each pixel	Cropping intensity index describes the extent to which arable land is used over a year. It is the ratio of the total crop area of all planting seasons in a year to the total area of arable land.		Cropping intensity is presented as maps by pixels or spatial average pixels values for MPZs, 48 countries, and 7 regions for China. Values are compared to the average of the previous five years, with departures expressed in percentage.
NDVI
Normalized Difference Vegetation Index
Crop/ Satellite	[0.12-0.90] number, pixel or CWSU average	An estimate of the density of living green biomass.		NDVI is shown as average profiles over time at the national level (cropland only) in crop condition development graphs, compared with previous year and recent five-year average, and as spatial patterns compared to the average showing the time profiles, where they occur, and the percentage of pixels concerned by each profile.
RADPAR
CropWatch indicator for Photosynthetically Active Radiation (PAR), based on pixel based PAR
Weather/Satellite	W/m², CWSU	The spatial average (for a CWSU) of PAR accumulation over agricultural pixels, weighted by the production potential.		RADPAR is shown as the percent departure of the RADPAR value for the reporting period compared to the recent fifteen-year average, per CWSU. For the MPZs, regular PAR is shown as typical time profiles over the spatial unit, with a map showing where the profiles occur and the percentage of pixels concerned by each profile.
RAIN
CropWatch indicator for rainfall, based on pixel-based rainfall
Weather/ satellite	Liters/m², CWSU	The spatial average (for a CWSU) of rainfall accumulation over agricultural pixels, weighted by the production potential.		RAIN is shown as the percent departure of the RAIN value for the reporting period, compared to the recent fifteen-year average, per CWSU. For the MPZs, regular rainfall is shown as typical time profiles over the spatial unit, with a map showing where the profiles occur and the percentage of pixels concerned by each profile.
TEMP
CropWatch indicator for air temperature, based on pixel-based temperature
Weather/ satellite	°C, CWSU	The spatial average (for a CWSU) of the temperature time average over agricultural pixels, weighted by the production potential.		TEMP is shown as the departure of the average TEMP value (in degrees Centigrade) over the reporting period compared with the average of the recent fifteen years, per CWSU. For the MPZs, regular temperature is illustrated as typical time profiles over the spatial unit, with a map showing where the profiles occur and the percentage of pixels concerned by each profile.
VCIx
Maximum vegetation condition index
Crop/ Satellite	Number, pixel to CWSU	Vegetation condition of the current season compared with historical data. Values usually are [0, 1], where 0 is "NDVI as bad as the worst recent year" and 1 is "NDVI as good as the best recent year." Values can exceed the range if the current year is the best or the worst.		VCIx is based on NDVI and two VCI values are computed every month. VCIx is the highest VCI value recorded for every pixel over the reporting period. A low value of VCIx means that no VCI value was high over the reporting period. A high value means that at least one VCI value was high. VCI is shown as pixel-based maps and as average value by CWSU.
VHI
Vegetation health index
Crop/ Satellite	Number, pixel to CWSU	The average of VCI and the temperature condition index (TCI), with TCI defined like VCI but for temperature. VHI is based on the assumption that "high temperature is bad" (due to moisture stress), but ignores the fact that low temperature may be equally "bad" (crops develop and grow slowly, or even suffer from frost).		Low VHI values indicate unusually poor crop condition, but high values, when due to low temperature, may be difficult to interpret. VHI is shown as typical time profiles over Major Production Zones (MPZ), where they occur, and the percentage of pixels concerned by each profile.
VHIn
Minimum Vegetation health index
Crop/ Satellite	Number, pixel to CWSU	VHIn is the lowest VHI value for every pixel over the reporting period. Values usually are [0, 100]. Normally, values lower than 35 indicate poor crop condition.		Low VHIn values indicate the occurrence of water stress in the monitoring period, often combined with lower than average rainfall. The spatial/time resolution of CropWatch VHIn is 16km/week for MPZs and 1km/dekad for China.
CPI
Crop Production Index
Crop/ Satellite	Number, pixel to CWSU	The average crop production situation for the same period in the past five years was used as a benchmark to make an overall estimate of the current season's agricultural production situation.		Based on the VCIx, CALF, land productivity and area of irrigated and rainfed cropland in the current monitoring period and the same period in the past five years for the spatial unit, a mathematical model proposed by CropWatch is used to calculate the index expressed as a normalized value. A value of 1.0 represents the basic normal crop production situation in the current period for the spatial unit, and the higher the value, the better the crop production situation in the current period. Conversely, the lower the value, the worse the crop production situation for the spatial unit in the current period.

Note: Type is either "Weather" or "Crop”; source specifies if the indicator is obtained from ground data, satellite readings, or a combination; units: in the case of ratios, no unit is used; scale is either pixels or large scale CropWatch spatial units (CWSU). Many indicators are computed for pixels but represented in the CropWatch bulletin at the CWSU scale.

CropWatch spatial units(CWSU)

CropWatch analyses are applied to four kinds of CropWatch spatial units (CWSU): Countries, China, Major Production Zones (MPZ), and global crop Monitoring and Reporting Units (MRU). The tables below summarize the key aspects of each spatial unit and show their relation to each other. For more details about these spatial units and their boundaries, see the CropWatch bulletin online resources.

SPATIAL LUNITS
CHINA
Overview	Description
Seven monitoring regions	The seven regions in China are agro-economic/agro-ecological regions that together cover the bulk of national maize, rice, wheat, and soybean production. Provinces that are entirely or partially included in one of the monitoring regions are indicated in color on the map below.

Countries (and first-level administrative districts, e.g., states and provinces)
Overview		Description
“Forty eight plus one” countries to represent main producers/exporters and other key countries.		CropWatch monitored 49 countries together represent more than 80% of the production of maize, rice, wheat and soybean, as well as 80% of exports. Some countries were included in the list based on criteria of proximity to China (Uzbekistan, Cambodia), regional importance, or global geopolitical relevance (e.g., four of five most populous countries in Africa). The total number of countries monitored is “48 + 1,” referring to 48 and China itself. For the nine largest countries — United States, Brazil, Argentina, Russia, Kazakhstan, India, China, and Australia, maps and analyses may also present results for the first-level administrative subdivision. The CropWatch agroclimatic indicators are computed for all countries and included in the analyses when abnormal conditions occur. Background information about the countries’agriculture and trade is available on the CropWatch Website, cloud.cropwatch.com.cn.

Major Production Zones (MPZ)
Overview	Description
Six globally important areas of agricultural production	The six MPZs include West Africa, South America, North America, South and Southeast Asia, Western Europe and Central Europe to Western Russia. The MPZs are not necessarily the main production zones for the four crops (maize, rice, soybean, wheat) currently monitored by CropWatch, but they are globally or regionally important areas of agricultural production. The seven zones were identified based mainly on production statistics and distribution of the combined cultivation area of maize, rice, wheat and soybean.

Global Monitoring and Reporting Unit (MRU)
Overview	Description
105agro-ecological/agro-economic units across the world	MRUs are reasonably homogeneous agro-ecological/agro-economic units spanning the globe, selected to capture major variations in worldwide farming and crops patterns while at the same time providing a manageable (limited) number of spatial units to be used as the basis for the analysis of environmental factors affecting crops. Unit numbers and names are shown in the figure below. A limited number of units are not relevant for the crops currently monitored by CropWatch but are included to allow for more complete coverage of global production. Additional information about the MRUs is provided online under cloud.cropwatch.com.cn .

Production estimation methodology

The main concept of the CropWatch methodology for estimating production is the calculation of current year production based on information about last year’s production and the variations in crop yield and cultivated area compared with the previous year. The equation for production estimation is as follows:

Where i is the current year, are the variations in crop yield and cultivated area compared with the previous year; the values of can be above or below zero.

For the 48 countries monitored by CropWatch, yield variation for each crop is calibrated against NDVI time series, using the following equation:

Where are taken from the time series of the spatial average of NDVI over the crop specific mask for the current year and the previous year. For NDVI values that correspond to periods after the current monitoring period, average NDVI values of the previous five years are used as an average expectation. is calculated by regression against average or peak NDVI (whichever yields the best regression), considering the crop phenology of each crop for each individual country. Multiple yield prediction models are further considered, including a novel assimilation method based on the double flow-dependent ensemble square root filtering, a rain-fed crop yield prediction method considering multidimensional drought indices, and a prediction model of crop yields throughout the entire growing season considering the computational efficiency of remote sensing models and the mechanistic advantages of crop growth models. In recent years, machine learning methods have become prevalent in crop yield prediction due to the numerous factors influencing crop yields, which is also used in yield estimation.

Different methods were used for crop areas. For China, the crop areas were estimated using supervised classification methods and CPTP (crop planting and type proportions). CPTP combines remote sensing-based estimates of the crop planting proportion (cropped area to arable land) with a crop type proportion (specific type area to total cropped area) to estimate the crop area. The planting proportion is estimated based on an unsupervised classification of high-resolution satellite images from GF-1/Sentinel-2/Landsat images. The crop-type proportion for China is obtained by the GVG apps from field transects. The area of a specific crop is computed through multiplying farmland area, planting proportion, and crop-type proportion of the crop.

For wheat, soybean, maize, and rice crops outside of China, CropWatch combines supervised classification methods or regressions of crop area on arable land area for each country (with due attention to phenology) to estimate crop area.

Where, a and b are the coefficients generated by linear regression with area from FAOSTAT or national sources and CALF (Cropped Arable Land Fraction) from CropWatch estimates. With the development of deep learning and remote sensing, multiple crop type classification models are developed and adopted for higher accuracy of production estimation.

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Annex B. Quick reference to CropWatch indicators, spatial units and methodologies

Agroecological zones for 49 key countries