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 www.cropwatch.com.cn.

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."

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 one plus one” countries to represent main producers/exporters and other key countries.		CropWatch monitored 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 “forty one plus one,” referring to forty one countries 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, www.cropwatch.com.cn.
Agro-ecological Zones for 42 key countries 42 key agricultural countries are divided into 212 sub-national regions based on cropping systems, climatic zones, and topographic conditions. Each countries are considered separately. A limited number of regions are not relevant for the crops currently monitored by CropWatch but are included to allow for more complete coverage of the 42 key countries. Some regions are more relevant for rangeland and livestock monitoring which is also essential for food security.
Major Production Zones (MPZ)
Overview	Description
Seven 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
65 agro-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 (e.g., MRU-63 to 65) 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 www.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, and  are the variations in crop yield and cultivated area compared with the previous year; the values of  and  can be above or below zero.

For the 31 countries monitored by CropWatch, yield variation for each crop is calibrated against NDVI time series, using the following equation:

Where and  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.

A different method is used for areas. For China, CropWatch 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). The planting proportion is estimated based on an unsupervised classification of high resolution satellite images from HJ-1 CCD and GF-1 images. The crop-type proportion for China is obtained by the GVG instrument from field transects. The area of a specific crop is computed by multiplying farmland area, planting proportion, and crop-type proportion of the crop.

To estimate crop area for wheat, soybean, maize, and rice outside China, CropWatch relies on the regression of crop area against cropped arable land fraction of each individual country (paying due attention to phenology):

where a and b are the coefficients generated by linear regression with area from FAOSTAT or national sources and CALF the Cropped Arable Land Fraction from CropWatch estimates.  can then be calculated from the area of current and the previous years.

The production for "other countries" (outside the 31 CropWatch monitored countries) was estimated as the linear trend projection for 2014 of aggregated FAOSTAT data (using aggregated world production minus the sum of production by the 31 CropWatch monitored countries).

Methods for crop pest and disease monitoring

For the crop pest and disease monitoring part, we bring together and produce cutting edge research to provide pest and disease monitoring and forecasting information, integrating multi-source (Earth Observation-EO, field survey, meteorological, entomological and plant pathological, etc.) data to monitor pests and diseases damaged areas and levels. For different type of pest or disease, different models are developed, which are as follows.

1) Wheat stripe rust

For wheat stripe rust (Puccinia striiformis), it forms yellow stripe or oval-shaped spots on the leaves of wheat, which results in leaf yellowing, chlorophyll content and water content reduction. In this report, the standard for wheat stripe rust is based on China’s ‘Rules for investigation and forecasting of wheat stripe rust’ (GB/T15795‐2011), and the detail standards is listed in Table 1. Based on the standards, the ground survey data and remote sensing observation data were obtained through the integrated experiment of crop diseases on satellite ground synchronization. Additionally, the levels 1 and 2 in Table 1 refer to the slight occurrence of wheat rust, the level 3 refers to moderate, and levels 4 and 5 refer to severe.

Table 1 wheat stripe rust field investigation indices

Note: Y is the disease index, which is used to reflect the severity of disease occurrence. The calculation formula is Y=F*D*100, F is the disease leaf rate, and D is the average severity of the disease leaf. R is the disease field rate, referring to the percentage of the number of damaged fields in the total field.

Reference: http://doc.mbalib.com/view/2e0ae53c7f397af70deb37edb07c5a12.html

Firstly, the spatial distribution of wheat stripe rust is taken as prior knowledge based on literature research and ground survey. Then Wheat Rust Index (WRI) is constructed based on Plant Senescence Reflectance Index (PSRI) (Equation 1) and Red-edge Vegetation Stress Index (RVSI) (Equation 2) to monitor wheat stripe rust, for which WRI (Equation 3) considering crop growth, chlorophyll content and their variation characteristics. At last, integrated with disease habitat information including land surface temperature (LST, MODIS product), rainfall and wind (meteorological data), also historical data to construct Disease Index (DI) (Equation 4) for wheat stripe rust monitoring.

PSRI = (RR – RB) / RNIR                                    (1)

RVSI = ((R712 + R752) / 2) - R732                      (2)

WRI = f (ΔPSRI, ΔRVSI)                                      (3)

DI = g (WRI, LST - LSTavg, R - Ravg, W)           (4)

Where, RR, RB and RNIR are the reflectance of red, blue, and near infrared (NIR) band, R712, R752 and R732 are the reflectance of 712nm, 752nm and 732nm band. LST is land surface temperature while LSTavg is the average historical land surface temperature. R is rainfall while Ravg is the average historical rainfall. W is wind direction. f and g are constructed with regression analysis based on filed survey datasets. The data range of DI is 0~100%. When 0 < DI ≤ 30%, the damaged level of stripe rust is slight, 30% < DI ≤ 60% is moderate, DI > 60% is severe.

2) Wheat Fusarium head blight

For wheat Fusarium head blight (Fusarium graminearum), it destroyed the cellular integrity of the impacted tissues leading to cell death and degradation of chlorophylls. In this report, the standard for wheat Fusarium head blight is based on China’s ‘Rules for monitoring and forecasting of wheat head blight’ (GB/T 15796‐2011), and the detail standards is listed in Table 2. Based on the standards, the ground survey data and remote sensing observation data were obtained through the integrated experiment of crop diseases on satellite ground synchronization. Additionally, the levels 1 and 2 in Table 2 refer to the slight occurrence of wheat rust, the level 3 refers to moderate, and levels 4 and 5 refer to severe.

Table 2 wheat Fusarium head blight field investigation indices

Note: Y is the rate of diseased panicle, which refers to the ratio of the number of wheat ears to the total number of ears investigated, and R is the incidence area ratio.

Reference: https://www.taodocs.com/p-86284688.html

Firstly, the spatial distribution of wheat Fusarium head blight which provided by CAB international (CABI) is taken as prior knowledge based on literature research and ground survey. Then Wheat Fusarium Head Blight Index (WFHBI) is constructed based on Normalized Difference Vegetation Index (NDVI) (Equation 5) and Difference Vegetation Index (DVI) (Equation 6) to monitor wheat Fusarium head blight, for which WFHBI (Equation 7) considering spectral reflectance of ear and canopy, and their variation characteristics. At last, integrated with disease habitat information including growth stage, LST and rainfall, also historical data to construct DI (Equation 8) for wheat fusarium head blight monitoring.

NDVI = (RNIR – RR) / (RNIR + RR)                     (5)

DVI = RNIR - RR                                                   (6)

WFHBI = f (ΔNDVI, ΔDVI)                                     (7)

DI = g (WFHBI, G, LST – LSTavg, R - Ravg)        (8)

Where, G is the wheat growth stage. f and g are constructed with regression analysis based on filed survey datasets. The data range of DI is 0~100%. When 0 < DI ≤ 30%, the damaged level of Fusarium head blight is slight, 30% < DI ≤ 60% is moderate, DI > 60% is severe.

3) Wheat sheath blight

For wheat sheath blight (Rhizotonia cerealis), its main damage to wheat is appearing brown spot around leaf sheath and water loss of stem wall, which results in nutrient and water deficiency and death. In this report, the standard for wheat sheath blight is based on China’s ‘Rules for investigation and forecasting of wheat sheath blight’ (NY/T 614‐2002), and the detail standards is listed in Table 3. Based on the standards, the ground survey data and remote sensing observation data were obtained through the integrated experiment of crop diseases on satellite ground synchronization. Additionally, the levels 1 and 2 in Table 3 refer to the slight occurrence of wheat sheath blight, the level 3 refers to moderate, and levels 4 and 5 refer to severe.

Table 3 wheat sheath blight field investigation indices

Reference: https://max.book118.com/html/2017/0718/122811227.shtm

Firstly, the spatial distribution of wheat sheath blight is taken as prior knowledge based on literature research and ground survey. Then Wheat Sheath Blight Index (WSBI) is constructed based on Triangular Vegetation Index (TVI) (Equation 9) and Normalized Difference Water Index (NDWI) (Equation 10) to monitor wheat sheath blight, for which WSBI (Equation 11) considering crop growth and its variation characteristics. At last, integrated with disease habitat information including LST and rainfall, also historical data to construct DI (Equation 12) for wheat sheath blight monitoring.

TVI = 0.5 (120 (RNIR - RG) -200 (RR - RG))                  (9)

NDWI = (RG – RNIR) / (RG + RNIR)                             (10)

WSBI = f (ΔTVI, ΔNDWI)                                                (11)

DI = g (WSBI, LST - LSTavg, R - Ravg)                         (12)

Where, RG is the reflectance of green band. f and g are constructed with regression analysis based on filed survey datasets. The data range of DI is 0~100%. When 0 < DI ≤ 30%, the damage level of sheath blight is slight, 30% < DI ≤ 60% is moderate, DI > 60% is severe.

4）Wheat aphid

For wheat aphid (Sitobion avenae and Rhopalosiphum padi), its main damage to wheat is feeding on the sap of young or senescent leaves, stalks and heads, which endangers the normal growth of wheat, and the honeydew emitted by aphids adheres to the leaf surface, seriously affects the photosynthesis of wheat leaves, cause the wheat seedlings becoming yellow and die. In this report, the standard for wheat aphid is based on China’s ‘Rules for investigation and forecasting of wheat aphid’ (NY/T612-2002), and the detail standards is listed in Table 4. Based on the standards, the ground survey data and remote sensing observation data were obtained through the integrated experiment of crop pests on satellite ground synchronization. Additionally, the levels 1 and 2 in Table 4 refer to the slight occurrence of wheat aphid, the level 3 refers to moderate, and levels 4 and 5 refer to severe.

Table 4 wheat aphid field investigation indices

Reference: http://www.doc88.com/p-7708315673411.html

Firstly, the spatial distribution of wheat aphid is taken as prior knowledge based on literature research and ground survey. Then Wheat Aphid Damage Index (WADI) is constructed based on NDVI and Photochemical Reflectance Index (PRI) (Equation 13) to monitor wheat aphid, for which WADI (Equation 14) considering crop growth and its variation characteristics. At last, integrated with pest habitat information including LST and rainfall, also historical data to construct Pest Index (PI) (Equation 15) for wheat aphid monitoring.

PRI = (R531 - R570) / (R531 + R570)                    (13)

WADI = f (ΔNDVI, ΔPRI)                                        (14)

PI = g (WADI, LST - LSTavg, R - Ravg)                 (15)

Where, R531 and R570 are the reflectance of 531nm and 570nm bands. f and g are constructed with regression analysis based on filed survey datasets. The data range of PI is 0~100%. When 0 < PI≤ 30%, the damaged level of aphid is slight, 30% < PI≤ 60% is moderate, PI > 60% is severe.