Botswana College of Agriculture Farai M. MARUMBWA BDMS

Botswana College of Agriculture Farai M. MARUMBWA BDMS
R. Tsheko & M. Tapela Botswana College of Agriculture Farai M. MARUMBWA BDMS Crop Monitoring – Interpretation of Products SADC THEMA Agriculture Service

Agriculture - SADC THEMA Service
Course Content Products Baseline Products Products from meteorological ground measurements Products from Remote Sensing sources Time series CNDVI Gsod and FEWSNet RFEs DMP

Baseline Products Agriculture Mask Crop Statistics Map
Crop Specific Maps

Baseline Products Agriculture Mask
The agriculture mask outlines those areas that are dedicated to cultivation This crop mask has a spatial resolution of 300m

This mask is generated based on the imporved JRC crop mask. The mask contains values ranging from 0 to 200. Zero means that there is no agriculture on the pixel and 200 means that the pixel is fully (100%) covered by agriculture.

Satellite information such as NDVI is specifically extracted on crop areas based on the crop mask The higher resolution of the crop mask (300m) is higher than the SPOT vegetation data (1000m) which allows for the calculation of a weighted crop specific NDVI or CNDVI (Genovese, 2001)

The output Crop statistics map (boolean) is computed by selecting only the pixels which are 60 percent covered by the agriculture. On the boolean crop mask 1 indicates agriculture and 0 represents anything else aprt from Agriculture. When Agriculture mask is overlaid on top of vegetation and rainfall anomaly maps, it can be used to further assist in the early detection of cropped areas affected by drought or other meteorological hazards.

Baseline Products Crop Statistics Map
The crop statistics map links the official crop statistic, presenting the average production and planted area figures for each administrative region or unit. The crop statistics are obtained from the official national sources.

Baseline Products Crop Statics Map
The main inputs for calculation of this product are the National and Regional Agricultural Crop Statistics and administrative Boundaries. The product is obtained by joining the crop statistics table with the administrative unit map

Baseline Products Crop Statics Map
This product may be used for mapping the major cropping areas. It is also useful for verifying yield estimates from remote sensing data sources.

Baseline Products Crop Specific Map
This product may be used for mapping the major cropping areas. This data is also useful for verifying yield estimates from remote sensing data sources.

Meteorological derived products – ground measurements
Rainfall (AP04,AP05,AP07, AP08,AP09, AP10,AP11,AP12) Temperature (AP06, AP13)

Rainfall Products AP04 - Current Condition Rainfall Map AP05 – Current Condition Cumulate Rainfall Map AP07 – Graph of Rainfall Events in the Current Season AP08 – Map of Current Rainfall Compared with the LTA, Max, Min (mm) AP09 - Map of Current Rainfall Compared with the LTA, Max, Min (%) AP10 - Map of Current Cumulate Rainfall Compared with the LTA, Max, Min (mm) AP11 - Map of Current Cumulate Rainfall Compared with the LTA, Max, Min (%) AP12 - Graph of Cumulate Rainfall of current season compared to the LTA, LTMax, LTMin

Very Important Remark to you all It is not always possible to obtain this Met data in all countries. Some National Meteorological services will not make this data available to AMESD, or sometimes the data will only be available at a cost. The SADC-THEMA is set up in such a way that it runs without this data. Wherever “Ground or in-situ Data” is needed, we can always fall back on similar datasets obtained from Remote Sensing. However, if the data is available to the National End-user of the AMESD service, it can improve the quality of the datasets obtained by Remote Sensing. (VALIDATION)

Meteorological derived products – ground measurements AP04: Current Conditions Rainfall Map
This product is a ten day rainfall total interpolated on a 1x1km grid from the ground point measurements (NOAA). This product is available through out the year Rainfall is crucial for crop growth and is therefore an important factor for the monitoring of agricultural. Rainfall is normally the main limiting factor for crop development in arid and semi arid regions and is the first indicator to look at.

Satellite derived products – Remote sensing AP04: Current Conditions Rainfall Map
This product can be used for assessment of start of growing season. Dekadal rainfall observations are also of great importance in determining the start of growing season (i.e. total of 25mm for a particular dekad followed by 20mm of rainfall for consecutive two dekads can be used to determine the start of the rainfall season.

Meteorological derived products – ground measurements AP05: Current Conditions Cumulate Rainfall Map
High cumulate rainfall This product can be used for the assessment of start of growing season. Lack of data? Data is in the estation, explore it before you make a statement! Low cumulate rainfall

Meteorological derived products – ground measurements AP07 – Graph of Rainfall Events in the Current Season Graph of current rainfall showing the amount received for each dekad over a specific area or place. The graphs are extracted over crop area and average Rainfall values for each administrative areas are generated for plotting the graph

Meteorological derived products – ground measurements AP08 – Map of Current Rainfall Compared with the LTA, Max, Min (mm) Current Rainfall – LTA Rainfall Current Rainfall – LTMax Rainfal Current Rainfall – LTmin Rainfal This product provides an indication of rainfall performance as compared to the Long Term Average, Maximum and Minimum values performing better

Meteorological derived products – ground measurements AP09 - Map of Current Rainfall Compared with the LTA, Max, Min (%) Current Rainfall – LTA Rainfall)x100 / LTA Rainfall Current Rainfall – LTM Rainfall)x100 / LTM Rainfall Current Rainfall – Rainfall)x100 / LTm Rainfall The fact that the difference is expressed as a percentage makes it easier for non specialist to interpret the Performing better

Meteorological derived products – ground measurements AP10 - Map of Current Cumulate Rainfall Compared with the LTA, Max, Min (mm) Current Cumulate Rainfall – LTA Cumulate Rainfall Current Cumulate Rainfall – LTMax Cumulate Rainfall Current Cumulate Rainfall – LTmin Cumulate Rainfall The rainfall is cumulated from September to April, this is the agricultural season for the SADC region Gsod stations? ? NOT A GOOD PICTURE FOR SADC (again check quality of gsod data, that is availability of reporting stations in your area)

Most of SADC received below average rainfall
Meteorological derived products – ground measurements AP11 - Map of Current Cumulate Rainfall Compared with the LTA, Max, Min (%) Current Rainfall – LTA Rainfall)x100 / LTA Rainfall Current Rainfall – LTM Rainfall)x100 / LTM Rainfall Current Rainfall – LTm Rainfall)x100 / LTm Rainfall Most of SADC received below average rainfall

Meteorological derived products – ground measurements AP12 - Graph of Cumulate Rainfall of current season compared to the LTA, LTMax, LTMin Graph of Cumulate Rainfall during the current season compared to the Long Term Average, Long Term Maximum and Long Term Minimum. The graphs show the trends of the cumulate rainfall of the current rainfall season as compared with the Long Term Average, Maximum and Minimum.

Meteorological derived products – ground measurements AP12 - Graph of Cumulate Rainfall of current season compared to the LTA, LTMax, LTMin This graph is useful in the identification of rainfall anomalies and early detection of bad growing conditions. The graph shows good rainfall performance (November to Mid February 2010) for the season when compared to the LTA.

Temperature AP06 – Current Conditions Air Temperature Map AP13 – Map of Current Air Temperature Compared with the LTA, LTMax, LTMin

Meteorological derived products – ground measurements AP06 – Current Conditions Air Temperature Map
This product is generated by interpolation of daily air temperature measurements. At the end of the ten days the daily air temperature are averaged to come up with a 10 day average temperature map Very hot spot

Meteorological derived products – ground measurements AP06 – Current Conditions Air Temperature Map
Cardinal Temperature (oC) Plant Minimum Optimum Maximum Maize 8-10 32-35 40-44 Sorghum 40 Very high temperatures immediately after ploughing may cause crop germination failure Plant wilt during the development stage due to high evaporation and transpiration Very low temperatures at harvesting period may cause frost bite to crops thus causing poor yield. Cardinal temperatures for the germination of some important crops for the SADC Region; Source: Adapted from Bierhuizen

Very low temperatures on the other hand causes frost bites to crops.
Meteorological derived products – ground measurements AP13 – Map of Current Air Temperature Compared with the LTA, LTMax, LTMin Current Air Temperature – LTA Air Temperature Current Air Temperature – LTMax Air Temperature Current Rainfall – LTMin Air Temperature The products help in identifying areas with very low and high temperature Very high temperatures cause high evapo-transpiration leading to wilting of crops. Very low temperatures on the other hand causes frost bites to crops.

Satellite derived products – Remote sensing
Rainfall (AP14,AP15,AP16, AP17,AP18) Vegetation (AP19,AP20,AP21,AP22) Soil moisture (AP24,AP25,AP26,AP27) DMP (AP28,AP29,AP30,AP31)

Satellite derived products – Remote sensing Rainfall products
AP14 – Current Rainfall Estimate Map AP15 - Rainfall Estimates compared with average [difference] AP16 - Rainfall Estimates Compared with average [%] AP17 - Cumulate Rainfall Map AP18 - Cumulate Rainfall Map compared with Average (% Anomaly)

Satellite derived products – Remote sensing AP14 Current Rainfall Estimate Map
In order to compensate for sparse and late reporting rain gauge stations, the agricultural service often relies upon indirect estimates of precipitation. The RFE is a rainfall estimate of NOAA's Climate Prediction Centre currently used by FEWS-NET and several United Nations agencies such as the Food and Agriculture Organization (FAO) and World Food Programme (WFP) for agricultural monitoring in a large number of African countries. It uses satellite imagery from the geostationary Meteosat Second Generation (MSG) and estimates convective rainfall as a function of top of cloud temperatures (the so called cold cloud duration model or (CCD) and using GTS stations for calibration (there are new products coming).

Satellite derived products – Remote sensing AP14 Current Rainfall Estimate Map
The only processing done on this data at DMS is to resample this data to a 1km resolution, Geographic projection Lat Long WGS 1984. Higher rainfall (compare with gsod rainfall)

Satellite derived products – Remote sensing AP15 Rainfall Estimates compared with average [difference] Map of Rainfall Estimates compared with the Long Term Average values is used for the assessment and detection of possible rainfall anomalies (mm). This product is computed by subtracting the Current Rainfall from the LTA Rainfall

Satellite derived products – Remote sensing AP16 Rainfall Estimates compared with average [%]
Map of Rainfall Estimates compared with the Long Term Average values is used for the assessment and detection of possible anomalies (%). The product is calculated as follows (Current Rainfall – LTA Rainfall)x100 / LTA Rainfall During the computation, all pixels with zero in the LTA denominator are replaced with 0.1 (to avoid division by zero)

Satellite derived products – Remote sensing AP17 Cumulate Rainfall Map
This map shows the progressive sum of decadal rainfall estimates since the start of season and updated every 10 days. The rainfall is cumulated from the start of season up to the end of season. The “Start of Season” is a Static Parameter, defined as the first of September for the summer crops in the rainy season, and the first of April for the winter crops in the dry season This product is of great importance for vegetation condition and for monitoring the development of the rainfall season.

Satellite derived products – Remote sensing AP17 Cumulate Rainfall Map
Cumulate Rainfall less than 500mm Compare with gsod product

The maps are produced for the period September to April.
Satellite derived products – Remote sensing AP18 Cumulate Rainfall Map compared with average [% Anomaly] Maps representing the Cumulate Rainfall compared with long term average for the same period, from the start of the season to the current period, expressed as a percentage. The maps are produced for the period September to April. Positive percentage values on the map can be interpreted as an earlier rainy season or highlight a year with higher precipitation than the long term average. This product is important for vegetation condition and water resource monitoring.

Satellite derived products – Remote sensing AP18 Cumulate Rainfall Map compared with average [% Anomaly] Worse than LTA Better than LTA

Satellite derived products – Vegetation
AP19 – Vegetation Index Map AP20 - Vegetation Index Compared with average [difference] AP21 - Vegetation Index Compared with average [%] AP22 - Crop / Vegetation performance Graphs (CNDVI) Drought service

Satellite derived products AP19 Vegetation Index Map
The AMESD SADC THEMA does not redistribute this product as it is already disseminated by VITO The vegetation index map (NDVI) is based on the characteristic reflection of plant leaves in the visible and near-infrared portions of light. Healthy vegetation has low reflection of visible light (from 0.4 to 0.7 μm), since it is strongly absorbed by leaf pigments (chlorophyll) for photosynthesis. At the same time, there is high reflection of near-infrared light (from 0.7 to 1.1 μm). + -

The portion of reflected near-infrared light depends on the cell structure of the leaf. The 10-daily composite NDVI images are used, where the daily images are combined in so called MVC (Maximum Value Composite) products to eliminate at least partially the effects of cloud cover and perturbing atmospheric artifacts. NDVI values are always in between –1 and +1, where higher values represent more vigorous and healthy vegetation. Very low values (0.1 and lower) correspond to barren areas of rock, sand and snow. Moderate values (0.2 to 0.3) indicate shrub and grassland Temperate and tropical rainforests are represented by high NDVI values (0.6 to 0.8) Values below zero indicate water bodies

NDVI can be used to measure and monitor plant growth, vegetation cover, and biomass production High vegetation Be careful when you interpret the product, the average for the shape/district is affected by Salt pans ie. Perform your analysis at sub-district level/ game park, ranches etc…future products for rangelands monitoring Low vegetation

Satellite derived products AP20 Vegetation Index Compared with average [difference]
The Vegetation Index Compared with average map shows the deviation of a NDVI value to its long term average for the same decade. The average value is subtracted from the observation, and the difference is computed (Difference= (Current NDVI – Average NDVI) A positive difference/ anomaly means a higher NDVI, which can be interpreted as a more advanced stage of the growing season, as greener vegetation or more biomass.

Satellite derived products AP20 Vegetation Index Compared with average [difference]
Large decrease Large decrease

% Anomaly Map = (Current NDVI – Average NDVI) x 100 / (Average NDVI)
Satellite derived products AP21 Vegetation Index Compared with average [%] The Vegetation Index Compared with average (%) map shows the deviation of a NDVI value as a percentage to its average for the same decade. % Anomaly Map = (Current NDVI – Average NDVI) x 100 / (Average NDVI) A positive percentage difference/ anomaly means a higher NDVI, which can be interpreted as a more advanced stage of the growing season, or as greener vegetation or more biomass The expression of the product as a percentage makes it much easier for non-specialists people to interpret the product.

Satellite derived products AP21 Vegetation Index Compared with average [%]

Satellite derived products AP22 Crop/Vegetation performance Graphs (CNDVI)
CORINE NDVI (C-NDVI) is a land cover weighted NDVI. The CNDVI method was developed to extract NDVI profiles from satellite imagery. NDVI is extracted and averaged on crop areas for each zone (eg administrative unit) to extract crop specific NDVI profiles to provide an indicator of crop status and yield. This technique aggregates NDVI information by administrative regions and also focuses on agricultural (production) areas only. The calculated CNDVI is a more appropriate indicator of crop yield (agro-statistical) There are 3 steps that are taken to compute this product:

There are 3 steps that are taken to compute this product:
Satellite derived products AP22 Crop/Vegetation performance Graphs (CNDVI) There are 3 steps that are taken to compute this product: Extraction of NDVI average data for each zone in the supplied crop or zones of interest map from the current season raster data and the long term averages for the valid pixels [marked as crop] in the crop mask. Sending the 2 extracted datasets to current season and LTA tables Plotting the data from the two tables in a graph

While difference/ anomaly images show the spatial distribution of plant growth anomalies, vegetation performance time series charts help to understand the development of vegetation on a long-term basis. The time series of NDVI data allows analysis of changes in vegetation vigor and density in response to bio-physical conditions (including plant type, weather and soil). The primary use of these graphs is to compare the current state of vegetation with previous time periods, for example the same time in an average year or a reference year (a particularly good or bad year) to detect anomalous conditions.

Satellite derived products – Remote sensing Soil Moisture
Satellite derived products - Soil moisture AP23 - Crop Water Requirements Satisfaction (WRSI) Index AP24 - WRSI Anomaly maps AP25 – Onset of Rains maps AP26 - Onset of Rains Anomaly maps AP27 – Soil Moisture Index maps

Satellite derived products – Remote sensing AP23 - Crop Water Requirements Satisfaction (WRSI) Index
Crop water requirement satisfaction index is the ratio of the actual evapotranspiration (AET) to the maximum evapotranspiration (MET) for a given dekad. WRSI is an indicator of crop performance based on the availability of water to the crop during a growing season. This map portrays WRSI values for a particular crop from the start of the growing season until this time period. It is based on the actual estimates of meteorological data to-date For example, if the cumulative crop water requirement up to this period was 200 mm and only 180 mm was supplied in the form of rainfall, the crop experienced a deficit of 20 mm during the period and thus the WRSI value will be ((180 / 200) * 100 = 90 %).

WRSI = f (prec, PET, WHC, Crop Type, SOS, EOS, LGP) data from NOAA, generated at EDC RFE (NOAA) FAO soils map of the world K pasture? Kc (FAO)

Satellite derived products – Remote sensing AP24 – WRSI Anomaly maps
WRSI anomaly map shows the relative magnitude of the WRSI as a percentage of the median WRSI. WRSI Anomaly (%) = (Current WRSI / Median WRSI) * 100 Although site/region specific validation is important, the user may use these anomaly maps as a semi-quantitative indicator for the performance of the crops in relation to the average condition. A value close to 100 signifies that this year's crop performance is about same compared to the median or last year, while percentage above and below 100 may indicate above and below the median or last year's production forecasts, respectively.

Satellite derived products – Soil Moisture AP25 – Onset of Rains maps
Map showing the dekad in which the “effective rains for planting” occurred. NOAA Climate Prediction Center satellite rainfall estimates (RFE) are used to identify the dekad of onset of rains for the agricultural growing season. On a per pixel basis, rainfall criteria developed at the Agriculture-Hydrology-Meteorology (AGRHYMET) Regional Center in Niamey, Niger (AGRHYMET, 1996), are applied to the RFE values. 1st dekad in which at least 25 mm of rain fall, followed by two dekads which total at least 20 mm of rain

The onset of rains map is critically important for farmers in the SADC region where the growing seasons that typically do not exceed 90 days in length. Rains started in September 2011 By 02 October 2011, most parts of SADC show that the rains have not yet started

Rains started in September 2011 By 02 October 2011, most parts of SADC show that the rains have not yet started

Satellite derived products – Soil Moisture AP26 Onset of Rains Anomaly maps
A map providing information on how early or late the first effective rains are compared to average. Effective rainfall is the rains that can provide enough soil moisture for start of planting season. The blue colours show areas where the rainy season has started early compared to what normally is expected The pink – dark red show areas that have experienced a delay in the onset of rains. The overlaying of this product with the crop mask enables the identification of farming area that would have experienced a delay of rains. Based on this information farmers can be advised to grow crop varieties which grows fast.

Satellite derived products – Soil Moisture AP26 – Onset of Rains Anomaly maps
The blue colours show areas where the rainy season has started early compared to what normally is expected The pink – dark red show areas that have experienced a delay in the onset of rains. The overlaying of this product with the crop mask enables the identification of farming area that would have experienced a delay of rains. Based on this information farmers can be advised to grow crop varieties which grows fast. Need for products validation (in-situ)

Satellite derived products – Soil Moisture AP27 Soil Moisture Index maps
Soil Moisture Index (SMI) is ability of soil to supply moisture to plant. The soil water content is obtained through a simple mass balance equation where the level of soil water is monitored in a bucket defined by the water holding capacity (WHC) of the soil and the crop root depth, i.e., SWi = SWi-1 + PPTi - AETi where SW is soil water content PPT is precipitation AET is actual evapotranspiration i is the time step index.

Satellite derived products – Soil Moisture AP27 Soil Moisture Index maps
The product can assist to determine areas where soil moisture is low enough to be considered critical and can provide first warning for areas where wilting may be imminent. The product can also assist in crop condition and crop stage monitoring

Satellite derived products – Remote Sensing
Satellite derived products - DMP AP28 - Current dry matter productivity map AP29 - Cumulate dry matter productivity map AP30 - Cumulate dry matter productivity graphs AP31 - Cumulate dry matter productivity comparison with average maps [%]

Satellite derived products – DMP AP28 - Current dry matter productivity map
DMP can be calculated by combining fAPAR, estimated from satellite imagery, with solar radiation and temperature information, as described by Monteith (1972). DMP1 = R1 • 0.48 • fAPAR1 • ε(T1) • 10000 R1 [J/m²/day] is the incoming short wave radiation of the sun ( nm), which is composed on the average for 48% of PAR (Photosyntheticly Active Radiation: nm), fAPAR1[-] is the PAR-fraction absorbed by the green vegetation The efficiency term ε(T1) [kgDM/JPAR] accounts for the conversion of this absorbed energy into biomass (radiation use efficiency) and for the losses related to the transport of photosynthesis, the maintenance of the standing phytomass, etc. ε(T1) is simplified and approximated as a function of the daily temperature T1 (Veroustraete et al.,2002). The function ε(T1) is non-linear and bell-shaped: it reaches a maximum at a temperature of 22°C and approaches zero for temperatures below 0°C and above 40°C. The factor [m²/ha] obviously transforms the square meters into hectares, a more common unit in agro-statistics

Satellite derived products – DMP
AP29 - Cumulate dry matter productivity map Cumulate dry matter productivity represents the sum of the dekadal dry matter productivity maps from the start of the season to the current period (September-April). Cumulating DMP over time, from the start of the season onwards, provides estimates of final dry matter production over time. This product can be used for crop growth monitoring because it provides an indicator of the performance of the crops and vegetation in general. This product can also be used for yield estimates.

Satellite derived products – DMP AP30 - Current dry matter productivity graph
This graphs represent the trend of the dekadal dry matter productivity from the start of the season to the current period. The cumulate DMP graphs help to understand the general development of biomas on a long term basis. They also offer the best way of detecting areas showing possible signs of season’s failure. The graphs can be used for early warning applications in areas where timely ground collected data are seldom available and remotely sensed images offer a quick and cheap way to monitor the growing season (Griguolo,1994) The fact that these graphs are generated based on administration boundaries makes the cumulative dry matter productivity image data become more compatible with other agro-statistical information (for example official areas/ yields) in a spatial and thematic sense (Griguolo,1994).

Satellite derived products – DMP AP31 - Current dry matter productivity comparison with average maps [%] The maps represent the cumulate dekadal dry matter productivity compared with long term average for the same period, from the start of the season to the current period, expressed as a percentage. The start of the season is defined as starting from September and ending in April.

Satellite derived products – DMP AP31 - Current dry matter productivity comparison with average maps [%] The product is also used to shows the spatial distribution of cumulated biomas anomalies. A positive anomaly means a higher biomas productivity which can be interpreted as a more advanced stage of the growing season, or as a better growing season. Good pasture

Time Series Analysis Gsod and FEWSNet RFEs NDVI DMP

Time Series Analysis options

Time Series Analysis

Thank You.

Botswana College of Agriculture Farai M. MARUMBWA BDMS

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Botswana College of Agriculture Farai M. MARUMBWA BDMS

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