M. TAPELA and R. TSHEKO
Botswana College of Agriculture
Agriculture Service
Crop Condition Monitoring: Refresher
Agenda SADC THEMA Services
PRESENTATION FORMAT
1.
2.
3.
4.
5.
Introduction
Overview of Agricultural Applications
Spectral Response of Vegetation
Vegetation Indices
Rainfall and Water Indices
1. Introduction: What can you see ?
Field A
Field B
1. Introduction: What can you see ?
© CCRS
Crop Condition Monitoring A
Crop Condition Monitoring B
Crop Monitoring help farmers to understand the health of the crop, extent of
infestation or stress damage, or potential yield and soil conditions.

1. Introduction: What can you see ?



Commodity brokers are also very interested in how
well farms are producing, as yield (both quantity
and quality) estimates for all commodity price and
worldwide trading.
Governments are interested in order to prepare for
possible food shortages.
Regional economic bodies such as SADC,
ECOWAS and Development Partners need crop
monitoring for Famine Early Warning Systems
(FEWS)
1. Introduction: What can you see ?

FEWSNET Estimated Food Security Outlook – 4Q 2011
2) Overview of Agricultural Monitoring
Remote Sensing Applications and Products:
 About
70% of the Earth’s land surface is covered with
vegetation.
 Remote sensing is used for agriculture, forests, rangelands,
wetlands and urban vegetation assessment.
 There are generally three (3) areas in which remote sensing
products are used in agriculture, namely:
o crop type classification,
o crop condition monitoring or assessment,
o crop yield estimation (prediction)
2) Overview of Agricultural Monitoring
Crop type classification and crop area estimation:
Based on the premise that a crop can be identified
based on its spectral signature.
 Requires knowledge of the developmental stages of
each crop in the area to be investigated.
 A crop calendar is used to list expected.
developmental status and appearance of each crop
in an area throughout the growing season.
 Crop area (crop masks) is estimated/measured from
land use maps derived from high spatial resolution
imagery.

© CCRS
2) Overview of Agricultural Monitoring
Crop condition assessment :
 Information on crop condition can be obtained
from satellite imagery to assess;
o Crop disease
o Insect/Pest damage
o Crop stress
o Disaster damage (flood, drought, hail storms,
fire)

Depending on the observed problems, remedial
measures could be put in place such as replanting,
herbicides/fertilizer treatment, and drainage.
© CCRS
2) Overview of Agricultural Monitoring
Crop yield estimation:
Achieved by determining the area of each crop type and
estimating the yield per unit area of each crop
 Crop yield depends on several factors such as soil
moisture, soil fertility, air and soil temperature
 Yield can also be affected by disease, insects’ infestation,
physical damage
 Field inspection of sample sites are used to;
o validate the satellite observations
o develop correlations between spectral signatures and
crop yield

2) Overview of Agricultural Monitoring
Crop yield prediction :
Historical data on crop yield from individual fields as well as
satellite information can be used for crop yield prediction
 Meteorological satellites provide both climatic and
meteorological data that is required for yield prediction
 Satellite information can be used to determine how in a
certain year yield deviates from the normal trend
 Monitoring biomass over time can provide important
information about the likelihood of good or poor harvest as
well as the stability of the natural ecosystem

2) Overview of Agricultural Monitoring
Agriculture Products – SADC THEMA
Agricultural Service Products
Agricultural Baseline Products
1
AP01: Agriculture Mask
2
AP02: Crop Statistics Map
3
AP03: Crop Specific Maps
Agricultural Products from Meteorological Ground Measurements
4
AP04: Current Conditions Rainfall Map
5
AP05: Current Condition Cumulate Rainfall Map
6
AP06: Current Conditions - Air Temperature Map
7
AP07: Graph of Rainfall Events in the Current Season
8
AP08: Map of current Rainfall compared with the LTA, Max and Min values (mm)
9
AP09: Map of current Rainfall compared with the LTA, Max and Min values (%)
10
11
12
AP10: Map of current Cumulate Rainfall compared with the LTA, Max and Min values (mm)
AP11: Map of current Cumulate Rainfall compared with the LTA, Max and Min values (%)
AP12: Graph of Cumulate Rainfall of the current season compared to the LTA, LTMax, LTMin
13
AP13: Map of current Air Temperature compared with the LTA, Max and Min values (oC)
Agricultural Products from Remote Sensing
14
AP14: Current Rainfall Estimates Map
15
AP15: Rainfall Estimates Compared with average [difference]
16
AP16: Rainfall Estimates Compared with average [%]
17
AP17: Cumulate Rainfall Map
18
AP18: Cumulate Rainfall Map compared with Average (% Anomaly)
2) Overview of Agricultural Monitoring
Agriculture Products – SADC THEMA
19
AP19: Vegetation Index Map
20
AP20: Vegetation Index Compared with average [difference]
21
AP21: Vegetation Index Compared with average [%]
22
AP22: Crop / Vegetation performance Graphs
23
AP23: Current Water Requirements Satisfaction Index (WRSI) Map
24
AP24: WRSI Anomaly maps
25
AP25: Onset of Rains Maps
26
AP26: Onset of rains anomaly maps
27
AP27: Soil Moisture Index Maps
28
AP28: Current dry matter productivity map
29
AP29: Cumulate dry matter productivity map
30
AP30: Cumulate dry matter productivity graphs
31
AP31: Cumulate dry matter productivity comparison with average maps (%)
3) Spectral Response of Vegetation
Definition of Remote Sensing :
 Remote sensing may be defined as the art, science, and
use of technology to observe an object, area or phenomena
and obtain information about it whilst not in contact with it.
 The term embraces a number of observation or sensor
techniques ranging from;
o photography,
o multispectral,
o thermal
o hyperspectral sensing
 Satellite-based observation methods have gained wide
application compared to aerial photography
3) Spectral Response of Vegetation
Spectral Response of vegetation:
 Green plants have unique spectral reflectance which is
influenced by the state, structure and composition of the
plant.
 Healthy plants will reflect more radiation in the near infrared
region of the spectrum.

3) Spectral Response of Vegetation
Radiation - Leave Interactions :
 Chlorophyll strongly absorbs radiation in the red and blue
wavelengths but reflects green wavelengths
 In autumn, there is less chlorophyll in the leaves, so there is
less absorption and proportionately more reflection of the red
wavelengths, making the leaves appear red or yellow (yellow is
combination of red and green wavelengths)
3) Spectral Response of Vegetation
Radiation - Leave Interactions :
3) Spectral Response of Vegetation
Radiation - Water Interactions :
 Water typically looks blue or blue-green due to stronger
reflectance at these shorter wavelengths, and darker if viewed
at red or near infrared wavelengths.
 Suspended sediment present in the upper layers of the water
body, then this will allow better reflectivity and a brighter
appearance of the water.
3) Spectral Response of Vegetation
Spectral Signatures:
 Reflectance of vegetation and water at different wavelengths
can be used to identify and separate different materials or
objects using multi-spectral data
3) Spectral Response of Vegetation
Spectral Signatures:
 A combination of wavelengths (more than one) may be used to
make the distinction in what we call multi-spectral remote
sensing
3) Spectral Response of Vegetation
Spectral Signatures:
 Spectral Reflectance
may also be used to
distinguish between
different vegetation
types (Crop Masks)
4) Vegetation Indices
VEGETATION PRODUCTS
4) Vegetation Indices
NDVI :
NDVI
is a dimensionless index that is indicative for
vegetation density and is calculated by comparing
the visible and near-infrared sunlight reflected by
the surface (reflectance).
NDVI = (NIR — VIS)/(NIR + VIS)
where NIR is the near-infrared reflectance and
VIS is the reflectance of visible light.
4) Vegetation Indices
NDVI :
 The product is distributed as digital numbers (DN). To decode
the values to the real numbers the following formular is used:
NDVI (real values) = (DN * Sc) + Off
Sc: 0.004
Off: -0.1
 Moderate
values (around 0.2 and 0.3) correspond to shrub
and grasslands.
4) Vegetation Indices
NDVI :
 NDVI values range between -1 and +1, with dense
vegetation and healthy having higher values (e.g., 0.4 - 0.7),
and lightly vegetated regions having lower values (e.g., 0.1 0.2).

Single season NDVI
NDVI Anomaly - Tanzania
4) Vegetation Indices
NDVI :
 Over the course of a growing season, we first see a steady
increase in the NDVI values as the young, green crops grow
(the growth makes the surface appear more and more
green, which is reflected by the NDVI).
 This increase reaches a maximum value just before it drops
suddenly at harvest time (or when the plants die naturally),
which can easily be explained by the harvesting of the
healthy, green plants, which makes the surface appear less
green.
4) Vegetation Indices
NDVI :
 Used for crop and agricultural monitoring and early warning
of failing growing seasons.
Typical Crop Phenological Stages
Vegetative
Reproductive
Ripening
45 days
35 days
30 days
4) Vegetation Indices
NDVI :
4) Vegetation Indices
NDVI :
NDVI time series for Pandamatenga Region in Botswana
1
0.9
0.8
min
max
mean
2STD
ndvi-2010
0.7
NDVI values
0.6
0.5
0.4
0.3
0.2
0.1
0
0
5
10
15
20
Dekad
25
30
35
40
4) Vegetation Indices
NDVI :
 % Anomaly Map = (Current NDVI – Average NDVI) x 100 /
(Average NDVI)
 Other
uses include:
 Monitoring of long-term vegetation changes and climate
change models, seasonality studies
 Assessment of desertification events
 Indicator and alert function for drought events
 Create land cover maps
4) Vegetation Indices
Corine NDVI (Crop Specific) CNDVI:
 CORINE NDVI (CNDVI) is a landcover weighted NDVI.
 The CNDVI method extracts NDVI profiles and averages
them on crop areas (such as maize) by region or
administrative zones to provide an indicator of crop status
and yield.
Extraction average of red pixels (cropped areas) within the zone of interest
(blue polygon) from the raster data for the current season and the long-term
averages for the same periods

4) Vegetation Indices
Corine NDVI (Crop Specific) CNDVI:
 NDVI images are converted to represent an agricultural
production region; this is achieved by computing regional
NDVI means and weighting the values according to each
pixels’s area occupied by the land cover type of interest (a
specific crop such as maize).
Nr ,i
CNDVI r ,c,t 
W
r ,c,t ,i .NDVI i
i
4) Vegetation Indices
Dry Matter Productivity (DMP):
 DMP, or Dry Matter Productivity, is an indication of the dry matter
biomass increase (growth rate) expressed in kg/dry
matter/ha/day.
 The increase in dry matter biomass on a daily basis (subscript 1)
can be formulated as:
 DMP1 = R1 · 0.48 · fAPAR1 · ε(T1) · 10000
 Where
 R1 (J/m²/day) is the incoming short wave radiation of the sun
(200-3000 nm), which is composed on the average for 48% of
PAR (Photosyntheticly Active Radiation: 400-700nm)
 fAPAR1[-] is the PAR-fraction absorbed by the green
vegetation.
4) Vegetation Indices
Dry Matter Productivity (DMP):
 DMP, or Dry Matter Productivity, is an indication of the dry matter
biomass increase (growth rate) expressed in kg/dry
matter/ha/day.
 The increase in dry matter biomass on a daily basis (subscript 1)
can be formulated as:
 DMP1 = R1 · 0.48 · fAPAR1 · ε(T1) · 10000
 Where
 R1[J/m²/day]
is the incoming short wave radiation of the sun (200-3000
nm), which is composed on the average for 48% of PAR (Photosyntheticly
Active Radiation: 400-700nm)
 fAPAR1[-] is the PAR-fraction absorbed by the green vegetation.
 ε(T1) [kgDM/JPAR] accounts for the conversion of this absorbed energy
into biomass
4) Vegetation Indices
Dry Matter Productivity (DMP):
 DMP, or Dry Matter Productivity, is an indication of the dry matter
biomass increase (growth rate) expressed in kg/DM/ha/day
5) Rainfall and Water Indices
RAINFALL and SOIL MOISTURE PRODUCTS
5) Rainfall and Water Indices
Water Requirement Satisfaction Index (WRSI):
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.
5) Rainfall and Water Indices


This product is useful in identifying areas with crop failure.
5) Rainfall and Water Indices
Water Requirement Satisfaction Index (WRSI):

FAO field studies showed that crops with WRSI values;

less than 50% had crop failure

80 indicates average crop performance

100 ("no deficit“) corresponds to the absence of yield
reduction related to water deficit
5) Rainfall and Water Indices
Soil Moisture Index (SWI):

Soil Moisture Index (SMI) is the ability of the soil to
supply moisture to the plant.


SWI = SW/WHC x 100%
SW = SWi-1 + PPTi - AETi

WHC = Water Holding Capacity

SW = Soil water content

PPT = Precipitation,

i = the time step index.
The END
Thank You.
REFERENCES
BROWN E. M.(2008) FAMINE EARLY WARNING SYSTEMS AND REMOTE
SENSING DATA. SPRINGER-VERLAG BERLIN HEIDELBERG
CANADA CENTRE FOR REMOTE SENSING. 2010. FUNDAMENTALS OF
REMOTE SENSING
LILLESAND, T.M. AND KIEFER, R.W. (1994) REMOTE SENSING AND IMAGE
INTERPRETATION. JOHN WILEY AND SONS INC., NEW YORK
REMOTE SENSING TUTORIAL. HTTP://RST.GSFC.NASA.GOV