CROP MONITORINGby

MUGIYO HILLARYAgriculture Service Capacity Building Partner - BCA

PRESENTATION FORMAT

1.Introduction2.Overview of Agricultural Applications3.Spectral Response of Vegetation4.Vegetation Indices5.Rainfall and Water Indices

INTRODUCTION

The nature of a yield predictor can be either:

e.g. maximum or minimum temperature, precipitation, global radiation

e.g. relative soil moisture, actual evapotranspiration (ET), phenological indicators

This lesson focuses on predictors that are derived from satellite images which are usually the most easily available at regional and national levels.

More information on predictors

e.g. vegetation status indices, biomass estimates, leaf area index

All three groups can include information from different sources like: meteo stations, agrometeo stations, crop models and remote sensing.

CROP PHENOLOGY

• Rainfall affects the water availability for crops;

• for temperate climates, low winter temperatures don’t allow crops to grow until spring time; and

• growth of annual crops often occurs between episodes of drought, heat, or cold.

Crop phenology

The term “phenology” relates to the timing of recurrent biological events. Crop phenology is therefore the timing of main crop stages during the season. Climatic variability strongly influences this timing.

For optimum yields, farmers select crop varieties and planting dates to optimally use the periods with good growing conditions i.e. favourable weather for crop growth.

For example…

Let’s start by defining what crop phenology is…

CROP STAGES AND DROUGHT SENSITIVITY

What are main crop stages? The main stages differs per crop.

Example: Maize crop stages

Some stages are more drought sensitive than others. Maize is relatively tolerant to water deficits during the vegetative and ripening periods.

Greatest yield decreases are caused by water deficits during the flowering period, mainly due to a reduction in grain number per maize cob and drying of the silk. Water stress during the yield formation period causes grain size to reduce which consequently lowers grain yield. Water deficit during the ripening period has little effect on yield.

Click on the image to enlarge it.

Crop condition during a crop stage depends on the previous stage(s). Severe stress during any stage may adversely affect crop yields.

CROP CALENDARS

Crop calendars provide average dates of planting and crop stages for the important crops in a particular region.

Sorghum planting dates

Source: compiled from a variety of crop calendar by Sacks et al. 2010

Crop calendars can help in the planning of farm activities, such as land preparation, planting, application of fertilizers, and harvesting.

Generally crop calendars are constructed based on current farmer practices in a region. Such calendars are useful, because average planting dates will differ depending on geographical location.

Click on the image to enlarge it.

What are crop calendars?

CROP CALENDARS

Global overview of growing season lengths for sorghumBesides planting date, also other temporal characteristics will vary depending on geographical location, such as:

•the length of the growing season; and

•the precise timing of crop stages.

This depends on region-specific climate, land management practices, and cultivated crop varieties..

Click on the image to enlarge it.

Source: compiled from a variety of crop calendars by Sacks et al. 2010

RAINFALL AND NDVI IN RELATION TO CROP STAGES

Rainfall and NDVI data can provide useful information for crop monitoring. As stated before, negative events (like drought) can have different impacts according to the crop stage.

Let’s explore these activities in more detail….

1. To focus the analysis of rainfall and NDVI anomalies to the most critical moments for crop growth.

2. To estimate the timing of crop stages.

With regard to the timing of crop stages these data could be useful in two ways:

Example: Maize during long rains 2009 in Eastern Province (Kenya)

Click the forward arrow to see how the example unfolds or click on the PDF icon to read it and print it

This figure shows the CNDVI seasonal graphs together with the maize crop calendar of the long rains (start/end season).

Source: JRC bulletin 07-2009

It is clear that already at the start-of-season the CNDVI for 2009 was much below normal. Significant delays in start-of-season often lead to below-average yields.

Based on this graph you can examine anomaly maps of rainfall estimates to understand causes of the poor season …

RAINFALL AND NDVI IN RELATION TO CROP STAGES

RAINFALL AND NDVI IN RELATION TO CROP STAGES

Spatial variability of the timing may thus be better captured as compared to general crop calendars.

Rainfall and NDVI information can be used to estimate the timing of crop stages.

The remainder of this lesson will focus on how to estimate timing of crop stages and additional phenological parameters from rainfall and NDVI data.

Average start of season (1982-2010)

Temporal variability may be assessed, for example through identifying delays in the start of the growing season. Such delays may have repercussions on final yields.

Season delay from NDVI time seriesClick on the images to

enlarge them.

ESTIMATING START-OF-SEASON FROM RAINFALL DATA

Rainfall data can be used to decide on the timing of key agricultural practices, such as planting and irrigation.

For crop establishment sufficient soil moisture is needed when planting. Shortage of water during this phase could result in early crop failure.

Local farmers, extension services, and meteorological agencies make rules to determine optimal planting times. These are crop and location dependent, based on experience, and generally involve information on rainfall.

False start

ESTIMATING START-OF-SEASON FROM RAINFALL DATA

You can also present start-of-season maps as (absolute) anomaly maps. In that case you compare the current start-of-season with the averages of previous years.

Rainfall and NDVI anomaly maps are described in Lesson 4.

Start-of-season 2007 anomaly map in Zimbabwe

Click on the map to enlarge it.

This start-of-season anomaly map for the 2006/2007 season in Zimbabwe shows that large part of the country experienced an early start-of-season.

Source: FEWS-NET

ESTIMATING START-OF-SEASON FROM RAINFALL DATA

Source: SADC

This example shows the start-of-season and corresponding anomalies for the 2010/2011 season in Southern Africa. These maps are derived from RFE (rainfall estimate) data, following the criterion: “25mm in one dekad + 20 mm in the next two dekads”.

Start of season and anomalies for 2010/2011 season in Southern Africa

NDVI AND PHENOLOGY: KEY TEMPORAL PARAMETERS

This graph illustrates how the NDVI profile relates to crop stages for a specific pixel with dominant maize cover.

NDVI profile compared to crop stages derived from Growing Degree Days

Note: crop stages were determined here following a temperature accounting method of growing degree days.

E= emergence CD= crop development F= flowering Y= yield formation R= ripening

You can see that at the moment of maximum NDVI all leaves have formed and the silk (flowering) stage starts.

NDVI AND PHENOLOGY: KEY TEMPORAL PARAMETERS

NDVI temporal graphs and CNDVI concept are described in greater detail in Lesson 5.

An NDVI pixel mostly contains multiple land covers.

Land cover map

You should keep in mind that agricultural land is often not the only cover within a pixel. Therefore the greening up in NDVI graphs is affected by other land covers contained in the pixel.

You can derive information on the timing of crop stages (or crop phenology) from NDVI series for a single pixel, or from aggregated NDVI series (such as CNDVI).

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NDVI temporal smoothing

Dekadal NDVI data can still suffer from contamination by clouds and atmospheric variability. Several approaches exist to remove the NDVI reductions that these effects cause and further smoothen the temporal NDVI profiles.

Original and smoothed NDVI time series

An original and filtered NDVI time series for a random AVHRR pixel.

original smoothed

METHODS FOR PHENOLOGY EXTRACTION FROM NDVI

This graph illustrates the simple often-used threshold method.

The local threshold is defined as the 20% between minimum and maximum NDVI: start- and end-of-season are the locations where the threshold crosses the NDVI profile.

Simple threshold approach to obtain phenological metrics

Click on the graph to enlarge it.

Different thresholds are used in literature, these can depend also on location. (more…)

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Standardized anomalies (or z-score)

In this map, the z-score indicates the number of standard deviations that the current cumulated FAPAR is above (positive) or below (negative) the 1998-2009 average.

The difference between current value and the average of previous years, divided by the standard deviation calculated from all previous year values. For example, for rainfall negative z-scores indicate drier than normal conditions.

current value average Stddev

NDVI AND PHENOLOGY: OTHER KEY PARAMETERS

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

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).

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Vegetation Indices

Dry Matter Productivity (DMP)

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) · 10000Where

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.

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) · 10000Where

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

Vegetation Indices

Dry Matter Productivity (DMP): The physical DMP values are between 0 and 327.67 kg dry matter

per hectare per day, where higher values indicate more production of dry matter biomass.

The product is distributed as digital numbers (DN). To decode the values to the real numbers(physical DMP values) the following formular is used:DMP (real values) = (DN * Sc) + OffSc: 0.01Off: 0

Vegetation Indices

Dry Matter Productivity (DMP): UsageDMP is used to assess crop development (growth rate) and crop

yield (e.g. for early-warning systems), using mostly the differencing(consecutive images in one season) and cumulative techniques.

For assessment over the growing season, an analysis needs to be made of a multi-temporal set of those images (Assessing the progress of the growing season and changing growth rates).

e.g. by looking at the differences of consecutive images in the same season, or by comparing to previous years or seasons or long term averages (allows the detection of anomalies in vegetation growth).

Vegetation Indices

• Calculation of the so-called RegionalUn-mixed Mean Calculation of the so-called RegionalUn-mixed Mean (RUM) statistics: computation of DMP values per (RUM) statistics: computation of DMP values per administrative unit and/or per land-cover or crop type administrative unit and/or per land-cover or crop type further enhances the product for the purpose of dry matter further enhances the product for the purpose of dry matter productivity monitoring in specific areas and/or for specific productivity monitoring in specific areas and/or for specific vegetation classes or crop types. This allows to compare vegetation classes or crop types. This allows to compare the generated statistics with statistics commonly available the generated statistics with statistics commonly available from the respective (agri-cultural) authority.from the respective (agri-cultural) authority.

• DMP images can also be cumulated over time from the DMP images can also be cumulated over time from the start of the season in order to estimate the final dry matter start of the season in order to estimate the final dry matter which had been produced by the vegetation over time, which had been produced by the vegetation over time, which has a direct relationship to yield. which has a direct relationship to yield.

Vegetation Indices

DMP: Visualization and Application

4) Vegetation Indices

DMP: Visualization and Application

Rainfall and Water Indices

RAINFALL and SOIL MOISTURE PRODUCTS

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.

Rainfall and Water Indices

This product is useful in identifying areas with crop failure.

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

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.

IF YOU WANT TO KNOW MORE

Online resourcesTIMESAT, Free software to extract phenological parameters from NDVI timeseries. http://www.nateko.lu.se/timesat/timesat.asp

Phenology website of USGS including a section on methods http://phenology.cr.usgs.gov

Additional readingBrown, M. E., and K. M. de Beurs. 2008. Evaluation of multi-sensor semi-arid crop season parameters based on NDVI and rainfall. Remote Sensing of Environment 112(5): 2261-2271. de Beurs, K. M., and G. M. Henebry. 2010. Spatio-temporal statistical methods for modeling land surface phenology, in I. L. Hudson and M. R. Keatley (eds.), Phenological research: methods for environmental and climate change analysis. Dordrecht, The Netherlands: Springer, pp. 177-208. Hodges, T., 1990. Predicting crop phenology. CRC Press, Boca Raton, Florida. ISBN 9780849367458 Raes, D., A. Sithole, A. Makarau, and J. Milford, 2004. Evaluation of first planting dates recommended by criteria currently used in Zimbabwe. Agricultural and Forest Meteorology 125: 177-185. Sacks, W. J., D. Deryng, J. A. Foley, and N. Ramankutty. 2010. Crop planting dates: an analysis of global patterns. Global Ecology and Biogeography 19(5): 607-620. Sivakumar, M. V. K. 1988. Predicting rainy season potential from the onset of rains in Southern Sahelian and Sudanian climatic zones of West-Africa. Agricultural and Forest Meteorology 42(4): 295-305. White, M. A., K. M. de Beurs, K. Didan, D. W. Inouye, A. D. Richardson, O. P. Jensen, J. O'Keefe, G. Zhang, R. R. Nemani, W. J. D. van Leeuwen, J. F. Brown, A. de Wit, M. Schaepman, X. M. Lin, M. Dettinger, A. S. Bailey, J. Kimball, M. D. Schwartz, D. D. Baldocchi, J. T. Lee, and W. K. Lauenroth. 2009. Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982-2006. Global Change Biology 15(10): 2335-2359.

THANK YOU

This presentation has been prepared with the financial assistance of the European Union. The contents are the sole responsibility of MESA SADC THEMA and can under no circumstance be regarded as reflecting the position of the European Union