CROP MONITORING HILLARY

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  1. 1. CROP MONITORING by MUGIYO HILLARY Agriculture Service Capacity Building Partner - BCA
  2. 2. PRESENTATION FORMAT 1.Introduction 2.Overview of Agricultural Applications 3.Spectral Response of Vegetation 4.Vegetation Indices 5.Rainfall and Water Indices
  3. 3. 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.
  4. 4. CROP PHENOLOGY Rainfall affects the water availability for crops; for temperate climates, low winter temperatures dont 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 Lets start by defining what crop phenology is
  5. 5. 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.
  6. 6. 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?
  7. 7. CROP CALENDARS Global overview of growing season lengths for sorghum Besides 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
  8. 8. 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. Lets 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:
  9. 9. 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
  10. 10. 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 series Click on the images to enlarge them.
  11. 11. 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
  12. 12. 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
  13. 13. 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
  14. 14. 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.
  15. 15. 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).
  16. 16. Popup WindowPopup Window 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
  17. 17. 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)
  18. 18. Popup Window Popup Window 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
  19. 19. 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
  20. 20. 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 w