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Challenge: Monitoring of agricultural fields

Martyna Gatkowska




Presentation Plan:

1. Challenge description2. Overview of satellite data applicable to challenge and operating services delivering

the data3. Use cases



Challenge description

• Agriculture is being monitored with the application of satellitedata since 1970s… so a lot has been done so far…

• But still there is a need for comprehensive, high temporal and spatial resolution, high quality, automatic system for cropsmonitoring

• Another challenge could be assessing the crops need for fertilizerswhich could be translated into precision farming machines

• Last but not least application of satellite data for agriculturalinsurance is also in demand



[email protected]

Crop recognition – Crops’s classification



www.asap.farmer.pl

Crop recognition – Crops’s classification



SATELLITE DATA APPLIED FOR MONITORING AGRICULTURAL FIELDS



SATELLITE DATA:

• Low resolution:• MODIS,

• NOAA AVHRR

• Medium resolution:• Landsat 8,

• Sentinel 2,

• High resolution:• WorldView II,

• PLANETSCOPE

AERIAL IMAGERY

PROXIMAL SENSING



Moderate Resolution Imaging Spectroradiometer- MODIS

• 36 spectral bands

• Spatial resolution: 250 – 1000 m;

• Temporal resolution: 12 h;

• Applications for monitoring of fields:– Crops’ condition in global, Continental

and national scale,

– Surface temperature,

– Snow cover



Advanced Very High Resolution Radiometer- NOAA AVHRR

• 5 spectral bands

• Spatial resolution: 1000 m;

• Temporal resolution: 24 h;

• Applications for monitoring of fields:– Crops’ condition in global,

Continental and nationalscale,

– Surface temperature,



Landsat 8:

• Spatial resolution: 30 days

• Temporal resolution: 16 days;

• Monitoring of crops condition, temperature stress and crops recognition



Sentinel 2a i 2b

• Temporal resolution: 5 dni;

• Spatial resolution: 10 m



WorldView

• On demand

• Spatial resolution:– Panchromatic – 0,46 m;

– Multispectral – 1,84 m



PLANETSCOPE

• Constellation of 200 + satellites

• Full globe coverage app. every 24 h

• Spatial resolution: 3m

• 4 spectral bands – RGB + NIR

• https://www.planet.com/products/planet-imagery/

https://www.planet.com/products/planet-imagery/



Does spatial resolutions matters for agricultural applications?

10 m 20 m 30 m

60 m 100 m 250 m



Aerial imagery



Proximal sensing



HOW SATELLITE CAN SERVE MONITORING OF AGRICULTURAL FIELDS?



Applications of satellite data for agriculture:

• Estimation of yields and biomass

• Estimation of nutrients

• Stress conditions assessment

• Weeds detection and estimation

• Soil composition



Vegetation indexes

• Index database – a database of remote sensingindices

• https://www.indexdatabase.de/

• Number of indices: 519

https://www.indexdatabase.de/



Index database – a database of remotesensing indices

Application:Number of indices:

Examplary indices:

Agriculture – general 22Normalized Difference Nitrogen Index, Atmospherically Resistant Vegetation Index , NDVI

Parametersdescribing crops

4Crop water stress index, Green leaf index, Calibrated NDVI, SAVI

Monitoring of cropsyield

2NDVIhyper - Normalized Difference Pigment specific normalised difference, Calibrated NDVI

Productionmonitoring

1Calibrated NDVI

Precision agriculture 4

Canopy Chlorophyll Content Index, Crop waterstress index, Normalized Difference NormalizedDifference 790/670, Normalized difference red edge index



Normalized Difference NIR/Red Normalized Difference Vegetation Index - NDVI



NDVI – applications and limitations



NDVI – zastosowanie i ograniczenia



Soil Adjusted Vegetation Index - SAVI



Comparison of NDVI and SAVI values:



Normalized Difference Red Edge index (NDRE)

• NDRE is an index that can only be formulated when the Red edge band is available in a sensor.

• It is sensitive to chlorophyll content in leaves (how green a leaf appears), variability in leaf area, and soil background effects.

• NDRE is applicable for monitoring the variability in fertilizer requirements or foliar Nitrogen, not necessarily Nitrogen availability in the soil.



CCCI – Chlorophyl Canopy Content Index

Applications:• leaf chlorophyll content• plant vigor• stress detection• fertilizer demand• Nitrogen uptake



NDSI – Normalized Difference Soil Index

• 𝑁𝐷𝑆𝐼 =middle infrared−green

middle infrared+green

• Differentiat the bare soil from crops;

• Enables to monitor the soils parameters within the field



NDSI vs. Electromagnetic scanning



NDSI vs. NDVI

Polygon Average NDVI values

1_1 0,79

1_2 0,80

2_1 0,64

2_2 0,64

2_3 0,65

3_1 0,68

4_1 0,63

5_1 0,76



Homogenous polygons

+ +

DTM NDSI NDVI



NDWI – Normalized Difference Water Index



MV 2017-11-07MV 2017-11-13MV 2017-11-19RGB 2017-11-14CIR 2017-11-14

RGB 2017-11-14CIR 2017-11-14NDWI 2017-11-14 MV 2017-11-07MV 2017-11-13MV 2017-11-19MV 2017-11-25

MV 2017-11-25NDWI 2017-11-14

Dane optyczne Dane radarowe



Pow.: 72,3 haWoda: 0,6 ha







Few tips about satellite data applications for agriculture!

• Keep in mind the limitations such as: spatial and temporal resolution

• Remember every crop type is different!

• Validate your work

• Collect in-situ data when possible

• Use additional data!

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