Remote sensing –Beyond images Mexico 14-15 December 2013 The workshop was organized by CIMMYT Global Conservation Agriculture Program (GCAP) and funded by the Bill & Melinda Gates Foundation (BMGF), the Mexican Secretariat of Agriculture, Livestock, Rural Development, Fisheries and Food (SAGARPA), the International Maize and Wheat Improvement Center (CIMMYT), CGIAR Research Program on Maize, the Cereal System Initiative for South Asia (CSISA) and the Sustainable Modernization of the Traditional Agriculture (MasAgro)

1. Remote sensing products
in support of crop subsidy
in Mexico
Carlos Dobler
cdobler@siap.gob.mx

2. • Two projects are presented
• Retrieve useful information in support of crop
subsidy and/or insurance using RS techniques
• 1. Evaluation of sowing conditions
• 2. Agricultural Drought Index

3. Evaluation of
sowing conditions
• PROCAMPO Productivo:
– SAGARPA’s program that subsidies
agricultural activities.
– Eligible areas: those planted as accorded
between producer – program (ha. per field).

4. Evaluation of
sowing conditions
• Traditional way of verifying sowing conditions:
– On-field work.
– Cost: $MXN
p/year
– ~ 20,000 fields verified.
…a lot

5. Evaluation of
sowing conditions
• Using satellite images and advanced remote sensing
techniques, this process can be optimized: considerable
increase in the number of verified fields.
• Classification:
– Object based
– Decision trees

6. Evaluation of
sowing conditions
23 CADERs (partials) in 15
states.
Spring-summer season.
Imagery cover

7. Imagery
schedule
N
Image
useful?
Y
Atmospheric correction
Image segmentation
Object aggregation
Image
Pre-processing
Sampling
design
Unsupervised classification
Sampling points selection
On-field
sampling
Data gathering: planted/non-planted
Auxiliar info: croptype, phenology, % cover, crop height, pictures
Decision trees
(classification)
6 info layers:
-4 SPOT bands
-NDVI
-Texture (std. dev.)
Results

8. Object-based
4 spectral bands
Segmentation
NDVI
Layer
aggregation
into objects
= 6 object-based data layers
Texture

9. Sampling design
Classes
(unsupervised)
Canatlán, Dgo. Sep 2013.
Sampling points

10. On-field sampling
González, Tamps. Sep. 2013.

11. Decision trees
Sampled points:
N:96
Y: 4
Sampled points:
N: 11
Y:117
0.96
confidence
0.91
confidence
LOW
HIGH
probability
sowed
probability
sowed
Sampled points:
N:20
Y:15
0.57
confidence
UNCERTAIN!

12. Classification
Miguel Auza, Zac. Aug. 2013.

13. Area cuantification
Folio
HIGH PROB.
702901605-1
81%
UNCERTAIN
0%
LOW PROB.
19%

14. Results
State
CADER
Verified Fields
Zacatecas
Miguel Auza
19,409
San Luis Potosí
Villa De Ramos
26,715
Guerrero
Acapulco
5,860
Durango
Canatlán
2,363
Chihuahua
El Terrero
2,733
Tamaulipas
González
4,793
Puebla
Libres
36,520
Tlaxcala
Huamantla-Cuapiaxtla
30,198
Oaxaca
Pinotepa
5,279
Chihuahua
Anahuac, Cusihuiriachi
3,350
Michoacán
Venustiano Carranza
4,708
Jalisco
La Barca, Ocotlán, Atotonilco El Alto
8,332
Jalisco
Lagos de Moreno, Teocaltiche
6,364
Aguascalientes
Aguascalientes
2,476
Sinaloa
Mazatlán
4,034
Coahuila
Monclova, San Buenaventura
1,020
Colima
Armería
4,348
15
23
168,502
in 4 months

15. Agricultural Drought
Index
• Some effects:
–
–
–
–
–
–
Lower yields
Late planting season
Early harvest
Crop re-conversion
Interruption in cycle
$$$
• Developed with assessment of National Drought
Mitigation Center (UNL; USA) & Servicio Meteorológico
Nacional (MX).

16. Agricultural Drought
Index
• Conditions:
• 1. Rain-fed agriculture
• 2. Monthly delivered
• 3. 1km2 resolution

17. Agricultural Drought
Index
• 4. National: in-season areas

18. Agricultural Drought
Index
• 4. National: in-season areas
Rain-fed agr. (out-of-season)
In-season
Feb
Apr
Jun
Aug
Oct
Dec

19. Variables
• 1. SPI (anomaly in precipitation)
+400 stations (MX + US Border)
Parameter tuning (for interpolations)

20. Variables
• 1. SPI (anomaly in precipitation)

21. Variables
• 2. VCI (anomaly in NDVI)

22. Variables
• 3. TCI (anomaly in LST)

23. Variables
• 4. VHI (VCI & TCI)
Ensenada, BC
Camargo, CHIH
Silao, GTO

24. Variables
• 4. VHI (VCI & TCI)

25. Variables
integration
1
Valores fuzzy
Valores fuzzy
1
0.5
0.5
0
0
-2
-1
0
Valores originales (SPI)
1
2
0
25
50
Valores originales (VHI) [%]
SPI + VHI normalization
75
100

26. Variables
integration
Drought level
Extreme
Very high
High
Med
Low

27. Results
August 2011
August 2013

28. Results

29. Thank you!
Carlos Dobler
cdobler@siap.gob.mx