Remote sensing products

in support of crop subsidy

in Mexico

Carlos Dobler

cdobler@siap.gob.mx

• 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

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).

Evaluation of

sowing conditions

• Traditional way of verifying sowing conditions:

– On-field work.

– Cost: $MXN p/year

– ~ 20,000 fields verified.

…a lot

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

Evaluation of

sowing conditions

23 CADERs (partials) in 15

states.

Spring-summer season.

Imagery cover

Imagery

schedule

Image

Pre-processing

Atmospheric correction

Image segmentation

Object aggregation

Sampling

design

Image

useful?

Unsupervised classification

Sampling points selection

Data gathering: planted/non-planted

Auxiliar info: croptype, phenology, % cover, crop height, pictures

6 info layers:

-4 SPOT bands

-NDVI

-Texture (std. dev.)

Results

Y

N

On-field

sampling

Decision trees

(classification)

Object-based

Segmentation

Layer

aggregation

into objects

4 spectral bands NDVI Texture

= 6 object-based data layers

Classes

(unsupervised)

Sampling points

Canatlán, Dgo. Sep 2013.

Sampling design

González, Tamps. Sep. 2013.

On-field sampling

Sampled points:

N:96

Y: 4

0.96

confidence

LOW

probability

sowed

Sampled points:

N: 11

Y:117

0.91

confidence

HIGH

probability

sowed

Sampled points:

N:20

Y:15

0.57

confidence

UNCERTAIN!

Decision trees

Miguel Auza, Zac. Aug. 2013.

Classification

Folio 702901605-1

HIGH PROB. 81%

UNCERTAIN 0%

LOW PROB. 19%

Area cuantification

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 23 168,502in 4 months

Results

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).

Agricultural Drought

Index

• Conditions:

• 1. Rain-fed agriculture

• 2. Monthly delivered

• 3. 1km2 resolution

Agricultural Drought

Index

• 4. National: in-season areas

Agricultural Drought

Index

• 4. National: in-season areas

Feb Apr Jun

Aug Oct Dec

Rain-fed agr. (out-of-season)

In-season

• 1. SPI (anomaly in precipitation)

Variables

+400 stations (MX + US Border) Parameter tuning (for interpolations)

• 1. SPI (anomaly in precipitation)

Variables

• 2. VCI (anomaly in NDVI)

Variables

• 3. TCI (anomaly in LST)

Variables

• 4. VHI (VCI & TCI)

Variables

Ensenada, BC

Camargo, CHIH

Silao, GTO

• 4. VHI (VCI & TCI)

Variables

Variables

integration

0 25 50 75 100

0

0.5

1

Valores originales (VHI) [%]

Valo

res fuzzy

-2 -1 0 1 2

0

0.5

1

Valores originales (SPI)

Valo

res fuzzy

SPI + VHI normalization

Variables

integration

Drought level

Low

Med

High

Very high

Extreme

Results

August 2011 August 2013

Results

Thank you!

Carlos Dobler

cdobler@siap.gob.mx