An Introduction to Remotely Sensed Data Analysis - Sampling Spatial Units for Agricultural Surveys - page 89

Furthermore, classified satellite images can be used as auxiliary variables
to improve the precision of ground survey estimates, generally with a regres-
sion or a calibration estimator (see Sect. 10.3 ). However, some alternative
estimator definitions based on confusion matrices 3 of classified images can
also be used. The remotely sensed information can also represent an auxiliary
variable for small area estimation procedures (see Chap. 11 ) .
Finally, RS data have been used to estimate the production of crops
because of their link with yield. The most common indicators are based on
NDVI (Benedetti and Rossini 1993 ; Benedetti et al. 1994 ), and can be
computed using an RS image. However, as shown by Carfagna and Gallego
( 2005 ), the link between NDVI and crop yield is only strong for some crops
under certain conditions.
The cases described above are just some of the possible applications of RS
to spatial sampling surveys for agricultural data. Remotely sensed informa-
tion will be extensively used in the remainder of this topic.
References
Arbia G, Benedetti R, Espa G (1999) Contextual classification in image analysis: an assessment of
accuracy of ICM. Comput Stat Data Anal 30:443-455
Atzberger C (2013) Advances in remote sensing of agriculture: context description, existing
operational monitoring systems and major information needs. Remote Sens 5:949-981
Ball GH, Hall DJ (1965) Isodata: a method of data analysis and pattern classification. Stanford
Research Institute, Menlo Park, CA
Becker-Reshef I, Justice C, Sullivan M, Vermote E, Tucker C, Anyamba A, Small J, Pak E,
Masuoka E, Schmaltz J, Hansen M, Pittman K, Birkett C, Williams D, Reynolds C, Doorn B
(2010) Monitoring global croplands with coarse resolution earth observations: the global
agriculture monitoring (GLAM) project. Remote Sens 2:1589-1609
Benedetti R, Rossini P (1993) On the use of NDVI profiles as a tool for agricultural statistics: the
case study of wheat yield estimate and forecast in Emilia Romagna. Remote Sens Environ
326:311-326
Benedetti R, Rossini P, Taddei R (1994) Vegetation classification in the middle Mediterranean
area by satellite data. Int J Remote Sens 15:583-596
Besag J (1986) On the statistical analysis of dirty pictures. J R Stat Soc Ser B 48:259-302
Canada Centre for Remote Sensing (2003) Principles of remote sensing. Centre for Remote
Imaging Sensing and Processing (CRISP), National University of Singapore
Carfagna E, Gallego FJ (2005) Using remote sensing for agricultural statistics. Int Stat Rev
73:389-404
Cressie N (1993) Statistics for spatial data. Wiley, New York
Cumani A (1991) Edge detection in multispectral images. CVGIP: Graph Models Image Process
53:40-51
3 A confusion matrix contains information about actual and predicted classifications from a
classification method. The performance of such systems is commonly evaluated using the data
in the matrix.