Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2015, Vol. 12, No. 1, pp. 31-40
Composite index of temperature conditions and its applicability to the description of the state of agricultural vegetation in Kazakhstan
A.G. Terekhov
1 , N.G. Makarenko
1,2 , I.T. Park
1 1 Institute of Information and Computing Technologies MES, Almaty, Kazakhstan
2 Central Astronomical Observatory RAS, Saint-Petersburg, Russia
In this paper, we consider a modification of the scale of the temperature condition index and verify its effectiveness for agricultural vegetation in Kazakhstan. Two classes of indices in relative scales are commonly used to describe the state of the underlying surface according to satellite monitoring data. The first one is the scale of states, which weighs the current value in the range of long-term variations. Its disadvantage is the sensitivity to large fluctuations of variations caused by rare events. The second, more popular, is scale anomalies. It shows how the current values differ from the average. The deviation from the average number reduces the nonstationarity of data, but complicates the interpretation of the indices. The proposed change to the scale adds to the basic parameters (minimum and maximum number) the arithmetic mean of the data that determines the middle of the scale. This modification is useful for data with significant kurtosis (asymmetry) in the density distribution. We present the results of comparative analysis of the proposed version of the index and the traditional ones. The data were obtained from the product NEO - Land Surface Temperature anomaly, with a resolution of 0,1 degrees and 8-day update period. Numerical experiments showed the efficiency of the modified scale for asymmetric data. We hope that this approach will improve the information basis for the diagnostics of the state of vegetation, conditions of growing and development of spring crops in Kazakhstan.
Keywords: remote sensing, radio-brightness surface temperature, temperature condition index, the state of vegetation, diagnostics moisture conditions
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