Physiographic zones of the Earth are “major units of the geographic (landscape) sphere, which give way to each other in a regular and definite order depending mainly on the ratio of heat and moisture” [1]. The rise in the Earth’s annual mean temperature recorded for the instrumental observation period [2] may provoke variations in the global distribution of the heat/moisture ratio and, as a consequence, in displacement of the position and configuration of physiographic zones. The variations may be accompanied by negative socioeconomic impacts. To minimize the negative consequences, it is expedient to carry out a timely forecasting based on data of the space monitoring of physiographic zones. Such monitoring requires analysis of a giant body of data. Therefore, it is necessary to elaborate automatic methods for the mapping of these zones by using satellite-borne survey. Currently, several quantitative criteria (indices) have been suggested to identify physiographic zones, e.g., Budyko’s radiational aridity index [3], which characterizes the ratio between the annual radiation balance and annual precipitation. The space version of this method is limited by the need for obtaining continuous yearlong series of observations over elements of the radiation balance of the day surface and atmospheric precipitation. Cloudiness prevents the recording of such continuous series. Therefore, we have studied the possibility of applying another method for monitoring the position of physiographic zones, namely mapping of the thermal inertia of the Earth’s surface based on a satellite-borne thermal survey data [4‐11]. 1 1 Thermal inertia p is the resistance of a surface to heating (or cooling) under the influence of a periodic heat source. p = ( λ · c · ρ ) 1/2 , J/(m 2 · s 1/2 · K) = 1 TIU, where λ is the thermal conductivity factor, W/(m · K); c is specific heat, J/(kg · K), and ρ is density, kg/m 3 . The method we applied for the remote mapping of thermal inertia (TI); daily mean evaporation rate (ER) from the Earth’s surface, mm/day; and heat flux (HF), W/m 2 involved the mathematical model of the daily variation of the Earth’s surface temperature (ESTs) [10], which took into account basic factors affecting the EST formation. The model has the following assumptions: the metrological conditions and concentration of optically active gases in the atmosphere within the whole area under study are identical; emittance, albedo of the Earth, and TI do not vary during the whole period of the satellite-borne thermal survey. To determine TI and ER, we carried out the thermal multispectral space survey for 3‐10 days under stable meteorological conditions in the absence of rainfall in order to characterize the daily EST dynamics more completely. Moreover, mapping of TI and ER was based on the following parameters of routine expedited meteorological observations over parameters involved in the mathematical EST model: total solar radiation; air temperature, moisture, and wind velocity at a height of 2 m above the surface; atmospheric pressure; and cloudiness. To solve the inverse problem, we performed mathematical EST modeling for all possible combinations of TI, ER, HF, and albedo, i.e., the “library” of ideal ESTs. Then, the measured EST values were compared to ideal values. The TI and ER values sought were deduced from the assigned fitting criterion of measured and ideal EST values. Algorithm errors of solving the inverse problem are given in the table.
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