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The substantial objective of desertification monitoring is to derive its development trend, which facilitates pre-making policies to handle its potential influences. Aiming at this extreme goal, previous studies have proposed a large number of remote sensing (RS) based methods to retrieve multifold indicators, as reviewed in Part I. However, most of these indicators individually capable of characterizing a single aspect of land attributes, e.g., albedo quantifying land surface reflectivity, cannot show a full picture of desertification processes; few comprehensive RS-based models have either been published. To fill this gap, this Part II was dedicated to developing a RS information model for comprehensively characterizing the desertification and deriving its trend, based on the indicators retrieved in Part I in the same case of the south Junggar Basin, China in the last decade (2000-2009). The proposed model was designed to have three dominant component modules, i.e., the vegetation-relevant sub-model, the soil-relevant sub-model, and the water-relevant sub-model, which synthesize all of the retrieved indicators to integrally reflect the processes of desertification; based on the model-output indices, the desertification trends were derived using the least absolute deviation fitting algorithm. Tests indicated that the proposed model did work and the study area showed different development tendencies for different desertification levels. Overall, this Part II established a new comprehensive RS information model for desertification risk assessment and its trend deriving, and the whole study comprising Part I and Part II advanced a relatively standard framework for RS-based desertification monitoring. (C) 2018 COSPAR. Published by Elsevier Ltd. All rights reserved.