Quantitative remote sensing is a branch of

remote sensing Remote sensing is the acquisition of information about an physical object, object or phenomenon without making physical contact with the object, in contrast to in situ or on-site observation. The term is applied especially to acquiring inform ...

. The quantitative remote sensing system does not directly measure

land Land, also known as dry land, ground, or earth, is the solid terrestrial surface of Earth not submerged by the ocean or another body of water. It makes up 29.2% of Earth's surface and includes all continents and islands. Earth's land sur ...

surface

parameter A parameter (), generally, is any characteristic that can help in defining or classifying a particular system (meaning an event, project, object, situation, etc.). That is, a parameter is an element of a system that is useful, or critical, when ...

s of interest. Instead, the signature remote sensors receive is

electromagnetic radiation In physics, electromagnetic radiation (EMR) is a self-propagating wave of the electromagnetic field that carries momentum and radiant energy through space. It encompasses a broad spectrum, classified by frequency or its inverse, wavelength ...

reflected, scattered, and emitted from both the

surface A surface, as the term is most generally used, is the outermost or uppermost layer of a physical object or space. It is the portion or region of the object that can first be perceived by an observer using the senses of sight and touch, and is ...

and the

atmosphere An atmosphere () is a layer of gases that envelop an astronomical object, held in place by the gravity of the object. A planet retains an atmosphere when the gravity is great and the temperature of the atmosphere is low. A stellar atmosph ...

. Both modeling and model-based inversion are important for quantitative remote sensing. Here, modeling mainly refers to

data Data ( , ) are a collection of discrete or continuous values that convey information, describing the quantity, quality, fact, statistics, other basic units of meaning, or simply sequences of symbols that may be further interpreted for ...

modeling, which is a method used to define and analyze data requirements; model-based inversion mainly refers to using physical or empirically physical models to infer unknown but interested parameters.

Model-based inversion

The inversion algorithm is needed to obtain land surface parameters from remotely sensed data. It is not a trivia task to reliably retrieve land surface parameters since the remote sensing signature is a function of not only the variable of interest but also many other atmosphere and surface characteristics. Multifaceted aspects of the remote sensing data, such as the temporal, spectral, spatial, polarized information, as well as ancillary and prior knowledge, are typically used in a synthetic way to improve the quality of land parameter retrievals. Hundreds of models related to atmosphere, vegetation, and radiation have been established during past decades. The model-based inversion in geophysical (atmospheric) sciences has been well understood. However, the model-based inverse problems for Earth surface received much attention by scientists only in recent years. Compared to modeling, model-based inversion is still in the stage of exploration. This is because that intrinsic difficulties exist in the application of ''a priori'' information, inverse strategy, and inverse algorithm. The appearance of hyperspectral and multiangular remote sensor enhanced the exploration means, and provided us more spectral and spatial dimension information than before. However, how to utilize these information to solve the problems faced in quantitative remote sensing to make remote sensing really enter the time of quantification is still an arduous and urgent task for remote sensing scientists.

Quantitative Models in Optical Remote Sensing

All

model A model is an informative representation of an object, person, or system. The term originally denoted the plans of a building in late 16th-century English, and derived via French and Italian ultimately from Latin , . Models can be divided in ...

s in optical remote sensing are traditionally grouped into two major categories:{{Cite book , last=Liang , first=Shunlin , url=https://onlinelibrary.wiley.com/doi/book/10.1002/047172372X , title=Quantitative Remote Sensing of Land Surfaces , date=2003 , publisher=Wiley , isbn=978-0-471-28166-5 , edition=1 , language=en , doi=10.1002/047172372x Statistical models: based on correlation relationships of land surface variables and remotely sensed data. They are easy to develop and effective for summarizing local data; however, the developed models are usually site-specific. They also cannot account for cause-effect relationships. Physical Models: physically based models follow the physical laws of the

system. They also establish

cause and effect Causality is an influence by which one event, process, state, or object (''a'' ''cause'') contributes to the production of another event, process, state, or object (an ''effect'') where the cause is at least partly responsible for the effect, ...

relationships. If the initial models do not perform well, we know where to improve by incorporating the latest knowledge and information. However, there is a long curve to develop and learn these physical models. Any models represent the abstract of the reality; thus a realistic model could potentially be very complex with a large number of variables.

bibliography

Liang, S. (2005). ''Quantitative remote sensing of land surfaces''. John Wiley & Sons.

References

Remote sensing