# Advances in Quantitative Earth Remote Sensing: Past, Present and Future
## 2 Contributions
[3], is focused on the use of all-weather passive microwave remote sensing together with a deep learning convolutional neural network to derive land-surface temperature (LST), which is essential for use in the study and management of water resources and food production. The authors used a convolutional neural network (CNN) algorithm to retrieve LST from brightness temperature measurements by the Advanced Microwave Scanning Radiometer 2 (AMSR2) over China. They used LST from the Moderate Resolution Imaging Spectroradiometer (MODIS) as a reference dataset to overcome any issues related to the comparability of in situ observations in comparison with AMSR2-based LSTs. They randomly divided the AMSR2 brightness temperature (TB) and MODIS LST into training and test datasets, and a CNN was constructed to simulate passive microwave brightness and to invert the surface temperature. When they used the twelve combination of vertical and horizonal (V and H) polarization brightness temperature measurements (7.3, 10.65, 18.7, 23.8, 36.5, 89 GHz), they obtained the most stable and accurate CNN retrieval model. Measurements from V polarizations performed better than H polarizations; however, because CNNs rely heavily on large amounts of data, the combination of both V and H polarizations performed better than a single polarization. The retrievals for different regions revealed that the CNN accuracy was highest over large bare land surface areas. A comparison of the retrieved LSTs with in situ measurements from meteorological stations resulted in R\({}^{2}\) = 0.987, RMSE = 2.69 K, and an average relative error of 2.57 K.  
The fourth paper, by [PERSON] et al. [4], is focused on development, test and evaluation of a sensor for in situ measurements of sun-induced fluorescence (SIF), which is used widely in the study of terrestrial ecosystems gross primary production and their contribution to the cycling of carbon in the Earth system. SIF is considered as a proxy for photosynthesis in terrestrial vegetation. In situ and long-term observations of SIF are required for gaining greater insight for seasonal dynamics of photosynthetic activity of ecosystems, including their gross primary production (GPP). The authors describe the design and operation of a tower-based and automated SIF measurement (SIFSpec) system. This system was developed to obtain synchronous SIF observations and flux measurements across different terrestrial ecosystems, and to use these observations for evaluating the satellite-based SIF products (e.g., from European, Japanese and US satellites). The paper provides details about the system components, instrument installation, calibration, data collection, and processing. Correcting for the effects of atmospheric conditions on SIF measurements is also included in their data processing, which is very important but usually ignored for tower-based SIF measurements. They report continuous measurements of SIF for maize during two growing seasons at Daman (DM) flux site (Gansu Province, China) to illustrate the robust performance of their system for capturing the diurnal variations in photosynthetically active radiation (PAR) and seasonal variations in GPP. They obtained very good correlation (0.81) between SIF and seasonal variation of GPP based on measurements from the oxygen-2 (O\({}_{2}\)-A) spectral band. They concluded that automated and continuous SIF measurements with concurrent eddy covariance (EC) flux measurements could provide a reliable approach for understanding the photosynthetic activity of the terrestrial ecosystems, and for better linking of ground-based and airborne and space-borne remotely sensed SIF measurements. The fifth paper, by [PERSON] et al. [5], is focused on using passive microwave remote sensing to obtain near-surface soil moisture information, which is also essential for the study of water resources and food production.