## 1 Introduction  
The Amazon rainforest, holding 50% of tropical forests carbon stocks [1], accounting for about 15% of global terrestrial photosynthesis [2], is commonly known as an important and continuous part of Earth system functioning [3]. Understanding the seasonal and interannual variations of photosynthesis and other gross CO\({}_{2}\) fluxes of wet tropical forests offers insights about how they may respond to climate change [4]. Despite its perennial canopy, the Amazonian tropical evergreen forests show significant seasonality in leaf age groups (e.g., young leaves, mature leaves and old leaves) [5], precipitation and radiation [6, 7], which dominate regulating the seasonal cycles of carbon and water fluxes [8]. Satellite optical remote-sensing sensors also discern significant canopy seasonality, i.e., a dry-season green-up phenomenon compared to the wet seasons in the parts of Amazon tropical evergreen forest [5; 9; 10; 11; 12; 13]. For example, some studies analyzed Moderate Resolution Imaging Spectrometer (MODIS) Enhanced Vegetation Index (EVI) and showed that the canopy of the tropical forests 'greened up' during higher-sunlight dry seasons [14], which increased greenness by 25% with sunlight during the dry season across Amazonian tropical forests [11]. However, such a dry-season green-up in Amazonia from optical signal was questioned by several studies [15; 16; 17]. The optical-based signals are easily affected by land cover conditions and atmospheric conditions such as aerosols, atmospheric gases, clouds, and water vapor [10; 18; 19; 20; 21]. That is the main cause of the biases on optical reflectance changes, i.e., the interpretation of near-infrared reflectance changes can be induced by cloud/aerosols contaminations and surface anisotropy and varying sensor and illumination geometries [5; 15; 16; 17]. Studies have shown that removing artifacts of changing sun-sensor geometry in optical remote sensing red and near-infrared reflectance data will eliminate the appearance of a green up (the change of EVI) during dry season months [15]. Thus, more investigations from other types of satellite signals independent of optical sensors are needed to verify such a dry-season green-up phenomenon across Amazonian tropical evergreen forests. Unlike optical sensors, microwave sensors are seen as an important alternative for monitoring vegetation dynamics [22]. It detects and receives the electromagnetic radiation and scattering characteristics of the measured object in the microwave band (1 mm < wavelength < 1 m) to identify distant objects [23]. This enables microwave sensors the ability of working under all atmospheric conditions and penetrating parts of vegetation canopies to detect targets below. Therefore, microwave remote sensing is often used to detect changes in canopy structure, biomass, soil moisture and vegetation water content [10; 23; 24]. Up to now, several studies have been carried out to evaluate the drought impacts on Amazonian tropical forests. For example, novel low-frequency microwave satellite data (L-VOD, L band Vegetation Optical Depth) was not only used to precisely monitor the aboveground carbon (AGC) changes, but was also definitely used to monitor the intensity and spatial distribution of AGC restoration in the tropical forest [25; 26]. The data from satellite microwave sensors which measure precipitation (TRMM, Tropical Rainfall Measuring Mission) and canopy backscatter (QSCAT, Quick Scatterometer) are also used to quantify the relative severity of recent droughts and potential impacts on Amazonian tropical forests, and they showed severe water shortage in western Amazonia during the drought period of 2005 [27]. Although focused on drought, the above microwave studies with relatively satisfactory experimental results fully demonstrate the great potential of microwave data and its application advantages in tropical rain forest. However, to date, few microwave remote sensing studies are focused on detecting the dry-season green-up of tropical evergreen forests.