The Sun-induced chlorophyll fluorescence (SIF) holds significant representativeness in monitoring vegetation structure and physiology, playing a crucial role in tracking vegetation changes. However, the limited availability of high spatial resolution SIF severely constrains its application in local areas. To address this issue, this study employs a downscaling approach based on the extreme gradient boosting (Xgboost) model to generate high-resolution SIF data. Choosing the Saihanba region in China as our experimental area, we first reconstruct high-quality Sentinel-2 Vegetation Indices (VIs) time-series data for the Saihanba area. These time-series VIs data serve as the primary parameters for the downscaling of SIF. Subsequently, leveraging the constructed Xgboost model, we successfully downscaled the coarse resolution SIF (0.05°) to a higher resolution (0.0005°). To validate the accuracy of the downscaled SIF data, we utilized the gross primary productivity (GPP) from two flux sites within the research area for verification. The results indicate a high level of precision in the downscaled SIF. Through this study, we have effectively generated precise SIF data for the Saihanba region, offering a convenient and reliable method for monitoring vegetation in local areas.

The scaling effect in remote sensing limits the estimation accuracy and application of remote sensing products, such as leaf area index (LAI). At present, the studies on scaling effect mostly focus on developing the algorithm for scaling bias correction, and rarely consider the model parameter type, which plays a critical role during the scaling bias calculation of LAI. In addition, some studies have suggested that the nonlinearity of normalized difference vegetation index (NDVI) equation affects the scaling bias calculation of LAI. However, few studies have been performed to clarify its effects in a quantitative way. In this paper, at two VALERI sites, discuss on the scaling bias calculation of LAI based on the Taylor series expansion method (TSEM) from two aspects: i) the nonlinearity of NDVI equation and ii) the model parameter selection (reflectance, NDVI and directional gap probability). The results indicate that the nonlinearity of NDVI equation has little effects on the LAI scaling bias calculation when NDVI was included in the retrieval model. On the other hand, when directional gap probability was considered in the retrieval model, it had effects. In comparison, when directional gap probability was used in the retrieval model, the scaling bias calculation of LAI showed higher quality.

Vegetation productivity is an important parameter for estimating carbon stocks in terrestrial ecosystems and is important for monitoring regional and global ecological changes. In this study, gross primary productivity (GPP) and net primary productivity (NPP) products with a spatial resolution of 500 m and a temporal resolution of 8 days from 2000 to 2019 were produced based on Global land surface satellite (GLASS) leaf area index (LAI) and the fraction of absorbed photosynthetically active radiation (FPAR) products, and an improved light use efficiency (LUE) model that introduced clearness index (CI) to represent the effect of radiation on LUE. Validated by FLUXNET GPP data, Bigfoot NPP and EMID NPP data, the GPP and NPP products have high accuracy. The dataset has the potential to monitor global and regional ecology and vegetation growth conditions.

Vegetation Optical Depth (VOD) plays an increasingly important role in studying global carbon, water and energy transformation [1], [2]. This study explores the performance of the X-MEB (X-band microwave emission of the biosphere) model at global scale. Similar to the L-MEB model, the X-MEB model, built by INRAE (Institut national de recherche pour l'agriculture, l'alimentation et l'environnement) Bordeaux, aims to retrieve VOD (referred to as IB X-VOD) at X-band. To avoid the ill-posed problem caused by retrieving two parameters of interest (soil moisture (SM) and VOD) from mono-angular and dual-polarized observations (AMSR2), which are strongly correlated, we used the ERA5 SM product as an input to the X-MEB inversion. At a first step, we produced global IB X-VOD in year 2015 using the parameters (soil roughness and effective scattering albedo) calibrated in the African continent and evaluated the retrieved X-VOD with three vegetation parameters including Above-Ground Biomass (AGB), Leaf Area Index (LAI) and Normalized Difference Vegetation Index (NDVI). The evaluation results indicate X-MEB model has a great potential for global VOD retrievals from AMSR2 satellite data.

The long time series vegetation productivity products are of great significance to the research of increasing CO2and global changes. In this paper, global net primary productivity (NPP) in 1981-2018 was firstly estimated with Global LAnd Surface Satellite (GLASS) data, ERA-Interim meteorological data and the other variables by using the improved Multi-source data Synergized Quantitative (MuSyQ) NPP algorithm. The average global NPP is 61.0 PgC/yr in 1981-2018, which is in great agreement with the other similar products. The global NPP has shown a significant increase trend, with an annual growth rate of 0.10 PgC/yr over the past 38 years. NPP in the northern hemisphere and southern hemisphere account for 62.0% and 38.0% of the global respectively, both show an increasing trend. The overall increasing trends in NPP are also consistent among most of the biomes.

Decade years of efforts on the retrieval of soil moisture based on radiative transfer model have largely improved the accuracy of soil moisture (SM). This paper focus on the other parameter, namely vegetation optical depth (VOD). We retrieved X-band VOD from AMSR-E and AMSR2 observations by inverting the L-MEB model (Wigneron et al. 2007 [1]) at X-band, considering that SM was known. As SM input to the L-MEB inversion we used the ECMWF SM product. This step avoids correlation between VOD and SM retrievals from the mono-angular AMSR-E observations. In a first step we evaluated the retrieved VOD with the Copernicus Global Land Service (CGLS) LAI. The evaluation results indicate our model has a great potential for VOD retrievals from AMSR-E/2 satellite data.

NPP is calculated to characterize vegetation activity as well as improve our understanding of terrestrial ecosystem carbon cycle. In this study, we estimated a time series of NPP and the spatial and temporal variations from 1982 to 2012 in China. Subsequently, the correlations between the NPP and climate factors (temperature and precipitation) were evaluated to show the responses of vegetation NPP to climate changes. The results showed that NPP in China decreased from southeast to northwest due to the spatial variability of vegetation types and climate characteristics. Annual NPP had a fluctuating increase tendency during our study period with values ranging from 1.92 to 2.73 PgC•a-1, with an annual increase of 0.02 PgC•a-2. In addition, NPP in north China correlates positively with precipitation and negatively correlates with temperature, this is owing to the fact that this region is relatively dry and increasing precipitation extends the growing season of vegetation. In south China the results were the opposite.

The fraction of absorbed photosynthetically active radiation (FAPAR) is a critical input parameter in many climate and ecological models. Long time series of global FAPAR products are required for many applications, such as vegetation productivity, carbon budget calculations, and global change studies. Three long time series of global FAPAR products have been existing since the 1980s: Global LAnd Surface Satellite (GLASS) Advanced Very High Resolution Radiometer (AVHRR), National Centers for Environmental Information (NCEI) AVHRR, and third-generation Global Inventory Monitoring and Modeling System (GIMMS3g). Currently, no intercomparison studies exist that have evaluated these FAPAR products to understand their differences for effective applications. In this paper, these three long time series of global FAPAR products are first intercompared to evaluate their spatial and temporal consistencies, and then compared with FAPAR values derived from high-resolution reference maps of VAlidation of Land European Remote sensing Instruments sites. Our results demonstrate that the GLASS AVHRR FAPAR product is spatially complete, whereas the NCEI AVHRR and GIMMS3g FAPAR products contain many missing pixels, especially in rainforest regions and in middle- and high-latitude zones of the Northern Hemisphere. The GLASS AVHRR, NCEI AVHRR, and GIMMS3g FAPAR products are generally consistent in their spatial patterns. However, a relatively large discrepancy among these FAPAR products is observed in tropical forest regions and around 55°N-65°N. In latitudes between 15°N and 25°N, the mean GIMMS3g FAPAR values are clearly larger than the mean GLASS AVHRR and NCEI AVHRR FAPAR values during July-October each year. The GLASS AVHRR FAPAR product provides smooth FAPAR temporal profiles, whereas the NCEI AVHRR and GIMMS3g FAPAR products showed fluctuating trajectories, especially during the growing seasons. All three FAPAR products show high agreement coefficients (ACs) in vegetation regions with obvious seasonal variations and low ACs in tropical forest regions and sparsely vegetated areas. A comparison of these FAPAR products with the FAPAR values derived from high-resolution reference maps demonstrates that the GLASS AVHRR FAPAR product has the best performance [root mean square deviation (RMSD) = 0.0819 and bias = 0.0043], followed by the NCEI AVHRR FAPAR product (RMSD = 0.1061 and bias = 0.0371), and then finally, the GIMMS3g FAPAR product (RMSD = 0.1152 and bias = 0.0248).

Remote measurement of SIF has opened a new perspective to assess plant actual photosynthesis at larger, ecologically relevant scales. However, understanding the underling mechanisms between SIF and GPP remains challenging before SIF used as a robust constraint for estimating GPP. In this study, GOME-2 SIF was found to be consistently related to MODIS GPP. We also noticed the SIF-GPP relationship was ecosystem-specific and influenced by land surface temperature. The former was due to some structural and physiological characteristics related to each ecosystem. The latter can be attributed to the biochemical process influenced by temperature conditions. Model simulations also indicated the SIF-GPP relationship was complex and affected by some factors like chlorophyll content and LAI. Our study contributes to a better understanding of the information inherent in remotely sensed SIF and its functional relationship to GPP.

The scale effects in earth science, which are related to various aspects in remote sensing monitoring, have become an international prosperous research area. As spatial heterogeneity of the earth system limits the transferring between different scale, it is necessary to study these spatial heterogeneity factors, and analyze their impact on NPP scale effect. Then we can introduce an approach to perform spatial scale calibration based on a correction factor for scale effect, and perform it to NPP. This study presented an approach driven by remotely sensed data and meteorological data to estimate GPP and NPP over regional scales. By using multi-scale data and different scaling strategies, NPP of Heihe River Basin in 2012 with various scales were derived. With a focus on differences among land cover types, we introduced and tested a kind of spatial scale calibration method, to get close to the real value of the net primary productivity.

Wetland is an important part of terrestrial ecosystem because of their unique water-heat effects and greenhouse gas (GHG) metabolic processes. However, the variations in carbon flux and ET in wetland are not yet fully understood. Zhangye wetland is an oasis-desert wetland with complex meteorological conditions. In this study, we used eddy covariance technology and process-based to examine the characteristics of carbon flux and ET over an artificial wetland in an oasis-desert area. The main objectives were to determine that (1) is it a carbon sink or source during the study period? (2) is there a significant relationship between the simulation using Biome-BGC model (Wetland-BGC version) and observation? The results showed that (1) it was a carbon sink in non-growing season in Zhangye wetland, whereas it was a carbon pool during the growing season; and CH4flux influenced the total carbon budget significantly; (2) the relationship between observation and simulation was higher than 0.6 (GPP, Recoand ET), but it was 0.3065 on NEE.

The accurate prediction of crop yield is of great help for grain policy making as the importance of food in human life. By assuming a homogeneous and vertical laminar structure and introducing a multilayer-two-big-leaf model, we developed a radiative transfer equation for winter wheat canopy and a model named RS-P-YEC (Remote Sensing — Photosynthesis — Yield Estimation for Crop) for winter wheat yield estimation. In this model, we converted the net primary productivity to winter wheat yield using harvest index. In this study, we estimated yield of winter wheat in North China Plain using the RS-P-YEC model. The simulated yield agrees well with observations from agro-meteorological stations and the R2reaches to 0.817. This study demonstrates that RS-P-YEC model is useful in the yield estimation of winter wheat in North China Plain with widely available remotely sensed images.

Net primary productivity (NPP) is important in the global carbon budget. The change of NPP can be a good indicator of climate variation to some extent. Therefore, it is necessary to study the relationship between climate factors and inter-annual change of NPP, which will help us to understand global change. An empirical exponential model between NPP and integrated NDVI in the Yellow River Basin in China has been established. The spatial distribution pattern and dynamic change of annual NPP from year 1982 to 1998 are analyzed by using multi-temporal 8 km resolution AVHRR-NDVI data. The results show that there exists an incline trend of mean NPP for whole basin while the rainfall decreases slightly, which demonstrates that human activity effects the vegetation cover and NPP much. Finally, in order to analyze the effect of rainfall and temperature on inter-annual change of NPP, correlation coefficient between rainfall, temperature and NPP are computed respectively. It is found that relativity between rainfall, air temperature and NPP is complicated for different climate and vegetation zone. NPP is not highly correlated with climate factors in most places, which may be caused by human activity and other factors. The effect of rainfall on NPP is significant in desert steppe region, while the effect of temperature on NPP is significant in alpine vegetation region and Qinhai-Xizang Plateau. The correlation coefficient between NPP and temperature is negative in area where NPP is positively correlated with rainfall, while it is positive in area where NPP is negatively correlated with rainfall.

A new method to analyze the differences of soil moisture in an area is advanced. Soil moisture is related closely to vegetation growth. An experiment was conducted in the Yellow River Basin in China. The change of vegetation cover is the result of many factors. We postulate that soil moisture in the Yellow River Basin is mainly determined by the precipitation. The delaying effect of the precipitation on the vegetation cover was analyzed in the Yellow River Basin using the cross partial correlation index between precipitation and NDVI. The cross partial correlation index has already excluded the impact of temperature on vegetation cover. Different delaying effects represent different soil moisture. The delaying interval can be adapted to measure soil moisture difference at different levels. On the basis of the above result, the difference of soil moisture in the Yellow River Basin was analyzed, and the difference map of soil moisture in the Yellow River Basin was made.

A revised energy use efficiency model was applied to estimate the distribution of net primary productivity in China. Twenty-eight groups of ground data were collected to validate the result. Our correlation comparison indicates that the estimated result is suitable for the actual situation of vegetation in China and the model parameters were chosen reasonably.