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The global navigation satellite system reflectometry (GNSS-R) technique has been proven to be a powerful tool for retrieving geophysical parameters of ocean and land/hydrology processes. The ultimate goal for such GNSS-R applications is to achieve large-scale, all-weather, and full-time mapping using spaceborne platforms. In order to ensure both GNSS-R receiver and algorithm meet the requirements of spaceborne observations, airborne experimental campaigns need to be first carried out for early testing and validation purposes. This paper presents a first comprehensive overview of China's airborne GNSS-R campaign conducted on May 30, 2014. There were two objectives for this campaign: (1) to examine the capability of the GNSS-R receiver developed by the National Space Science Center, Chinese Academy of Sciences, for airborne observations and (2) to study algorithms for soil moisture and altimetry retrievals. In this paper, initial results of soil moisture retrievals are presented. The left-hand circularly polarized-predominant satellite information was successfully used to retrieval soil moisture over the cropland. The right-hand circularly polarized components of the reflected signals were also received and examined. The GPS-derived soil moisture results, on the one hand, correctly represented the spatial variations of the soil moisture along the tracking of the flight; on the other hand, the results underestimated the ground-truth. Errors from the retrieval model and from the positioning and effects from the vegetation layer and from the atmospheric water vapor were the primary causes of the uncertainties in soil moisture retrievals using the airborne GNSS-R data. This airborne experimental campaign firstly investigate that China has the capability to perform airborne GNSS-R observation using the self-developed receiver, although the receiver developed by the NSSC needs to be further examined for its capability for spaceborne observation. The early findings of this study will provide illustrations for planned future airborne campaigns. [ABSTRACT FROM AUTHOR]

• Distribution of maize planting areas were retrieved in a large spatiotemporal scale. • Spatiotemporal patterns of drought stress in the major maize planting areas of China were evaluated. • NPP which characterize maize biomass can determine the thresholds of maize drought stress. • Conceptual model of maize drought stress based on the TVDI and NPP was established for the first time. Droughts are characterized by limited water resources and extreme arid climates and have a notable influence on crop yield and food security. Therefore, evaluating the crop drought stress at a large spatiotemporal scale is important. In this study, maize planting areas in the Northeast China Plain (NECP) and North China Plain (NCP) over the past 20 years (2000–2019) were identified from the moderate resolution imaging spectroradiometer (MODIS) enhanced vegetation index, comprising 8-day time-series moving windows. The temperature vegetation drought index (TVDI) was retrieved using the MODIS normalized difference vegetation index (NDVI) and land surface temperature datasets to evaluate maize drought. Furthermore, by combining MODIS NDVI with measured meteorological data, monthly net primary productivity (NPP) was retrieved based on the Carnegie–Ames–Stanford approach (CASA) to characterize the maize biomass. Thus, the weight of drought stress in different growth stages of maize was obtained by combining the maize phenological information with TVDI and NPP. Moreover, TVDI drought stress was obtained to comprehensively assess maize drought stress. Finally, we fitted TVDI drought stress with NPP to establish a conceptual model for the response of maize biomass to drought stress in the NCP and NECP at a large spatiotemporal scale for the first time. The conceptual model revealed that the phenomenon of excessive moisture during maize growth was not evident (TVDI drought stress < 0.375) and that most maize plants were still affected by drought stress (TVDI drought stress > 0.375) in the NECP. Correspondingly, in the NCP, there was hardly any excessive moisture, and maize exhibited a certain resistance to low-intensity drought stress (TVDI drought stress was approximately 0.330). However, after the drought stress exceeded the tolerance range (TVDI drought stress > 0.375), the maize biomass was significantly affected by drought stress. Our findings illustrate the two response models of drought stress in the major maize producing areas of China for the first time, thereby providing new insights for preventing drought stress risk and improving maize yield on a large scale. [ABSTRACT FROM AUTHOR]

Spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) provides a new opportunity for land observation. This study is the first to compare and evaluate the performance of the only two spaceborne GNSS-R satellite missions whose data are publicly available, i.e., the UK's TechdemoSat-1 (TDS-1) and the US's Cyclone Global Navigation Satellite System (CYGNSS), for sensitivity analysis with SMAP SM on a daily basis and soil moisture (SM) estimates on a monthly basis over Mainland China. For daily sensitivity analysis, the two data were matched up and compared for the period (i.e., May 2017 through April 2018) when they coexisted (R = 0.561 vs. R = 0.613). For monthly SM estimates, a back-propagation artificial neural network (BP-ANN) was used to construct a model using data from more than two years. The model was subsequently used to derive long-term and continuous SM maps over Mainland China. The results showed that TDS-1 and CYGNSS agree and correlate very well with the SMAP SM in Mainland China (R = 0.676, MAE = 0.052 m3m−3, and ubRMSE = 0.060 m3m−3 for TDS-1; R = 0.798, MAE = 0.040 m3m−3, and ubRMSE = 0.062 m3m−3 for CYGNSS). The retrieved results were further validated using monthly in situ SM data from dense sites across Mainland China. It was found that the SM derived from the TDS-1/CYGNSS also correlated well with in situ SM (R = 0.687, MAE = 0.066 m3m−3, and ubRMSE = 0.056 m3m−3 for TDS-1; R = 0.724, MAE = 0.052 m3m−3, and ubRMSE = 0.053 m3m−3 for CYGNSS). The results in this study suggested that TDS-1/CYGNSS and the upcoming spaceborne GNSS-R mission could be new and powerful data sources to produce SM data set at a large scale and with relatively high precision. [ABSTRACT FROM AUTHOR]