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In cold regions, the coexistence and interconversion of liquid water and ice in frozen soils have important implications for energy partitioning and surface runoff at the Earth's surface. Passive microwave remote sensing is crucial for the global monitoring of soil moisture (SM). However, current research on SM focuses mainly on unfrozen soil conditions. Limited studies have been conducted on variations in soil liquid water content throughout the freezing season. This article investigated the potential use of brightness temperature observations from the Soil Moisture Active Passive (SMAP) satellite for retrieving all-season liquid SM. The single-channel algorithm and the Zhang-Zhao dielectric model, which was specifically developed for freezing and thawing soils, achieved successful retrieval of liquid SM in both frozen and thawed soils, even when snow cover was present. The results indicate improved spatial coverage (during winter) and consistent spatial patterns in SM compared with the SMAP products. Validation at 17 SM networks suggests that the retrieved all-season liquid SM effectively captures the dynamic characteristics of each region with an average bias of 0.011 m3/m3, an average unbiased root mean square error of 0.056 m3/m3, and an average correlation coefficient of 0.76. The additional retrieval of unfrozen water content during the freezing season would enhance the monitoring and understanding of the hydrological cycle and energy balance in cold regions.

Fusion of thermal infrared (TIR) and passive microwave (PMW) observations are important techniques for estimating all-weather land surface temperature (LST) of relatively higher resolutions. For such fusion techniques under cloudy weather, PMW observations are especially useful in correcting the cloud-shadowing bias of the real cloudy LST against a theoretically clear-sky one in the daytime. However, PMW observations have very limited transit times in a day, and cannot be fused with asynchronous TIR data as LST can vary significantly during the daytime. To overcome this flaw, we developed a model for simulating the variation of cloud-shadowing bias based on cloud total amount (CTA) information observed by geostationary satellites. As constant coefficients of the model were found to have dependence on space and time, they were dynamically fitted for each pixel in each month using cloud-shadowing bias calculated by TIR and synchronous PMW data in the early-afternoon (1:30 P.M.). The model was then used to provide middle-morning (10:30 A.M.) cloud-shadowing bias estimates, based on which, all-weather LST at 1 km were produced at that specific moment. Validation results show that reconstructed cloudy LST in the middle-morning, with no synchronous PMW observations, has its overall RMSE values against ground measurements and real remote sensing data under cloud between 2.4 and 4.0 K. This accuracy result is generally comparable to that obtained through fusing information from synchronous PMW data in the early-afternoon moment. Therefore, it is prospective to be further applied for generating high-resolution all-weather LST with much shorter re-visit cycles than the satellite PMW data.

Approximately 10% of China’s grasslands are severely degraded and 90% of them are overgrazed. To protect ecosystems and boost human well-being, payments for ecosystem services programs have been implemented to generate win-win outcomes for pastoralists and the grasslands. Taking a payment for ecosystem services program in Damao County, Inner Mongolia as an example, our study evaluated the ecological effects of the Grassland Ecological Compensation Policy (GECP) based on historical trends at the pixel, parcel, and county levels. We also evaluated the socioeconomic effects of GECP using both objective and subjective well-being at the household level. Our results show that: (1) at the pixel level, the percentages of additionally increased Normalized Difference Vegetation Index (NDVI) and Net Primary Production (NPP) were 93.4% and 93.3% after GECP implementation, corresponding to the average additional effects of 0.08 and 58.99 g C/m ^2 , respectively. At the parcel level, the GECP additionally increased NDVI between 0.02–0.17 (average of 0.08) and increased NPP between 28.36–115.15 (average of 60.30) g C/m ^2 , respectively. At the county level, the GECP additionally increased grassland NDVI and NPP by 0.07 (∼3.4% annually) and 53.63 g C/m ^2 (∼4.5% annually) from 2008 to 2020, respectively; and (2) the GECP implementation significantly improved pastoralists’ objective well-being ( P < 0.01) while the effects on subjective well-being indices were mixed. Our results also show that GECP effects on objective and subjective well-being significantly differ from households with large rangeland to those with small rangeland. We further discussed the experience, challenges, and opportunities of GECP. The long-term sustainability of GECP, particularly socioeconomic sustainability, still remains challenging and relies on guiding pastoralists to find alternative livelihoods. For future research and policy improvement, we call for the establishment of a better policy compensation mechanism that jointly considers the ecological effectiveness, economic efficiency, and social equity.

The ecological environment is the basis of human survival and development. Effective methods to monitor the ecological environment are essential for the healthy development of human settlements. At present, methods based on remote sensing images and other basic data have played key roles in ecological environment monitoring, providing support for decision-making on local ecological environment protection. However, these data and methods have obvious limitations. On the one hand, they cannot reflect the feelings of human beings about the ecological environment in which they live. On the other hand, it is difficult to capture more detailed information about the ecological environment. Non-professional observation data represented by social media describe the ecological environment from the perspective of the public, which can be a powerful supplement to traditional data. However, these different data sources have their own characteristics and forms, and it is difficult to achieve efficient integration. Therefore, in this paper, we proposed a framework that comprehensively considers social media, remote sensing, and other data to monitor the ecological environment of a study area. First, the framework extracted the ecological environment-related information contained in social media data, including public sentiment information and topic keyword information, by integrating algorithms such as natural language processing and machine learning. Then, we constructed a social semantic network related to the ecological environment based on the extracted information. We used a remote sensing image and other basic data to analyze the ecological sensitivity in the study area. Finally, based on the keyword with spatial location attribute contained in the social semantic network, we established the link between the constructed network and the results of ecological sensitivity analysis to comprehensively analyze the ecological environment in the study area. The comprehensive analysis results not only reflect the distribution of ecological vulnerability in the study area, but also help identify specific areas worthy of attention and the ecological problems faced by these areas. We used the city of Sanya in China as a case study to verify the effectiveness of the method in this paper.

Satellite-derived land surface temperature (LST) data are most commonly observed in the longwave infrared (LWIR) spectral region. However, such data suffer frequent gaps in coverage caused by cloud cover. Filling these ‘cloud gaps’ usually relies on statistical re-constructions using proximal clear sky LST pixels, whilst this is often a poor surrogate for shadowed LSTs insulated under cloud. Another solution is to rely on passive microwave (PM) LST data that are largely unimpeded by cloud cover impacts, the quality of which, however, is limited by the very coarse spatial resolution typical of PM signals. Here, we combine aspects of these two approaches to fill cloud gaps in the LWIR-derived LST record, using Kenya (East Africa) as our study area. The proposed “cloud gap-filling” approach increases the coverage of daily Aqua MODIS LST data over Kenya from 90%. Evaluations were made against the in situ and SEVIRI-derived LST data respectively, revealing root mean square errors (RMSEs) of 2.6 K and 3.6 K for the proposed method by mid-day, compared with RMSEs of 4.3 K and 6.7 K for the conventional proximal-pixel-based statistical re-construction method. We also find that such accuracy improvements become increasingly apparent when the total cloud cover residence time increases in the morning-to-noon time frame. At mid-night, cloud gap-filling performance is also better for the proposed method, though the RMSE improvement is far smaller (

Estimating accurately evapotranspiration (ET) in urban ecosystems is difficult due to the complex surface conditions and a lack of fine measurement of vegetation dynamics. To overcome such difficulties using recent developments of remote sensing technology, we estimate leaf area index (LAI) from Sentinel-2-based Normalized Difference Vegetation Index (NDVI) using the NDVI–LAI nonlinear relationship. By applying Sentinel-2-based LAI and land cover classification (LCC) to a carbon-water coupling model (PML-V2.1) with surface meteorological forcing data as input, we, for the first time, estimate monthly ET at 10m × 10m resolution for the Beijing Sponge City. Results show that for the whole sponge city during June 2018, the LAI, ET and gross primary productivity (GPP) are 0.83 m2 m−2, 1.6 mm d−1 and 2.8 gC m−2 d−1, respectively. For different LCCs, lakes and rivers have the highest ET (≥8 mm d−1), followed by mixed forests and croplands (ET is 4–6 mm d−1 and LAI is 2–3 m2 m−2) with dominant contribution (>80%) from plant transpiration, while grasslands (2–4 mm d−1) have 50–70% from transpiration due to smaller LAI (1~2 m2 m−2). The impervious surfaces occupying ~60% of the sponge city area, have the smallest ET (−1) in which interception evaporation by impervious surface contributes 20–30%, and transpiration from greenbelts (0.5–1.0 m2 m−2 of LAI) contributes 40–50%. These findings can provide a valuable scientific basis for policymaking and urban water use planning. This study proposes a Sentinel-2-based technology for estimating ET as a feasible framework to evaluate city-level hydrological dynamics in urban ecosystems.

Oilseed rape (Brassica napus L.) is one of the three most important oil crops in China, and is regarded as a drought-tolerant oilseed crop. However, it is commonly sensitive to waterlogging, which usually refers to an adverse environment that limits crop development. Moreover, crop growth and soil irrigation can be monitored at a regional level using remote sensing data. High spatial resolution optical satellite sensors are very useful to capture and resist unfavorable field conditions at the sub-field scale. In this study, four different optical sensors, i.e., Pleiades-1A, Worldview-2, Worldview-3, and SPOT-6, were used to estimate the dry above-ground biomass (AGB) of oilseed rape and track the seasonal growth dynamics. In addition, three different soil water content field experiments were carried out at different oilseed rape growth stages from November 2014 to May 2015 in Northern Zhejiang province, China. As a significant indicator of crop productivity, AGB was measured during the seasonal growth stages of the oilseed rape at the experimental plots. Several representative vegetation indices (VIs) obtained from multiple satellite sensors were compared with the simultaneously-collected oilseed rape AGB. Results showed that the estimation model using the normalized difference vegetation index (NDVI) with a power regression model performed best through the seasonal growth dynamics, with the highest coefficient of determination (R2 = 0.77), the smallest root mean square error (RMSE = 104.64 g/m2), and the relative RMSE (rRMSE = 21%). It is concluded that the use of selected VIs and high spatial multiple satellite data can significantly estimate AGB during the winter oilseed rape growth stages, and can be applied to map the variability of winter oilseed rape at the sub-field level under different waterlogging conditions, which is very promising in the application of agricultural irrigation and precision agriculture.