Root-zone soil moisture (RZSM) and surface soil moisture (SSM) are essential indicators for drought monitoring; however, studies on their effectiveness for drought monitoring is limited. For the first time, we investigated the use of a physically based model to predict RZSM at 50 cm depth and 100-m spatial resolution in the southern Great Plains (SGPs) of the United States for drought monitoring. The physically based model was primarily optimized using differential evolution algorithm and in situ observations (2015-2019) at site. Then, the optimized model was expanded to grid level (100-m resolution) with site-level parameters and covariates. Subsequently, we predicted RZSM from 2015 to 2019 at 100-m spatial resolution. The predicted RZSM showed acceptable accuracy during the optimization and validation periods, with root mean square error (RMSE) of 0.039 and 0.043 cm(3)/cm(3), respectively, conforming to NASA's quality benchmark (RMSE = 0.06 cm(3)/cm(3)) for predicting soil moisture from satellite measurements. Additionally, we evaluated the accuracy of the standardized soil moisture index (SSMI), produced from the predicted RZSM and SMAP-HB SSM in capturing drought episodes with respect to the United States drought monitor (USDM) observations. The predicted SSMI-RZSM captured between 53% and 90% of the drought episodes, with an average of 71% between 2017 and 2019 across the validation stations while the SSMI-SMAP-HB only captured between 33% and 64%, with an average of 46%, indicating that SSMI-RZSM is more effective than SSMI-SMAP-HB at capturing drought episodes. We concluded that accurate and reliable measurements of root-zone field capacity and porosity are key to reliable RZSM predictions using the proposed approach. This study establishes a solid foundation for regional RZSM predictions and drought monitoring at higher spatial resolution.

Maize is recognized as one of the four major crops in the world and plays an important role in global agri-food systems. Therefore, understanding how maize yield responds to climate change is essential for addressing the challenges of exponential population growth and food security. In this study, maize yield data in 77 countries from 1982 to 2016 and seven essential climate variables (ECVs) were collected to assess the effects of climate change on maize yield variation. To this end, potential ECVs were first divided into three groups: energy availability (net surface solar radiation, air temperature and land surface temperature), water availability (soil moisture and precipitation), and exchange efficiency (relative humidity and wind speed), which are closely related to crop growth. Correlation analysis was conducted to determine the best ECVs for further investigation. Furthermore, the generalized additive model (GAM) was used to express yield as function to the ECVs in each country. Specifically, the first-order difference in maize yield and ECVs were considered in data process to maximize the effects of reductions from other factors such as crop management and cultivars. Finally, the performance of the proposed approach was compared with that of widely used multiple regression method. The results indicate that: (1) a global average of 46% of maize yield variability can be explained by ECVs variability, yet significant discrepancies exist for different countries; (2) over 73% of countries are dominated by more than two groups of ECVs; (3) GAM generally outperforms the traditional multiple regression method in more than 80% of the investigated countries. This study offers a fresh perspective for investigating maize yield responses to climate change.

Land surface temperature (LST) and soil moisture (SM) are two important parameters in land surface ecosystem at the regional and global scale. The accurate acquisition of LST and SM can benefit various fields, including agriculture and climate which are closely related to human life. The independent retrievals of LST and SM from passive microwave observations are mutually restricted and highly dependent on auxiliary data. To solve this problem, a simulation retrieval method of LST and SM was proposed based on the characteristics of multifrequency and dual-polarization. The simultaneous solution of LST and SM was realized by approximating and correcting the radiative transfer equation (RTE). The performance of the proposed method was evaluated using simulated data, resulting in a root mean square error (RMSE) of approximately 1.63 K and 0.063 m(3)/m(3) . This method was further used to retrieve LST and SM from advanced microwave scanning radiometer for EOS (AMSR-E) observations. The retrieved LST was compared to the MODIS LST product under clear sky, with an RMSE of 5.68 K. The retrieved LST was validated using the Integrated Surface Database (ISD) air temperature under cloudy sky, with an RMSE of 4.29 K. The accuracy of retrieved LST changes with the variation of vegetation. The retrieved SM was evaluated using the Climate Change Initiative (CCI) SM product and in situ observations. The result shows that the accuracy ranges from 0.0157 to 0.1115 m(3)/m(3 )with the change of vegetation. This study gives a feasible method to retrieve LST and SM simultaneously with reasonable accuracy.

Land surface temperature (LST) is crucial for the energy balance between the Earth's surface and the atmosphere. Thermal infrared (TIR) and passive microwave (PMW) remote sensing are key methods for acquiring surface temperature globally and regionally. TIR observations have certain limitations due to their inability to penetrate cloud cover. Conversely, PMW measurements partially overcome this drawback to some extent, but their lower retrieval accuracy and coarse resolution limit its wider application. This study developed an artificial intelligence (AI) framework for precise and high-resolution LST estimation from PMW measurements, comprising PMW LST retrieval and downscaling components. Within this framework, high-resolution LST products have been obtained from Advanced Microwave Scanning Radiometer 2 (AMSR2), and the station-based validations and sensitivity analysis have also been conducted on the algorithm. The results were given as follows. First, the GeoFusionNet algorithm achieved higher LST retrieval accuracy than empirical or physical models. The mean absolute error (MAE) was 2.37 K (1.60 K) during daytime (nighttime). Second, the downscaled PMW LST retained high accuracy, with a daytime (nighttime) MAE increase of 0.28 K (0.14 K) compared to the Moderate Resolution Imaging Spectroradiometer (MODIS) 1-km product. Station-based validations showed that the coefficient of determination R-2 was above 0.9, with an average root-mean-squared error (RMSE) of 3.4 K (2.4 K) for daytime (nighttime) and an MAE of 2.80 K (1.98 K). Third, sensitivity analysis demonstrated the algorithm's stable performance, especially in summer and autumn. Spatially, the accuracy remained within 3 K for various land types, including cropland, evergreen forests, and deciduous forests. These results indicate that PMW LST retrieved by this framework has sufficient accuracy and fine-spatial resolution for monitoring dynamic changes in large-scale hydrological, climatic, and agricultural fields.

High-standard farmland construction is critical for sustainable land use and food security in China. Currently, scientific siting delineation for high-standard farmland at the county level is lacking. Delineation through evaluation and zoning is essential to improve land productivity. This study focuses on Dali County in Shaanxi Province, Fenwei Plain. It analyzes cultivated land changes using the land use transfer matrix and the PLUS. By integrating macro “township-parcel-grid” and micro “farmer” perspective, it establishes a comprehensive evaluation system for high-standard farmland delineation. We delineate construction zones and evaluate potential effect of high-standard farmland. The results reveal that: (1) From 2000 to 2020, the cultivated land area generally decreased. The permanent basic farmland positively contributes to the increase of cultivated land. Land use simulation in 2030 results show that the urgency for land consolidation. (2) The delineated high-standard farmland area in Dali is 85,081 hm2, with comprehensive evaluation index values ranging from 0.2229 to 0.5970, indicating that Dali has the potential for continuous construction of high-standard farmland. (3) This study zones high-standard farmland construction according to the difficulty and time sequence. Delineated high-standard farmland can play potential effect of agglomeration and demonstration, sustainable and coordinated development and grain production capacity increasing. We offer a scientific method for delineating zones of high-standard farmland within county, providing recommendations for sustainable high-standard farmland construction. It serves as a reference for the gradual conversion of all permanent basic farmland to high-standard farmland in China.

[目的]摸清耕地"非粮化"类型的时序数量转移和空间动态分布特征,为实现耕地"非粮化"分类管控提供科学方法和依据.[方法]文章以西北旱区农业生产典型县陕西大荔为研究区域,基于2000年、2010年和2020年3期的遥感解译数据,运用土地利用转移矩阵和标准差椭圆的方法,探究大荔县耕地"非粮化"的时序数量转移及空间动态分布特征.[结果](1)数量变化上,20年间大荔县耕地"非粮化"类型呈多元化增加趋势,且近10年"非粮化"率急剧上升;(2)转移类型上,20年间粮食作物是耕地"非粮化"面积增加的最大转出者,其中2000-2010年主要转向杂果树类,增量较小;2010-2020年主要转向杂果树类、设施农业和水产养殖,增量巨大;(3)空间动态上,20年间大荔县耕地"非粮化"的空间集聚性特征加强,其中粮食作物和撂荒地向南部地势较低区域集聚;杂果树类、苗圃花卉和设施农业向北部海拔较高区域扩张;而水产养殖向东部河流集聚.[结论]根据西北旱区不同耕地"非粮化"类型的时空演变特征,为政府分类管控"非粮化"现象、优化区域布局和生产结构提供参考依据.

Due to the effects of radio frequency interference and the limitations of algorithms under specific conditions, most of the currently available microwave-based soil moisture (SM) products are spatially discontinuous and have coarse spatial resolution, whereas optical observations also reveal various data gaps due to cloud contamination. Hence, the prediction of SM over invalid pixels and disaggregation from coarse to high scales are two main processes for obtaining SM at fine spatiotemporal resolution (e.g., daily/1-km). In the present study, two methods with respect to disaggregation-first or prediction-first were investigated from the synergetic use of the widely recognized European Space Agency-Climate Change Initiative (ESA-CCI) SM product and Moderate Resolution Imaging Spectroradiometer (MODIS) images over the Tibetan Plateau (TP) region. Specifically, the Disaggregation based on Physical And Theoretical scale Change (DisPATCh) algorithm and the generalized regression neural network (GRNN) were implemented in the disaggregation and prediction, respectively. In DisPATCh, spatially complete land surface temperature (LST), normalized difference vegetation index (NDVI) and digital elevation model (DEM) were provided as essential inputs to downscale the microwave-based ESA-CCI to a spatial resolution of 1 km, whereas MODIS-derived LST, NDVI, land surface albedo and DEM were considered in the GRNN prediction. Following the two methods, the daily/1-km SM dataset over a period of three years was finally estimated. Assessments with ground in-situ SM measurements over the TP region reveal an acceptable accuracy with unbiased root mean square errors of similar to 0.06 m3/m3, indicating the potential to obtain operational daily/1-km spatially continuous SM products in future developments.

[目的]从土地利用变化程度、过程及空间转移3个维度揭示西北旱区典型县域土地利用时空变化过程及特征,为区域土地政策的制定与实施提供参考依据.[方法]以陕西铜川耀州区为例,用2000年5月、2008年7月以及2015年8月的Landsat遥感影像为主要数据源,根据土地资源及其利用、自然属性状况,将研究区土地利用类型共分为7种(林地、耕地、草地、农村居民点、城镇用地、其他建设用地和水库坑塘),采用土地利用程度指数、土地利用转移矩阵和土地利用重心迁移模型,分析2000-2015年陕西铜川耀州区主要土地利用类型的时空格局变化特征.[结果](1)从利用程度来看,2000,2008及2015年耀州区土地利用程度综合指数依次为252.65,247.69和250.66,其中2000-2008年耀州区土地利用程度变化量为-4.97,处于调整期;2008-2015年耀州区土地利用程度变化量为2.97,处于发展期;2000-2015年,耀州区土地利用程度变化量为-2.00,土地利用总体处于调整期,仍有较大开发利用空间.2000-2015年,耕地利用程度变化量间呈持续下降趋势,开发利用水平较低,呈衰退趋势;林地利用程度变化量先上升后下降,总体呈持续发展趋势;而农村居民点、城镇用地及其他建设用地的利用程度变化量则呈持续增长趋势,处于发展阶段;水库坑塘和草地的利用程度变化量呈先下降后上升趋势.(2)从转移过程来看,在2000-2008和2008-2015年耀州区不同土地利用类型之间发生转移的面积分别为23 362.44和21 579.20 hm2,其中耕地和林地发生转移面积占总转移面积的比例均最高,其次是农村居民点用地、草地、其他建设用地和城镇用地,水库坑塘转移面积占比最小;各土地利用类型均进行了一定程度的相互转移,其中耕地与林地相互转化以及耕地向农村居民点、其他建设用地和城镇用地转化成为主要趋势.(3)从空间变化来看,在2000-2015年,耀州区各土地利用类型重心均进行了一定程度的空间迁移,其中其他建设用地和草地的重心转移距离均较大,林地的重心转移距离最小.[结论]2000-2015年陕西铜川耀州区土地利用变化存在明显的空间分异特征,土地利用格局演变加速.

【Objective】The study was conducted to promote the high-quality development of agriculture at county level in the Yellow River basin, deepen the understanding of agricultural inputs and outputs, and improve the increasingly tight constraints on agricultural resources and the deterioration of agricultural ecological environment, and to propose optimized development ideas and countermeasures.【Method】The land use changes of Dali County, Shannxi Province were analyzed by the land use transfer matrix, then the emergy analysis (EMA) method was used and an index system was constructed, several indexes based on the emergy flow were defined and calculated, the operational characteristics of agroecosystems in 2014 and 2019 and the sustainability of development were characterized and measured, the emergy values of inputs and outputs in agro-ecosystems were quantitatively analyzed and evaluated output emergy, then the key factors that constrain the sustainable development of agro-ecosystems were identified.【Result】Compared with the base period of 2014, the emergy self-sufficiency rate of the agro-ecosystem in 2019 (year of land use planning) decreased from 18% to 13%, and the net emergy output rate decreased from 2.10 to 1.27, the sustainability index decreased from 0.90 to 0.35, the emergy value investment rate increased from 4.51 to 6.71, the environmental load rate increased from 2.33 to 3.61, the emergy value density from 1.93×1012 sej/m2 to 2.69×1012 sej/m2, and the emergy consumption per capita from 1.03×1016 sej/person to 1.41×1016 sej/person. In 2019, compared with 2014, the area of forest land, artificial land and water bodies decreased by 5.75%, 0.60% and 60.06% respectively, and the area of grassland, cultivated land and wetland increased by 19.80%, 1.44% and 21.15%, respectively.【Conclusion】Land use change is not the main factor influencing the change of agricultural output emergy, the overall development level of agriculture in Dali County is also increasing while the costs of agricultural production are also increasing, and the process of agricultural modernization is relatively rapid, which is a consumer-oriented economic system. The investment in non-renewable industrial auxiliary energy values represented by pesticides, diesel and chemical fertilizers has increased significantly, increasing the pressure on the agricultural ecological environment.

Soil moisture (SM) is a key variable in the surface energy balance and water cycle, and its spatiotemporal dynamics are of great significance to climate, agriculture and other fields. Optical remote sensing has been widely used to estimate SM with relatively fine spatial resolution. However, optical observations are easily contaminated by clouds, making it difficult to obtain spatially continuous SM over large regions. In the present study, a semimonthly SM dataset over the study area of the entire Inner Mongolia region with nearly full spatial coverage was derived from the synergistic use of China's Feng-Yun (FY) geostationary (FY-4A) and polar-orbit (FY-3D) observations, following a previously developed trapezoid feature space in a pixel-to-pixel manner. A preliminary assessment was conducted to evaluate the performances of the proposed method over two main dominant land cover types (grassland and cropland) in the study region, where the China Meteorological Administration Land Data Assimilation System (CLDAS) and the Soil Moisture Active Passive (SMAP) SM products were provided as references. The results indicated that the estimated SM was well correlated to the referenced SM datasets, with a significant correlation coefficient varying from 0.5 to 0.8. Furthermore, for the grassland (cropland), unbiased root mean square errors of approximately 0.062 (0.097) m(3)/m(3) and 0.055 (0.069) m(3)/m(3) can be found when comparing the estimated SM with the CLDAS and the SMAP product, respectively.

Land surface temperature (LST) and soil moisture (SM) are two important parameters in land surface ecosystem at regional and global scale. The accurate acquisition of LST and SM can benefit various fields, including agriculture and climate which are closely related to human life. This study proposed a simultaneous retrieval method of LST and SM based on the approximate and correction of passive microwave radiation transfer equation. Compared to LST and SM in simulated database, the accuracy of retrieved LST is approximately 1.63 K and the accuracy of retrieved SM is about 0.063 m3/m3.

[目的]研究掌握退耕还林政策实施过程中的土地动态变化程度和景观梯度空间分布,促进区域土地政策优化调整,实现土地资源可持续和集约化利用.[方法]文章以西北旱区关中平原典型县域陕西耀州为研究区域,采用土地利用动态度模型、土地利用扩展程度综合指数和土地景观梯度模型,充分利用土地景观空间信息,研究分析2000—2015年陕西耀州主要地类旱地、林地动态变化程度和景观梯度空间信息.[结果]退耕还林政策实施效果明显.该政策直接影响研究区域旱地和林地的数量和空间分布,林地和旱地在2000—2008年间的动态变化程度较2008—2015年均表现更为剧烈,2000—2015年间旱地景观梯度空间分布为西北向东南方向递增和聚集,即东南区域以旱地景观聚集区为主,林地景观梯度空间分布为南部向北部方向递增和聚集,即中部和北部以林地景观聚集区为主,旱地退化过程也是向东南方向逐渐推进,旱地和林地景观经历了2000—2008年急速变化期和2008—2015年持续稳定期两个阶段.[结论]提出的分析方法能快速、客观反映研究区域旱地和林地的动态变化程度和景观变化空间信息,为土地政策调控和优化土地利用结构提供科学的参考依据.

Since 1982, Landsat series of satellite sensors continuously acquired thermal infrared images of the Earth’s land surface. In this study, Landsat 5, 7, and 8 land surface temperature (LST) products in the conterminous United States from 2009 to 2019 were validated using in situ measurements collected at 6 SURFRAD (Surface Radiation Budget Network) sites, 6 ARM (Atmospheric Radiation Measurement) sites, and 9 NDBC (National Data Buoy Center) sites. The results indicate that a relatively consistent performance among Landsat 5, 7, and 8 LST products is obtained for most sites due to the consistent LST retrieval algorithm in conjunction with the same atmospheric compensation and land surface emissivity (LSE) correction methods for Landsat 5, 7, and 8 sensors. Large bias and root mean square error (RMSE) of Landsat LST product are obtained at some vegetated sites due to incorrect LSE estimation where LSE is invariant with the increasing of normalized difference vegetation index (NDVI). Except for the sites with incorrect LSE estimation, a mean bias (RMSE) of the differences between Landsat LST and in situ LST is 1.0 K (2.1 K) over snow-free land surfaces, −1.1 K (1.6 K) over snow surfaces, and −0.3 K (1.1 K) over water surfaces.

China is one of the largest agricultural countries in the world. The NH3 emissions from agricultural activities in China significantly affect regional air quality and horizontal visibility. To reliably estimate the influence of NH3 on agriculture, a high-resolution agricultural NH3 emissions inventory, compiled with a 1 km × 1 km horizontal resolution, was applied to calculate the NH3 mass burden in China. The key emission factors of this inventory were enhanced by considering the results of many native experiments, and the activity data of spatial and temporal information were updated using statistical data from 2015. Fertilizer and husbandry, as well as farmland ecosystems, livestock waste, crop residue burning, fuel wood combustion, and other NH3 emission sources were included in the inventory. Furthermore, a source apportionment tool, ISAM (Integrated Source Apportionment Method), coupled with the air quality modeling system RAMS-CMAQ (Regional Atmospheric Modeling System and Community Multiscale Air Quality), was applied to capture the contribution of NH3 emitted from total agriculture (Tagr) in China. The aerosol mass concentration in 2015 was simulated, and the results showed that a high mass concentration of NH3, which exceeded 10 μg m−3, appeared mainly in the North China Plain (NCP), Central China (CNC), the Yangtz River Delta (YRD), and the Sichan Basin (SCB), and the annual average contribution of Tagr NH3 to PM2.5 mass burden in China was 14–18 %. Specific to the PM2.5 components, Tagr NH3 provided a major contribution to ammonium formation (87.6 %) but a tiny contribution to sulfate (2.2 %). In addition, several brute-force sensitivity tests were conducted to estimate the impact of Tagr NH3 emissions reduction on the PM2.5 mass burden. Compared with the results of ISAM, it was found that even though the Tagr NH3 only contributed 10.1 % of nitrate under current emissions scenarios, the reduction of nitrate could reach 98.8 % upon removal of the Tagr NH3 emissions. The main reason for this deviation could be that the NH3 contribution to nitrate is small under rich NH3 conditions and large in poor NH3 environments. Thus, the influence of NH3 on nitrate formation could be enhanced with the decrease of ambient NH3 mass concentration.

Land surface temperature (LST) is an important variable in the physics of land–surface processes controlling the heat and water fluxes over the interface between the Earth’s surface and the atmosphere. Space-borne remote sensing provides the only feasible way for acquiring high-precision LST at temporal and spatial domain over the entire globe. Passive microwave (PMW) satellite observations have the capability to penetrate through clouds and can provide data under both clear and cloud conditions. Nonetheless, compared with thermal infrared data, PMW data suffer from lower spatial resolution and LST retrieval accuracy. Various methods for estimating LST from PMW satellite observations were proposed in the past few decades. This paper provides an extensive overview of these methods. We first present the theoretical basis for retrieving LST from PMW observations and then review the existing LST retrieval methods. These methods are mainly categorized into four types, i.e., empirical methods, semi-empirical methods, physically-based methods, and neural network methods. Advantages, limitations, and assumptions associated with each method are discussed. Prospects for future development to improve the performance of LST retrieval methods from PMW satellite observations are also recommended.

Land surface temperature (LST) plays an important role in land surface processes, and it is a key input for estimating important hydrological states and fluxes, such as soil moisture and evapotranspiration. In this study, a three-channel method is proposed to retrieve cloudy LST values from passive microwave data based on the relationship among surface emissivities at 18.70, 36.50, and 89.00 GHz over a bare soil surface. The performance of the method was evaluated using simulated data, resulting in a root mean square error (RMSE) of approximately 1.4K over the bare soil surface. This method was further extended to retrieve cloudy LST values over a natural surface. Due to the lack of in situ LST measurements, ground-based air temperatures were used as proxy data to validate the cloudy LST values retrieved from AMSR-E data. The RMSE values of the differences between the retrieved cloudy LST values and the ground-based air temperatures are approximately 3.4K and 4.3K for the descending and ascending overpasses, respectively. The results demonstrate that the three-channel method can be used to retrieve cloudy LST values from passive microwave data with reasonable accuracy.

As a new remote sensing monitoring information, sun-induced chlorophyll fluorescence (SIF) has been widely used in the detection of changes in vegetation state at global and regional scales in recent years. SIF study in agricultural field has quickly become a research hotspot. TanSat is a global scientific experiment satellite for carbon dioxide monitoring launched in December 2016 with SIF monitoring capability. This paper aims to retrieve SIF based on a simplified physical model using TanSat data. The KI Fraunhofer Line at 770.1 nm was selected, which is less affected by the atmosphere. L1B-level data of TanSat was used to extract SIF of southeast China in July 2017. We used cloud mask products from MODIS data at the same period to eliminate cloud-affected areas and obtain SIF under clear sky conditions. The SIF value of our retrieval result is between -2 mW.m -2 . sr -1 . nm -1 and 5 mW. m -2 . sr -1 . nm -1 . Comparing the result with SIF products showed that there is an encouraging consistency between them. This indicated that the inversion algorithm based on the simplified physical model can be used to retrieve SIF from TanSat data.

Land surface temperature (LST) is a crucial parameter in the interaction between the ground and the atmosphere. The Sentinel-3A Sea and Land Surface Temperature Radiometer (SLSTR) provides global daily coverage of day and night observation in the wavelength range of 0.55 to 12.0 μm. LST retrieved from SLSTR is expected to be widely used in different fields of earth surface monitoring. This study aimed to develop a split-window (SW) algorithm to estimate LST from two-channel thermal infrared (TIR) and one-channel middle infrared (MIR) images of SLSTR observation. On the basis of the conventional SW algorithm, using two TIR channels for the daytime observation, the MIR data, with a higher atmospheric transmittance and a lower sensitivity to land surface emissivity, were further used to develop a modified SW algorithm for the nighttime observation. To improve the retrieval accuracy, the algorithm coefficients were obtained in different subranges, according to the view zenith angle, column water vapor, and brightness temperature. The proposed algorithm can theoretically estimate LST with an error lower than 1 K on average. The algorithm was applied to northern China and southern UK, and the retrieved LST captured the surface features for both daytime and nighttime. Finally, ground validation was conducted over seven sites (four in the USA and three in China). Results showed that LST could be estimated with an error mostly within 1.5 to 2.5 K from the algorithm, and the error of the nighttime algorithm involved with MIR data was about 0.5 K lower than the daytime algorithm.

[目的]地表组分温度是定量遥感反演的一个关键参数,在能量平衡过程模型和地表自然灾害监测中具有重要意义.[方法]过去的几十年中,国内外大量研究人员针对地表组分温度的反演提出了不同的方法和模型.文章系统回顾了现有的地表组分温度热红外遥感反演算法,包括多角度算法、多波段算法和时空信息算法,分析了各种反演算法的优缺点,评述了地表组分温度的验证方法.[结果/结论]地表组分温度反演方法发展至今已经取得了阶段性进展,有些研究成果已得到广泛运用.由于地表结构复杂性、卫星传感器硬件技术及卫星发射成本等客观因素的影响,地表组分温度反演仍存在一些难点和亟待解决的问题,如有效比辐射率会随观测角度的变化而改变的问题、多角度和多波段数据的相邻角度和波段数据之间均存在相关性较高的问题、多角度传感器不同角度观测到的目标面积和观测时间不一致的问题等.未来地表组分温度遥感反演仍然是一个需要不断深入研究的内容.

The change of global thermal environment plays an important role in land surface processes. In this study, global thermal environment was analyzed using the vertically polarized brightness temperature at 36.5 GHz. The daily brightness temperature from 2003 to 2010 were decomposed using the annual temperature cycle (ATC) model, and the annual cycle parameters (ACPs) were obtained. The results show that the brightness temperature decreases with the increasing latitudes respectively for the northern hemisphere and the southern hemisphere. The land covered by vegetation is colder than the desert and barren. Some plateaus lead to lower brightness temperature than surrounding areas. In addition, the atmospheric and ocean circulation also affect global brightness temperature. The ACPs from brightness temperature can generally characterize the global thermal environment.