The land surface albedo (LSA) represents the ability of the land surface to reflect solar radiation. It is one of the driving factors in the energy balance of land surface radiation and in land–air interactions. In this paper, we estimated the land surface albedo based on GF-1 WFV satellite data that have a high spatial and temporal resolution and cross-validated the albedo estimation results. The albedo estimations and validations were performed in the Ganzhou District, Zhangye City, China, and the Sindh Province, Pakistan. We used the direct estimation method which used a radiative transfer simulation to establish the relationship between the narrow band top of the atmosphere bidirectional reflectance and the land broadband albedo to estimate the albedo data. The results were validated with ground data, Landsat data, MODIS products, and GLASS products. The results show that the method can produce highly accurate albedo estimation results on different land cover types (RMSE: 0.026, R2: 0.835) and has a good consistency with the existing albedo products. This study makes a significant contribution to improving the utilization of GF data and contributes to the understanding of land–air interactions.

Land surface albedo (LSA) is one of the driving factors in the energy balance of surface radiation and the interaction between the earth and atmosphere. LSA is an important parameter that is widely used in surface energy balance, medium- and long-term weather forecasting, and global change studies. GF-1 wide field view (WFV) data provide a spatial resolution of 16 m and temporally intensive land surface observations, but efficient algorithm was still lacking for quantitatively land surface parameters estimation. It is essential to improve the data use ability by generating efficient land surface parameter retrieval algorithms. This study proposed an LSA retrieval algorithm by using GF-1 WFV data. Land surface bidirectional reflectance distribution function characteristic parameters were used to represent the non-Lambertian characteristic of land surface. The top of atmosphere (TOA) reflectance is simulated by the 6S radiative transfer model by considering non-Lambertian land surfaces. Linear regression is applied in the TOA reflectance, and LSA is simulated with the surface bidirectional reflectance characteristic parameters to build a lookup table. The proposed algorithm can estimate LSA with high accuracy according to the TOA reflectance without the complex multistep inversion process. The validation results of ground measurements in Northwest China for different land cover types show that the algorithm is effective, and the overall root mean square error was 0.036 when compared with field observation. The algorithm also shows great consistency with Landsat albedo data. The proposed algorithm is of great significance for improving GF data utilization.

Despite the advances in technologies to derive the maximum rate of Rubisco carboxylation (V-cmax) seasonality at large-scale, factors controlling the temporal dynamics of V-cmax is largely unknown without extensive field measurements at stand scale. In addition, state-of-the-art process-based terrestrial ecosystem models had not accounted the complex canopy structure for estimating radiation interception by vegetation. Both of which could lead to uncertainties in gross ecosystem production (GEP) estimations. Here, we examined the respective and combined effects of leaf age and canopy structure on GEP by integrating the Farquhar photosynthesis model with a two-leaf (sunlit and shaded) canopy radiation interception model based on the Geometric Optical and Radiative Transfer (GORT) theory. We observed that the V-cmax of new leaves was approximately 34.4% higher than that of mature leaves. Considering the seasonal dynamics of V-cmax caused by new leaf expansion, the average bias between the modeled GEP and that derived from the eddy covariance (EC) in the growing season at 8-day temporal scale was reduced from -8% to -1%, with slightly higher correlation (from 0.9 to 0.92) and reduction in root mean squared error (from 6.0 to 3.9 g C m(-2) 8d(-1)). Most importantly, the total effect of new leaf expansion and canopy gaps during the growing season was +322 g C m 2 yr(-1) and -114 g C m(-2) yr(-1), which was approximately 22.5% and 8.1% of the total GEP, respectively. Thus, it is important to consider both leaf age and canopy structure when developing a robust terrestrial ecosystem model. We highlighted that V-cmax seasonality and realistic canopy radiation interception simulation are critical for accurate estimation of canopy GEP for evergreen forests.

Land surface albedo (LSA) is an important parameter that affects surface–air interactions and controls the surface radiation energy budget. The spatial and temporal variation characteristics of LSA reflect land surface changes and further influence the local climate. Ganzhou District, which belongs to the middle of the Hexi Corridor, is a typical irrigated agricultural and desert area in Northwest China. The study of the interaction of LSA and the land surface is of great significance for understanding the land surface energy budget and for ground measurements. In this study, high spatial and temporal resolution GF-1 wide field view (WFV) data were used to explore the spatial and temporal variation characteristics of LSA in Ganzhou District. First, the surface albedo of Ganzhou District was estimated by the GF-1 WFV. Then, the estimated results were verified by the surface measured data, and the temporal and spatial variation characteristics of surface albedo from 2014 to 2018 were analyzed. The interaction between albedo and precipitation or temperature was analyzed based on precipitation and temperature data. The results show that the estimation of surface albedo based on GF-1 WFV data was of high accuracy, which can meet the accuracy requirements of spatial and temporal variation characteristic analysis of albedo. There are obvious geographic differences in the spatial distribution of surface albedo in Ganzhou, with the overall distribution characteristics being high in the north and low in the middle. The interannual variation in annual average surface albedo in Ganzhou shows a trend of slow fluctuations and gradual increases. The variation in annual albedo is characterized by “double peaks and a single valley”, with the peaks occurring from December to February at the end and beginning of the year, and the valley occurring from June to August. Surface albedo was negatively correlated with precipitation and temperature in most areas of Ganzhou.

叶面积指数LAI（Leaf Area Indexï¼‰æ˜¯è°ƒèŠ‚æ¤è¢«å† å±‚ç”Ÿç†è¿‡ç¨‹çš„æœ€é‡è¦çš„ç”Ÿç‰©ç‰©ç†å˜é‡ä¹‹ä¸€ï¼Œé«˜ç©ºé—´åˆ†è¾¨çŽ‡æ—¶é—´åºåˆ—LAIå¯¹äºŽæ¤è¢«ç”Ÿé•¿æ£€æµ‹ã€åœ°è¡¨è¿‡ç¨‹æ¨¡æ‹Ÿä¸ŽåŒºåŸŸå’Œå ¨çƒå˜åŒ–ç ”ç©¶è‡³å ³é‡è¦ï¼Œä½†æ˜¯ç”±äºŽæ•°æ®ç¼ºå¤±å’Œåæ¼”æ–¹æ³•é™åˆ¶ï¼Œç›®å‰è¿˜æ²¡æœ‰æ—¶ç©ºè¿žç»­çš„é«˜åˆ†è¾¨çŽ‡LAIæ•°æ®äº§å“ã€‚æœ¬ç ”ç©¶æå‡ºäº†ä¸€ç§ç”Ÿæˆæ—¶é—´è¿žç»­çš„é«˜ç©ºé—´åˆ†è¾¨çŽ‡LAIæ•°æ®çš„ç®—æ³•ï¼Œé¦–å ˆå¯¹MODIS LAI产品滤波平滑，生成时间序列LAIçš„ä¸ŠåŒ ç»œæ›²çº¿ï¼Œæ ¹æ®ä¸ŠåŒ ç»œæ›²çº¿æä¾›çš„å˜åŒ–ä¿¡æ¯æž„å»ºLAI动态模型。然后利用地面实测的LAI数据与Landsat反射率数据构建LAI反演的BP （Back Propagation）神经网络模型。将反演得到的高分辨率LAI数据作为LAI观测数据，利用集合卡尔曼滤波EnKF（Ensemble Kalman Filter）方法实时更新动态模型，生成时间连续的30 m空间分辨率LAI数据集。基于该算法生成了塞罕坝地区2000年—2018年长时间序列LAI数据集，利用Prophetæ·±åº¦å­¦ä¹ æ¨¡åž‹è¿›è¡Œæ¨¡æ‹Ÿå’Œé¢„æµ‹ï¼Œæ ¹æ®é¢„æµ‹å’ŒåŽŸå§‹LAI差异，利用支持向量机SVM（Support Vector Machine）方法检测植被干扰状况。结果表明：EnKF算法能够生成时空连续的高空间分辨率LAI数据，估算结果与地面测量值一致性较高，R2为0.9498，RMSE为0.1577，在区域尺度上与Landsat LAI参考值较为吻合，R2高于0.87，RMSE低于0.61。Prophet与SVMæ¨¡åž‹æ£€æµ‹åˆ°ç ”ç©¶åŒº2009年，2010年，2013年，2014年， 2015å¹´æ¤è¢«å—å¹²æ‰°è¾ƒä¸ºä¸¥é‡ï¼Œä¸»è¦ç”±äºŽå¹´é™æ°´é‡åå°‘å’Œæž—åŒºä½œä¸šç ä¼é€ æˆï¼Œæ£€æµ‹ç»“æžœä¸Žå½“åœ°é™æ°´é‡ä¸Žç ä¼æ•°æ®å»åˆã€‚æœ¬æ–‡æå‡ºçš„ç®—æ³•å¯ç”¨äºŽå¤§èŒƒå›´é«˜æ—¶ç©ºLAIæ•°æ®åæ¼”å’Œæ¤è¢«å˜åŒ–æ£€æµ‹ï¼Œå¯¹å¡žç½•åä¹ƒè‡³å ¨å›½æž—åŒºè§„åˆ’ç®¡ç†å ·æœ‰é‡è¦çš„å‚è€ƒä»·å€¼ã€‚

High spatial resolution and high temporal frequency fractional vegetation cover (FVC) products have been increasingly in demand to monitor and research land surface processes. This paper develops an algorithm to estimate FVC at a 30-m/15-day resolution over China by taking advantage of the spatial and temporal information from different types of sensors: the 30-m resolution sensor on the Chinese environment satellite (HJ-1) and the 1-km Moderate Resolution Imaging Spectroradiometer (MODIS). The algorithm was implemented for each main vegetation class and each land cover type over China. First, the high spatial resolution and high temporal frequency normalized difference vegetation index (NDVI) was acquired by using the continuous correction (CC) data assimilation method. Then, FVC was generated with a nonlinear pixel unmixing model. Model coefficients were obtained by statistical analysis of the MODIS NDVI. The proposed method was evaluated based on in situ FVC measurements and a global FVC product (GEOV1 FVC). Direct validation using in situ measurements at 97 sampling plots per half month in 2010 showed that the annual mean errors (MEs) of forest, cropland, and grassland were −0.025, 0.133, and 0.160, respectively, indicating that the FVCs derived from the proposed algorithm were consistent with ground measurements [ R 2 = 0.809, root-mean-square deviation (RMSD) = 0.065]. An intercomparison between the proposed FVC and GEOV1 FVC demonstrated that the two products had good spatial-temporal consistency and similar magnitude (RMSD approximates 0.1). Overall, the approach provides a new operational way to estimate high spatial resolution and high temporal frequency FVC from multiple remote sensing datasets.

Background Determining the spatial distribution of tree heights at the regional area scale is significant when performing forest above-ground biomass estimates in forest resource management research. The geometric-optical mutual shadowing (GOMS) model can be used to invert the forest canopy structural parameters at the regional scale. However, this method can obtain only the ratios among the horizontal canopy diameter (CD), tree height, clear height, and vertical CD. In this paper, we used a semi-variance model to calculate the CD using high spatial resolution images and expanded this method to the regional scale. We then combined the CD results with the forest canopy structural parameter inversion results from the GOMS model to calculate tree heights at the regional scale. Results The semi-variance model can be used to calculate the CD at the regional scale that closely matches (mainly with in a range from − 1 to 1 m) the CD derived from the canopy height model (CHM) data. The difference between tree heights calculated by the GOMS model and the tree heights derived from the CHM data was small, with a root mean square error (RMSE) of 1.96 for a 500-m area with high fractional vegetation cover (FVC) (i.e., forest area coverage index values greater than 0.8). Both the inaccuracy of the tree height derived from the CHM data and the unmatched spatial resolution of different datasets will influence the accuracy of the inverted tree height. And the error caused by the unmatched spatial resolution is small in dense forest. Conclusions The semi-variance model can be used to calculate the CD at the regional scale, together with the canopy structure parameters inverted by the GOMS model, the mean tree height at the regional scale can be obtained. Our study provides a new approach for calculating tree height and provides further directions for the application of the GOMS model.

目前，估算高分辨率叶面积指数LAI（Leaf Area Index）的常用方法是采用大量地面测量数据和遥感数据建立统计模型，再用统计模型估算LAIã€‚ç„¶è€Œï¼Œä¸Žå†œç”°åœ°é¢æµ‹é‡å®žéªŒç›¸æ¯”ï¼Œæ£®æž—åœ°é¢æµ‹é‡å®žéªŒèŽ·å–çš„è§‚æµ‹æ•°æ®æ›´åŠ æœ‰é™ï¼Œè¿™ä½¿å¾—åŸºäºŽç»Ÿè®¡æ¨¡åž‹çš„æ£®æž—é«˜åˆ†è¾¨çŽ‡LAIçš„ä¼°ç®—ç²¾åº¦ä½Žï¼Œéš¾ä»¥æ»¡è¶³åº”ç”¨éœ€æ±‚ã€‚ä¸ºæ­¤ï¼Œæœ¬æ–‡æå‡ºä¸€ç§åŸºäºŽæ£®æž—æ¨¡åž‹å‚æ•°å ˆéªŒçŸ¥è¯†ã€ä½¿ç”¨æ£®æž—ç ”ç©¶åŒºå°‘é‡çš„LAI地面测量数据和归一化植被指数NDVI数据估算森林高分辨率LAIçš„æ–¹æ³•ã€‚é¦–å ˆï¼ŒèŽ·å–å ¨çƒ20个森林实验区的LAI地面测量数据和NDVI数据，建立LAI-NDVIç»Ÿè®¡æ¨¡åž‹å¹¶æå–æ£®æž—æ¨¡åž‹å‚æ•°çš„å ˆéªŒçŸ¥è¯†ã€‚ç„¶åŽï¼Œä»¥ä¸€ä¸ªæ–°çš„æ£®æž—ç«™ç‚¹Concepciónä½œä¸ºç ”ç©¶åŒºï¼Œå°†è¯¥ç ”ç©¶åŒºçš„æ•°æ®åˆ†ä¸ºå»ºæ¨¡æ•°æ®å’ŒéªŒè¯æ•°æ®ä¸¤ä¸ªéƒ¨åˆ†ã€‚ä½¿ç”¨ç ”ç©¶åŒºæœ‰é™çš„å»ºæ¨¡æ•°æ®å¯¹æ£®æž—æ¨¡åž‹å‚æ•°å ˆéªŒçŸ¥è¯†è¿›è¡Œæœ¬åœ°åŒ–æ ¡æ­£å¾—åˆ°ä¼˜åŒ–æ¨¡åž‹ï¼Œä¼˜åŒ–æ¨¡åž‹ç”¨äºŽä¼°ç®—æ£®æž—é«˜åˆ†è¾¨çŽ‡LAI，使用验证数据评价LAI的估算精度。同时，选取了Camerons站点、Gnangara站点、Hirsikangas站点评价本文方法的LAI估算精度。使用地面测量LAIéªŒè¯åŸºäºŽæ£®æž—æ¨¡åž‹å‚æ•°å ˆéªŒçŸ¥è¯†ä¼°ç®—é«˜åˆ†è¾¨çŽ‡LAI的结果精度，经验证4ä¸ªæ£®æž—ç«™ç‚¹çš„å‡æ–¹æ ¹è¯¯å·®åˆ†åˆ«ä¸º0.6680，0.4449，0.2863，0.5755ã€‚ç ”ç©¶ç»“æžœè¡¨æ˜Žï¼šåœ¨ä» æœ‰å°‘é‡è§‚æµ‹æ•°æ®æ—¶ï¼Œé‡‡ç”¨æœ¬æ–¹æ³•èƒ½æœ‰æ•ˆåœ°æé«˜æ£®æž—é«˜åˆ†è¾¨çŽ‡LAIçš„ä¼°ç®—ç²¾åº¦ã€‚å› æ­¤ï¼Œæœ¬æ–¹æ³•å¯ä¸ºæ£®æž—é«˜åˆ†è¾¨çŽ‡LAI的遥感估算提供参考。

The amount of snow affects the energy, water, and carbon balance on the Earth's surface and the interactions between the land and the atmosphere. It is a key parameter for the numerical weather prediction and the long-term climate research. Therefore, it is very important to monitor snow water equivalent (SWE). This chapter describes the methods to measure SWE using both the ground-based and the remote sensing techniques, and introduces the spatiotemporal distribution characteristics of the SWE and its application in the hydrological, meteorological, biological and economical fields. Microwave remote sensing has a strong physical basis to detect snow depth and SWE, due to the penetration ability of microwave and its sensitivity to the snow volume scattering. Therefore, we focus on the theories and algorithms using the passive and active microwave remote sensing. The optical remote sensing utilizes the snow cover fraction to build empirical relationship with SWE. The SWE can also be "reconstructed" by summing the snowmelt calculated by the snow cover fraction from the last to the current snow days. These methods are also briefly introduced, as an extension to the microwave remote sensing.

Despite the long-term records of satellite observations, factors controlling the seasonal and interannual variations in canopy reflectance remain poorly understood. Leaf optical properties (LOP, including leaf reflectance and transmittance) changes as leaves age, and thus impact the seasonal pattern of canopy reflectance, i.e., the “leaf age effect”. Here, we combined the Geometric Optical Radiative Transfer (GORT) model with continuous field measurements of leaf- to stand-scale characteristics to simulate canopy reflectance in a Chinese fir plantation with stand development (1-33 yr). We found that canopy structure controls the variations in canopy reflectance during young stages (<; 10 yr) and that leaf age controls the variations in canopy reflectance after canopy closure. Moreover, we found that the “leaf age effect” get enhanced with stand development, with R2 increased from about 0.1 to 0.56, 0.67, 0.92, and 0.82 for young, half-mature, near-mature, and mature stages, respectively. This study reveals the stand age dependence of leaf age effect on canopy reflectance which improves our interpretation and understanding of satellite observations to study the ecosystem function of forests.

Because of the influences of cloud cover, seasonal snow, and many other factors, time series of land surface parameters extracted from remote sensing data often suffer from discontinuities and missing data, which have seriously restricted the application of extracted land surface parameters to research in global change and other fields. This chapter introduces several different techniques for compositing, smoothing, and gap filling of remotely sensed time series data, which generate land surface parameter products that are temporally and spatially continuous. A multitemporal compositing method is designed to select the optimal remote sensing data as the pixel value in a composite time window, according to certain criteria. This is a common processing method used to obtain cloud-free and spatially continuous images. Section 3.1 of this chapter gives a detailed description of several commonly used compositing algorithms. Here, time series data smoothing and gap-filling methods are applied to reconstruct high-quality time series data with temporal continuity and spatial integrity, thereby eliminating the influences of cloud. Section 3.2 introduces several typical smoothing and gap-filling algorithms for time series data.

Soil moisture content (SMC) is an important parameter in various applications. This chapter reviews conventional techniques for in situ point measurements and then introduces the basic principles, sensors, and inversion methods of microwave remote sensing in both the passive and active modes. Various optical and thermal IR methods are also reviewed. A list of SMC products is given at the end.

The fraction of absorbed photosynthetically active radiation (FAPAR) characterizes the energy absorption capacity of vegetation canopy. It is a basic physiological variable describing the vegetation structure and related material and energy exchange processes and is an important parameter for estimating the net primary production of terrestrial ecosystem using a remote sensingebased method. This chapter first discusses some related concepts and then introduces the principles and current status of FAPAR estimation. At present, the remote sensingebased methods for estimating FAPAR can be divided into two categories: empirical methods and methods using radiative transfer models or other physical models. Section 11.3 introduces FAPAR products as well as their intercomparison results. Case studies of the study area near the AmeriFlux sites are given in Section 11.4.

This chapter introduces the principles and algorithms for estimating downward surface shortwave radiation and photosynthetically active radiation (PAR) from remotely sensed data. The advantages and disadvantages of the existing algorithms are also briefly discussed. Section 5.1 briefly introduces basic concepts, such as the solar radiation spectrum, solar constant, and shortwave radiation and PAR. Section 5.2 presents the current observation networks of global solar radiation, including global energy balance archive, baseline surface radiation network, surface radiation budget network, and FLUXNET. Section 5.3 introduces the related solar radiation estimation methods. Section 5.4 introduces the current existing solar radiation products from remote sensing observations. This chapter ends with a short summary.

Land surface temperature (LST) and land surface emissivity (LSE) determine the longwave radiation in land surface radiation and energy budgets and are the key input parameters in climatic, hydrological, ecological, and biogeochemical models. Section 7.1 provides the traditional definitions of temperature and emissivity as well as several definitions for the temperature and emissivity of heterogeneous and nonisothermal mixed pixels using remote sensing pixel scales. Section 7.2 illustrates the algorithm used to retrieve the average LST, including the retrieval algorithms designed for thermal infrared data and passive microwave data. Section 7.3 first presents two methods for LSE measurements and then introduces typical LSE retrieval algorithms. Finally, a formula for converting a narrowband emissivity into a broadband emissivity is described. Section 7.4 lists the most frequently used LST and LSE products. Section 7.5 briefly describes the fusion of thermal infrared LST and microwave LST.

This chapter provides the theoretical basis for measurement and estimation of terrestrial evapotranspiration. It includes the Monin-Obukhov similarity theory (MOST) and the Penman-Monteith equation. We focus on the application of these theories to satellite remote sensing. Both the MOST and the Penmane Monteith equations are empirical in nature, and they require several assumptions to be met. Further parameterizations and assumptions are necessary to relate the satellite-derived land surface variables to terrestrial evapotranspiration. There is no difference in the physics between the two formulations as the Penman-Monteith equation is derived from MOST. However, there are important differences between the two formulations in their practical application: MOST-like methods use airesurface temperature differences and require accurate estimates of these terms. Therefore, MOST-like methods are sensitive to errors in these differences. This sensitivity is substantially reduced by the Penman-Monteith equation related methods, which use the available energy and stomatal conductance that can be empirically parameterized as a function of the vegetation index (leaf area index), incident radiation, and relative humidity. The accuracy of a given model is usually the first consideration in its development, but the required ancillary data and the method's suitability for different conditions are considerations for satellite remote methods as well. Satellite remote sensing can now reliably determine the spatial variability of land surface variables and terrestrial evapotranspiration; however, it still has difficulty in providing long-term, stable estimates.

This chapter describes the production and management of high-level remote sensing data products. The data production system provides task management, algorithm execution, data quality inspection, and system monitoring functions. An overview of the ground segment system is presented. For a very long time, Earth observation agencies have played a major role in remote sensing data management. The Google Earth Engine system consists of four main parts-a data management system, computing engine system, programming interface, and user interface system-which dramatically accelerates the research efficiency for remote sensing scientists.

Quantitative remote sensing is an inevitable trend in the application of remote sensing in the 21st century. Its core role is to establish a quantitative relationship between the electromagnetic spectrum and the information obtained by the sensor and then use electromagnetic wave information to quantitatively detect all kinds of surface characteristic parameters. Quantitative remote sensing is based on the precise calibration of sensors and the atmospheric correction of remote sensing data. This chapter introduces the atmospheric effects on the process of remote sensing imaging and atmospheric correction to specifically discuss the utilization of atmospheric correction in processing remote sensing data. Section 1 presents the background of atmospheric effects. Section 2 discusses various algorithms for the estimation of aerosol properties, which are based on spectral, temporal, angular, spatial and polarization characteristics. Section 3 introduces the algorithms used for estimating atmospheric moisture content. Then, the influences of other atmospheric components and commonly used atmospheric correction models are illustrated. Finally, an application of atmospheric correction is presented.

卫星遥感技术的快速发展使得获取全球大范围叶面积指数成为可能,但基于现有的算法和数据估算高分辨率LAI的精度还需要提高.针对农作物、草地和林地等3种典型地表类型,选取地面观测数据较多的4个研究区,包括3个各地类用于建模验证的研究区与一个用于适用性验证的独立研究区,针对4个研究区,分别获取地面测量数据以及对应的30 m空间分辨率地表反射率数据.在3个主要研究区建立并比较了NDVI植被指数经验模型、BP神经网络模型和基于模拟退火算法的BP神经网络模型,利用地面实测数据对模型进行验证.结果表明:在研究所选的3个主要研究区,基于模拟退火算法的BP神经网络模型的估算精度比BP神经网络模型和NDVI经验模型的估算精度高,农田、草地和林地站点估算结果的决定系数分别为0.899、0.858和0.863,BP神经网络模型的估算结果决定系数分别为:0.763、0.710和0.742,NDVI经验模型的精度最差,其估算结果的决定系数分别为0.622、0.536和0.637.为了验证SA-BP神经网络的适用性,选取独立研究区进行验证,结果显示验证精度较高,R2为0.842,RMSE为0.6895,说明该模型外推能力较好.研究证明了基于模拟退火算法的BP神经网络模型提高了模型泛化能力,有效防止了BP神经网络模型滑入局部最小值,是提高高空间分辨率LAI估算精度的有效手段.