Estimation of regional evapotranspiration over the North China Plain using geostationary satellite data

Data from the first operational Chinese geostationary satellite Fengyun-2C (FY-2C) satellite are applied in combination with Moderate Resolution Imaging Spectroradiometer (MODIS) satellite products for the assessment of regional evapotranspiration over the North China Plain. The approach is based on the improved triangle method, where the temperature–vegetation index space includes thermal inertia. Two thermal infrared channels from FY-2C are used to estimate surface temperature (Ts) based on a split window algorithm originally proposed for the MSG-SEVIRI sensor. Subsequently the high temporal resolution of FY-2C data is exploited to give the morning rise in Ts. Combined with the 16 days composite MODIS vegetation indices product (MOD13) at a spatial resolution of 5 km, evaporative fraction (EF) is estimated by interpolation in the ΔTs–NDVI triangular-shaped scatter space. Finally, regional actual evapotranspiration (ET) is derived from the evaporative fraction and available energy estimated from MODIS surface albedo products MCD43. Spatial variations of estimated surface variables (Ts, EF and ET) corresponded well to land cover patterns and farmland management practices. Estimated ET and EF also compared well to lysimeter data collected for the period June 2005–September 2007. The improved triangle method was also applied to MODIS products for comparison. Estimates based on FY-2C products proved to provide slightly better results than those based on MODIS products. The consistency of the estimated spatial variation with other spatial data supports the use of FY-2C data for ET estimation using the improved triangle method. Of particular value is the high temporal frequency of image acquisitions from FY-2C which improves the likelihood of obtaining cloud free image acquisitions as compared to polar orbiting sensors like MODIS.     

Analysis of urban built-up areas and surface urban heat island using downscaled MODIS derived land surface temperature data

Regional scale urban built-up areas and surface urban heat islands (SUHI) are important for urban planning and policy formation. Owing to coarse spatial resolution (1000 m), it is difficult to use Moderate Resolution Imaging Spectroradiometer (MODIS) Land surface temperature (LST) products for mapping urban areas and visualization, and SUHI-related studies. To overcome this problem, the present study downscaled MODIS (1000 m resolution)-derived LST to 250 m resolution to map and visualize the urban areas and identify the basic components of SUHI over 12 districts of Punjab, India. The results are compared through visual interpretation and statistical procedure based on similarity analysis. The increased entropy value in the downscaled LST signifies higher information content. The temperature variation within the built-up and its environs is due to difference in land use and is depicted better in the downscaled LST. The SUHI intensity analysis of four cities (Ludhiana, Patiala, Moga and Vatinda) indicates that mean temperature in urban built-up core is higher (38.87 °C) as compared to suburban (35.85 °C) and rural (32.41 °C) areas. The downscaling techniques demonstrated in this paper enhance the usage of open-source wide swath MODIS LST for continuous monitoring of SUHI and urban area mapping, visualisation and analysis at regional scale. Such initiatives are useful for the scientific community and the decision-makers.     

石羊河流域蒸散发量遥感估算及时空格局分析

根据能量平衡原理,采用MODIS图像数据反演石羊河流域的地表蒸散发(Evapotranspiration,ET),对石羊河流域1月、4月、7月和10月的日均ET进行了估算(1月的日ET为0.15～7.21 mm,4月的日ET为0.89～7.86 mm,7月的日ET为0.12 ～9.08 mm,10月的日ET为0.54～...     

Estimation of soil moisture using deep learning based on satellite data: a case study of South Korea

The Korea Meteorological Administration uses soil moisture (SM) observed by the Advanced Microwave Scanning Radiometer-2 (AMSR2) to monitor drought. However, it may not be appropriate for monitoring drought in South Korea due to significant underestimation of SM. In this study, we used a deep learning method that performs better than traditional statistical and physical models for reliable estimation of SM based on remotely sensed satellite data. For estimating SM, we carefully selected input variables that exhibit a feedback loop with SM. To build an effective deep learning model, we examined the influences of sampling criteria and input parameters as well as the accuracy of several deep neural networks. The selected model was cross-validated to determine its stability. The estimated SM using deep learning had a high correlation coefficient (R) of 0.89 and a low root mean square error (RMSE; 3.825%) and bias (?0.039%) compared to in-situ measurements. A time series analysis using dynamic time warping was conducted which showed that the estimated SM was almost similar to the in-situ SM. In order to investigate the improvement in SM estimation using our method, it was compared with the Global Land Data Assimilation System and AMSR2. Significant improvements in R and a reduction in error values by more than half were achieved using our method. The estimated SM has finer spatial resolution at 4 km, and it can be rapidly produced, which will be useful for drought monitoring over the Korean Peninsula in near-real-time.     

A practical algorithm for estimating surface soil moisture using combined optical and thermal infrared data

Surface soil moisture (SSM) is a critical variable for understanding the energy and water exchange between the land and atmosphere. A multi-linear model was recently developed to determine SSM using ellipse variables, namely, the center horizontal coordinate (x₀), center vertical coordinate (y₀), semi-major axis (a) and rotation angle (θ), derived from the elliptical relationship between diurnal cycles of land surface temperature (LST) and net surface shortwave radiation (NSSR). However, the multi-linear model has a major disadvantage. The model coefficients are calculated based on simulated data produced by a land surface model simulation that requires sufficient meteorological measurements. This study aims to determine the model coefficients directly using limited meteorological parameters rather than via the complicated simulation process, decreasing the dependence of the model coefficients on meteorological measurements. With the simulated data, a practical algorithm was developed to estimate SSM based on combined optical and thermal infrared data. The results suggest that the proposed approach can be used to determine the coefficients associated with all ellipse variables based on historical meteorological records, whereas the constant term varies daily and can only be determined using the daily maximum solar radiation in a prediction model. Simulated results from three FLUXNET sites over 30 cloud-free days revealed an average root mean square error (RMSE) of 0.042 m³/m³ when historical meteorological records were used to synchronously determine the model coefficients. In addition, estimated SSM values exhibited generally moderate accuracies (coefficient of determination R² = 0.395, RMSE = 0.061 m³/m³) compared to SSM measurements at the Yucheng Comprehensive Experimental Station.     

时序双极化SAR开采沉陷区土壤水分估计

开采沉陷地质灾害诱发矿区生态环境恶化的关键因子是土壤水分变化。研究提出了一种利用Sentinel-1A双极化SAR和OLI地表反射率数据联合反演土壤含水量的方法,即基于归一化水体指数(NDWI)反演植被含水量;采用Water-Cloud Model(WCM)模型消除植被对Sentinel-1A后向散射系数产生的影响,将其转化为裸土区的后向散射系数;利用基于AIEM模型和Oh模型建立的经验模型反演研究区地表参数,并用OLI光学反演结果进行验证;最后比较了开采沉陷区内外土壤水分含量。研究表明:(1)与基于OLI的土壤水分监测指数(SMMI)的土壤水分含量反演结果相比,两种极化方式中VH极化反演的水分结果具有更好的一致性,且两种极化方式反演结果也表明荒漠化草原区比黄土丘陵沟壑区反演效果更好,说明地形对后向散射的影响不可忽略。(2)在2016年内72期数据中,VH极化反演结果对比区土壤水分含量大于沉陷区的有41期,所占比例为57%;VV极化反演结果对比区土壤水分含量大于沉陷区的有36期,所占比例为50%,且不同矿区内的沉陷区受到的影响不同。说明开采沉陷造成的地表粗糙度的增加会对地表土壤水分产生负面影响,但不同矿区之间又有差异。     

Retrieval of land surface temperature (LST) from landsat TM6 and TIRS data by single channel radiative transfer algorithm using satellite and ground-based inputs

The present study proposes land surface temperature (LST) retrieval from satellite-based thermal IR data by single channel radiative transfer algorithm using atmospheric correction parameters derived from satellite-based and in-situ data and land surface emissivity (LSE) derived by a hybrid LSE model. For example, atmospheric transmittance (τ) was derived from Terra MODIS spectral radiance in atmospheric window and absorption bands, whereas the atmospheric path radiance and sky radiance were estimated using satellite- and ground-based in-situ solar radiation, geographic location and observation conditions. The hybrid LSE model which is coupled with ground-based emissivity measurements is more versatile than the previous LSE models and yields improved emissivity values by knowledge-based approach. It uses NDVI-based and NDVI Threshold method (NDVI^THM) based algorithms and field-measured emissivity values. The model is applicable for dense vegetation cover, mixed vegetation cover, bare earth including coal mining related land surface classes. The study was conducted in a coalfield of India badly affected by coal fire for decades. In a coal fire affected coalfield, LST would provide precise temperature difference between thermally anomalous coal fire pixels and background pixels to facilitate coal fire detection and monitoring. The derived LST products of the present study were compared with radiant temperature images across some of the prominent coal fire locations in the study area by graphical means and by some standard mathematical dispersion coefficients such as coefficient of variation, coefficient of quartile deviation, coefficient of quartile deviation for 3rd quartile vs. maximum temperature, coefficient of mean deviation (about median) indicating significant increase in the temperature difference among the pixels. The average temperature slope between adjacent pixels, which increases the potential of coal fire pixel detection from background pixels, is significantly larger in the derived LST products than the corresponding radiant temperature images.     

基于作物缺水指数的土壤含水量估算方法

为研究江苏省徐州市的土壤水分时空分布及动态变化,基于MODIS数据和站点气象数据,利用蒸散发双层模型和考虑土壤水分可供率的改进双层模型分别计算实际蒸散发量,利用Penman-Monteith模型计算区域潜在蒸散发量,计算获得作物缺水指数(crop water stress index,CWSI),并与2010年7月和11月的土壤相对含水量实测数据分别进行回归分析建模,得到了土壤含水量分布图。结果表明:基于蒸散发双层模型的土壤含水量估算结果与实测值的决定系数分别为0.53和0.72,平均相对误差分别为5.89%和9.6%;对双层模型进行改进后,土壤含水量估算结果与实测值的决定系数都为0.84,平均相对误差分别为3.47%和6.03%,利用改进后的双层模型对土壤相对含水量进行估算效果更好。     

中国西部地区基于经验模型的土壤水分反演

地表土壤水分含量的时空分布信息是十分重要的,常常作为水文模型、气候模型、生态模型的输入参数,同时,也是干旱预报、农作物估产等工作的重要指标。被动微波遥感是监测土壤含水量最有效的手段之一。相比红外与可见光,它具有波长长,穿透能力强的优势。相比主动微波雷达,被动微波辐射计具有监测面积大、周期短,受粗糙度影响小,对土壤水分更为敏感,算法更为成熟的优势。目前,已研究出许多反演土壤水分的方法.本课题的主要内容是借助AMSR-E土壤水分影像数据、MODIS归一化植被指数(NDVI)影像数据和MODIS分类影像数据,利用ENVI软件进行遥感图像数据处理,运用统计分析方法建立NDVI与土壤水分的经验模型,研究中国西部地区稀疏植被覆盖区土壤水分的反演。     

闪电河流域农牧交错带微波遥感土壤水分产品评价

空间网格分辨率为9 km 的 SMAP(Soil Moisture Active and Passive)、0.1D(Degree)的 ASCAT(The Advanced Scatterometer)、25 km 的 FY-3B 以及25 km ESA-CCI(European Space Agency-Climat...     

Long-term soil moisture content estimation using satellite and climate data in agricultural area of Mongolia

The purpose of this study is to estimate long-term SMC and find its relation with soil moisture (SM) of climate station in different depths and NDVI for the growing season. The study area is located in agricultural regions in the North of Mongolia. The Pearson’s correlation methodology was used in this study. We used MODIS and SPOT satellite data and 14 years data for precipitation, temperature and SMC of 38 climate stations. The estimated SMC from this methodology were compared with SM from climate data and NDVI. The estimated SMC was compared with SM of climate stations at a 10-cm depth (r² = 0.58) and at a 50-cm depth (r² = 0.38), respectively. From the analysis, it can be seen that the previous month’s SMC affects vegetation growth of the following month, especially from May to August. The methodology can be an advantageous indicator for taking further environmental analysis in the region.     

基于CCI和MODIS数据的淮河流域地表土壤湿度降尺度方法研究

土壤湿度是控制陆地和大气间水热能量交换的一个关键参数,同时也是陆面生态系统水循环的重要组成部分。本文选用25 km分辨率的CCI(Climate Change Initiative)土壤湿度产品数据,并结合1 km分辨率的MODIS数据,构建微波土壤湿度产品数据降尺度回归算法,获取淮河流域1 km空间分辨率的土壤湿度数据。降尺度后所获取淮河流域1 km空间分辨率的土壤湿度数据总体上提高了25 km空间分辨率的CCI土壤湿度产品数据的精度.     

Empirical modeling and spatio-temporal patterns of urban evapotranspiration for the Phoenix metropolitan area,Arizona

In this study, an empirical model for predicting urban evapotranspiration (ET) is examined for the Phoenix metropolitan area that is in a subtropical desert climate using in situ ET measurements from a local flux tower and remotely sensed moderate-resolution imaging spectroradiometer land products. Annual ET maps of Phoenix are then created for the period from 2001 to 2015 using the empirical model developed. A time-series trend analysis is finally performed using predicted ET maps to discover the spatio-temporal patterns of ET changes during the study period. Results suggest that blue-sky albedo and land surface temperature are two statistically significant variables explanatory to model urban ET for Phoenix. Areas that have experienced significant increases of ET are highly spatially clustered, and are mainly found on the outskirts of the city, while areas of decreasing ET are generally associated with highly developed areas, such as downtown Phoenix.     

Retrieval of land surface temperature from the Kalpana-1 VHRR data using a single-channel algorithm and its validation over western India

Indian geostationary satellite Kalpana-1 (K1) offers a potential to capture the diurnal cycle of land surface temperature (LST) through thermal infrared channel (10.5–12.5 μm) observations of the Very High Resolution Radiometer (VHRR) sensor. A study was carried out to retrieve LST by adapting a generalized single-channel (SC) algorithm (Jiménez-Muñoz and Sobrino, 2003) for the VHRR sensor over India. The basis of SC algorithm depends on the concept of Atmospheric Functions (AFs) that are dependent on transmissivity, upwelling and downwelling radiances of the atmosphere. In the present study AFs were computed for the VHRR sensor through the MODTRAN simulations based upon varying atmospheric and surface inputs. The AFs were fitted with the atmospheric columnar water vapour content and a set of coefficients was derived for LST retrieval. The K1-LST derived with the SC algorithm was validated with (a) in situ measurements at two sites located in western parts of India and (b) the MODIS LST products. Comparison of K1-LST with the in situ measurements demonstrated that SC algorithm was successful in capturing the prominent diurnal variations of 283–332 K in the LST at desert and agriculture experimental sites with a rmse of 1.6 K and 2.7 K, respectively. Inter comparison of K1-LST and MODIS LST showed a reasonable agreement between these two retrievals up to LST of 300 K, however a cold bias up to 7.9 K was observed in MODIS LST for higher LST values (310–330 K) over the hot desert region.