A New Long-Term Downward Surface Solar Radiation Dataset over China from 1958 to 2015.

Downward surface solar radiation (Rs) plays a dominant role in determining the climate and environment on the Earth. However, the densely distributed ground observations of Rs are usually insufficient to meet the increasing demand of the climate diagnosis and analysis well, so it is essential to build a long-term accurate Rs dataset. The extremely randomized trees (ERT) algorithm was used to generate Rs using routine meteorological observations (2000-2015) from the Climate Data Center of the Chinese Meteorological Administration (CDC/CMA). The estimated Rs values were validated against ground measurements at the national scale with an overall correlation coefficient value of 0.97, a mean bias of 0.04 Wm-2, a root-mean-square-error value of 23.12 Wm-2, and a mean relative error of 9.81%. It indicates that the estimated Rs from the ERT-based model is reasonably accurate. Moreover, the ERT-based model was used to generate a new daily Rs dataset at 756 CDC/CMA stations from 1958 to 2015. The long-term variation trends of Rs at 454 stations covering 46 consecutive years (1970-2015) were also analyzed. The Rs in China showed a significant decline trend (-1.1 Wm-2 per decade) during 1970-2015. A decreasing trend (-2.8 Wm-2 per decade) in Rs during 1970-1992 was observed, followed by a recovery trend (0.23 Wm-2 per decade) during 1992-2015. The recovery trends at individual stations were found at 233 out of 454 stations during 1970-2015, which were mainly located in southern and northern China. The new Rs dataset would substantially provide basic data for the related studies in agriculture, ecology, and meteorology.

ANN-Based Estimation of Low-Latitude Monthly Ocean Latent Heat Flux by Ensemble Satellite and Reanalysis Products.

Ocean latent heat flux (LHF) is an essential variable for air-sea interactions, which establishes the link between energy balance, water and carbon cycle. The low-latitude ocean is the main heat source of the global ocean and has a great influence on global climate change and energy transmission. Thus, an accuracy estimation of high-resolution ocean LHF over low-latitude area is vital to the understanding of energy and water cycle, and it remains a challenge. To reduce the uncertainties of individual LHF products over low-latitude areas, four machine learning (ML) methods (Artificial Neutral Network (ANN), Random forest (RF), Bayesian Ridge regression and Random Sample Consensus (RANSAC) regression) were applied to estimate low-latitude monthly ocean LHF by using two satellite products (JOFURO-3 and GSSTF-3) and two reanalysis products (MERRA-2 and ERA-I). We validated the estimated ocean LHF using 115 widely distributed buoy sites from three buoy site arrays (TAO, PIRATA and RAMA). The validation results demonstrate that the performance of LHF estimations derived from the ML methods (including ANN, RF, BR and RANSAC) were significantly better than individual LHF products, indicated by R2 increasing by 3.7-46.4%. Among them, the LHF estimation using the ANN method increased the R2 of the four-individual ocean LHF products (ranging from 0.56 to 0.79) to 0.88 and decreased the RMSE (ranging from 19.1 to 37.5) to 11 W m-2. Compared to three other ML methods (RF, BR and RANSAC), ANN method exhibited the best performance according to the validation results. The results of relative uncertainty analysis using the triangle cornered hat (TCH) method show that the ensemble LHF product using ML methods has lower relative uncertainty than individual LHF product in most area. The ANN was employed to implement the mapping of annual average ocean LHF over low-latitude at a spatial resolution of 0.25° during 2003-2007. The ocean LHF fusion products estimated from ANN methods were 10-30 W m-2 lower than those of the four original ocean products (MERRA-2, JOFURO-3, ERA-I and GSSTF-3) and were more similar to observations.

Estimation of Daily Terrestrial Latent Heat Flux with High Spatial Resolution from MODIS and Chinese GF-1 Data.

Reliable estimates of terrestrial latent heat flux (LE) at high spatial and temporal resolutions are of vital importance for energy balance and water resource management. However, currently available LE products derived from satellite data generally have high revisit frequency or fine spatial resolution. In this study, we explored the feasibility of the high spatiotemporal resolution LE fusion framework to take advantage of the Moderate Resolution Imaging Spectroradiometer (MODIS) and Chinese GaoFen-1 Wide Field View (GF-1 WFV) data. In particular, three-fold fusion schemes based on Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) were employed, including fusion of surface reflectance (Scheme 1), vegetation indices (Scheme 2) and high order LE products (Scheme 3). Our results showed that the fusion of vegetation indices and further computing LE (Scheme 2) achieved better accuracy and captured more detailed information of terrestrial LE, where the determination coefficient (R2) varies from 0.86 to 0.98, the root-mean-square error (RMSE) ranges from 1.25 to 9.77 W/m2 and the relative RSME (rRMSE) varies from 2% to 23%. The time series of merged LE in 2017 using the optimal Scheme 2 also showed a relatively good agreement with eddy covariance (EC) measurements and MODIS LE products. The fusion approach provides spatiotemporal continuous LE estimates and also reduces the uncertainties in LE estimation, with an increment in R2 by 0.06 and a decrease in RMSE by 23.4% on average. The proposed high spatiotemporal resolution LE estimation framework using multi-source data showed great promise in monitoring LE variation at field scale, and may have value in planning irrigation schemes and providing water management decisions over agroecosystems.

Geologic factors leadingly drawing the macroecological pattern of rocky desertification in southwest China.

Rocky desertification (RD) is a special process of land deterioration in karst topography, with a view of bedrock exposure and an effect of ecological degradation. Among the three largest karst regions in the world, southwest China boasts the largest RD area and highest diversity of karst landscapes. However, inefficient field surveying tends to restrict earlier studies of RD to local areas, and the high complexity of karst geomorphology in southwest China further lead to the shortage of the knowledge about its macroecological pattern so far. To address this gap, this study innovatively took county as the unit to statistically explore the links between the 2008-censused distributions of county-level RD in southwest China and its potential impact factors of three kinds (geologic, climatic, and anthropogenic), all transformed into the same mapping frame. Spatial pattern analyses based on spatial statistics and artificial interpretation unveiled the macroscopic characteristics of RD spatial patterns, and attribution analyses based on correlation analysis and dominance analysis exposed the links of the impact factors to RD and their contributions in deciding the macroscopic pattern of RD. The results suggested that geologic factors play a first role in drawing the macroecological pattern of RD, also for the slight-, moderate-, and severe-level RD scenarios, in southwest China. Despite this inference somehow collides with the popular awareness that anthropogenic factors like human activities are leadingly responsible for the RD-relevant losses, the findings are of practical implications in guiding making the macroscopic policies for mitigating RD degradation and advancing its environmental restoration.

Evaluation of a satellite-derived model parameterized by three soil moisture constraints to estimate terrestrial latent heat flux in the Heihe River basin of Northwest China.

Satellite-derived terrestrial latent heat flux (LE) models are useful tools to understand regional surface energy and water cycle processes for terrestrial ecosystems in the Heihe River basin (HRB) of Northwest China. This study developed a satellite-derived hybrid LE model parameterized by three soil moisture (SM) constraints: SM, relative humidity (RH), and diurnal air temperature range (DT); and assessed model performance and sensitivity. We used MODerate Resolution Imaging Spectroradiometer (MODIS) and eddy covariance (EC) data from 12 EC flux tower sites across the HRB. The hybrid model was trained using observed LE over 2012/2013-2014, and validated using observed LE for 2015 and leave-one-out cross-validation. The results show that the three SM constraints schemes exhibited some modeling differences at the flux tower site scale. LE estimation using SM achieved the highest correlation (R2 = 0.87, p < 0.01) and lowest root mean square error (RMSE = 20.1 W/m2) compared to schemes using RH or DT schemes. We then produced regional daily LE maps at 1 km × 1 km across the HRB for 2013-2015. Regional analysis shows that our LE estimates from all three constraint models exhibited large spatial variability and strong seasonal and annual variations, attributed to differences in parameterizing the model water constraints. This study provides data and model based evidence to improve satellite-derived hybrid LE models with regard to water constraints.

Long-term spatial distributions and trends of the latent heat fluxes over the global cropland ecosystem using multiple satellite-based models.

Estimating cropland latent heat flux (LE) from continental to global scales is vital to modeling crop production and managing water resources. Over the past several decades, numerous LE models were developed, such as the moderate resolution imaging spectroradiometer LE (MOD16) algorithm, revised remote sensing-based Penman-Monteith LE algorithm (RRS), the Priestley-Taylor LE algorithm of the Jet Propulsion Laboratory (PT-JPL) and the modified satellite-based Priestley-Taylor LE algorithm (MS-PT). However, these LE models have not been directly compared over the global cropland ecosystem using various algorithms. In this study, we evaluated the performances of these four LE models using 34 eddy covariance (EC) sites. The results showed that mean annual LE for cropland varied from 33.49 to 58.97 W/m2 among the four models. The interannual LE slightly increased during 1982-2009 across the global cropland ecosystem. All models had acceptable performances with the coefficient of determination (R2) ranging from 0.4 to 0.7 and a root mean squared error (RMSE) of approximately 35 W/m2. MS-PT had good overall performance across the cropland ecosystem with the highest R2, lowest RMSE and a relatively low bias. The reduced performances of MOD16 and RRS, with R2 ranging from 0.4 to 0.6 and RMSEs from 30 to 39 W/m2, might be attributed to empirical parameters in the structure algorithms and calibrated coefficients.

An Empirical Orthogonal Function-Based Algorithm for Estimating Terrestrial Latent Heat Flux from Eddy Covariance, Meteorological and Satellite Observations.

Accurate estimation of latent heat flux (LE) based on remote sensing data is critical in characterizing terrestrial ecosystems and modeling land surface processes. Many LE products were released during the past few decades, but their quality might not meet the requirements in terms of data consistency and estimation accuracy. Merging multiple algorithms could be an effective way to improve the quality of existing LE products. In this paper, we present a data integration method based on modified empirical orthogonal function (EOF) analysis to integrate the Moderate Resolution Imaging Spectroradiometer (MODIS) LE product (MOD16) and the Priestley-Taylor LE algorithm of Jet Propulsion Laboratory (PT-JPL) estimate. Twenty-two eddy covariance (EC) sites with LE observation were chosen to evaluate our algorithm, showing that the proposed EOF fusion method was capable of integrating the two satellite data sets with improved consistency and reduced uncertainties. Further efforts were needed to evaluate and improve the proposed algorithm at larger spatial scales and time periods, and over different land cover types.

Evaluation of three satellite-based latent heat flux algorithms over forest ecosystems using eddy covariance data.

We have evaluated the performance of three satellite-based latent heat flux (LE) algorithms over forest ecosystems using observed data from 40 flux towers distributed across the world on all continents. These are the revised remote sensing-based Penman-Monteith LE (RRS-PM) algorithm, the modified satellite-based Priestley-Taylor LE (MS-PT) algorithm, and the semi-empirical Penman LE (UMD-SEMI) algorithm. Sensitivity analysis illustrates that both energy and vegetation terms has the highest sensitivity compared with other input variables. The validation results show that three algorithms demonstrate substantial differences in algorithm performance for estimating daily LE variations among five forest ecosystem biomes. Based on the average Nash-Sutcliffe efficiency and root-mean-squared error (RMSE), the MS-PT algorithm has high performance over both deciduous broadleaf forest (DBF) (0.81, 25.4 W/m(2)) and mixed forest (MF) (0.62, 25.3 W/m(2)) sites, the RRS-PM algorithm has high performance over evergreen broadleaf forest (EBF) (0.4, 28.1 W/m(2)) sites, and the UMD-SEMI algorithm has high performance over both deciduous needleleaf forest (DNF) (0.78, 17.1 W/m(2)) and evergreen needleleaf forest (ENF) (0.51, 28.1 W/m(2)) sites. Perhaps the lower uncertainties in the required forcing data for the MS-PT algorithm, the complicated algorithm structure for the RRS-PM algorithm, and the calibrated coefficients of the UMD-SEMI algorithm based on ground-measured data may explain these differences.

[Application of hyper-spectral remote sensing technology in environmental protection].

Hyper-spectral remote sensing (RS) technology has been widely used in environmental protection. The present work introduces its recent application in the RS monitoring of pollution gas, green-house gas, algal bloom, water quality of catch water environment, safety of drinking water sources, biodiversity, vegetation classification, soil pollution, and so on. Finally, issues such as scarce hyper-spectral satellites, the limits of data processing and information extract are related. Some proposals are also presented, including developing subsequent satellites of HJ-1 satellite with differential optical absorption spectroscopy, greenhouse gas spectroscopy and hyper-spectral imager, strengthening the study of hyper-spectral data processing and information extraction, and promoting the construction of environmental application system.

[Research on using a mono-window algorithm for land surface temperature retrieval from Chinese satellite for environment and natural disaster monitoring (HJ-1B) data].

Land surface temperature(LST) is a key parameter in earth environment, the thermal infrared band that can detect LST plays an important role in spectroscopy. Aiming to the latest optical and thermal bands of HJ-1B satellite, the LST retrieval over Ningxia plain was implemented using a mono-window algorithm without atmospheric water vapor content input, based on the COST model for atmospheric correction. Considering the difficulty of obtaining simultaneous ground measured data, the MODIS LST product was adopted as a standard to test the approach. The comparison and validation indicate that this method has good reliability with accuracy of less than 1 K. In addition, the sensitivity analysis is performed for land surface emissivity, and the result shows that this variable was not sensitive to LST, because the LST error is less than 0. 5 K when it varies at me dium level. This study proves that the satellite data has higher availability for detecting LST.

[New index for soil moisture monitoring based on deltaT(s)-albedo spectral information].

Monitoring soil moisture by remote sensing has been an important problem for both agricultural drought monitoring and water resources management. In the present paper, we acquire the land surface temperature difference (deltaT(s)) and broadband albedo using MODIS Terra reflectance and land surface temperature products to construct the deltaT(s)-albedo spectral feature space. According to the soil moisture variation in spectral feature space, we put forward a simple and practical temperature difference albedo drought index (TDADI) and validate it using ground-measured 0-10 cm averaged soil moisture of Ningxia plain The results show that the coefficient of determination (R2) of both them varies from 0.36 to 0.52, and TDADI has higher accuracy than temperature albedo drought index (TADI) for soil moisture retrieval. The good agreement of TDADI, Albedo/LST, LST/ NDVI and TVDI for analyzing the trends of soil moisture change supports the reliability of TDADI. However, TDADI has been designed only at Ningxia plain and still needs further validation in other regions.

[Investigation of Paeonia sinjiangensis resources using remote sensing and expert knowledge].

OBJECTIVE: To study the effectiveness and feasibility of remote sensing technology in the rare species of wild plant resources. METHOD: The mechanism and characteristics of Paeonia sinjiangensis were analyzed to find the possibility of extracting from TM imagery. An expert system has been used with Landsat Thematic Mapper data to derive P. sinjiangensis. Then logical decision rules were used with the various datasets to assign values. RESULT: The land for P. sinjiangensis possible growth were mapped and accuracy tested was approving. CONCLUSION: The results suggest that the remote sensing expert interpretation system using satellite imagery and ancillary data will be feasible for research of rare wild medicinal plants distribution.