The fraction of mulched film is of great significance for evaluating the agricultural water-saving effect and controlling environmental plastic pollution. Unfortunately, there is no work has been done to obtain this parameter due to the mixed pixel issue of satellite imagery with medium and low resolutions, till now. In this study, we proposed a novel two-step framework for mapping fraction of mulched film based on very high resolution satellite observation and deep learning. The first step is extracting the extent of the mulched film based on a new few-shot learning model named PT-CNN (Parameter Transition Convolutional Neural Network), which aims to increase the extraction accuracy and overcome the lack of labeled training data. The second step is retrieving the fraction of the mulched film at pixel scale based on a spectrum analysis method. The result shows that the proposed PT-CNN outperforms several state-of-the-art methods in mulched film extraction, with F1-scores at 97.09% and 98.65% for white and black mulched film, respectively. Meanwhile, the retrieved pixel scale fraction of mulched film shows high consistency to in-situ measurement, with a MAE of 0.0321. The proposed method can be useful in agricultural water resource management and environmental governance.

Plastic mulch has been widely used in agricultural cultivation for its significantly increasement toward crop yield since last century, and has recently draw lots of attention from the government side, due to the environmental concerns caused by the agricultural plastic mulch. In this study, a few-shot learning based deep learning model is designed for film mulching mapping using very high resolution satellite imagery. Firstly, the image restoration model is pre-trained by massive unlabeled very resolution satellite imagery samples. Then, the film mulching mapping model is set by the pre-training model weights and trained by few labeled film mulching imagery samples. Results show the proposed method could achieve well film mulching mapping performance, and the F1-score reaches 0.9, which is useful in the agricultural irrigation management and yield prediction.

Soil moisture (SM) plays an important role in the water–heat–energy exchange and water cycle of the land ecosystem. Long-term SM products are vital in the time series study of ecology and hydrology. Therefore, it is vital to extend the time span with limited SM monitoring sensors, since there is no single long-term SM product currently. In this study, an SM product prolonging method based on an artificial neural network (ANN) and moderate-resolution imaging spectroradiometer (MODIS) optical products was proposed. The prolonging results of Soil Moisture Active Passive (SMAP) and Fenyun-3B (FY3B) products were validated in Tibetan Plateau to present the feasibility of this method. The result shows this method is feasible in areas under medium vegetation cover (0.2$R $= 0.84, RMSE3${cm}^{-3}$) within situmeasurements for both SMAP and FY-3B products. The generated long-term SM will benefit the global water cycle study.

Error estimation of precipitation products is an important procedure in the data quality evaluation. It is a challenging task due to the lack of the in-situ ground observations and the variations of the geophysical characteristics in regions with complex terrain. Compared with the traditional methods, the double instrumental variable (DIV) method has the merits of being able to estimate the errors between two products. In this study, the double instrumental variable method for data error estimation is applied and validated on precipitation products in regions with complex terrain. The DIV-based Errors for two state-of-the-art precipitation products IMERG and SM2RAIN are being further verified by using another high-accuracy ground-based precipitation products CMPA. The results indicate that the DIV-based Errors of IMERG and SM2RAIN range from 0 to 25 mm per day and 0 to 15 mm per day, respectively. The RMSEs of IMERG and SM2RAIN compared with CMPA, which are defined as CMPA-based Errors, are ranging from 0 to 23 mm and 0 to 22 mm, respectively. It is concluded that the spatial distribution of the DIV-based Errors shows the consistency with the CMPA-based Errors, which further demonstrates the potential of using double instrumental variable method for precipitation products fusion.

Highly quality soil moisture is significant for hydrological, meteorological and agricultural applications. At present, active and passive remote sensing are the only ways to monitor soil moisture directly at regional scale. However, the quality of single satellite-based soil moisture product is insufficient to meet the requirements of these applications. Hence, fusing these two soil moisture products to improve their quality of change capture ability and accuracy is a necessary and challenging work. This study proposes an improved double instrumental variable method to fuse active and passive soil moisture products. First, the method is improved in finding the best instrumental variables in time series based on correlation coefficient. Second, fused weights of input soil moisture products are estimated using the improved method. Finally, fused soil moisture products are obtained with higher change capture ability and higher accuracy. The Tibetan Plateau was selected as the study area to test the algorithm using both of the Climate Change Initiative (CCI) active and passive soil moisture products from the European Space Agency (ESA). The ground validation results show that, compared with the original soil moisture products, the change capture ability, expressed by the correlation coefficient (R), and the accuracy, expressed by the unbiased root mean square deviation (ubRMSD), have been both improved by about 10% on average. This study indicates that the proposed fusion method can effectively improve the quality of soil moisture products to further understand the global changing water cycle.

Irrigation is critical to agricultural production in arid and semiarid regions, and it is imperative to map high-resolution irrigated area to improve water productivity. This study proposes a field-scale (30-m resolution) irrigated area mapping method based on soil moisture change detection using remote sensing data only. First, normalized soil moisture is obtained using the optical trapezoid model (OPTRAM) and then converted to soil water content. Next, individual irrigation events are identified in the time series of soil water content using threshold detection. Finally, irrigation events are accumulated over the time series, and then, the irrigated area map can be obtained. This method was tested using Google Earth Engine (GEE) to analyze remote sensing images and map irrigated areas in a typical arid and semiarid region called Hexi Corridor in northwestern China in the past 30 years.In situvalidation shows that this method has an accuracy close to 100%. The shortcoming of low recall is also overcome by long-term observations. An application of the proposed method shows that the irrigated cropland of Hexi Corridor has increased by 4840 km2(42.2%) over a 31-year time period (1990–2020). This field-scale irrigated area mapping method can improve the management of water resources.

Synthetic Aperture Radar (SAR) and Multi-spectral (MS) remote sensing images are commonly used for flood mapping. SAR images can provide valid backscattering measurements of inundated areas through cloud cover, while MS data is able to monitor the spectral changes of ground surface, but usually affected by clouds. The complementary characteristics of the two data indicate the potential of their combining application for flood monitoring in emergency. This paper proposes a novel weighted evidential fusion method to take full advantages of the SAR and MS data for change detection during the flood. First, pre-processing and classification are performed with the SAR and MS data, independently. Second, a modified PCR6 rule for evidential fusion is proposed, which introduces the confusion matrixes to calculate the weight of evidences so that the conflicting degree in the fusion process can be reduced. Then, the flood inundating, standing and receding patterns are identified, which can be used to describe the flooding process in details. Practically, the proposed method is applied to flood mapping of the Typhoon Rumbia in 2018, in Shouguang City, China. The experiments show that the proposed fusion scheme efficiently use both of the SAR and MS data, and improve the flood mapping accuracy.

Ship navigation route planning in ice-covered sea is important for the safety of voyages. Sea ice channels, only in which vessels can pass through, change their topology as a result of the appearance and disappearance of sea ice. This paper provides a route planning method for ships that travel through the sea ice channels based on high resolution satellite images. The main processes of the method include: (1) Classification of sea ice using high resolution satellite images. (2) Extraction of sea ice channels topology based on the classification results. (3) Automated route planning based on the topology of sea ice channels. The key algorithms in the processes are presented in this paper. Practically, we used the aforementioned scheme to find the optimal path for voyages on several simulated missions. The simulations indicate that the proposed method is very practical and time-efficient for route planning with high resolution satellite images, while traditional pixel-based-algorithm is unable to cope with large-size images and the dynamic change of the sea ice. Moreover, the proposed method can take the advantages of the widely available high resolution satellite data and the near real-time sea ice observation. Therefore, the proposed method is potential to be applied on the navigation system for vessels travelling in the north and south polar regions.

Surface soil moisture (SM) plays an essential role in the water and energy balance between the land surface and the atmosphere. The published soil moisture products, having a low spatial resolution of 25-40 km, and low temporal resolution of 2-3 days, limits their applications at regional scale. In this study, the spatio-temporal resolution of Fengyun (FY) SM products was improved using a machine-learning model named the General Regression Neural Network (GRNN), with the help of selected six high spatial resolution parameters, including Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), albedo, Digital Elevation Model (DEM), Longitude (Lon) and Latitude (Lat) after gap-filled as input variables. An implements tested over the Tibetan Plateau (TP) showed that the spatio-temporal resolution of FY-3B SM was improved from 0.25° and 2-3 days to 0.05° and 1-day. The high spatio-temporal resolution SM can enhance our understanding of water-energy cycle under climate change.

Channel network topology plays an important role in hydrological analysis. This letter proposes an innovative method to construct it based only on remote sensing images. The method uses spectral water indexes and mask of large lakes and ocean areas derived from remote sensing data to generate the map of channels. Then, a morphological thinning algorithm is introduced to extract the initial skeleton of channels. Moreover, an iterative pruning process based on a graph algorithm is proposed to simplify the initial skeleton. Finally, according to the simplified skeleton and its adjacency matrix, a new connectivity graph can be constructed to describe the channel network topology. The proposed method can construct complete skeleton structure and the topology of channels with good connectivity in an automatic way. The output will facilitate detailed hydrological modeling and further applications.

Soil moisture is a key variable in the exchange of water and energy between the land surface and the atmosphere. Long time series of and spatio-temporal continuous soil moisture is of great importance to meteorological and hydrological applications, such as weather forecasting, global change and drought monitoring. In this study, the Essential Climate Variable (ECV) soil moisture product of the Tibetan Plateau (TP) from 2002 to 2015 was reconstructed using the General Regression Neural Network (GRNN) based on reconstructed MODIS products, i.e., LST, NDVI, and Albedo. Results show that the ECV soil moisture could be well reconstructed with R2higher than 0.71, RMSE less than 0.05 cm3cm-3and absolute Bias less than 0.03 cm3cm-3for both grids of 0.25°×0.25° and 1°×1°, compared with the in-situ measurements in 2012 over the TP. The reconstructed long time series of and spatio-temporal continuous soil moisture could be valuable in hydrometeorological studies of the TP.

Housing vacancy data, providing useful information on building occupancy rates, are used extensively by public and private organizations to evaluate the need for new housing development programs and also the vacancy rate is regarded as a leading indicator to measure the economic climate. Such a topic has received considerable attentions in China due to its overheated real estate development. Previous house vacancy studies use governmental statistics or private user positioning data, the collection of which is time consuming and difficult. In this work, an efficient indicator Vacancy Area Index (VAI), ranging from 0 to 10, is proposed to measure the county level housing occupancy and ghost cities over China based on MODIS products and DMSP-OLS nighttime light data. Results show that VAI is a good measure of house vacancy with a detection rate 94.44% of ghost cities and a precision 92.48% of non-ghost cities. About 320 among 2430 county areas in China have a VAI no less than 7, indicating national wide spread high house vacancy. The VAI can also reveal the spatial characteristics and temporal development of cities with different features such as tourism cites, industrial cities, and real "Ghost Cities" accurately.

The directional anisotropy of canopy emissivity and brightness temperature in the thermal infrared band has widely been studied. However, the contribution of different scattering orders has been an open scientific question for many years. The recently proposed CE-P model enables us to analytically evaluate the different scattering orders. Herein, we derive expressions for the first double collisions (DCE12) and first triple collisions (DCE123). Our result shows that DCE123can simulate the observed emissivity with an error less than 0.001 and that DCE12is reasonably accurate when leaf emissivity is greater than 0.96. Numerical analysis shows that the contribution of quadruple or greater collisions can be ignored when the leaf (soil) emissivity is no less than 0.90. Furthermore, we develop the CBT-P model to simulate the directional brightness temperature (DBT) based on the new optimized CE-P model (DCE123) and validate it by 4SAIL (4-Stream Radiative Transfer Theory of Scattering by Arbitrary Inclined Leaves) and DART (Discrete Anisotropic Radiative Transfer) models. Both of isothermal (soil temperature is equal to leaf temperature) and nonisothermal (soil temperature is higher than leaf temperature) cases are considered. The maximum differences between the CBT-P model and 4SAIL (DART) are less than 0.35 K (0.42 K), the average differences between CBT-P and 4SAIL (DART) are less than 0.10 K (0.13 K), and the R2is over 0.99 (0.95) with component emissivities larger than 0.90 and the difference between soil and leaf temperatures less than 20 K. The directional anisotropy of DBT is dominated by the zero-scattering and the single scattering terms according to the new developed CBT-P model.

This paper proposes a novel method to fuse multi-source acoustical remote sensing images for marine sediment mapping. Acoustic images from sidescan sonar, multibeam bathymetry, and sub-bottom profiler describe the 2-D, 2.5-D, and 3-D properties of the seabed sediment respectively. In attempt to make use of the multi-dimensional information from the multi-source data, the evidential fusion method, combined with object-based classification and spatial overlay analysis is proposed. Firstly, marine sediments are independently classified in the multi-source acoustic images with object based methods. Then, the spatial overlay analysis is conducted to group the classification results as evidence of different sediments. Finally, the evidential fusion method is employed to determine the exact distribution of sediments on the map. The proposed method introduces the classification results of sub-bottom profiler data that provide useful information, even though the data is limited in spatial coverage and is rarely used in automatic sediment mapping. The experiments show that the fused data from the three different sources of acoustic images significantly improve the mapping accuracy.

A new directional canopy emissivity model (CE-P) based on spectral invariants can separate the multiple scattering effect and single scattering in vegetation canopy. So we can further evaluate the contribution of scattering effect to the canopy emissivity and brightness temperature based on CE-P model. Numerical analysis shows that the contribution of more than three times scattering can be ignored when the leaf (soil) emissivity is no less than 0.90. Then, we optimize CE-P model and obtain the expressions containing the first twice collisions (ε2) and first three times collisions (ε3). The result shows that ε3 can simulate the emissivity in any case with an error less than 0.001. Furthermore, we simulate the brightness temperature distribution using the optimized model and compare it with DART model. The difference between them is less than 0.3K and the R2of them is over 0.96 in all of the selected samples.

Soil moisture is a key variable in the exchange of water and energy between the land surface and atmosphere, especially over the Tibetan Plateau. The Fengyun-3B Microwave Radiation Imager (FY-3B/MWRI) soil moisture product is one of relatively new passive microwave products. This paper validated the FY-3B/MWRI soil moisture product using in-suit measurements within two soil moisture networks (1°×1° and 0.25°×0.25°) on the central Tibetan Plateau, and compared it with other fourteen products from satellite and land surface models based on published studies. Results showed that the FY-3B/MWRI soil moisture product is comparable with AMSR2, and outperforms LPRM_C, LPRM_X, ASCAT, AMSR-E (NASA), AMSR-E (JAXA), SMOS, and outputs from CLM, Noah, VIC, and Mosaic models. The good performance indicates that the FY-3B/MWRI soil moisture products could be valuable in studying meteorology, hydrology, the environment, agriculture, etc. on the central Tibetan Plateau.

The ocean bio-physical co-variability and its response to climate change have received increasing attention with the accumulation of satellite ocean data sets. Most of the present studies focus either on regional ocean or short time span, lacking a systemetic study of the co-variability of bio-physical parameters in the Pacific. Based on the satellite datasets and re-analyzed datasets from 1997 to 2012, the co-variability and coupled patterns between the surface chlorophyll-a (CHL) and the physical factors, i.e. sea surface temperature (SST), sea level anomaly (SLA) and sea winds in the Pacific Ocean were studied through the canonical correlation analysis. We found that the bio-physical coupled patterns in the Pacific Ocean were corresponding to the Pacific climate variability patterns, i.e. El Niño Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), North Pacific Gyre Oscillation (NPGO).

Interception loss is an important component of the regional water balance for the Heihe River Basin which is an inland basin with limited precipitation. We used a modified Gash analytical model by combining remote sensing observations to estimate the interception loss of several vegetation types, e.g., grass, crop, forest and shrub for the years 2003-2012 in the Heihe River Basin. The estimated monthly interception ratio (in percent) was compared with field measurements made in Dayekou and Pailugou forest hydrology experimental sites and the results showed reasonable accuracy with RMSE of 5.0% and 4.3% at the two sites, respectively. The regional distribution of the interception loss showed strong spatial and temporal variability at monthly scale. At annual scale, the interception ratio could be treated as a stable indicator for long-term water balance research. The annual average interception loss is about 7.2% of gross rainfall for the vegetation covered area in the Heihe River Basin.

Airborne LIDAR (Light Detection and Ranging) is an active remote sensing technique that measures the properties of scattered light to determine the range and intensity information of a distant target. Many studies have been reported on estimating a suite of forest characteristics such as fractional vegetation cover, leaf area index and canopy height using LIDAR data. The three characteristics of crop canopy also play key roles in vegetation radiative transfer models and yield estimation. But crops are so small and low that more than 95% pulses have ground hit, it is difficult to separate the crop and soil completely, so the methods used in forest may not be suitable for crops. In this paper, based on theoretical analysis, we propose a new method, trying to derive gap fraction of crop field using the airborne LIDAR intensity of ground hits, so we can manage to retrieve the fractional vegetation cover, LAI and the height of crop canopy. We choose corn field as study object, field validation shows that our method can accurately retrieve the three structural parameters of corn field. This study documents the great potential of LIDAR remote sensing for accurately characterizing crop canopies.

Soil moisture is a key parameter for drought monitoring. Crops distribute so fragmentally in China that mixed pixels account for a large proportion in moderate and coarse resolution remote sensing images. The soil moisture retrieved from vegetation-soil mixed pixels is a very important problem for drought monitoring and ecological study. Focusing on vegetation-soil mixed pixels, a new method for retrieving soil moisture from hyperspectral data is provided based on scale transformation method. Yingke Oasis, Zhangye, Gansu province was selected as validation area. A Hyperion/EO-1 data acquired on Jul.15, 2008 was pre-processed and linearly interpolated to 180m and 1080m resolution images. Then a multi-scale image series was obtained. Using the above method, the soil moisture of pixels whose space resolution is 1080m were calculated. The retrieved results were verified by synchronized ground observation data. The results show that the proposed method is reliable.

Grassland is an essential part of terrestrial ecosystems. It has a significant impact on the carbon cycle, as well as on climate and on regional economies. Till now, vegetation indices are the most popular remote sensed detecting method of grassland degradation. Although vegetation indices are useful for estimating the biomass, but detecting changes of vegetation indices are not always effective, as grassland vegetation with different characteristics may still produce similar vegetation index values. The development of hyperspectral sensors provides a new approach to solve this problem. The Hulunbeier grassland was chosen as a study object. Reflectance spectra of leaves and pure canopies of some dominant grassland species, as well as reflectance spectra of mixed grass community were measured. Using spectral feature parameterization methods such as spectral slope, spectral derivative, spectral integration, and spectral index, the spectral feather of leaves and pure canopies had been extracted. So the typical grassland vegetation species can be distinguished. Then the spectra of mixed grass community were unmixed using linear mixing models, and the proportion of all the components had been calculated. The field validation proved spectral feature parameterization and pixel unmixing methods in this research are effective.