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Early prediction of winter wheat yield at the regional scale is essential for food policy making and food security, especially in the context of population growth and climate change. Agricultural big data and artificial intelligence (AI) are key technologies for smart agriculture, bringing cost-effective solutions to the agricultural sector. Deep learning-based crop yield forecast has currently emerged as one of the key methods for guiding agricultural production. In this study, we proposed a Bayesian optimization-based long- and short-term memory model (BO-LSTM) to construct a multi-source data fusion-driven crop growth feature extraction algorithm for winter wheat yield prediction. The yield prediction performance of BO-LSTM, support vector machine (SVM), and least absolute shrinkage and selection operator (Lasso) was then compared with multi-source data as input variables. The results showed that effective deep learning hyperparameter optimization is made possible by Bayesian optimization. The BO-LSTM (RMSE = 177.84 kg/ha, R2 = 0.82) model had the highest accuracy of yield prediction with the input combination of "GPP + Climate + LAI + VIs". BO-LSTM and SVM (RMSE = 185.7 kg/ha, R2 = 0.80) methods outperformed linear regression Lasso (RMSE = 214.5 kg/ha, R2 = 0.76) for winter wheat yield estimation. There were also differences between machine learning and deep learning, BO-LSTM outperformed SVM. indicating that the BO-LSTM model was more effective at capturing data correlations. In order to further verify the robustness of the BO-LSTM method, we explored the performance estimation performance of BO-LSTM in different regions. The results demonstrated that the BO-LSTM model could obtain higher estimation accuracy in regions with concentrated distribution of winter wheat cultivation and less influence of human factors. The approach used in this study can be expected to forecast crop yields, both in regions with a deficit of data and globally; it can also simply and effectively forecast winter wheat yields in a timely way utilizing publicly available multi-source data. [ABSTRACT FROM AUTHOR]

Accurate spatial distribution and area of crops are important basic data for assessing agricultural productivity and ensuring food security. Traditional classification methods tend to fit most categories, which will cause the classification accuracy of major crops and minor crops to be too low. Therefore, we proposed an improved Gray Wolf Optimizer support vector machine (GWO-SVM) method with oversampling algorithm to solve the imbalance-class problem in the classification process and improve the classification accuracy of complex crops. Fifteen feature bands were selected based on feature importance evaluation and correlation analysis. Five different smote methods were used to detect samples imbalanced with respect to major and minor crops. In addition, the classification results were compared with support vector machine (SVM) and random forest (RF) classifier. In order to improve the classification accuracy, we proposed a combined improved GWO-SVM algorithm, using an oversampling algorithm(smote) to extract major crops and minor crops and use SVM and RF as classification comparison methods. The experimental results showed that band 2 (B2), band 4 (B4), band 6 (B6), band 11 (B11), normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI) had higher feature importance. The classification results oversampling- based of smote, smote-enn, borderline-smote1, borderline-smote2, and distance-smote were significantly improved, with accuracy 2.84%, 2.66%, 3.94%, 4.18%, 6.96% higher than that those without 26 oversampling, respectively. At the same time, compared with SVM and RF, the overall accuracy of improved GWO-SVM was improved by 0.8% and 1.1%, respectively. Therefore, the GWO-SVM model in this study not only effectively solves the problem of equilibrium of complex crop samples in the classification process, but also effectively improves the overall classification accuracy of crops in complex farming areas, thus providing a feasible alternative for large-scale and complex crop mapping. [ABSTRACT FROM AUTHOR]

Monitoring agricultural drought via ground hyper-spectral remote sensing has always been a hot topic in the fields of agriculture and meteorology. In this study, a greenhouse experiment was conducted on wheat subjected to water stress during its different growth stages, namely tillering, jointing, and milk maturity. An instrument (HOBO ware PRO) used to continuously measure soil moisture was employed to measure the soil water content (SWC). An analytical spectral device (ASD) was utilized to obtain the spectral curve of wheat subject to different water treatment methods. The canopy temperature was obtained using thermal infrared sensors (METER SI-400). The relationships between the SWC, wheat drought stage, canopy temperature, and spectral response characteristics were elucidated. The results showed that the significant differences in spectral characteristics were due to water stress during the different growth stages of wheat. Red-edge parameters of red-valley position (RVP) and red-edge position (REP) both changed by 21 nm for the tillering-stage drought and the jointing-stage drought; however, the RVP and REP values for the milk maturity stage drought and the treatment under no water stress changed by 2 nm. Further, it was proved that the red-edge blue-shift phenomenon was affected not only by the different wheat growth processes, but also by the water stress at different growth stages. Red-edge reflectance clearly reflects wheat water stress at different growth stages. From SWC and canopy temperature analysis results, SWC and canopy temperature had a significant difference between wheat drought at different growth stages, and the canopy temperature at the jointing stage drought had the strongest change. The water index (WI) based on eleven vegetation water indexes exhibited a good performance for distinguishing wheat water stress at different growth stages. In conclusion, ground-based hyperspectral remote sensing can provide a large amount of high temporal and spectral resolution data on vegetation and its surrounding environment, making it an important technical tool for wheat drought monitoring, which has a great significance on the monitoring and early warning of wheat drought, reducing drought-related yield losses, and ensuring food security. [ABSTRACT FROM AUTHOR]

SPAD value was measured by a portable chlorophyll instrument, which can reflect the relative chlorophyll content of vegetation well. Chlorophyll is an important organic chemical substance in plants that acquires and transmits energy during photosynthesis. The continuous spectral curve of winter wheat can be obtained rapidly in a specific band range by using hyperspectral remote sensing technology to estimate the SPAD value of winter wheat, which is of great significance to the growth monitoring and yield estimation research of winter wheat. In this study, with winter wheat as the research object, the spectral data and corresponding SPAD value in different growth stages were used as the data source, 20 kinds of data preprocessing spectra and sensitive spectral indices set the data as model input values, the partial least square regression (PLSR) model was established to estimate the SPAD value, and the model estimation results of different model input values at different growth stages were compared in detail. The results showed that the set of sensitive spectral indices selected in this study as input values can effectively improve the accuracy and stability of the PLSR model. In addition, the effects of 20 spectral data pretreatment methods on the estimation results of the SPAD value were compared and analyzed in different growth stages. It was found that the spectral data pretreated by the combination of wavelet packet denoising, first-order derivative transformation and principal component analysis can improve the accuracy and stability of PLSR model, and it is suitable for all growth stages. The results also showed that the estimation model is highly sensitive to the standard deviation of the SPAD value (STDchl) in sample sets. When the standard deviation is greater than 5.5 SPAD, the larger the STDchl is, the higher the model estimation accuracy is, and the more stable the model is. At this time, the model estimation accuracy is higher (R2V is greater than 0.5, ratio of performance to deviation is greater than 1.4), which can meet the estimation requirements of the SPAD value. [ABSTRACT FROM AUTHOR]

Rust is a common disease in wheat that significantly impacts its growth and yield. Stem rust and leaf rust of wheat are difficult to distinguish, and manual detection is time-consuming. With the aim of improving this situation, this study proposes a method for identifying wheat rust based on ensemble learning (WR-EL). The WR-EL method extracts and integrates multiple convolutional neural network (CNN) models, namely VGG, ResNet 101, ResNet 152, DenseNet 169, and DenseNet 201, based on bagging, snapshot ensembling, and the stochastic gradient descent with warm restarts (SGDR) algorithm. The identification results of the WR-EL method were compared to those of five individual CNN models. The results show that the identification accuracy increases by 32%, 19%, 15%, 11%, and 8%. Additionally, we proposed the SGDR-S algorithm, which improved the f1 scores of healthy wheat, stem rust wheat and leaf rust wheat by 2%, 3% and 2% compared to the SGDR algorithm, respectively. This method can more accurately identify wheat rust disease and can be implemented as a timely prevention and control measure, which can not only prevent economic losses caused by the disease, but also improve the yield and quality of wheat. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is a key parameter for many fields of study. Currently, LST retrieved from satellite thermal infrared (TIR) measurements is attainable with an accuracy of about 1 K for most natural flat surfaces. However, over urban areas, TIR measurements are influenced by 3-D structures and their radiation that could degrade the performance of existing LST retrieval algorithms. Therefore, quantitative models are needed to investigate such impact. Current 3-D radiative transfer models are generally based on time-consuming numerical integrations whose solutions are not analytical, and are therefore difficult to exploit in the methods of physical retrieval of LST in urban areas. This article proposes an analytical TIR radiative transfer model over urban (ATIMOU) areas that considers the impact of 3-D structures and their radiation. The magnitude of this impact on TIR measurements is investigated in detail, using ATIMOU, under various conditions. Simulations show that failure to acknowledge this impact can potentially introduce a 1.87-K bias to the ground brightness temperature for street canyon whose ratio “wall height/road width” is 2, wall and road temperature is 300 K, wall emissivity is 0.906, and road emissivity is 0.950. This bias reaches 4.60 K if road emissivity decreases to 0.921, and road temperature decreases to 260 K. ATIMOU is also compared to the discrete anisotropic radiative transfer (DART) model. Small mean absolute error of 0.10 K was found between the models regarding the simulated ground brightness temperatures, indicating that ATIMOU is in good agreement with DART. [ABSTRACT FROM AUTHOR]

The precise estimation of sugarcane yield at the field scale is urgently required for harvest planning and policy-oriented management. Sugarcane yield estimation from satellite remote sensing is available, but satellite image acquisition is affected by adverse weather conditions, which limits the applicability at the field scale. Secondly, existing approaches from remote sensing data using vegetation parameters such as NDVI (Normalized Difference Vegetation Index) and LAI (Leaf Area Index) have several limitations. In the case of sugarcane, crop yield is actually the weight of crop stalks in a unit of acreage. However, NDVI's over-saturation during the vigorous growth period of crops results in significant limitations for sugarcane yield estimation using NDVI. A new sugarcane yield estimation is explored in this paper, which employs allometric variables indicating stalk magnitude (especially stalk height and density) rather than vegetation parameters indicating the leaf quantity of the crop. In this paper, UAV images with RGB bands were processed to create mosaic images of sugarcane fields and estimate allometric variables. Allometric equations were established using field sampling data to estimate sugarcane stalk height, diameter, and weight. Additionally, a stalk density estimation model at the pixel scale of the plot was created using visible light vegetation indices from the UAV images and ground survey data. The optimal stalk density estimation model was applied to estimate the number of plants at the pixel scale of the plot in this study. Then, the retrieved height, diameter, and density of sugarcane in the fields were combined with stalk weight data to create a model for estimating the sugarcane yield per plot. A separate dataset was used to validate the accuracy of the yield estimation. It was found that the approach presented in this study provided very accurate estimates of sugarcane yield. The average yield in the field was 93.83 Mg ha−1, slightly higher than the sampling yield. The root mean square error of the estimation was 6.63 Mg ha−1, which was 5.18% higher than the actual sampling yield. This study offers an alternative approach for precise sugarcane yield estimation at the field scale. [ABSTRACT FROM AUTHOR]

• A-BiGRU enables more accurate modeling of crop growth to achiever high accuracy. • The Attention mechanism can focus on the critical growth period of the crop. • The bidirectional GRU structure is better than GRU. • Image reconstruction can effectively fill the data gap. Timely and accurate access to regional scale crop plant area and spatial distribution is essential for regional agricultural production and food security, especially in the context of global population growth and climate change. Deep learning has become prevalent in crop mapping under complex conditions due to its powerful feature extraction and nonlinear ability. This study proposes a time-series image classification framework using Attention-based Bidirectional Gated Recurrent Unit (A-BiGRU) to map rice, maize, and soybean in Fujin, China, from reconstructed Sentinel-2 time-series images. Firstly, the reconstructed Sentinel-2 time-series images with 10 spectral dimensions and 22 temporal dimensions were obtained by linear interpolation and Savitzky-Golay (SG) filter. Then, a neural network, the A-BiGRU was developed to identify different crops by taking advantage of their unique growth patterns. The attention mechanism enables temporal neural networks to focus on the critical growth periods of crops. Additionally, the structure of GRU is simpler than that of long short-term memory (LSTM) and simple recurrent neural network (SRNN), which reduces the number of parameters and alleviates overfitting. Compared to GRU, BiGRU can fully uses the time-series information of the entire crop growth cycle. To assess the effectiveness of the proposed method, we compared two deep learning methods (LSTM and SRNN) and three widely used non-deep learning classifiers (Spectral Angle Mapping (SAM), Support Vector Machine (SVM)) and eXtreme Gradient Boosting (XGBoost). The results demonstrate that A-BiGRU achieved the highest accuracy, with an overall accuracy of 0.9804, a macro F1 score of 0.9788 and a kappa score of 0.9714. We also selected four typical regions and compared the classification results with optical images, which showed that the proposed method has a good recognition effect. Therefore, the A-BiGRU method is capable of achieving high-precision crop mapping. [ABSTRACT FROM AUTHOR]

• The MP-Net model aggregates context information. • The MP-Net model reduces the feature losses. • The MP-Net model efficiently and precisely classifies crops. Accurate crop classification map is of great significance in various fields such as the survey of agricultural resource, the analysis of existing circumstance on land application, the yield estimation of crop and the disaster warning. The methods based on machine learning and deep learning are popularly used in crop classification and recognition of remote sensing images. However, the crop classification task based on neural networks still faces significant challenges due to the spatial and temporal distribution of crops and the inherent characteristics of remote sensing images. Therefore, this study proposes the multi-layer pyramid crop classification network (MP-Net) to solve the above problems. To reduce the feature loss during the crop extraction, the proposed model uses the pyramid pooling module to improve the ability of global information acquisition, and the information concatenation module to retain the upper features. Using the GF-6 and Sentinel-2 satellite data, the proposed model was tested in Erhai Lake Basin and Beian City. Compared with other five deep learning models, such as FCN, SegNet, U-Net, PSPNet and DeepLabv3+, the experimental results indicate that the proposed model achieves the highest accuracy in both study areas. Meanwhile, the proposed model has the advantages of short training time and high efficiency under the same running conditions. Overall, this study is beneficial to improve the efficiency and accuracy of crop classification task in the unbalanced temporal and spatial distribution. It also brings a feasible scheme for crop classification tasks in complex growing areas. The code has been publicly available at https://github.com/Xu-Chang-Hong/MP-Net. [ABSTRACT FROM AUTHOR]

• Using hyperspectral data to retrieve wheat chlorophyll content. • Ridge regression algorithm and Gradient Boosting Regression Tree algorithm are used to establish the model. • The nonlinear inversion model obtained by the gradient boosting regression tree algorithm has higher accuracy than the linear model. Chlorophyll is the basic component of plant leaves. It is closely related to the use of solar radiation, the absorption of atmospheric carbon dioxide and the process of photosynthesis. The level of chlorophyll content has a very important indication of photosynthetic efficiency and development status of plants. Because hyperspectral data has the problem of large amount of data and high redundancy, this paper deals with hyperspectral data from two aspects to establish a prediction model of leaf chlorophyll content in different growth stages of winter wheat. On the one hand, this article does not perform dimensionality reduction processing on hyperspectral data. That is, for the full-band spectral data, the Ridge regression algorithm (abbreviated as Ridge) is used to establish a multiple linear prediction model. Use the Gradient Boosting Regression Tree algorithm (abbreviated as GBRT) and the feedforward neural network based on Back Propagation algorithm (abbreviated as BP) to establish two multiple nonlinear prediction models. On the other hand, dimensionality reduction is performed on hyperspectral data. Use Elastic Net algorithm (abbreviated as EN) to reduce the dimensionality of hyperspectral data to extract sensitive bands, and then use GBRT algorithm and feedforward neural network based on Back Propagation algorithm to establish multivariate nonlinear prediction models (abbreviated as EN-GBRT, EN-BP). After the model accuracy test, the conclusions are as follows: (1) The nonlinear prediction model obtained by the gradient boosting regression tree algorithm has higher accuracy and universality than the linear model in each growth period of winter wheat (R2 GBRT ＞R2 Ridge). (2) The EN-GBRT prediction model has greater advantages in predicting the chlorophyll content of winter wheat at each growth stage (R2 EN-GBRT ＞R2 EN-BP) within two nonlinear prediction models. (3) Comprehensive comparison of the prediction accuracy of GBRT and EN-GBRT models in each growth period. In the jointing period, heading period, flowering period, filling period, milking period and maturity period of winter wheat, the accuracy of prediction using the GBRT model was higher (R2 jointing = 0.81, R2 heading = 0.50, R2 flowering = 0.74, R2 filling = 0.41, R2 milking = 0.84, R2 maturity = 0.95). In the whole growth period of winter wheat, the prediction accuracy of EN-GBRT model was better (R2 whole = 0.85). [ABSTRACT FROM AUTHOR]

Accurate monitoring of soil moisture and the development of timely interventions are important to reduce the social and economic losses caused by drought. Compared to short-wave infrared (SWIR) and thermal infrared (TIR), near-infrared (NIR) and visible bands are widely used in almost all optical satellites. Drought monitoring using NIR and visible bands is therefore more relevant for optical satellites. Among the visible bands, the red band is often used in combination with the NIR band for drought monitoring due to its sensitivity to vegetation. However, current drought indexes based on the NIR and the red band applied to areas of high vegetation suffer from insufficient accuracy or tedious calculations. In this study, the ratio drought index (RDI) was developed after constructing a new feature space by examining the spectral properties of soil and vegetation at different water levels in the NIR and red bands. The accuracy of soil moisture inversion under two types of bare soil and vegetation was evaluated using in situ data from Tai’an City, Shandong Province. The perpendicular drought index (PDI) and modified perpendicular drought index (MPDI) were also used to compare for the RDI. The results showed that the RDI correlation coefficients (R2) of 0.653 and 0.641 were better than the MPDI of 0.616 and 0.594 and the PDI of 0.602 and 0.546 for soil moisture measurements from vegetation and bare soil cover. The RDI attenuates the effect of vegetation on soil moisture inversion, as its root mean square error (RMSE) in vegetated areas is lower than that of the PDI and MPDI. The RDI calculation can be used as a theoretical guide for large-scale soil moisture estimation because it is fast, accurate and does not require additional quantitative remote sensing inversion factors. [ABSTRACT FROM AUTHOR]

Highlights: [•] N loading from crop fields and livestock farms within a watershed was calculated. [•] Manure-DNDC, a process-based model with livestock operation was utilized. [•] The model also has hydrological enhancement with runoff and soil erosion simulation. [•] Only 30% of livestock waste N was applied to the crop fields in the watershed. [•] Huge potentials could exist to improve the nutrient complementation in the system. [ABSTRACT FROM AUTHOR]

The increasing demand for pork products has contributed to the increase in greenhouse gas emissions (GHG). Based on survey data of pig farms in Yancheng, Jiangsu Province, China, we developed a hybrid model DICPig (DNDC and IPCC combined model for pig husbandry) by combining the Manure-DNDC (denitrification and decomposition model for manure) model with the IPCC (Intergovernmental Panel on Climate Change) agricultural greenhouse gas calculation method to calculate the total GHG emissions from the pig husbandry life cycle. This hybrid model allowed the analysis of the pig husbandry carbon emissions component structure and the identification of the factors affecting the level of emissions. The results showed that CO 2 (Carbon Dioxide) emission per pig in its life cycle is about 672.27 kg CO 2 -eq. In the process of pig waste disposal, the N 2 O (Nitrous oxide) emitted is 1.17 kg, and the CH 4 (Methane) emitted is 1.36 kg. Pig waste disposal and feed production processes were the two most important contributors to total pig husbandry GHG emissions, accounting for 56.92% and 28.62% of the total emissions respectively. GHG emissions from pig farms could be reduced significantly by optimizing manure management measures, constructing and utilizing biogas digesters, reducing the amount of fertilizer applied to cropping, and adopting a planting-breeding combination policy. • Developing DICPig model combining the IPCC method and Manure-DNDC model. • DICPig model is used to calculate GHG emissions for the pig husbandry life cycle. • A case study for DICPig was in Dongnan Town, Yancheng, China. • Sensitivity and uncertainty analysis verified the accuracy of DICPig. • Effective measures were put forward to reduce GHG emissions for case study. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) plays a significant part in regional hydrological and meteorological systems throughout Earth. It is considered an indispensable state variable in earth science. The high sensitivity of microwave remote sensing to soil moisture, and its ability to function under all weather conditions at all hours of the day, has led to its wide application in SM retrieval. The aim of this study is to evaluate the ability of ALOS-2 data to estimate SM in areas with high vegetation coverage. Through the water cloud model (WCM), the article simulates the scene coupling between active microwave images and optical data. Subsequently, we use a genetic algorithm to optimize back propagation (GA-BP) neural network technology to retrieve SM. The vegetation descriptors of the WCM, derived from optical images, were the normalized difference vegetation index (NDVI), the normalized difference water index (NDWI), and the normalized multi-band drought index (NMDI). In the vegetation-covered area, 240 field soil samples were collected simultaneously with the ALOS-2 SAR overpass. Soil samples at two depths (0–10 cm, 20–30 cm) were collected at each sampling site. The backscattering of the ALOS-2 with the copolarization was found to be more sensitive to SM than the crosspolarization. In addition, the sensitivity of the soil backscattering coefficient to SM at a depth of 0–10 cm was higher than at a depth of 20–30 cm. At a 0–10 cm depth, the best results were the mean square error (MAE) of 2.248 vol%, the root mean square error (RMSE) of 3.146 vol%, and the mean absolute percentage error (MAPE) of 0.056 vol%, when the vegetation is described as by the NDVI. At a 20–30 cm depth, the best results were an MAE of 2.333 vol%, an RMSE of 2.882 vol%, a MAPE of 0.067 vol%, with the NMDI as the vegetation description. The use of the GA-BP NNs method for SM inversion presented in this paper is novel. Moreover, the results revealed that ALOS-2 data is a valuable source for SM estimation, and ALOS-2 L-band data was sensitive to SM even under vegetation cover. [ABSTRACT FROM AUTHOR]

Yellow rust is a disease with a wide range that causes great damage to wheat. The traditional method of manually identifying wheat yellow rust is very inefficient. To improve this situation, this study proposed a deep-learning-based method for identifying wheat yellow rust from unmanned aerial vehicle (UAV) images. The method was based on the pyramid scene parsing network (PSPNet) semantic segmentation model to classify healthy wheat, yellow rust wheat, and bare soil in small-scale UAV images, and to investigate the spatial generalization of the model. In addition, it was proposed to use the high-accuracy classification results of traditional algorithms as weak samples for wheat yellow rust identification. The recognition accuracy of the PSPNet model in this study reached 98%. On this basis, this study used the trained semantic segmentation model to recognize another wheat field. The results showed that the method had certain generalization ability, and its accuracy reached 98%. In addition, the high-accuracy classification result of a support vector machine was used as a weak label by weak supervision, which better solved the labeling problem of large-size images, and the final recognition accuracy reached 94%. Therefore, the present study method facilitated timely control measures to reduce economic losses. [ABSTRACT FROM AUTHOR]

A series of algorithms for satellite retrievals of sun-induced chlorophyll fluorescence (SIF) have been developed and applied to different sensors. However, research on SIF retrieval using hyperspectral data is performed in narrow spectral windows, assuming that SIF remains constant. In this paper, based on the singular vector decomposition (SVD) technique, we present an approach for retrieving SIF, which can be applied to remotely sensed data with ultra-high spectral resolution and in a broad spectral window without assuming that the SIF remains constant. The idea is to combine the first singular vector, the pivotal information of the non-fluorescence spectrum, with the low-frequency contribution of the atmosphere, plus a linear combination of the remaining singular vectors to express the non-fluorescence spectrum. Subject to instrument settings, the retrieval was performed within a spectral window of approximately 7 nm that contained only Fraunhofer lines. In our retrieval, hyperspectral data of the O2-A band from the first Chinese carbon dioxide observation satellite (TanSat) was used. The Bayesian Information Criterion (BIC) was introduced to self-adaptively determine the number of free parameters and reduce retrieval noise. SIF retrievals were compared with TanSat SIF and OCO-2 SIF. The results showed good consistency and rationality. A sensitivity analysis was also conducted to verify the performance of this approach. To summarize, the approach would provide more possibilities for retrieving SIF from hyperspectral data. [ABSTRACT FROM AUTHOR]

Soil organic matter (SOM) is the main source of soil nutrients, which are essential for the growth and development of agricultural crops. Hyperspectral remote sensing is one of the most efficient ways of estimating the SOM content. Visible, near infrared, and mid-infrared reflectance spectroscopy, combined with the partial least squares regression (PLSR) method is considered to be an effective way of determining soil properties. In this study, we used 54 different spectral pretreatments to preprocess soil spectral data. These spectral pretreatments were composed of three denoising methods, six data transformations, and three dimensionality reduction methods. The three denoising methods included no denoising (ND), Savitzky–Golay denoising (SGD), and wavelet packet denoising (WPD). The six data transformations included original spectral data, R; reciprocal, 1/R; logarithmic, log(R); reciprocal logarithmic, log(1/R); first derivative, R'; and first derivative of reciprocal, (1/R)'. The three dimensionality reduction methods included no dimensionality reduction (NDR), sensitive waveband dimensionality reduction (SWDR), and principal component analysis (PCA) dimensionality reduction (PCADR). The processed spectra were then employed to construct PLSR models for predicting the SOM content. The main results were as follows—(1) the wavelet packet denoising (WPD)-R' and WPD-(1/R)' data showed stronger correlations with the SOM content. Furthermore, these methods could effectively limit the correlation between the adjacent bands and, thus, prevent "overfitting". (2) Of the 54 pretreatments investigated, WPD-(1/R)'-PCADR yielded the model with the highest accuracy and stability. (3) For the same denoising method and spectral transformation data, the accuracy of the SOM content estimation model based on SWDR was higher than that of the model based on NDR. Furthermore, the accuracy in the case of PCADR was higher than that for SWDR. (4) Dimensionality reduction was effective in preventing data overfitting. (5) The quality of the spectral data could be improved and the accuracy of the SOM content estimation model could be enhanced effectively, by using some appropriate preprocessing methods (one combining WPD and PCADR in this study). [ABSTRACT FROM AUTHOR]

[Display omitted] Land cover changes (LCCs) affect surface temperatures at local scale through biophysical processes. However, previous observation-based studies mainly focused on the potential effects of virtual afforestation/deforestation using the space-for-time assumption, while the actual effects of all types of realistic LCCs are underexplored. Here, we adopted the space-and-time scheme and utilized extensive high-resolution (1-km) satellite observations to perform the first such assessment. We showed that, from 2006 to 2015, the average temperature in the areas with LCCs increased by 0.08 K globally, but varied significantly across latitudes, ranging from −0.05 to 0.18 K. Cropland expansions dominated summertime cooling effects in the northern mid-latitudes, whereas forest-related LCCs caused warming effects elsewhere. These effects accounted for up to 44.6% of overall concurrent warming, suggesting that LCC influences cannot be ignored. In addition, we revealed obvious asymmetries in the actual effects, i.e., LCCs with warming effects occurred more frequently, with stronger intensities, than LCCs with cooling effects. Even for the mutual changes between two covers in the same region, warming LCCs generally had larger magnitudes than their cooling counterparts due to asymmetric changes in transition fractions and driving variables. These novel findings, derived from the assessment of actual LCCs, provide more realistic implications for land management and climate adaptation policies. [ABSTRACT FROM AUTHOR]

• Using hyperspectral data to invert the nitrogen content of different gradients in maize leaves. • Perform dimensionality reduction processing on information-rich hyperspectral data to reduce data redundancy and avoid over-fitting. • The elastic net algorithm is selected for dimensionality reduction, and the convex combination of L1 norm and L2 norm is used as the penalty term. • Nine inversion models were established, among which the best model was EN-PLSR, R2 = 0.96, RMSE = 0.19. Fast, accurate, and non-destructive detection of the nitrogen (N) content in corn leaves is of great significance for the precise dynamic management of nitrogen fertilizer application for corn. Hyperspectral data can provide an important means for detecting the nitrogen content in plants. Existing research has mainly focused on using various vegetation indices or 3–5 band combinations to estimate leaf nitrogen content, ignoring the different in spectral characteristics of hyperspectral data and failing to characterize most of the spectral information. Some scholars have used principal component analysis and wavelet analysis dimensionality reduction algorithms, but used different bands for these models. Therefore, more and different inversion models need to be introduced to improve the use of spectral data and increase the universality of the model. The present study selected three different methods to reduce data dimensionality, including the Successful Projections Algorithm (SPA) and the Least Absolute Shrinkage and Selection Operator (LASSO) and the Elastic Net (EN) algorithms. Then the processed spectral reflectance information and observational data for synchronous leaf nitrogen content were used to construct an inversion model used to predict leaf nitrogen content. Nine inversion models were constructed based on different dimensionality reduction and regression methods. Based on the coefficient of determination (R 2) and root mean square error (RMSE), the accuracy of each model was tested. The main results follow: (1) Dimensionality reduction processing of hyperspectral data can effectively prevent data from overfitting, limit the correlation between adjacent frequency bands, and reduce data redundancy. An EN dimensionality reduction algorithm (EN-Partial Least Squares Regression (PLSR)) model R 2 = 0.96, RMSE = 0.19) was better than a SPA (SPA-PLSR model R 2 = 0.90, RMSE = 0.26) and LASSO (LASSO-PLSR model R 2 = 0.89, RMSE = 0.37) dimensionality reduction algorithm. (2) For the same dimensionality reduction method, the accuracy of the regression model based on PLSR was higher than that of other models. Among the nine inversion models in this paper, the EN-PLSR inversion model has the best fitting effect (R 2 = 0.96, RMSE = 0.19). (3) Obvious changes in nitrogen content have little effect on the overall hyperspectral reflectance curve. This study provides a reference for high-efficiency and non-destructive testing of corn nitrogen content using hyperspectral technology. [ABSTRACT FROM AUTHOR]

Accurate retrieval of land surface temperature (LST) over urban areas is of great significance for urban thermal environment monitoring. In previous studies, most of the urban LST retrieval methods were developed based on the assumption of a flat surface without considering the influence of urban 3-D geometry structure, which has a significant impact on the retrieval accuracy of LST over urban areas. In this study, a radiative transfer equation (RTE)-based single-channel method was developed to retrieve LST with urban geometry effect correction from the Landsat 8 thermal infrared (TIR) data in band 10. The increase in adjacent radiance from the surrounding pixels and the decrease in atmospheric downwelling radiance caused by urban geometry structure were taken into account in this method. Because it is difficult to directly validate the retrieval accuracy of LST over urban areas using in situ LST measurements, the performance of the RTE-based LST retrieval method was evaluated via comparing brightness temperature (BT) at the top of the atmosphere (TOA) simulated by the discrete anisotropic radiative transfer (DART) model and the urban RTE over three subregions. There is a good agreement between BT at the TOA simulated by the DART model and the urban RTE, with a root-mean-squared error (RMSE) of less than 0.25 K. The variations in LST retrieved with urban geometry effect correction over different local climate zones (LCZs) were analyzed. In general, built-up LCZs have relatively higher LST than land cover LCZs. The differences between LST retrieved without/with urban geometry effect correction over different LCZs are greater than 0.2 K. The largest average LST difference over built-up LCZs is approximately 0.9 K, whereas that over land cover LCZs is approximately 0.65 K. LST retrieved without/with urban geometry effect correction was used to calculate urban heat island intensity (UHII) in terms of the LCZ-based method. The results indicate that UHII calculated from LST with urban geometry effect correction is lower than that calculated from LST without urban geometry effect correction, with an average difference of approximately 0.5 K. [ABSTRACT FROM AUTHOR]

Accurate estimation of land surface temperature (LST) is crucial for ecological environment monitoring and climate change studies in mountainous areas. The current LST retrieval algorithms were developed without accounting for the topographic effect, which can only be used to retrieve LST over relatively flat surfaces. Due to the impact of 3-D structure of mountainous surfaces, rugged terrain makes the processes of thermal radiation more complex. In this study, a radiative transfer equation (RTE)-based single-channel algorithm was proposed to retrieve LST with topographic effect correction from the Landsat 8 thermal infrared (TIR) data in mountainous areas. This algorithm accounts for the changes in the thermal radiation components in the TIR RTE caused by the topographic effect. According to the analysis of simulation data, sky-view factor (SVF), atmospheric water vapor content, surface emissivity of target pixel, and average LST of the surrounding terrain have significant influence on the magnitude of the topographic effect. The differences between the LST retrieved without/with topographic effect correction from the Landsat 8 TIR data are related to SVF. The topographic effect should be taken into account in the LST retrieval algorithm when SVF is smaller than 0.7. The largest LST difference of approximately 1 K occurs in the deep valley. The results indicate that LST without topographic effect correction could be overestimated to be as high as 1 K. Due to a lack of in situ LST measurements, the performance of the LST retrieval algorithm in mountainous areas was only evaluated by comparing the brightness temperature (BT) at the top of the atmosphere (TOA) simulated by the DART+MODTRAN model and the TIR RTE over mountainous surfaces at three subregions. There is a good consistency between BT at the TOA simulated by the DART+MODTRAN model and the TIR RTE over mountainous surfaces at the three subregions, with a root-mean-squared error (RMSE) of less than 0.23 K. [ABSTRACT FROM AUTHOR]

Accurate estimations of daily mean land surface temperature (LST) are important for investigating the urban heat island effect, land-atmosphere energy exchanges, and global climate change. Moderate Resolution Imaging Spectroradiometer (MODIS) sensors can provide up to four instantaneous LSTs of a single day across the world. However, numerous studies, such as those on climate change and hydrology, require the input of daily mean LSTs rather than instantaneous value. In this paper, we propose a practical method to estimate the daily mean LST using instantaneous LST products derived from MODIS. Based on the in situ LST measurements collected from 235 sites distributed globally, multiple linear regressions of two to four valid instantaneous LSTs at different MODIS observations moments (at least one daytime and one nighttime observations) can provide reliable estimates of daily mean LSTs under all-weather conditions with a root mean square error (RMSE) of less than 1.60 K. In addition, the conditions of clouds would affect the estimation accuracy of daily mean LST to a certain extent. Subsequently, an algorithm is proposed to produce the most complete coverage of daily mean LSTs from instantaneous LST products derived from MODIS. Validation results with in situ measurements show that the daily mean LSTs estimated from the MOD11A1 and MYD11A1 products are similar to the daily mean of the in situ LST, with an RMSE of 2.17 K. Furthermore, the daily mean LST derived from MODIS data is successfully applied to calculate the global annual cycle parameters (ACPs) in the annual temperature cycle (ATC) model. The results of this study show that the daily mean LST can be retrieved accurately from combinations of daytime and nighttime LSTs derived from MODIS. We expect that our findings will be useful for various applications involving global LST trend analysis and climate change. [ABSTRACT FROM AUTHOR]

Timely and accurate estimation of the winter wheat planting area and its spatial distribution is essential for the implementation of crop growth monitoring and yield estimation, and hence for the development of national agricultural production and food security. In remotely sensed winter wheat mapping based on spectral similarity, the reference curve is obtained by averaging multiple standard curves, which limits mapping accuracy. We propose a spectral reconstruction method based on singular value decomposition (SR-SVD) for winter wheat mapping based on the unique growth characteristics of crops. Using Sentinel-2 A/B satellite data, we tested the SR-SVD method in Puyang County, and Shenzhou City, China. Performance was increased, with the optimal overall accuracy and the Kappa of Puyang County and Shenzhou City were 99.52% and 0.99, and 98.26% and 0.97, respectively. We selected the spectral angle mapper (SAM) and Euclidean Distance (ED) as the similarity measures. Compared to spectral similarity methods, the SR-SVD method significantly improves mapping accuracy, as it avoids excessive extraction, can identify more detailed information, and is advantageous in distinguishing non-winter wheat pixels. Three commonly used supervised classification methods, support vector machine (SVM), maximum likelihood (ML), and minimum distance (MD) were used for comparison. Results indicate that SR-SVD has the highest mapping accuracy and greatly reduces the number of misidentified pixels. Therefore, the SR-SVD method can achieve high-precision crop mapping and provide technical support for monitoring regional crop planting structure information. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is an important climate variable related to surface energy and water balance. The LST products retrieved from Terra-/Aqua-MODIS thermal infrared data have been widely used in climatological, hydrological, meteorological, and ecological applications. The newest collection (C6) MODIS LST products have been freely available for the user community since 2016. It is necessary to evaluate the accuracy of the C6 MODIS LST products prior to their application. In this study, the C6 MODIS LST products were validated using a radiance-based (R-based) method. We selected 38 validation sites worldwide over eight land cover types, i.e., in-land water, forest, shrubland, grassland, cropland, urban, snow, and bare soil surfaces. Except for the results for grassland sites during the daytime and night-time and the bare soil sites during the daytime, the C6 MODIS LST products are in good agreement with the R-based LST values, with bias and root mean square error (RMSE) values of less than 1 K for all sites during the daytime and night-time. Large LST discrepancies over grassland sites are caused by misclassification in the MODIS land cover type products, which can lead to large uncertainties in the determination of surface emissivity. We also compared the accuracies of the C5 and C6 MODIS LST products. Results indicate that there are no significant accuracy discrepancies between the C5 and C6 MODIS LST products for all sites, with the exception of bare soil sites. Compared with the C5 MODIS LST products, significant accuracy improvement in the C6 MODIS LST products over most bare soil sites can be found. The absolute bias values are reduced from approximately 1.4–3.0 K for C5 to approximately 0.3–0.8 K for C6, and the RMSE values from approximately 1.4–3.1 K for C5 to approximately 0.5–0.8 K for C6. However, further overestimation in the C6 MODIS LST products can be found over two bare soil sites due to the poor emissivity adjustment provided by the emissivity adjustment model incorporated into the C6 split window algorithm. [ABSTRACT FROM AUTHOR]

During the past century Chinese agriculture has been struggling to produce more food to support the ever growing population, while dealing with the increased degradation of air, water and soil quality in the agricultural regions. Lessons learnt from the long-term efforts indicated that scientifically sound methodology could play a crucial role in understanding the complex interactions among climate, soil, water and farming management practices, all of which collectively control the agroecosystem services including crop yield, greenhouse gas emissions, nutrient loading and other environmental issues. Therefore, the development of a more process-based approach is needed. The process-based model, DNDC (Denitrification-Decomposition) has been widely used internationally to simulate detailed carbon and nitrogen biogeochemical cycles occurring in agricultural systems. However, this model is not fully suitable for China as it lacks a number of features which are crucial for representing Chinese agro-ecosystems, including paddy rice cultivation, complex and multiple cropping systems, and intensive management practices, etc. Recently a new version of DNDC, China-DNDC, was developed. The new model improved its capability of predicting the fluxes of all three terrestrial greenhouse gases: nitrous oxide (N 2 O), carbon dioxide (CO 2 ), and methane (CH 4 ), as well as other important indicators such as crop phenology, soil C sequestration, and nutrient leaching from different cropping systems across all the major agricultural regions in China. This paper reported how China-DNDC was developed, tested and applied for sustaining Chinese agriculture. And then, it identified the weaknesses and potential improvements for the model. [ABSTRACT FROM AUTHOR]

• Both one-phase and two-phase trapezoids are examined in separating ET soil and ET veg. • Two typical methods determining four endmembers are integrated into two trapezoids. • Analytical deductions of differences of ET soil and ET veg fraction are made. • ET veg /ET are intercompared with those by five typical partitioning methods. Partition of land surface evapotranspiration (ET) into soil evaporation (ET soil) and vegetation transpiration (ET veg) is of great significance for scheduling agricultural irrigation, improving water-use efficiency of crop, and managing water resources. This study made a comprehensive evaluation of one-phase and two-phase surface temperature versus fractional vegetation cover trapezoids in the separation of soil evaporation from vegetation transpiration through analytical deductions and model applications, with surface temperatures at four end-members determined from the layered approach and the patch approach. The two trapezoids were tested on the MODIS data during June to September in 2012 at Daman superstation in Northwest China. The trapezoid-estimated ratios of transpiration to total ET (ET veg /ET) were intercompared with the ET veg /ET from five typical partitioning methods, namely, the stable isotope-method, the underlying water-use efficiency (uWUE) method, the transpiration estimation algorithm (TEA), the Pérez-Priego method, and the Wei method. Results showed that: 1) The two-phase trapezoid integrated with the layered approach and the one-phase trapezoid integrated with the layered approach performed the best and worst with root-mean-square errors of 52.6 W/m2 and 78.6 W/m2, respectively, when the estimated total ET was validated against the Bowen Ratio corrected eddy covariance measurements. 2) The five partitioning methods produced largely different ET veg /ET, with the highest values from the TEA method and the lowest values from the Pérez-Priego method. 3) The estimated vegetation transpiration and ET veg /ET by the two-phase trapezoid were generally higher than those by the one-phase trapezoid. 4) In the intercomparison of ET veg /ET, the layered approach agreed better with the five partitioning methods than the patch approach. 5) The two-phase trapezoid integrated with the layered approach overall produced the most consistent estimates of ET veg /ET with those from the five partitioning methods, with the lowest bias varying between -30.9% and 8.7% and root-mean-square differences varying between 16.8% and 36.8%. In summary, the two-phase trapezoid is theoretically more rational and appears to outperform the one-phase trapezoid. This study is beneficial for a better understanding of the differences, similarities, advantages and weaknesses of the one-phase and two-phase trapezoids in the partition of total ET to its soil and vegetation components. [ABSTRACT FROM AUTHOR]

Nitrogen (N) losses and greenhouse gas (GHG) emissions from paddy rice fields contaminate water bodies and atmospheric environment. A 2-year (2012–2013) field experiment was conducted at a typical paddy rice field in a rural suburb of Shanghai, China. N losses and GHG emissions from the paddy field with alternative fertilizer management practices were simultaneously measured. Four treatments were tested in the experiment: applications of only chemical synthetic fertilizer urea (CT), only organic manure (OT), a combination of the two types of fertilizers (MT) and a control (CK). Results from the field study indicated that CT produced the highest seasonal N loading rate (18.79 kg N/ha) and N 2 O emissions (1.81 kg N 2 O/ha) but with the lowest seasonal CH 4 emissions (69.09 kg CH 4 /ha). With organic manure applied, MT and OT respectively reduced N loading by 21.86% and 30.41%, reduced N 2 O emissions by 28.34% and 69.41%, but increased CH 4 emissions by 137% and 310% in comparison with CT. However, the net impact of CH 4 and N 2 O emissions on global warming was enhanced when organic manure was applied. In addition, CT and MT produced the optimal rice yield during the experimental period, while OT treatment led to a yield reduction by 9.29% compared with CT. In conclusion, the impacts of alternative fertilizer management practices on ecosystem services ought to be assessed specifically due to the great variations across rice yields, N loss and GHG emissions. [ABSTRACT FROM AUTHOR]

Abstract: Nitrogen (N) loss through nitrate leaching in arable cropping systems in China has been recognized as one of the most common agricultural sources of groundwater contamination. The Denitrification–Decomposition or DNDC model, equipped with detailed soil hydrological and biogeochemical processes, was adopted in the study to quantify nitrate leaching for an intensively cultivated region in northern China. Several key parameters embedded in DNDC were calibrated against leaching data measured at a typical field with winter wheat–corn rotation within the target domain region. Five more sites representing the predominant cropping systems in the study region were selected for validating the modified model. In comparison with the original version of DNDC, the revised version yielded better results in simulated soil water and N leaching losses. To upscale the model simulation to regional level, we linked the validated DNDC to a regional database containing meteorological data, soil properties, vegetation types, and management practices for the target domain. Results from the regional simulation indicated that the total potential nitrate leaching load from the simulated 16.31millionha croplands (sown area) ranged from 1.5 to 2.15TgN per year, with an average of 1.8TgN, which was equivalent to 26.1% of the total amount of N fertilizer applied in the region in 2009. The modeled results showed clear spatial patterns of nitrate leaching rates across the region due to the spatially differentiated fertilizer application rates as well as the soil water regimes. Alternative water management practices were suggested to effectively reduce nitrate leaching losses from the agricultural region in northern China. [Copyright &y& Elsevier]

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. [ABSTRACT FROM AUTHOR]

The influence of adjacency effect on high-spatial-resolution satellite imagery in the visible and near infrared region has been studies since the 1980s. However, the adjacency effect is usually neglected in the thermal infrared (TIR) region. The conventional TIR radiative transfer equation (RTE) only takes into account radiance from target pixel and neglects radiance contribution from surrounding background pixels. In this study, two TIR RTEs taking into account the adjacency effect for uniform and non-uniform Lambertian surfaces were deduced based on the radiative transfer theory. In terms of the TIR RTEs, we analyze the dominating influence factors of the adjacency effect and quantify the magnitude of the adjacency effect in the TIR region using simulation and actual satellite data. Two different definitions of the adjacency effect are presented in this study: (1) the effect due to radiance from background pixels scattered into sensor's instantaneous field of view, and (2) the effect due to apparent thermal contrast between target and background pixels. The results show that atmospheric visibility, water vapor content, sensor spectral band, and background pixel land surface temperature (LST) have significant influence on the adjacency effect. For a specific simulation scene, the magnitude of the first definition of the adjacency effect is larger than 0.5 K when atmospheric visibility is lower than 23 km. This magnitude is as much as 2 K under haze weather conditions with atmospheric visibility of 5 km. The results indicate that the adjacency effect should be taken into account in the radiative transfer simulation. The magnitude of the second definition of the adjacency effect is less than 0.3 K, even at atmospheric visibility of 5 km. The results reveal that the accuracy of the TIR RTE for a uniform Lambertian surface is enough for the development of LST retrieval algorithm under relatively haze weather conditions. In situ LST measurements collected at the Hailar, Urad Front Banner, and Wuhai sites in China were used to validate the accuracies of the LST retrieved by the RTE-based single channel algorithm with/without adjacency effect correction. There are nearly no discrepancies between the LST retrieved with/without adjacency effect correction when aerosol optical depth (AOD) is low than 0.3. However, the RMSE of the differences between the retrieved LST and the in situ LST decreases from approximately 1.4 K for the LST retrieved without adjacency effect correction to approximately 0.6 K for the LST retrieved with adjacency effect correction when AOD is high than 0.3. • Two TIR RTEs taking into account adjacency effect were deduced. • Dominating influence factor of adjacency effect in the TIR region was analyzed. • The magnitude of adjacency effect in the TIR region was quantified. • RMSE of LST derived with adjacency effect correction reduces 0.8 K for AOD > 0.3. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is vital for studies of hydrology, ecology, climatology, and environmental monitoring. The radiative-transfer-equation-based single-channel algorithm, in conjunction with the atmospheric profile, is regarded as the most suitable one with which to produce long-term time series LST products from Landsat thermal infrared (TIR) data. In this study, the performances of seven atmospheric profiles from different sources (the MODerate-resolution Imaging Spectroradiomete atmospheric profile product (MYD07), the Atmospheric Infrared Sounder atmospheric profile product (AIRS), the European Centre for Medium-range Weather Forecasts (ECMWF), the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2), the National Centers for Environmental Prediction (NCEP)/Global Forecasting System (GFS), NCEP/Final Operational Global Analysis (FNL), and NCEP/Department of Energy (DOE)) were comprehensively evaluated in the single-channel algorithm for LST retrieval from Landsat 8 TIR data. Results showed that when compared with the radio sounding profile downloaded from the University of Wyoming (UWYO), the worst accuracies of atmospheric parameters were obtained for the MYD07 profile. Furthermore, the root-mean-square error (RMSE) values (approximately 0.5 K) of the retrieved LST when using the ECMWF, MERRA2, NCEP/GFS, and NCEP/FNL profiles were smaller than those but greater than 0.8 K when the MYD07, AIRS, and NCEP/DOE profiles were used. Compared with the in situ LST measurements that were collected at the Hailar, Urad Front Banner, and Wuhai sites, the RMSE values of the LST that were retrieved by using the ECMWF, MERRA2, NCEP/GFS, and NCEP/FNL profiles were approximately 1.0 K. The largest discrepancy between the retrieved and in situ LST was obtained for the NCEP/DOE profile, with an RMSE value of approximately 1.5 K. The results reveal that the ECMWF, MERRA2, NCEP/GFS, and NCEP/FNL profiles have great potential to perform accurate atmospheric correction and generate long-term time series LST products from Landsat TIR data by using a single-channel algorithm. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is a key variable influencing the energy balance between the land surface and the atmosphere. In this work, a split-window algorithm was used to calculate LST from Sentinel-3A Sea and Land Surface Temperature Radiometer (SLSTR) thermal infrared data. The National Centers for Environmental Prediction (NCEP) reanalysis atmospheric profiles combined with the radiation transport model MODerate resolution atmospheric TRANsmission version 5.2 (MODTRAN 5.2) were utilized to obtain atmospheric water vapor content (WVC). The ASTER Global Emissivity Database Version 3 (ASTER GED v3) product was utilized to estimate surface emissivity in order to improve the accuracy of LST estimation over barren surfaces. Using a simulation database, the coefficients of the algorithm were fitted and the performance of the algorithm was evaluated. The root-mean-square error (RMSE) values of the differences between the estimated LST and the actual LST of the MODTRAN radiative transfer simulation at each WVC subrange of 0–6.5 g/cm2 were less than 1.0 K. To validate the retrieval accuracy, ground-based LST measurements were collected at two relatively homogeneous desert study sites in Dalad Banner and Wuhai, Inner Mongolia, China. The bias between the retrieved LST and the in situ LST was about 0.2 K and the RMSE was about 1.3 K at the Dalad Banner site, whereas they were approximately -0.4 and 1.0 K at the Wuhai site. As a reference, the retrieved LST was compared with the operational SLSTR LST product in this study. The bias between the SLSTR LST product and the in situ LST was approximately 1 K and the RMSE was approximately 2 K at the Dalad Banner site, whereas they were approximately 1.1 and 1.4 K at the Wuhai site. The results demonstrate that the split-window algorithm combined with improved emissivity estimation based on the ASTER GED product can distinctly obtain better accuracy of LST over barren surfaces. [ABSTRACT FROM AUTHOR]

Cloud-free remote sensing images are required for many applications, such as land cover classification, land surface temperature retrieval and agricultural-drought monitoring. Cloud cover in remote sensing images can be pervasive, dynamic and often unavoidable. Current techniques of cloud removal for the VNIR (visible and near-infrared) bands still encounters the problem of pixel values estimated for the cloudy area incomparable and inconsistent with the cloud-free region in the target image. In this paper, we proposed an efficient approach to remove thick clouds and their shadows in VNIR bands using multi-temporal images with good maintenance of DN (digital number) value consistency. We constructed the spectral similarity between the target image and reference one for DN value estimation of the cloudy pixels. The information reconstruction was done with 10 neighboring cloud-free pair-pixels with the highest similarity over a small window centering the cloudy pixel between target and reference images. Four Landsat5 TM images around Nanjing city of Jiangsu Province in Eastern China were used to validate the approach over four representative surface patterns (mountain, plain, water and city) for diverse sizes of cloud cover. Comparison with the conventional approaches indicates high accuracy of the approach in cloud removal for the VNIR bands. The approach was applied to the Landsat8 OLI (Operational Land Imager) image on 29 April 2016 in Nanjing area using two reference images. Very good consistency was achieved in the resulted images, which confirms that the proposed approach could be served as an alternative for cloud removal in the VNIR bands using multi-temporal images. [ABSTRACT FROM AUTHOR]