Surface soil moisture (SSM) is essential for understanding the interactions between the atmosphere and Earth’s surface. The rapid development of remote sensing technology in recent decades has provided feasible alternatives for SSM retrieval. The triangle model is one such method that uses the relationship between land surface temperature (LST) and vegetation index (VI) on a triangular space to estimate SSM. However, the traditional LST-VI triangle models inherently suffer from two major drawbacks. First, the subjective requirements for a sufficient number of pixels are characterized by a wide range of vegetation and SSM under uniform atmospheric conditions. Second, this is the need for date-to-date calibration. To overcome these limitations, the present study proposed a novel scheme of the feature space, the single pixel in time-series (SPTS) triangle model. The basic assumption of this feature space is that a given satellite pixel for cropland or grassland will undergo distinct vegetation cover and SSM status due to natural growth and soil moisture dynamics over a relatively long period. Unique triangles for 44 sites in two networks of the International Soil Moisture Network (ISMN)—the TxSon (US) dominated by grassland and REMEDHUS (Spain) dominated by cropland—were constructed based on Landsat data over a period of ~10 years (2013–2023). Compared to the traditional triangle model, the proposed model reveals enhanced skills for SSM retrieval, with a decrease in root-mean-square error (RMSE) by 13.5% (~0.050 m3/m3) over the study sites.

Land Surface Temperature (LST) is a critical parameter in meteorology, hydrology, and environmental science. Compared to thermal infrared remote sensing, passive microwave (PMW) remote sensing for LST retrieval offers advantage under cloudy conditions. In this study, we utilized the Channel Resolution Enhanced (CRE) Microwave Radiation Imager (MWRI) brightness temperature data from the Chinese FengYun-3D (FY-3D) polar-orbiting meteorological satellite as the primary input to obtain global LST. Two physics-based PMW retrieval methods were introduced: the three-channel method (18.7, 36.5, and 89.0 GHz) and the PWV-CLW method, which integrates the 18.7 GHz and 23.8 GHz channels with precipitable water vapor (PWV) and cloud liquid water (CLW). The results indicate that both methods have generally achieved good accuracy. The three-channel method performs well in grasslands and barren lands during the daytime, with a Root Mean Square Error (RMSE) ranging from 4 to 5 K. At night, it excels in grasslands, croplands, and barren lands, with an RMSE from 2 to 3 K. The PWV-CLW method demonstrates superior accuracy for forests and croplands during the daytime, with RMSE values from 3.6 to 5.3 K, respectively. At night, this method excels in accuracy for forests, with an RMSE of 2.9 K. Additionally, a fusion method was proposed to improve the overall accuracy of LST estimation across different land cover types. The RMSE values for ascending and descending overpasses are 4.22 K and 2.76 K, with biases of -0.29 K and -0.6 K, respectively. This approach effectively mitigates spatial heterogeneity and atmospheric effects, enabling all-weather LST retrieval and showcasing the potential of CRE FY-3D/MWRI data for LST monitoring.

Traditional climate data, such as air temperature and precipitation, have been widely used in various models for crop yield prediction. One of the major challenges is that most of these climate data were derived from either reanalysis products with relatively coarser spatial resolution or from in situ measurements with limited representativeness, which would inevitably reveal significant mismatches regarding spatial scale with other synchronously used vegetation and soil parameters at high resolution (e.g., ~1 km). To this end, satellite-derived land surface temperature (LST) and soil moisture (SM) at a high spatial resolution of 1 km were used as proxies of air temperature and precipitation to evaluate the feasibility of predicting maize yield in three major regions (northeast, northwest, and north China). Specifically, each region includes three provinces. Three widely used machine learning models, namely, the gradient boosting decision tree, extreme gradient boosted tree, and random forest (RF), were considered to avoid the contingency of a single model. In this study, the three models were trained at two spatial scales: 1) region by region and 2) entire maize planting area. Results indicated that using satellite-based LST and SM instead of traditional climate data of air temperature and precipitation can obtain a significantly improved maize yield prediction with the average root mean square error decreased from 862 to 827 kg/ha when the models were trained region by region and from 894 to 840 kg/ha when the models were trained over the entire maize planting area.

A new two-step physical method was developed to retrieve the land surface temperature (LST) from infrared atmospheric sounding interferometer (IASI) observations. This method relinearized the radiative transfer equation (RTE) by the tangents around the initial estimates of the LST, land surface emissivity (LSE), atmospheric equivalent temperature ($Ta$), and water vapor content ($q$). The Tikhonov regularization method and discrepancy principle (DP) iteration algorithm were employed to stabilize the ill-posed problem and obtain the final maximum likelihood solution of the LST with updating the initial estimation of LST, LSE,$Ta$, and$q$. A new channel selection scheme was proposed for this physical method to obtain an accurate LST estimation. This physical-based algorithm was tested on both simulated and real data obtained from the IASI. The root-mean-square error (RMSE) of the simulated LST is ~1 K based on an initial LST estimate with an RMSE of 2 K (1.9 K). The sensitivity analysis shows that the LST retrieval accuracy is ~1 K based on an LST with a random error of 3 K, constant initial LSE (0.97), 10%$Ta$error, and 40%$q$error. Compared with the Advanced Very High Resolution Radiometer onboard Metop (AVHRR/Metop) LST product, the physical method achieves the LST retrieval accuracy of 1.5 and 1 K for real daytime and nighttime IASI data obtained in the study area. Based on the new method, the LST can be retrieved with an accuracy similar to that of the AVHRR/Metop LST product.

Trapezoidal configurations constituted by land surface temperature and fractional vegetation cover has been frequently used to estimate surface soil moisture (SSM). Determination of the SSM status over the trapezoidal dry and wet edges is required to decouple the volumetric SSM content from the trapezoid-derived M0 because of the coupling of volumetric SSM content and soil texture (i.e., soil moisture availability,M0). Currently, soil hydraulic characteristics generated from soil pedotransfer functions (PTF) provide a preferred solution for describing the SSM status over trapezoidal dry and wet edges; however, most PTF have been developed from laboratory-based soil measurements which have not been fully integrated into remote sensing models for SSM retrieval. This study investigated a practical calibration approach for PTF-derived soil hydraulic characteristics to enhance SSM retrieval using these trapezoidal configurations. Three years of high-resolution SSM measurements were estimated using trapezoidal configurations with Landsat-8 data over a semi-arid network in Spain. For the uncalibrated trapezoid, fair accuracy with a root mean square error (RMSE) of 0.062 m3/m3and bias of 0.040 m3/m3was achieved when compared with in situ measurements. Furthermore, a practical PTF-calibration approach with local measurements was proposed and subsequently integrated into the trapezoid to obtain SSM values. Our results indicated enhanced SSM estimates with an RMSE of 0.050 m3/m3and bias of 0.012 m3/m3with the calibrated PTF. Finally, we found that the calibrated trapezoid can eliminate overestimation and underestimation when the SSM was lower or higher, respectively, which occurred frequently for optical SSM retrievals.

Relative humidity (RH) is one of the key variables for understanding the water, energy, and carbon exchange between the Earth and the atmosphere. Traditional methods for deriving RH from remotely sensed data usually require ground meteorological observations or are limited to clear-sky conditions, thereby making it a significant challenge to obtain spatially complete RH under all-sky conditions, especially over the regions with sparse meteorological instruments for observation. To this end, a new approach for deriving all-sky RH entirely based on Moderate Resolution Imaging Spectroradiometer (MODIS) data was proposed in the present study. Two key assumptions in the approach under cloudy conditions are that the actual water vapor is linearly related to the total precipitable water vapor (PWV) and that air temperature is linearly related to land surface temperature (LST). Results from a total of 30 AmeriFlux stations proved the aforementioned assumptions based on MODIS data collected over a study period of three years from 2009 to 2011. For different aridity conditions, RH retrieval revealed reasonable accuracy with a root-mean-square error (RMSE) of approximately 15.3% over an arid and semiarid region, whereas a comparable RMSE of 17.0% was obtained over a humid area. Further results also indicated that the aforementioned linear relationships were generally temporally stable, thereby indicating that the proposed method can be used to obtain all-sky RH at a regional or global scale entirely based on MOD06_L2-derived LST and MOD05_L2-derived PWV data given that the assumed linear relationships can be easily determined by historical MOD07_L2-derived atmospheric profiles.

Net surface shortwave radiation (NSSR) is a key parameter that drives the surface material exchange and energy balance. Herein, we propose an improved artificial neuron network (ANN) with parameterized (ANN-P) method to first calculate the albedo at the top of atmosphere (TOA) by considering the surface non-Lambertian effect. Subsequently, the NSSR is estimated based on the relationship between TOA broadband albedo and the Earth's surface-absorbed shortwave radiation using a parameterized method under clear sky. The modeling process is implemented with Chinese Gaofen-5 (GF-5) visible/near-infrared channels data simulated via MODTRAN. For comparison, a previously reported lookup table (LUT) with parameterized (LUT-P) method and an ANN method are also employed. The performances of all these methods are evaluated. In terms of model simulation part, the root-mean-square errors (RMSEs) are 15.01 (17.07), 10.04 (13.67), and 20.39 (29.99) W/m2for land, water, and snow/ice surfaces, respectively, for the ANN-P (versus LUT-P) method. Their mean bias errors (MBEs) are within 0.9 W/m2. With respect to the direct ANN method, it shows the highest accuracy yet relatively large deviation for water surface. Additionally, the sensitivity analysis of water vapor content (WVC) confirms that the ANN-P method is more stable than the LUT-P and ANN methods and is, thereby, recommended for clear-sky NSSR estimation. Finally, the ground validations indicate that the mean RMSEs (MBEs) for the LUT-P, ANN-P, and ANN methods are 49.33 (-3.01), 47.55 (1.75), and 104.24 (-75.72) W/m2, respectively.

Evapotranspiration (ET) is one of the most critical parameters in water- and energy-related domains. Two basic assumptions with respect to soil-moisture variation have been widely investigated for the retrieval of ET based on the trapezoid methods. Specifically, soil moisture within the surface and root-zone layers was assumed to vary synchronously in most of the earlier analyses. However, several recent investigations assumed that soil moisture within the upper soil layer should be dried up before the root-zone layer is stressed. To this end, the retrieval of ET under different aridity conditions over North American grasslands was investigated with the two assumptions, and the estimated ET was assessed using the flux data collected from eight AmeriFlux sites. Based on the available data from 2002 to 2018, results showed that the “asynchronous-assumed” method can obtain better ET estimates than the “synchronous-assumed” method over semiarid and subhumid areas, whereas the “synchronous-assumed” method can obtain better ET estimates in humid areas. Moreover, because of the different closure techniques used for the ET correction, no consistent conclusions could be found for the arid conditions to determine which trapezoid was better. Specifically, it was found that the cases of surface soil with zero water availability that were defined by the asynchronous-assumed trapezoid method rarely occur, even in arid areas, which indicated that the critical boundary that determines whether the root-zone layer begins to be water-stressed may need to be redefined.

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.

The angular effect is a primary obstacle for wide applications of land surface temperature (LST) products. Current directional thermal radiation models do not fully consider the difference between visible/near infrared and thermal radiative, i.e. thermal inertial effect, and are not practical enough. Therefore, this study proposed a practical method for angular normalization of LST products based on the component temperature and fractional vegetation cover (FVC). Analyzing with simulated data indicated that the proposed method could improve the LST retrieval accuracy caused by angular effect from 1.2 K to 0.8 K. In addition, the retrieval accuracy of component temperature would affect the performance of the proposed method whereas the retrieval accuracy of component emissivity had almost no effect on the performance.

Net surface shortwave radiation (NSSR) is a key parameter for the estimation of surface energy budget. This paper proposes a method to directly estimate the NSSR from simulated Chinese Gaofen-5 (GF-5) data without using any ancillary information. Firstly, the narrowband reflectances of visible/near infrared channels at the top of the atmosphere (TOA) were converted to the TOA broadband albedo. Secondly, by categorizing the land surface into three types, the NSSR was estimated under clear and cloudy skies separately based on the relationship between TOA broadband albedo and the Earth's surface absorbed shortwave radiation. The estimation error of the absorption coefficient for each land type is lower than 0.05. Finally, by employing a look-up-table acquired in the process of narrowband-to-broadband conversion, and the parameters in the NSSR estimation model for each land type, the performance of the proposed method was evaluated, where the root mean square errors (RMSEs) were 25.85 (13.97) W/m2, 20.39 (7.97) W/m2, and 40.54 (11.26) W/m2for land, ocean and snow/ice surfaces for clear (cloudy) skies, respectively.

Sensor-observed energy from adjacent pixels, known as the adjacency effect, influences land surface reflectivity retrieval accuracy in optical remote sensing. As the spatial resolution of thermal infrared (TIR) images increases, the adjacency effect may influence land surface temperature (LST) retrieval accuracy in TIR remote sensing. However, to our knowledge, few studies have focused on quantifying this adjacency effect on TIR measurements. In this study, a forward adjacency effect radiative transfer model (FAERTM) was developed to quantify the adjacency effect on high-spatial-resolution TIR measurements. The model was verified to be in good agreement with moderate resolution atmospheric transmission (MODTRAN) code, with a discrepancy3 K in some cases. These findings indicate that the adjacency effect should be considered when retrieving LSTs from TIR measurements, at least in some specific conditions. The proposed FAERTM provides a useful model for quantifying and addressing the adjacency effect on TIR measurements.

Generally, clouds and snow are mixed in one image together, the clouds are difficult to be identified from the image. Here, clouds were divided into high clouds, medium clouds, low clouds and thin clouds. They were identified and removed according to respective thresholds, which were obtained from experiments basing on AVHRR/2 data over Qinghai-Tibet Plateau. In the light of visual inspection, it can be found that the results of cloud removal were reliable and accurate.