Surface soil moisture (SSM) is critical for agricultural monitoring, drought detection, and precision irrigation management. The Soil Moisture Active Passive (SMAP) THySM (Thermal Hydraulic disaggregation of Soil Moisture) product provides daily 1 km SSM over Contiguous United States (CONUS) but suffers from 41–99% spatial data gaps, limiting operational agricultural use. Applying Data Interpolation Empirical Orthogonal Functions (DINEOF+), this paper presents DINEOF+ derived Observation-Faithful Soil Moisture (DOF_SM), a gap-free daily 1 km SSM dataset for CONUS spanning 2015–2025. DINEOF+ applies iterative truncated Singular Value Decomposition (SVD) to reconstruct missing SSM values while preserving the observational fidelity of the original THySM retrievals without model-assimilation artifacts. Validation against 594 in-situ stations from three core International Soil Moisture Network (ISMN) networks (SCAN, USCRN, SNOTEL) yields overall Pearson $\mathrm{r}=0.533$ and $\text{RMSE}= 0.106 \mathrm{m}^{3}/\mathrm{m}^{3}$. Novel stratification by irrigation status reveals superior accuracy at irrigated sites $(\mathrm{r}=0.690 \text{vs}. 0.530$ for rainfed), while phenology-based stratification identifies an accuracy nadir during peak corn canopy (Silking: $\mathrm{r}=0.421)$, directly informing yield forecasting uncertainty. The dataset is freely accessible via Zenodo under CC-BY 4.0.

An accurate and timely national or regional-scale map of crop planting date at field resolution could provide foundational information for assessing yield potential, optimizing agricultural decision-making, and improving model prediction. Although USDA publishes the state-level planting and emergence progress in the Crop Progress Reports, it is too coarse spatially to be used in many decision making scenarios. In addition, the exact planting dates are varied field by field in the real world. In the past, studies have been conducted to derive the field-level planting date maps through either supervised classification of growing-season satellite remote sensing images or calibrated crop growth models. However, those methods all suffer from timeliness, interpretability, and operational scalability for large geographic areas. To address the challenges, in this study, a near-real-time, physicals-guided framework is proposed to infer field-level planting dates by backward tracking of growing degree days from satellite-detectable green-up dates. The framework requires only early-season Harmonized Landsat and Sentinel (HLS) imagery and daily PRISM air temperature data with minimal ancillary inputs. By validating using multi-scale observations including USDA progress curves, geo-tagged field photos, and Beck’s farmer-reported planting records, the mapping results over the U.S. Corn Belt achieved mean absolute errors of less than 3 days compare with the ground truth data. This framework provides a concise, light-weight, transparent, and reproducible solution to the challenges in field-level planting date mapping, capable of the operational early-season deployment at continental scale.

Near real-time (NRT) crop type mapping plays a crucial role in modeling crop development, managing food supply chains, and supporting sustainable agriculture. The low-latency updates on crop type distribution also help assess the impacts of weather extremes and climate change on agricultural production in a timely fashion, aiding in identification of early risks in food insecurity as well as rapid assessments of the damage. Yet NRT crop type mapping is challenging due to the obstacle in acquiring timely crop type reference labels during the current season for crop mapping model building. Meanwhile, the crop mapping models constructed with historical crop type labels and corresponding satellite imagery may not be applicable to the current season in NRT due to spatiotemporal variability of crop phenology. The difficulty in characterizing crop phenology in NRT remains a significant hurdle in NRT crop type mapping. To tackle these issues, a novel emergence-based thermal phenological framework (EMET) is proposed in this study for field-level NRT crop type mapping. The EMET framework comprises three key components: hybrid deep learning spatiotemporal image fusion, NRT thermal-based crop phenology normalization, and NRT crop type characterization. The hybrid fusion model integrates super-resolution convolutional neural network (SRCNN) and long short-term memory (LSTM) to generate daily satellite observations with a high spatial resolution in NRT. The NRT thermal-based crop phenology normalization innovatively synthesizes within-season crop emergence (WISE) model and thermal time accumulation throughout the growing season, to timely normalize crop phenological progress derived from temporally dense fusion imagery. The NRT normalized fusion time series are then fed into an advanced deep learning classifier, the self-attention based LSTM (SAtLSTM) model, to identify crop types. Results in Illinois and Minnesota of the U.S. Corn Belt suggest that the EMET framework significantly enhances the model scalability with crop phenology normalized in NRT for timely crop mapping. A consistently higher overall accuracy is yielded by the EMET framework throughout the growing season compared to the calendar-based and WISE-based benchmark scenarios. When transferred to different study sites and testing years, EMET maintains an advantage of over 5% in overall accuracy during early- to mid-season. Moreover, EMET reaches an overall accuracy of 85% a month earlier than the benchmarks, and it can accurately characterize crop types with an overall accuracy of 90% as early as in late July. F1 scores for both corn and soybeans also achieve 90% around late July. The EMET framework paves the way for large-scale satellite-based NRT crop type mapping at the field level, which can largely help reduce food market volatility to enhance food security, as well as benefit a variety of agricultural applications to optimize crop management towards more sustainable agricultural production.

Crop-type mapping using time-series remote sensing data is crucial for a wide range of agricultural applications. Crop mapping during the growing season is particularly critical in timely monitoring of the agricultural system. Most existing studies focusing on within-season crop mapping leverage historical remote sensing and crop type reference data for model building, due to the difficulty in obtaining timely crop type samples for the current growing season. Yet the crop type samples from previous years may not be used directly considering the diverse patterns of crop phenology across years and locations, which hampers the scalability and transferability of the model to the current season for timely crop mapping. This article proposes an innovative within-season emergence (WISE) phenology normalized deep learning model towards scalable within-season crop mapping. The crop time-series remote sensing data are first normalized by the WISE crop emergence dates before being fed into an attention-based one-dimensional convolutional neural network classifier. Compared to conventional calendar-based approaches, the WISE-phenology normalization approach substantially helps the deep learning crop mapping model accommodate the spatiotemporal variations in crop phenological dynamics. Results in Illinois from 2017 to 2020 indicate that the proposed model outperforms calendar-based approaches and yields over 90% overall accuracy for classifying corn and soybeans at the end of season. During the growing season, the proposed model can give satisfactory performance (85% overall accuracy) one to four weeks earlier than calendar-based approaches. With WISE-phenology normalization, the proposed model exhibits more stable performance across Illinois and can be transferred to different years with enhanced scalability and robustness.

Adaptive management of large herbivores requires an understanding of how spatial-temporal fluctuations in forage biomass and quality influence animal performance. Advances in remote sensing have yielded information about the spatial-temporal dynamics of forage biomass, which in turn have informed rangeland management decisions such as stocking rate and paddock selection for free-ranging cattle. However, less is known about the spatial-temporal patterns of diet quality and their influence on large herbivore performance. This is due to infrequent concurrent ground observations of forage conditions with performance (e.g., mass gain), and previously limited satellite data at fine spatial and temporal scales. We combined multi-temporal field observations of diet quality (weekly) and mass gain (monthly) with satellite-derived phenological metrics (pseudo-daily, using data fusion and interpolation) to model daily mass gains of free-ranging yearling cattle in shortgrass steppe. We used this model to predict grazing season (mid-May to October) mass gains, a key management indicator, across 40 different paddocks grazed over a 10-year period (n = 138). We found strong relationships between diet quality and the satellite-derived phenological metrics, especially metrics related to the timing and rate of green-up and senescence. Satellite-derived diet quality estimates were strong predictors of monthly mass gains (R2  = 0.68) across a wide range of aboveground net herbaceous production. Season-long predictions of average daily gain and cattle off-mass had mean absolute errors of 8.9% and 2.9%, respectively. The model performed better temporally (across repeated observations in the same paddock) than spatially (across all paddocks within a given year), highlighting the need for accurate vegetation maps and robust field data collection across both space and time. This study demonstrates that free-ranging cattle performance in rangelands is strongly affected by diet quality, which is related to the timing of vegetation green-up and senescence. Senescing vegetation suppressed mass gains, even if adequate forage was available. The satellite-based pseudo-daily approach presented here offers new opportunities for adaptive management of large herbivores, such as identifying within-season triggers to move livestock among paddocks, predicting wildlife herd health, or timing the grazing season to better match earlier spring green-up caused by climate change and plant species invasion.

Crop phenology regulates seasonal agroecosystem carbon, water, and energy exchanges, and is a key component in empirical and process-based crop models for simulating biogeochemical cycles of farmlands, assessing gross and net primary production, and forecasting the crop yield. The advances in phenology matching models provide a feasible means to monitor crop phenological progress using remote sensing observations, with a priori information of reference shapes and reference phenological transition dates. Yet the underlying geometrical scaling assumption of models, together with the challenge in defining phenological references, hinders the applicability of phenology matching in crop phenological studies. The objective of this study is to develop a novel hybrid phenology matching model to robustly retrieve a diverse spectrum of crop phenological stages using satellite time series. The devised hybrid model leverages the complementary strengths of phenometric extraction methods and phenology matching models. It relaxes the geometrical scaling assumption and can characterize key phenological stages of crop cycles, ranging from farming practice-relevant stages (e.g., planted and harvested) to crop development stages (e.g., emerged and mature). To systematically evaluate the influence of phenological references on phenology matching, four representative phenological reference scenarios under varying levels of phenological calibrations in terms of time and space are further designed with publicly accessible phenological information. The results indicate that the hybrid phenology matching model can achieve high accuracies for estimating corn and soybean phenological growth stages in Illinois, particularly with the year- and region-adjusted phenological reference (R-squared higher than 0.9 and RMSE less than 5 days for most phenological stages). The inter-annual and regional phenological patterns characterized by the hybrid model correspond well with those in the crop progress reports (CPRs) from the USDA National Agricultural Statistics Service (NASS). Compared to the benchmark phenology matching model, the hybrid model is more robust to the decreasing levels of phenological reference calibrations, and is particularly advantageous in retrieving crop early phenological stages (e.g., planted and emerged stages) when the phenological reference information is limited. This innovative hybrid phenology matching model, together with CPR-enabled phenological reference calibrations, holds considerable promise in revealing spatio-temporal patterns of crop phenology over extended geographical regions.

Land surface phenology (LSP) enables global-scale tracking of ecosystem processes, but its utility is limited in drylands due to low vegetation cover and resulting low annual amplitudes of vegetation indices (VIs). Due to the importance of drylands for biodiversity, food security, and the carbon cycle, it is necessary to understand the limitations in measuring dryland dynamics. Here, using simulated data and multitemporal unmanned aerial vehicle (UAV) imagery of a desert shrubland, we explore the feasibility of detecting LSP with respect to fractional vegetation cover, plant functional types, VI uncertainty, and two different detection algorithms. Using simulated data, we found that plants with distinct VI signals, such as deciduous shrubs, can require up to 60% fractional cover to consistently detect LSP. Evergreen plants, with lower seasonal VI amplitude, require considerably higher cover and can have undetectable phenology even with 100% vegetation cover. Our evaluation of two algorithms showed that neither performed the best in all cases. Even with adequate cover, biases in phenological metrics can still exceed 20 days and can never be 100% accurate due to VI uncertainty from shadows, sensor view angle, and atmospheric interference. We showed how high-resolution UAV imagery enables LSP studies in drylands and highlighted important scale effects driven by within-canopy VI variation. With high-resolution imagery, the open canopies of drylands are beneficial as they allow for straightforward identification of individual plants, enabling the tracking of phenology at the individual level. Drylands thus have the potential to become an exemplary environment for future LSP research.

Time series of high spatiotemporal resolution satellite data are required to monitor land surface biophysical properties and their seasonal and inter-annual dynamics at sub-field to field scales. To generate such time series, various algorithms have been developed to fuse infrequent cloud-free Landsat observations with daily Moderate Resolution Imaging Spectroradiometer (MODIS) observations. An early and widely used Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) was developed based on the assumptions that observations from the two sensors are consistent in terms of spatial aggregation and temporal variation. These assumptions are not always valid in the real world, especially in a complex heterogeneous region. In this study, we investigated a Spatiotemporal Shape-Matching Model (SSMM) to generate synthetic time series of high spatiotemporal resolution satellite data. The SSMM, which is conceptually different from the image pair-based STARFM and STARFM-like approaches, makes full use of all spatiotemporally matched fine and coarse resolution data in an entire time series to establish a temporally uniformed fusion model for a given fine resolution pixel. The SSMM assumes that the temporal shapes of both the fine and coarse resolution time series are similar, but their magnitudes and phenological phases could differ largely even for the same vegetation type. This study assessed the capability of the SSMM to generate high spatiotemporal resolution time series of two-band Enhanced Vegetation Index (EVI2). Specifically, we generated the synthetic 30 m time series using the SSMM and STARFM algorithms in the northeastern United States based on 500 m Visible Infrared Imaging Radiometer Suite (VIIRS) data and 30 m Landsat 8 and Sentinel-2 observations. We then evaluated the SSMM-derived 30 m time series across 15 land cover types and various degrees of heterogeneity. The result indicates that the SSMM is able to effectively generate synthetic time series in all different land cover types, which has advantages over the STARFM approach. Although the SSMM performance is degraded in heterogeneous regions, it can explain 82–91% of variations in 30 m EVI2 time series and produce constant root mean square error (0.053–0.056) across various levels of heterogeneity. Moreover, the SSMM can explain 87%-93% and 69%-87% of variation in 30 m EVI2 time series, respectively, with the models established using more than 10 pairs and 4–10 pairs of fine and coarse resolution observations. This suggests that the SSMM is also capable of generating high spatiotemporal resolution time series using historical Landsat time series with limited cloud-free observations, which are critical for studying vegetation dynamics and monitoring crop conditions.

Multisensor image fusion results may deviate from accurately reflecting the phenological stages of winter wheat because different responses of satellite sensors to the spectrum lead to the radiometric inconsistency between different remote sensing images. To reduce the effect of the difference in the physical electromagnetic spectrum responses between sensors on monitoring the phenological stages of winter wheat by fusion results, Sensor Spectral Response (SSR) should be considered in spatiotemporal fusion methods. This paper proposes a novel image fusion model by introducing SSR into the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). The contribution of SSR in minimizing the effect of the system difference between sensors on image fusion products is parameterized as a calibration factor by matrixing operation, which is able to offset the systematic inconsistency between different sensor images. Linear regression equation for different land cover type and spectral band is established to calculate the weights needed in STARFM for improving the selection of neighboring spectrally similar pixels. This proposed method is evaluated using one satellite datasets including four ZY-3 (5.8 m) and Landsat 8 OLI (30 m) scenes which are acquired during the growth stages of winter wheat from seedling to harvest. Qualitative and quantitative evaluation shows that the proposed method can better monitor the phenology of winter wheat with an improved spatial and temporal consistency with the observations than STARFM.

Land surface albedo is a critical variable in determining surface energy balance, and regulating climate and ecosystem processes through feedback mechanisms. Therefore, climatic modelers and radiative monitoring require accurate estimates of land surface albedo. With the instrument development, algorithm upgrade, spectral-band-adjustment in wavelength center or band width, and the increasing distinct requirement from diversified communities, various albedo terms have been generated in related satellite-based products. The lack of understanding on the divergence of these terminologies can introduce potential considerable errors in the subsequent applications, or an elevated probability to invert the deduced conclusion. We surveyed the basic concepts of reflectance quantities, retrieval strategies, and models developed since the 1970s, and discuss both strength and opportunity for improvements on land surface albedo extraction, and product generation. In addition, we exemplified the difference of albedo terms using the daily MODIS product (MCD43A) to emphasize the potential risk of the ambiguous usage, over typical IGBP land covers in Northern Kazakhstan. Our investigation shows that relative differences among various albedo terms can reach up to 181% and 50%, while 0.266 and 0.118 of absolute variance respectively in the narrow and broad-band surface albedo, which illuminated cautions against the ambiguous understanding of albedo terminologies or erroneous usage of albedo products.

Forest ecosystem services such as clean water, wildlife habitat, and timber supplies are increasingly threatened by drought and disturbances (e.g., harvesting, fires and conversion to other uses), which can have great impacts on stand development and water balance. Improved understanding of the hydrologic response of forested systems to drought and disturbance at spatiotemporal resolutions commensurate with these impacts is important for effective forest management. Evapotranspiration (ET) is a key hydrologic variable in assessing forest functioning and health, but it remains a challenge to accurately quantify ET at landscape scales with the spatial and temporal detail required for effective decision-making. In this study, we apply a multi-sensor satellite data fusion approach to study the response of forest ET to drought and disturbance over a 7-year period. This approach combines Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) ET product time series retrieved using a surface energy balance model to generate a multi-year ET datacube at 30-m resolution and daily timesteps. The study area (similar to 900 km(2)) contains natural and managed forest as well as croplands in the humid lower coastal plains in North Carolina, USA, and the simulation period from 2006 to 2012 includes both normal and severe drought conditions. The model results were evaluated at two AmeriFlux sites (US-NC2 and US-NC1) dominated by a mature and a recently clearcut pine plantation, respectively, and showed good agreement with observed fluxes, with 813% relative errors at monthly timesteps. Changes in water use patterns in response to drought and disturbance as well as forest stand aging were assessed using the remotely sensed time series describing total evapotranspiration, the transpiration (T) component of ET, and a moisture stress metric given by the actual-to-reference ET ratio (f(RET)). Analyses demonstrate differential response to drought by land cover type and stand age, with larger impacts on total ET observed in young pine stands than in mature stands which have substantially deeper rooting systems. Transpiration flux shows a clear ascending trend with the growth of young pine plantations, while stand thinning within the plantation leads to decreases in both remotely sensed leaf area index and T, as expected. Time series maps of f(RET) anomalies at 30-m resolution capture signals of drought, disturbance and the subsequent recovery after clearcut at the stand scale and may be an effective indicator for water use change detection and monitoring in forested landscapes.

Land surface temperature (LST) is a key diagnostic indicator of agricultural water use and crop stress. LST data retrieved from thermal infrared (TIR) band imagery, however, tend to have a coarser spatial resolution (e.g., 100 m for Landsat 8) than surface reflectance (SR) data collected from shortwave bands on the same instrument (e.g., 30 m for Landsat). Spatial sharpening of LST data using the higher resolution multi-band SR data provides an important path for improved agricultural monitoring at sub-field scales. A previously developed Data Mining Sharpener (DMS) approach has shown great potential in the sharpening of Landsat LST using Landsat SR data co -collected over various landscapes. This work evaluates DMS performance for sharpening ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) LST (similar to 70 m native resolution) and Visible Infrared Imaging Radiometer Suite (VIIRS) LST (375 m) data using Harmonized Landsat and Sentinel-2 (HLS) SR data, providing the basis for generating 30-m LST data at a higher temporal frequency than afforded by Landsat alone. To account for the misalignment between ECOSTRESS/VIIRS and Landsat/HLS caused by errors in registration and orthorectification, we propose a modified version of the DMS approach that employs a relaxed box size for energy conservation (EC). Sharpening experiments were conducted over three study sites in California, and results were evaluated visually and quantitatively against LST data from unmanned aerial vehicles (UAV) flights and from Landsat 8. Over the three sites, the modified DMS technique showed improved sharpening accuracy over the standard DMS for both ECOSTRESS and VIIRS, suggesting the effectiveness of relaxing EC box in relieving misalignment-induced errors. To achieve reasonable accuracy while minimizing loss of spatial detail due to the EC box size increase, an optimal EC box size of 180-270 m was identified for ECOSTRESS and about 780 m for VIIRS data based on experiments from the three sites. Results from this work will facilitate the development of a prototype system that generates high spatiotemporal resolution LST products for improved agricultural water use monitoring by synthesizing multi-source remote sensing data.

Mapping the spatial variability of actual evapotranspiration (ETa) across vineyards is useful for optimizing irrigation scheduling and efficiency, leading to conservation of water resources and more sustainable wine grape production. To support efficient irrigation strategies, we investigate the utility of thermal infrared-based ETa maps over a range of vineyards located throughout California, each representing a unique local climate, trellis design, grape variety, row orientation and management practice. ETa maps are derived by combining the Disaggregated Atmosphere Land Exchange Inverse (ALEXI/DisALEXI) surface energy balance model and the Spatial Temporal Adaptive Reflectance Fusion Model (STARFM) to generate ETa estimates at high spatial (30 m) and temporal (daily) resolution. Model output is evaluated for years 2017 and 2018 over vineyard sites located in Sonoma, Sacramento, and Madera counties in California that are being monitored as part of the Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX). Overall, modeled daily ET estimates compare well with flux tower observations, with average root mean square error (RMSE), mean absolute error (MAE) and mean bias error (MBE) of 0.88 mm day(-1), 0.70 mm day(-1), and 0.17 mm day(-1) respectively, over all four individual vineyard locations, aligning with past GRAPEX studies. Despite general agreement, record wildfires in northern California during 2018 likely resulted in positive model bias, while misrepresentation of leaf area index within a double-trellis designed canopy at the southern-most vineyard resulted in negative model bias. Spatial analysis of monthly total ET highlights the advantages of utilizing a satellite-based approach to characterize the variability in water use within and surrounding the targeted vineyards. A reliable spatial ET product at scale has the potential to improve water allocation and conservation efforts by identifying areas of uneven water use due to variations in soil texture and composition and other environmental or anthropogenic factors.

Land surface phenology (LSP) provides critical information for investigating vegetation growth and development, studying ecosystem biodiversity, modeling terrestrial carbon and surface energy budgets, detecting land cover and land use change, and monitoring climate change. Although operational 500 m LSP products have been produced from coarse resolution data observed from Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS), an LSP product is also needed at the Landsat scale (30 m) to enhance the environmental monitoring and modeling. However, temporal frequency of 30 m satellite data is always inadequate for reliable ISP detection, despite enrichment by the operational harmonized Landsat and Sentinel-2 (HIS) product. In this study, we propose a new algorithm of LSP detection for the generation of a 30 m LSP product using routinely produced HLS and VIIRS surface reflectance products. Specifically, the new algorithm compares a HLS EVI2 (two-band enhanced vegetation index) time series at a given 30 m pixel with the set of 500 m VIIRS EVI2 time series neighboring the HLS pixel and selects the most similar temporal shape of VIIRS time series even though the amplitude and/or phase between HLS and VIIRS EVI2 time series may be mismatched. The shape of the selected VIIRS EVI2 time series is then used to match to the given HLS EVI2 time series to generate a synthetic HLS-VIIRS time series. The HLS-VIIRS time series is subsequently processed using the hybrid piecewise logistic model to detect the phenological transition dates and to quantify the confidence of LSP detection. This new algorithm is evaluated by implementing 30 m LSP detection in eight HLS tiles in the northeastern (forests), central (croplands), and western (shrublands) United States. Evaluation finds that the new-algorithm-detected greenup onset (1) agrees well with the standard VIIRS LSP product without bias, (2) closely correlates to PhenoCam observations with a slope close to one, and (3) compares well with both PhenoCam and field species-specific observations with a mean absolute difference of 8 days and a difference less than 10 days in more than 70% of the validation samples. This implementation suggests that the new algorithm could be implemented for regional and global LSP product generation at a 30 m resolution.

The unique vertical canopy structure and clumped plant distribution/row structure of vineyards and orchards creates an environment that is likely to cause the wind profile inside the canopy air space to deviate from how it is typically modelled for most crops. This in turn affects the efficiency of turbulent flux exchange and energy transport as well as their partitioning between the plant canopy and soil/substrate layers. The objective of this study was to evaluate a new wind profile formulation in the canopy air space that explicitly considers the unique vertical variation in plant biomass of vineyards. The validity of the new wind profile formulation was compared to a simpler wind attenuation profile that assumes attenuation through a homogeneous canopy. We evaluated both attenuation models using measurements of wind speed in a vineyard interrow, as well as turbulent flux estimates retrieved from a two-source energy balance model, which uses land surface temperature as the key boundary condition for flux estimation. This is relevant in developing a robust remote sensing-based energy balance modelling system for accurately monitoring vineyard water use or evapotranspiration that can be applied using satellite and airborne imagery for field-to-regional scale applications. These tools are needed in intensive agricultural production regions with arid climates such as the Central Valley of California, which experiences water shortages during extended drought periods requiring an effective water management policy based on robust water use estimates for allocating water resources. Results showed that the new wind profile model improved sensible heat flux estimates (RMSE reduction from 42 to 35W m(-2)) when the vine canopy is in early growth stage resulting in a strongly clumped canopy.