The vegetation greenness rate (GR: green-up rate, SR: senescence rate) quantifies canopy development, reflecting the acceleration and deceleration of photosynthesis during the growing season. However, long-term changes and climatic drivers of natural vegetation greenness rates remain poorly understood globally. Here, utilizing multi-source remote sensing and climatic data sets, we examined trends in the greenness rate of global natural vegetation over the past four decades, identified primary climatic drivers, and evaluated their sensitivities. The results reveal significant global changes in greenness rates. Notably, the GR increased significantly in 24.1% of pixels, while the SR rose in 23.9% of pixels (P < 0.05). Conversely, GR and SR experienced significant decreases in 13.4% and 15.1% of pixels, respectively. Temperature was the primary driver of GR changes in 32.9% of pixels worldwide. A higher accumulated temperature rate during the green-up phase generally accelerated GR, enhancing vegetation greenness at maturity onset. Similarly, temperature influenced SR in 28.6% of pixels; however, a higher accumulated temperature rate during the senescence phase typically slowed SR, delaying dormancy onset. In contrast, moisture-related factors, solar radiation, and VPD exhibited strong regional influences, with precipitation and soil moisture exerting particularly positive effects in drylands. Additionally, advancing or delaying green-up onset significantly decreased or increased GR, subsequently affecting greenness amplitude, the senescence rate (SR), and autumn phenology. Our study highlights that the rate of vegetation greenness is a critical transitional variable linking phenological timing and vegetation productivity, and a robust indicator in assessing the climate change impacts on Earth's terrestrial ecosystems.

Timely and accurate crop mapping is essential for predicting crop production, estimating water use, and informing market forecasts. However, operational crop maps are typically accessible more than four months subsequent to harvest, rather than in real-time or near real-time (NRT). Recently, in-season crop mapping has emerged by leveraging rich satellite data sources at various scales in the United States (US) Corn Belt - a prominent food-producing agricultural region dominated by corn and soybeans. However, challenges persist due to inadequate clear-sky satellite observations and the absence of field-scale in-season crop phenometrics. Recognizing that SWIR (shortwave infrared reflectance) is able to reflect the asynchronous temporal variations in plant canopy water contents and that combining phenological shift and growth magnitude can enhance the classification of crop types, this study developed two canopy Greenness and Water (GW) content indices that are GW-I, which is a ratio of the kernel NDVI (normalized difference vegetation index) to SWIR to distinguish phenological shift of different crops, and GW-II, which is a product of kernel NDVI and SWIR to separate growth magnitude of different crops. To reconstruct gap-free field-scale GW-I and GW-II time series, historical and timely available multi-scale satellite observations, including Harmonized Landsat and Sentinel-2 (HLS), Visible Infrared Imaging Radiometer Suite (VIIRS), and Advanced Baseline Imager (ABI), are dynamically fused every week. The potential future GW-I and GW-II values are further predicted using a recently developed algorithm of Spatiotemporal Shape Matching Model (SSMM) and combined with the timely available time series for retrieving NRT phenometrics (greenup onset, mid-date of greenup phase, and maturity onset) every week during the crop greenup phase. Multiple Gaussian mixture models are used to independently estimate the weekly probability of corn and soybean types using three NRT crop phenometrics and the latest (<= 3 days' latency) GW-II. Finally, the corn and soybean probabilities (estimated from GW-I phenometrics and GW-II crop growth magnitude together) are integrated to produce NRT corn and soybean mapping every week during the early growing season. The accuracy of NRT corn and soybean mapping is evaluated using the Cropland Data Layer (CDL). The result shows that our method can map corn and soybean in diverse croplands across the US Corn Belt with an overall accuracy of similar to 90 % at a relatively early date (late July), although the local heterogeneity of agricultural landscapes potentially impacts the accuracy during the early stages. These findings underscore the feasibility of applying the developed method to produce near real-time corn and soybean mapping not only across the US Corn Belt but also in other countries and diverse agricultural regions.

Phenology detection from remotely sensed data remains challenging in semi-arid ecosystems due to the unique spatial heterogeneity and irregular temporal growth in plants. PlanetScope imagery, with fine spatial and temporal resolutions, is revolutionizing the earth observation sector. It has demonstrated its effectiveness in monitoring phenology dynamics across various terrestrial ecosystems. However, the quality and accuracy of PlanetScope data for depicting plant growth development and detecting phenological metrics (phenometrics) in semi-arid environments have not been systematically examined. In this study, we evaluated the capability of PlanetScope for monitoring plant-specific phenology across the semi-arid western United States, by comparing phenometrics (onsets of greenup, maturity, senescence, and dormancy) retrieved from time series of two PlanetScope vegetation indices (VI), which are EVI2 (two-band Enhanced Vegetation Index) and NDVI (Normalized Difference Vegetation Index), with a set of PhenoCam observations at 15 sites. To conduct a comprehensive comparison, PhenoCam time series and phenometrics were extracted from infrared-enabled PhenoCam EVI2 and NDVI, as well as commonly used PhenoCam GCC (Green Chromatic Coordinate). Our results show that (1) time series of PlanetScope VI were consistent with PhenoCam GCC and VI time series during greenup phase but moderately comparable during senescence phase, with an average R2 of 0.67 and 0.57 for greenup and senescence phases, respectively; (2) phenometrics derived from PlanetScope VI exhibited better agreement with those from PhenoCam GCC and VI in greenup phase (greenup and maturity onsets) than in senescence phase (senescence and dormancy onsets), with an average R2 of 0.81, 0.84, 0.72, and 0.53 for greenup, maturity, senescence, and dormancy onsets, respectively; (3) PlanetScope-detected senescence onset and dormancy onset were systematically later than PhenoCam-based retrievals with a mean systematic bias of 12.6 days and 18.2 days, respectively; (4) phenometrics derived from PlanetScope VI were more comparable with PhenoCam VI retrievals than with PhenoCam GCC retrievals, which are reflected in better correlations and smaller bias between phenometrics, especially during senescence phase; and (5) PlanetScope and PhenoCam EVI2 time series produced the most comparable phenometrics, suggesting that EVI2 is an optimal index for vegetation phenology detections from different sensors. In summary, this study suggests PlanetScope has the ability to detect plant-specific phenology and to improve our understanding of phenology dynamics in heterogeneous semi-arid ecosystems at fine scales.

This paper provides a review and summary status of the research underway by the NASA Terra Aqua Suomi-NPP Land Discipline Team to provide continuity of global land data products from the NASA Moderate resolution Imaging Spectroradiometer (MODIS) to the Visible Infrared Imaging Radiometer Suite (VIIRS). The two MODIS instruments on the NASA Earth Observing System Terra (morning overpass) and Aqua (afternoon overpass) platforms have provided more than twenty years of data. The peer-reviewed land products generated from MODIS are now being transitioned to production using VIIRS inputs, with the intention of providing dynamic continuity for the Aqua observations. As part of that process, the products from the two instruments are undergoing intercomparison and evaluation. These results are provided where available and show promising levels of agreement and accuracy in all cases. The paper also offers options for establishing continuity of Terra MODIS data products.

The Advanced Baseline Imager (ABI) instruments onboard the Geostationary Operational Environmental Satellite-R series (GOES-R) provide new opportunities to expand their applications in global terrestrial ecology and environmental sciences. It offers a 5-15 min temporal resolution and 0.5-1 km spatial resolution that greatly benefit those environmental applications where spatial resolution can be compromised in favor of more frequent imagery. However, ABI captures reflected radiation at varying sun positions throughout the day, causing significant diurnal variations. The varying off-nadir view across ABI full disk presents further challenges for generating comparable and consistent vegetation observations, such as the two-band Enhanced Vegetation Index (EVI2). To address these challenges, three Bidirectional Reflectance Distribution Function (BRDF) models (RossThick-LiSparse-Reciprocal (RTLSR), Walthall, and Tian models) are examined to generate high-quality BRDF-adjusted EVI2 based on GeoNEX GOES-16 ABI surface reflectance. The ABI BRDF retrievals are then systematically compared to the corresponding VIIRS (Visible Infrared Imaging Radiometer Suite) Nadir BRDFAdjusted Reflectance (NBAR) products (VNP43 V001) retrieved from RTLSR model. The viewing geometry effects are further analyzed over various land cover types using time series analysis, Pearson Correlation Coefficients (PCC), and Relative Difference (RD) between ABI and VIIRS high-quality time series inversions. The results indicate that the RTLSR model fits well the diurnal ABI observed reflectance and EVI2 values (even for the hot-spot phenomena), and outperforms the Walthall and Tian models for generating ABI high-quality BRDF retrievals with consistently low Root Mean Square Error (RMSE<0.02) and Weight of Determination (WoD < 0.5) across the CONUS. The annual proportion of ABI high-quality BRDF retrievals with a 3-day moving window using the RTLSR model is spatially and statistically comparable to that of VIIRS NBAR with a 16-day retrieval period. Compared to the time series of high-quality VIIRS NBAR EVI2, the BRDF adjusted ABI EVI2 time series still show considerable impacts of view zenith angles over evergreen forest, shrubland, and savanna pixels. However, such impacts are not significant over spatially homogeneous pixels such as cropland and deciduous forests. These results emphasize the need for BRDF correction when considering the impact of off-nadir observations from geostationary satellites on vegetation monitoring. They also demonstrate the advantages of geostationary satellites in capturing rapid changes in greenness trajectories.

Land surface phenology (LSP) products are currently of large uncertainties due to cloud contaminations and other impacts in temporal satellite observations and they have been poorly validated because of the lack of spatially comparable ground measurements. This study provided a reference dataset of gap-free time series and phenological dates by fusing the Harmonized Landsat 8 and Sentinel-2 (HLS) observations with near-surface PhenoCam time series for 78 regions of 10 × 10 km 2 across ecosystems in North America during 2019 and 2020. The HLS-PhenoCam LSP (HP-LSP) reference dataset at 30 m pixels is composed of: (1) 3-day synthetic gap-free EVI2 (two-band Enhanced Vegetation Index) time series that are physically meaningful to monitor the vegetation development across heterogeneous levels, train models (e.g., machine learning) for land surface mapping, and extract phenometrics from various methods; and (2) four key phenological dates (accuracy ≤5 days) that are spatially continuous and scalable, which are applicable to validate various satellite-based phenology products (e.g., global MODIS/VIIRS LSP), develop phenological models, and analyze climate impacts on terrestrial ecosystems.

Crop phenology has been widely detected from multiple historical satellite observations. Conversely, Near-Real-Time (NRT) monitoring of crop progress from timely available remote sensing data is barely investigated because of the lack of high-frequency cloud-free satellite observations and future potential crop development. To address the challenge, this study proposes a novel algorithm for operational NRT monitoring of crop progress at the field scale. This algorithm first fuses the high spatial resolution (30 m) Harmonized Landsat and Sentinel-2 (HLS) data and the high temporal frequent (10 min) Advanced Baseline Imager (ABI) observations to generate cloud-free time series of HLS-ABI EVI2 (two-band Enhanced Vegetation Index) with a Spatiotemporal Shape-Matching Model (SSMM). It then predicts future potential EVI2 values at a given pixel using a reference EVI2 time series obtained from the neighboring pixels in the preceding year. Integrating the currently available HLS-ABI observations and the predicted future EVI2 values to generate annual EVI2 time series, the algorithm finally detects six crop phenometrics including greenup onset, mid-greenup phase, maturity onset, senescence onset, mid-senescence phase, and dormancy onset. The NRT monitoring, which are separated as near-real-time prediction (phenological event detected after the occurrence), real-time prediction (phenological event detected around the occurrence), and short-term prediction (phenological event detected before the occurrence), are continuously updated and improved with new HLS and ABI observations at a weekly basis throughout the growing season. We evaluate the NRT monitoring against standard phenology products, PhenoCam observations, as well as the weekly Crop Progress Reports (CPRs) released from the National Agricultural Statistics Service (NASS) of the United States Department of Agriculture (USDA) in 2020 across Iowa. The evaluation demonstrates the robustness of the developed algorithm in NRT monitoring of crop phenology. Although the uncertainties are relatively large for short-term prediction compared with standard detections, the real-time prediction shows that the Mean Absolute Difference (MAD) is <10 days for greenup and dormancy onset, and ∼ 5 days in the other four phenometrics. Further, the real-time prediction aligns well with PhenoCam observations (R2 = 0.96, P < 0.001) with a MAD of 7.8 days. Moreover, the HLS-ABI real-time prediction of crop phenometrics is capable of tracking NASS crop progress closely with small time shifts (≤ 5 days) and significant correlations (R2 > 0.85, P < 0.001) for various phenological stages of corn and soybean. These results prove that the algorithm could be implemented for NRT monitoring of various crop phenometrics from field, state, to national scales.

The upstream area of the Vietnamese Mekong Delta (VMD) underwent decades of human interventions to control annual flooding for agricultural production. The hard intervention of dyke construction and later soft intervention by sustainable flooding policy have complicated the dynamics of flooding extent in the area. This study utilized Synthetic Aperture Radar (SAR) Sentinel-1 data to investigate flooding extent in the upstream VMD in such human-induced complex context. The pilot area in Long Xuyen Quadrangle (LXQ), Plain of Reed (PoR) and the Middle Area (MA) of upstream VMD were chosen for flooding investigation in three-year periods 2020, 2021, and 2022. Harmonic regressions (HR) were adopted on segment-based VH backscatter time-series data taken at all specifications to construct annual temporal profiles that maintain necessary frequency representations of areas of Annual Crops with Seasonal Flooding (ACSF), Annual Crops without seasonal Flooding (ACnF) and other non-seasonal types. A set of criteria based on frequencies’ amplitudes and periods from annual temporal profiles was developed to identify these flood-relevant Land-use and Land Cover (LULC) types. Validation of classification results gave high producer and user accuracies of more than 90% in most cases for all classification classes. The flood extent from the HR and temporal profile analysis method was compared with a published peer-reviewed flood dataset to give a high level of agreement of approximately 80% in flooding season. In addition, in the context of complex flooding of VMD, the method also demonstrated the ability of getting over ambiguities in flood detection adhered to water detection paradigm adopted in other flood detection methods.

Land surface phenology (LSP) has been increasingly retrieved from satellite observations over past two decades. It plays an important role in understanding atmosphere-vegetation carbon and energy exchanges. Although LSP has been frequently compared with in-situ observations in a simple way, their difference and comparability are poorly understood. We in this study investigated the scalability, consistency, and representativeness of in-situ observations of species-specific phenology from national phenology networks and PhenoCam networks and compared them with LSP from Visible Infrared Imaging Radiometer Suite (VIIRS) at 500m pixels. Specifically, we investigated four methods (mean, median, 30th percentile, and minimum bias) to upscale in-situ observations collected from the Pan European Phenological database (PEP725, 9664 site-years) and the USA National Phenology Network (USA-NPN, 3144 site-years) spanning 2013–2020. The up-scaled in-situ observations were compared with the VIIRS LSP to address the optimal method of upscaling. The comparison differences were analyzed by associating with land cover and land surface heterogeneity to reveal the fundamental impact factors. Further, interannual variations and long-term trends in the species-specific phenological timing in the PEP725 and USA-NPN observations were correlated to VIIRS LSP, which was to expose the similarity of phenological variations under the same or similar weather conditions. Moreover, the daily variations in species-specific plant development were extracted from PhenoCam observations in the USA. The daily PhenoCam observations were fused with temporal trajectories obtained from harmonized Landsat and Sentienal-2 (HLS) at 30m pixels, which bridged field observations with satellite time series. The fused HLS-PhenoCam time series were applied to identify the phenometrics at 30m pixels, which were then linked to VIIRS LSP. Finally, we discussed the scalability and comparability of in-situ phenology observations to the LPS from moderate satellite pixels.

Tillage practices alter soil surface structure that can be potentially captured by satellite images with both high spatial and temporal resolution. This study explored tillage practice mapping using the daily Planet Fusion surface reflectance (PF-SR) gap-free 3 m data generated by fusing PlanetScope with Landsat-8, Sentinel-2 and MODIS surface reflectance data. The study area is a 220 x 220 km2 agricultural area in South Dakota, USA, and the study used 3285 PF-SR images from September 1, 2020 to August 31, 2021. The PF-SR images for the sur-veyed 433 fields were sliced into 10,747 training (70%) and evaluation (30%) non-overlapping time series patches. The training and evaluation patches were from different fields for evaluation data independence. The performance of four deep learning models (i.e., 2D convolutional neural networks (CNN), 3D CNN, CNN-Long short-term memory (LSTM), and attention CNN-LSTM) in tillage practice mapping, as well as their sensitivity to different spatial (i.e., 3 m, 24 m, and 96 m) and temporal resolutions (16-day, 8-day, 4-day, 2-day and 1-day) were examined. Classification accuracy continuously increased with increases in both temporal and spatial resolutions. The optimal models (3D CNN and attention CNN-LSTM) achieved-77% accuracy using 2-day or daily 3 m resolution data as opposed to-72% accuracy using 16-day 3 m resolution data or daily 24 m resolution data. This study also analyzed the feature importance of different acquisition dates for the two optimal models. The 3D CNN model feature importances were found to agree well with the tillage practice time. High feature importance was associated with observations during the fall and spring tillage period (i.e., fresh tillage signals) whereas the crop peak growing period (i.e., tillage signals weathered and confounded by dense canopy) was characterized by a relatively low feature importance. The work provides valuable insights into the utility of deep learning for tillage mapping and change event time identification based on high resolution imagery.

Vegetation phenology is one of the most sensitive indicators to environmental and climate changes. In order to characterize the seasonal variation in relatively pure or homogenous vegetation types, fine spatial resolution satellite data (<= 30 m), such as Landsat, Sentinel-2, PlanetScope, or Harmonized Landsat and Sentinel-2 (HLS), have been increasingly applied to detect land surface phenology (LSP). However, the most critical challenge in LSP detections is the gaps in temporal satellite observations caused by noise and persistent cloud/snow cover. Therefore, this study presented a novel algorithm for generating synthetic gap-free time series at the field scale (30 m) for LSP detections. Specifically, we first developed a framework to establish a large collection of temporal shapes of vegetation growth with as many as 100 grid-based Green Chromatic Coordinate (GCC) time series in a single PhenoCam site. For a given HLS pixel, the two-band Enhanced Vegetation Index (EVI2) time series was matched and fused with the most comparable temporal GCC shape selected from the collection of PhenoCam GCC time series to generate a synthetic gap-free HLS-PhenoCam EVI2 time series, which was used to detect the 30 m phenometrics. The detected phenometrics were evaluated using manually selected and spatially matched GCC observations as well as phenology detections from HLS alone. The result indicates that the HLS-PhenoCam phenometrics are very close to the observations from PhenoCam network with a correlation coefficient (R) of 0.82-0.97, a mean absolute difference (MAD) of 2.8-3.5 days, a root mean squared error (RMSE) of 3.5-4.0 days, and a mean systematic bias (MSB) of 0.1-2.2 days. The HLS-PhenoCam detections are significantly improved relative to the HLS phenometrics that have a statistic accuracy of R = 0.57-0.78, MAD = 6.4-9.3 days, RMSE = 8.8-13.9 days, MSB =-5.2-5.9 days. The difference between HLS-PhenoCam and HLS alone LSP detections over a HLS tile could be on average larger than two weeks if high-quality observation (HQO) proportion in the annual HLS time series is <10%, which exponentially reduces with the increase of HQO in HLS observations. The an-alyses in this study suggest that the gap-free HLS-PhenoCam time series is able to be generated for producing high-quality phenology datasets across a local and regional scale, to bridge near-surface PhenoCam observations with satellite observations data at various scales, and to be used as a scalable phenology dataset for the validation of global MODIS and VIIRS LSP products.

The 30 m resolution U.S. Department of Agriculture (USDA) crop data layer (CDL) is a widely used crop type map for agricultural management and assessment, environmental impact assessment, and food security. A finer resolution crop type map can potentially reduce errors related to crop area estimation, field size characterization, and precision agriculture activities that requires crop growth information at scales finer than crop field. This study is to develop a method for crop type mapping using Sentinel-2 10 m bands (i.e., red, green, blue, and near-infrared) and to examine the benefit of the derived 10 m crop type map. The crop type mapping was conducted for two study areas with significantly different field sizes and crop types in South Dakota and California, respectively. The Sentinel-2 10 m surface reflectance and the derived normalized difference vegetation index (NDVI) acquired in the 2019 growing season were used to generate monthly median composites as classification input. The training and evaluation samples were derived from CDL by (i) finding good quality 30 m CDL pixels and (ii) identifying a single representative Sentinel-2 10 m pixel time series for each 30 m good quality CDL pixel. The random forest algorithm was trained using 80% of the samples and evaluated using the 20% remaining samples, and the results showed high overall accuracies of 94% and 83% for South Dakota and California study areas, respectively. The major crops in both study areas obtained high user’s and producer’s accuracies (>87%). There is a good agreement between the class proportions in the 10 m crop type map and 30 m CDL for both study areas with R2 ≥ 0.94 and root mean square error (RMSE) ≤ 3%. More importantly, compared to the 30 m CDL, the 10 m crop type map has much less salt-pepper and crop boundary-aliasing effects and defines better the small surface features (e.g., small fields, roads, and rivers). The potential of the method for large area 10 m crop type mapping is discussed.

The annual flood and the alteration in hydrological regimes are the most vital concerns in the Vietnamese Mekong Delta (VMD). Although synthetic aperture radar (SAR) Sentinel-1 imagery is widely used for water management, only a few studies have used Sentinel-1 data for mapping surface water and monitoring flood events in the VMD. This study developed an algorithm to implement (i) automatic Otsu threshold on a series of Sentinel-1 images to extract surface water and (ii) time series analyses on the derived surface water maps to detect flood water extent in near-real-time (NRT). Specifically, only cross-polarized VH was selected after an assessment of different Sentinel-1 polarizations. The dynamic Otsu thresholding algorithm was applied to identify an optimal threshold for each pre-processed Sentinel-1 VH image to separate water from non-water pixels for producing a time series of surface water maps. The derived Sentinel-1 surface water maps were visually compared with the Sentinel-2 Full Resolution Browse (FRB) and statistically examined with the Sentinel-2 Multispectral Instrument (MSI) surface water maps, which were generated by applying the Otsu threshold on the normalized difference water index (NDWI) and modified normalized difference water index (MNDWI) images. The visual comparison showed a strong correspondence between the Sentinel-1 surface water maps and Sentinel-2 FRB images in three periods, including rice’s sowing season, flood period, and rice’s maturation stage. A good statistical agreement suggested that the performance of the dynamic Otsu thresholding algorithm on Sentinel-1 image time series to map surface water is effective in river areas (R2 = 0.97 and RMSE = 1.18%), while it is somewhat lower in paddy field areas (R2 = 0.88 and RMSE = 3.88%). Afterward, a flood mapping algorithm in NRT was developed by applying the change-detection-based time series analyses on the derived Sentinel-1 surface water maps. Every single pixel at the time t is respectively referred to its state in the water/non-water and flooded/non-flooded maps at the previous time t−1 to be classified into a flooded or non-flooded pixel. The flood mapping algorithm enables updates at each time step to generate temporal flood maps in NRT for monitoring flood water extent in large-scale areas. This study provides a tool to rapidly generate surface water and flood maps to support water management and risk reduction in the VMD. The future improvement of the current algorithm is discussed.