Science of Remote Sensing最新文献

Combining the advantages of crop growth models and remote sensing observations, data assimilation (DA) has emerged as a vital tool for crop growth monitoring and early-season crop yield forecasting. As an increasing number of related studies have been conducted, data assimilation systems for remote sensing and crop growth models have grown increasingly sophisticated. However, within this context, the research on data assimilation algorithms, as a core component of data assimilation system, highly need investigating the potential. In this review, we discuss the essential differences and inherent connections of various data assimilation algorithms based on Bayes's Theorem. Building upon this foundation, we review the application progress of different DA algorithms data assimilation of remote sensing and crop models. Additionally, we identify the challenges and limitations faced by current data assimilation algorithms in crop practical applications and propose potential directions for future study. As a summary of the entire paper, we provide recommendations for DA algorithm choice strategy in conjunction with specific application scenarios.

The area burned by wildfires in Canada in 2023 is unprecedented in historical records. To help ensure the safety of communities and support the mobilization of firefighting resources, rapid detection of areas affected by wildfires is required. Satellite data are ideally suited to provide near real-time wildfire information over large areas. At the same time, clouds, smoke, and haze can obscure the collection of observations from sensors typically used for mapping purposes. Established methods using coarse spatial resolution satellites (e.g., MODIS, VIIRS) rely upon the combination of daily revisit to enable the rapid and reliable detection of large active fires, in full or in part, and the application of modeling (including spatial buffering) to infer additional, yet still obscured, areas. While timely, these initial maps of wildfire-impacted areas do not capture small fires (those smaller than 200 ha) and, importantly, are not intended to differentiate unburned areas within fire perimeters. To address these limitations, we used data from Sentinel-2A and -2B, and Landsat-8 and -9, which form a virtual constellation of four satellites to revisit and map burned area in Canada's forested ecosystems for the 2023 fire season. Availing upon the high temporal data density and using the Tracking Intra- and Inter-year Change algorithm (TIIC), an aggregate seasonal mapping of wildfires resulted in a total area affected by wildfires in 2023 of 12.74 Mha. Within this total area, 9.51 Mha of treed land cover was impacted. Shrubs and wetlands comprised most of the remaining non-treed area that was burned. Using a 2022 map of aboveground treed biomass (AGB), approximately 0.649 Pg of AGB was impacted by 2023 wildfires, representing an 11-fold increase in AGB impacts relative to a long-term annual average of treed AGB loss. Differences between the estimate of total burned area reported herein and the total burned area indicated by the Natural Resources Canada (NRCan) Fire M3 hotspot fire perimeters (18.64 Mha) were analyzed. Overall, estimates of burned area differed by 5.9 Mha, including over 1.13 Mha of water identified as burned within the NRCan perimeters. Differences in land cover and AGB impacts between the two products were also investigated and quantified. TIIC enables the near-continuous capture of areas impacted by fire through the fire season, allowing for within-year refinement of total burned area, rapid interrogation of land cover types impacted, and estimation of associated biomass consequences.

Global forests face severe challenges owing to climate change, making dynamic and accurate monitoring of forest conditions critically important. Forests in Japan, covering approximately 70% of the country's land area, play a vital role yet often overlooked in global forestry. Japanese forests are unique, with approximately 50% comprising artificial forests, predominantly coniferous forests. Despite the Japanese government's extensive use of airborne Light Detecting and Ranging (LiDAR) to assess forest conditions, these data need more availability and frequency. The Global Ecosystem Dynamics Investigation (GEDI), the first Spaceborne LiDAR data explicitly designed for vegetation monitoring, is expected to provide significant value for high-frequency and high-accuracy forest monitoring. To assess the accuracy of GEDI data in Japanese artificial coniferous forests, the reference data were gathered from 53,967,770 artificial coniferous trees via airborne LiDAR data in Aichi Prefecture, Japan. This data was then compared to the corresponding GEDI-derived terrain elevations, canopy heights (GEDI RH98), and aboveground biomass density (AGBD) estimates to assess the accuracy of GEDI data. This research also explored how different factors influence the accuracy of GEDI terrain elevation estimates, including the type of beam, time of acquisition (day or night), beam sensitivity, and terrain slope. Additionally, the effects of various forest structural parameters, such as the height-to-diameter ratio, crown length ratio, and the number of trees on the accuracy of the GEDI canopy height and AGBD, were investigated. The results showed that GEDI terrain elevation demonstrated high accuracy across various slope conditions, with rRMSE ranging from 2.28% to 3.25% and RMSE ranging from 11.68 m to 16.54 m. After geolocation adjustment, the comparison of canopy height estimates derived from GEDI to airborne LiDAR-derived canopy height also showed high accuracy, exhibiting a rRMSE of 22.04%. In contrast, the GEDI AGBD product showed moderate accuracy, with a rRMSE of 52.79%. The findings also indicated that the accuracy of GEDI RH98 was influenced by terrain slope and crown length ratio, whereas the accuracy of GEDI AGBD was mainly impacted by the number of trees and crown length ratio. This study provided the first baseline accuracy assessment of GEDI terrain elevation, RH98, and AGBD estimates in Japanese artificial forests. Furthermore, this study provided valuable insights into the accuracy of GEDI metrics by examining potential factors.

Thousands of people are injured every year from explosive remnants of war which include unexploded ordnance (UXO) and abandoned ordnance. UXO has negative long-term impacts on livelihoods and ecosystems in contaminated areas. Exact locations of remaining UXO are often unknown as survey and clearance activities can be dangerous, expensive and time-consuming. In Vietnam, Lao PDR and Cambodia, about 20% of the land remains contaminated by UXO from the Vietnam War. Recently declassified historical KH-9 satellite imagery, taken during and immediately after the Vietnam War, now provides an opportunity to map this remaining contamination. KH-9 imagery was acquired and orthorectified for two study areas in Southeast Asia. Bomb craters were manually labeled in a subset of the imagery to train convolutional neural networks (CNNs) for automated crater detection. The CNNs achieved a F1-Score of 0.61 and identified more than 500,000 bomb craters across the two study areas. The detected craters provided more precise information on the impact locations of bombs than target locations available from declassified U.S. bombing records. This could allow for a more precise localization of suspected hazardous areas during non-technical surveys as well as a more fine-grained determination of residual risk of UXO. The method is directly transferable to other areas in Southeast Asia and is cost-effective due to the low cost of the KH-9 imagery and the use of open-source software. The results also show the potential of integrating crater detection into data-driven decision making in mine action across more recent conflicts.

The August 2023 wildfires over the island of Maui, Hawaii were one of the deadliest U.S. wildfire incidents on record with 100 deaths and an estimated U.S. $5.5 billion cost. This study documents the incidence, extent, and characteristics of the 2023 Maui wildfires using multi-resolution global satellite fire products, and in so doing demonstrates their utility and limitations for detailed fire monitoring, and highlights outstanding satellite fire observation needs for wildfire monitoring. The NASA 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) burned area product is compared with PlanetScope 3 m burned areas that were mapped using a published deep learning algorithm. In addition, all the August 2023 active fire detections provided by MODIS on the Terra and Aqua satellites and by the Visible Infrared Imaging Radiometer Suite (VIIRS) on the S-NPP and NOAA-20 satellites are used to investigate the geographic and temporal occurrence of the fires and their incidence relative to the 3 m mapped burned areas. The geographic and diurnal variation on the fire radiative power (FRP), available with the active fire detections, is presented to examine how energetically the fires were burning. The analysis is undertaken for all of Maui and for the town of Lahaina that was the major population center that burned. Satellite active fires were first detected August 8th^, 2023 in the early morning (1:45 onwards) on the western slopes of Mt. Haleakalā and were last detected August 10th in the early morning (at 2:46) over Lahaina and on the western slopes of Mt. Haleakalā. The FRP available with the VIIRS satellite active fire detections indicate that the fires burned less intensely from the beginning to the end of this three day period, the nighttime fires generally burned more intensely than the daytime fires, and the most intensely burning fires occurred over Lahaina likely due to the high fuel load in the buildings compared to the vegetation that burned elsewhere. The MODIS 500 m burned area product was too coarse to map most of the 18 burned areas that were mapped unambiguously at 3 m resolution with PlanetScope and covered 29.60 km², equivalent to about 1.6% of Maui. This study highlights the limitations of systematically derived satellite fire products for assessment before, during and after wildfire disaster events such as those experienced over Maui. The needs for future fire monitoring of wildfire disaster events, and the recommendation for a fire monitoring satellite constellation, are discussed.

The Tigray War was an armed conflict that took place primarily in the Tigray region of northern Ethiopia from November 3, 2020 to November 2, 2022. Given the importance of agriculture in Tigray to livelihoods and food security, determining the impact of the war on cultivated area is critical. However, quantifying this impact was difficult due to restricted movement within and into the region and conflict-driven insecurity and blockages. Using satellite imagery and statistical area estimation techniques, we assessed changes in crop cultivation area in Tigray before and during the war. Our findings show that cultivated area was largely stable between 2020 and 2021 despite the widespread impacts of the war. We estimated 1, 132, 000 ± 133, 000 ha of cultivation in pre-war 2020 compared to 1, 217, 000 ± 132, 000 ha in wartime 2021. Comparing changes inside and outside of a 5 km buffer around conflict events, we found a slightly higher upper confidence limit of cropland loss within the buffer (0–3%) compared to outside the buffer (0–1%). Our results support other reports that despite widespread war-related disruptions, Tigrayan farmers were largely able to sustain cultivation. Our study demonstrates the capability of remote sensing combined with machine learning and statistical techniques to provide timely, transparent area estimates for monitoring food security in regions inaccessible due to conflict.

Data limitations and attributability issues due to the full-scale Russian invasion of Ukraine in February 2022 presents continuing challenges in assessing production of major commodity crops in Ukraine. Up-to-date satellite imagery provides evidence of rapid changes in cropland within temporary occupied territories (TOT) by Russia within Ukraine. Ukraine is the world's top producer and exporter of sunflower and, therefore, monitoring, and quantifying changes in areas and production of sunflower is extremely important. We used Sentinel-1 (S1) synthetic aperture radar (SAR) images to quantify changes in sunflower planted areas in Ukraine during 2021–2022. We developed an operational workflow and produced the first available 20-m resolution sunflower maps over Ukraine. We developed a SAR-based generalized approach for sunflower mapping using a previously developed phenological metric and estimated sunflower planted areas and corresponding changes in 2021 and 2022 using a sample-based approach. Sunflower area was estimated at 7.10 ± 0.45 million hectares (Mha) in 2021 which was reduced to 6.75 ± 0.45 Mha in 2022, reflecting a 5% decrease compared to the preceding year. The reduction was mainly observed in the Russian-occupied regions while we did not find significant changes in sunflower areas in Ukrainian-controlled areas. In addition to traditional sunflower producing regions in the south and south-east of Ukraine we found new sunflower emerging hotspots along the south-central and north-eastern regions. Overall, the decrease in sunflower planted area was less severe than previously expected and reported in media for the entire Ukraine. This study demonstrates the utility of Earth observation data, namely Sentinel-1/SAR, for monitoring sunflower cultivation areas in regions where ground access is not possible or feasible due to armed conflict.

New construction activities can alter surface albedo and structure, which then affect surface temperature and roughness, and hence have a significant impact on urban climate. Construction activity is also an important indicator of human development and movement and is of high interest to the intelligence community. A new approach for Broad-Area-Search of New Construction activities (BASC) by combining time series analysis and rule-based filters using Landsat data was developed and tested in five selected cities (Boston, Shanghai, São Paulo, Dubai, and Ho Chi Minh City). The algorithm transforms Landsat images into fractions of a set of four endmembers using Linear Spectral Mixture Analysis (LSMA) and then applies the Continuous Change Detection and Classification (CCDC) algorithm for change detection. A set of rule-based filters and spatial processing was then applied to narrow the search to changes related to construction activities. Overall, BASC reached a recall of 0.83, a precision of 0.58, and an F1-Score of 0.68. Among the five cities, Dubai had the highest recall of 1.0 and the highest F1-score of 0.75, while Boston had the highest precision of 0.63. BASC performed worst in Shanghai with an F1-Score of 0.6, mainly due to it having the lowest recall of 0.62, while São Paulo has the lowest precision of 0.5. Common sources of omission errors include low-density, redevelopment, and small sites, while common commission errors include roofing, land clearing, water level changes, and re-surfacing projects. For comparison, BASC using Sentinel-2 Top-of-Atmosphere (TOA) Reflectance data recorded an overall F1-Score of 0.63, but with higher recall and lower precision. Integration of Sentinel-2 Surface Reflectance and Sentinel-1 SAR data has the potential to further improve the performance of BASC. The new algorithm provided a method for routine monitoring of construction activities over large areas. The result of such monitoring can be used as a baseline to narrow down the candidate targets of construction activities, where very high-resolution imagery can then be requested to perform further examination.

Wildfires have been progressively shrinking the C sink capacity of forest accelerating climate change effects on forest biodiversity, especially where megafires are recurrent and of increased frequency such as in the Mediterranean. Data from The Global Ecosystem Dynamics Investigation (GEDI) mission can inform on changes on forest structure to inform on fire impacts on vegetation. In this study, we assessed the performance of GEDI at measuring biomass and structural change from wildfires using the 2020/21 summer fire seasons in Spain and Portugal. The GEDI hybrid-inference method was used to calculate mean and total biomass in pre- and post-fire stages, while GEDI footprint data was further used to explain the fire severity classes derived from optical data. Our results showed the increasing impact of wildfires on biomass stocks and GEDI ecological metrics by increasing fire severity. More than over biomass stocks, severe fires substantially altered trends in structural metrics such as plant area volume density. The integration of GEDI metrics to explain fire severity had an accuracy of 52% considering five severity classes and an accuracy of 69% when considering the three main classes: unburned, moderate and high. Structural metrics from GEDI can be used to improve optical-based fire severity estimates used globally and to evaluate potential fire impacts based on time-series of GEDI tracks as showcased in the study, but also to measure forest recovery between fire seasons. The extension of GEDI is a major support for wildfire mapping efforts, integrated approaches to capture the increasing impact of fire on forest biodiversity and the monitoring of changes in carbon stocks.

The presence, duration, and amount of surface canopy water (SCW) is important in microwave remote sensing for agricultural applications. Our current understanding of the effect of SCW on total backscatter and the underlying mechanisms is limited. The aim of this study is to investigate the effect of SCW on backscatter as a function of frequency and polarization, and to understand the underlying mechanisms. For this purpose, the radiative transfer model developed at the Tor Vergata University was used to simulate the total backscatter at L-, C-, and X-band. First, simulations from the standard Tor Vergata model were compared to L-band observations. Then, two additional implementations of the model were developed to account for the effect of SCW and the presence of water on the soil surface on radar backscatter. Representing SCW by the inclusion of additional water in the vegetation leads to an increase in vegetation volume scattering and a reduction in the contribution from double bounce and direct scattering from the ground. This increases total backscatter, particularly at lower frequencies. Results suggest that the difference between backscatter in the presence and absence of SCW can be up to around 2.5 dB in L-band and likely less at higher frequencies. The effect of water on the canopy (SCW) reaches its maximum during the mid and late season as the crop reached its maximum biomass. The influence of dew on the reflectivity of the soil surface resulted in a difference of up to 3.8 dB between backscatter in the presence and absence of SCW. In particular, at low frequencies and low vegetation cover, the presence of water on the soil surface needs to be taken into account to correctly capture the sub-daily dynamics in backscatter. The findings of this study are relevant for current and future SAR missions including Sentinel-1, ROSE-L, NISAR, SAOCOM, ALOS, CosmoSkyMed, TerraSAR-X, TanDEM-X and constellations such as those of ICEYE, and Capella which have dawn/dusk overpasses or multiple overpasses per day.