Abstract. Compound dry-hot events are intensifying under climate change and pose growing risks to agricultural production. From April to June 2024, the North China Plain (NCP) experienced an extreme compound dry-hot event. Using satellite-based normalized difference vegetation index (NDVI), gross primary productivity (GPP), and crop yield statistics, this study quantified crop growth responses and identified the dominant climatic drivers during this event. The climate anomaly was characterized by pronounced warming in April and June, a continuous decline in precipitation and soil water from April onward, and a record-high vapor pressure deficit (VPD) in June, forming a persistent dry-hot stress. NDVI and GPP increased markedly in April and remained slightly positive in May, but both collapsed to their lowest levels since 2000 in June. Consistent with these vegetation signals, provincial yield statistics and experimental plot observations showed increased winter wheat yields but reduced summer maize yields. Sensitivity and contribution analyses revealed distinct phenology-modulated mechanisms: in April, elevated temperatures and vegetation carryover effects comparably enhanced vegetation activity in winter-wheat-dominated croplands; in May, vegetation dynamics were controlled almost entirely by the previous-month carryover effect, reflecting the growing influence of accumulated vegetation state; and in June, as winter wheat reached maturity and newly sown maize entered early establishment, VPD emerged as the primary limiting factor, strongly suppressing photosynthetic activity and seedling establishment. These findings demonstrate how phenological transitions modulate crop vulnerability to compound dry-hot events and provide useful insights for agricultural early warning, crop management, and climate adaptation strategies in the NCP.

Accurate measurement of canopy-scale solar-induced chlorophyll fluorescence (SIF) is essential for linking near-surface measurements with satellite observations and for reliably constraining terrestrial photosynthetic carbon uptake. However, the optimal ground observation scale (height and footprint) required to capture spatially representative SIF signals remains poorly defined. Here, we develop a physically based Optimal Observation Scale (OOS) model that integrates canopy height (Htoc) and fractional vegetation cover (FVC) to determine the optimal observation height (Hopt) and footprint size (Sopt) for maize. The model was parameterized using 3-D radiative transfer simulations (DART) and validated with a UAV-based hyperspectral SIF system across eight flight altitudes (5–50 m) over a full growing season. The results show that the spatial coefficient of variation (CV) of canopy SIF decreased significantly with increasing scale and stabilizes at Sopt of approximately 91.5 m2 (Hopt of approximately 26.6 m). Application of the OOS model reduced the CV from 17.3% to 7.9%, a 54.4% reduction in spatial uncertainty. The SIF–GPP (gross primary productivity) coupling strengthened with observation heights (Hobs) and approached saturation above the OOS-derived threshold (Hopt), highlighting the importance of observation scale for capturing photosynthetic dynamics. Validation against TROPOspheric Monitoring Instrument (TROPOMI) SIF further revealed that the strongest correlations (r ≈ 0.8) were achieved when UAV SIF observations were conducted at Hobs exceeding the Hopt calculated by the OOS model and spatially matched with satellite footprints. The OOS model provides a transferable, physically-based framework for optimizing ground SIF observations across scales. Its structure-based design also provides a pathway toward generalized SIF measurement strategies that can be extended to other ecosystems and future satellite validation efforts.

Accurate and real-time aboveground biomass (AGB) monitoring data provide powerful information to support rational agricultural resource use and precision agriculture. This study proposed the concept of relative day of the year (RDOY) to replace Zadoks stage (ZS), one growing stage for cereal, in the CBA-Wheat model for predicting AGB in winter wheat. Results showed that (1) The two-band enhanced vegetation index (EVT2) showed a highly significant correlation with the AGB in all RDOY, with the highest correlation of 0.91 at the RDOY of 0.47. (2) The coefficient of determination $\left(R^{2}\right)$ and root mean squared error (RMSE) of the CBA-Wheat model using RDOY with EVI2 were 0.79 and $1.79 \mathrm{t} / \mathrm{ha}$, respectively, while the accuracy of the validation set was $R^{2}=0.76$, RMSE $=1.79$ tha. The model was able to predict AGB well Overall, the CBA-Wheatpooy model has a good potential for development in improving the timeliness of winter wheat biomass inversion.

With the rapid advancement of unmanned aerial vehicles (UAVs) in recent years, UAV-based remote sensing has emerged as a highly efficient and practical tool for environmental monitoring. In vegetation remote sensing, UAVs equipped with hyperspectral sensors can capture detailed spectral information, enabling precise monitoring of plant health and the retrieval of physiological and biochemical parameters. A critical aspect of UAV-based vegetation remote sensing is the accurate acquisition of canopy reflectance. However, due to the mobility of UAVs and the variation in flight altitude, the data are susceptible to scale effects, where changes in spatial resolution can significantly impact the canopy reflectance. This study investigates the spatial scale issue of UAV hyperspectral imaging, focusing on how varying flight altitudes influence atmospheric correction, vegetation viewer geometry, and canopy heterogeneity. Using hyperspectral images captured at different flight altitudes at a Chinese fir forest stand, we propose two atmospheric correction methods: one based on a uniform grey reference panel at the same altitude and another based on altitude-specific grey reference panels. The reflectance spectra and vegetation indices, including NDVI, EVI, PRI, and CIRE, were computed and analyzed across different altitudes. The results show significant variations in vegetation indices at lower altitudes, with NDVI and CIRE demonstrating the largest changes between 50 m and 100 m, due to the heterogeneous forest canopy structure and near-infrared scattering. For instance, NDVI increased by 18% from 50 m to 75 m and stabilized after 100 m, while the standard deviation decreased by 32% from 50 m to 250 m, indicating reduced heterogeneity effects. Similarly, PRI exhibited notable increases at lower altitudes, attributed to changes in viewer geometry, canopy shadowing and soil background proportions, stabilizing above 100 m. Above 100 m, the impact of canopy heterogeneity diminished, and variations in vegetation indices became minimal (<3%), although viewer geometry effects persisted. These findings emphasize that conducting UAV hyperspectral observations at altitudes above at least 100 m minimizes scale effects, ensuring more consistent and reliable data for vegetation monitoring. The study highlights the importance of standardized atmospheric correction protocols and optimal altitude selection to improve the accuracy and comparability of UAV-based hyperspectral data, contributing to advancements in vegetation remote sensing and carbon estimation.

Solar-induced chlorophyll fluorescence (SIF) emitted from photosystem I (PSI) and photosystem II (PSII) is characterized by two peaks centered in the red and far-red spectral regions. SIF provides a unique remotely sensible signal to track plant photosynthetic dynamics. Compared with far-red SIF, red SIF (RSIF) is more strongly linked to PSII and thus with plant photosynthetic activity, but is subject to stronger reabsorption within leaves and canopies. This hinders the understanding and use of canopy RSIF observations (RSIFobs), which is only a small fraction of the total RSIF emitted by the photosystems (RSIFtotal). Deriving RSIFtotal from RSIFobs is still challenging due to retrieval uncertainty, limited availability of RSIFobs and spectral overlap with chlorophyll absorption. To address the challenges associated with deriving RSIFtotal, we propose an exploratory method framework that combines canopy far-red SIF observations (FRSIFobs) and leaf chlorophyll content (LCC) to derive RSIFtotal. We first downscale FRSIFobs from canopy to leaf, and then leverage LCC information to estimate RSIF at the leaf level. Finally, we incorporate LCC information in the subsequent downscaling of RSIF from leaf to photosystem. To evaluate our approach, we use ground-based observation data in three crop types (rice, wheat, and maize) and SCOPE model simulations. Our results demonstrate that the seasonal patterns of RSIFtotal show a close agreement with the seasonal patterns of gross primary production (GPP) and absorbed photosynthetic active radiation (APAR). More importantly, RSIFtotal slightly outperforms FRSIFobs in estimating GPP for the three crop types. Our study has also revealed a strong linear relationship between the escape probability of RSIFtotal (fesc_R) and the RSIFobs/FRSIFobs ratio affected by LCC. The simplicity and robustness of our approach, along with its potential application in satellite remote sensing, will contribute to the improvement of large-scale GPP estimation and photosynthetic phenology detection. Moreover, our investigation of fesc_R will contribute to a better understanding the physiological and non-physiological dynamics of RSIFobs.

Mapping agricultural information such as cropping area and type is of great significance for land use and food security and a common method to retrieve such information is satellite imagery classification. The current image classification techniques require a statistically large number of training samples to obtain representative spectral signatures, ideally collected during the satellite overpass time/date. Although this prerequisite sounds simple and easy in principle, it is extremely challenging and sometimes impossible when classifying historical satellite images, as one simply cannot go back in time to collect training datasets or ground truthing. In this paper, we introduce an iterative and subsequent proximation (ISP) method to circumvent this problem for historical image classifications for historical crop mapping. This method assumes that the same crops grown two years apart have similar spectral properties within the same growing season, with limited variation. This allows the use of the crop information classified in any given year (n) to be used as training samples for the next year (n + 1) or the previous year (n-1). Iteratively repeating n-1, n-2... process leads to the mapping of historical cropping information without a priori ground truthing data. To demonstrate the ISP feasibility, we first used the historical Landsat time-series data to map four major stable crops (rice, maize, soybean, and wheat) in Hailun County, Heilongjiang Province, China, and then further expanded the ISP application to the entire Heilongjiang Province to map cropping areas from 1982 to 2020, using the classification and regression trees (CART) algorithm. The results were validated for those years when actual cropping measurement data were available and further verified with statistical data for other years. The results indicated that the proposed ISP method was appropriate for the historical mapping of four major crops, with a mapping accuracy of approximately 80 % when validated with field data, and correlation coefficients of 0.86, 0.85, 0.91, and 0.94, respectively when compared with historical statistical cropping data. The crop mapping results showed distinct trends of northward expansion in each of the four crops in Heilongjiang Province, which agreed well with previous studies. In conclusion, the ISP method is quick, easy, and convenient for historical crop mapping, which is important in understanding the agricultural production history.

Farmland use policy in China has evolved substantially over the past 70 years, undergoing five stages from communal (1950 s-1980 s) to household-based (1980 s-1990 s), land circulation (1990 s-2000 s), family farms (2000 s-2010 s), and then cooperative systems (2010 s-present). Among many benefits and consequences that have been explored in previous studies, an overlooked impact is the effective farmland planting area (EFPA), resulting from farmland fragmentation at different stages. We used remotely sensed imagery to quantify farm-land fragmentation and EFPA in a representative agricultural area in northeast China. Specifically, we used the K -means (WKKM) and the regression decision trees (CART) classification methods to extract patch size of planted areas and crop planted information in the Google Earth Engine (GEE) environment, and compared the results with field measurements to assess the classification accuracy, and calculated the EFPA. The results showed that the total number of patches increased rapidly after the dissolution of the communal system but declined until the cooperative policy was implemented. The EFPA was negatively related to the number of patches, with an average of 90% EFPA during the commune period,-70% in the household responsibility period and-86% during the cooperative period. In conclusion, land use policy played a key role in balancing the EFPA and farmers' incentives and thus affected the EFPA in northeast China.

Solar‐induced chlorophyll fluorescence (SIF) has been used as a proxy for gross primary productivity (GPP) estimations. However, knowledge on how links between SIF and GPP across different plant types vary in response to sky conditions remain unclear. Here, we investigated the effects of sky conditions on the GPP‐SIF relationship based on continuous measurements of SIF and flux across four different plant types. Our analysis shows that the GPP‐SIF links are affected by sky conditions and these linking patterns respond differently across plant types. We propose that the inconsistent responses of SIF and GPP to sky conditions are primarily driven by variations in light use efficiency (LUE = GPP/absorbed photosynthetic active radiation (APAR)). Furthermore, we explore a quantitative variation in LUE and SIFyield (SIF/APAR) separately via a decoupling of clearness index (CI) and photosynthetic active radiation under different sky conditions. LUE is more sensitive to sky conditions for the C3 plants (Forest, Wheat and Rice) than the C4 plant (Maize), and SIFyield shows more sensitivity to sky conditions for the forest than croplands. Due to the tight link between CI and other environmental factors, the incorporation of CI into the SIF‐based GPP model improves GPP estimates for all C3 plants at both instantaneous and daily scales. Our study implies that a consideration of sky conditions into the SIF‐based GPP model can significantly advance the GPP modeling under all sky conditions.

Solar-induced chlorophyll fluorescence (SIF) has shown great potential for detecting changes in vegetation function under herbicide stress. However, how physiological (phi F, canopy SIF emission efficiency) and nonphysiological (e.g., structure and illumination) dynamics regulate canopy SIF, and the coupling between SIF and gross primary production (GPP) under herbicide stress remains unclear. Here, we conducted continuous eddy covariance flux and far-red SIF measurements during the early stage of maize in an herbicide-resistant maize field, where herbicide exclusively affects weeds. We investigated the performance of SIF, GPP, and vegetation indices (VIs) in capturing herbicide stress and then explored the sensitivity of SIF to the effects of herbicide treatments by disentangling canopy SIF into the physiological (phi F) and non-physiological components (NIRvP). We found that SIF rapidly increased in response to the herbicide and that GPP decreased, and that both were more responsive than VIs in capturing the early effects of herbicides. Thus, the opposing responses in SIF and GPP disrupted their otherwise linear relationship during herbicide treatment. More importantly, we found that the increased phi F dominated the variation of SIF during the early stages of herbicide stress, while the influence of NIRvP was prominent in the variability of SIF in the absence of herbicide. By unraveling its physiological and non-physiological contributions, our findings advance our understanding of how SIF responds to herbicide stress in heterogeneous cropland and will improve our interpretation of SIF as a tool for monitoring photosynthesis.

Solar-induced chlorophyll fluorescence (SIF) has provided novel methods for monitoring vegetation growth and the carbon cycle of terrestrial ecosystems. However, the effects of spatial heterogeneity on canopy SIF measurements remain unclear. The unmanned aerial vehicle (UAV) platform provides a unique opportunity to assess the impact of spatial heterogeneity on SIF variability due to its adjustable observational height at the intermediate canopy scale. In this work, we used a UAV-based SIF system to investigate the influences of fractional vegetation cover (FVC) on SIF measurements over (1) a homogeneous rice paddy field and (2) a heterogeneous planted forest characterized by unevenly distributed differing plant species. We first simultaneously conducted an experiment with UAV- and tower-based SIF systems in a homogeneous paddy field to test the reliability of SIF measurements from UAV-based system. The results showed that the SIF measured by UAV- and tower-based systems have a strong linear relationship (R2 = 0.98), demonstrating that UAV-based SIF system is capable of capturing the diurnal variations of canopy SIF. Then, we operated the UAV flying over the heterogeneous planted forest field in five flights, with each flight observing the same plant species across different heights to investigate the influences of FVC on the spatial variability of SIF. We found that FVC exerted substantial effects on the spatial variability of SIF, with the coefficient of variation (CV) of SIF observations at different flying heights increasing from 5% (high FVC) to 30% (low FVC), which was consistent with DART (Discrete Anisotropic Radiative Transfer) model simulations. Furthermore, our results indicated that the escaping probability of SIF and total emitted SIF showed nonlinear responses to FVC at individual observational heights. In particular, the escaping probability reached its lowest value when FVC was at an intermediate level (FVC = 0.6). These findings highlight the significant effect of spatial heterogeneity on canopy SIF measurements, especially at low FVC. Therefore, an exhaustive consideration of the SIF measurement footprint on different underlying surfaces (homogeneous or heterogeneous) is essential to advance SIF applications in terrestrial vegetation science.

Vegetation phenology plays a critical role in inter-annual changes of the terrestrial carbon cycle. Land surface phenology (LSP) has been widely used to monitor vegetation phenology from remotely-sensed data (RSD) across multiple spatial scales. However, it remains unclear how the temporal resolution of RSD influences the accuracy of LSP estimation. This study systematically analyzed the influences of temporal resolution, including the observation temporal resolution (OTR) and composite temporal resolution (CTR), of RSD on LSP estimation from continuous ground-based remote sensing observations. Specifically, this study quantitatively assessed the sensitivity of LSP estimation to temporal resolution from both structural indicators including normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and near-infrared reflectance of vegetation (NIRV), and physiological indicators including photochemical reflectance index (PRI), gross primary productivity (GPP), and solar-induced chlorophyll fluorescence (SIF). The results showed that the effects of temporal resolution of RSD on LSP estimation can be divided into systematic error and random error. The systematic error of CTR was caused by the methods of LSP estimation and affected by data compositing methods, while the random error of both OTR and CTR was caused by data noise. The sensitivity of SIF and GPP to temporal resolution (both OTR and CTR) in LSP estimation was higher than that of vegetation indices (NDVI, EVI, NIRV, PRI) due to higher data noise. Furthermore, in LSP estimation, the selection of the required temporal resolution of RSD was directly related to the data quality. The results highlight the importance of the temporal resolution in LSP estimation from RSD and provide two possible insights to reduce the errors of LSP estimation. First, in the case of the appropriate data compositing strategies and very little data noise, temporal resolution (both OTR and CTR) can be considered to have little influence on LSP estimation. Second, most of the difference in sensitivity of different indicators to temporal resolution comes from external factors, including observation and model or algorithm errors, rather than the properties of indicators.

Recent advances in solar-induced chlorophyll fluorescence (SIF), which is a complement to optical remote sensing based on greenness observation, have made it possible to monitor the photosynthesis of plants in terrestrial ecosystems using state-of-the-art technologies.With the rapid development of tower-based, unmanned aerial vehicle (UAV), airborne and space-borne SIF observation technology and improving understanding of SIF mechanism, SIF is providing essential data support and mechanism understanding for the estimation of biological traits and gross primary production of terrestrial ecosystem, early detection of abiotic stress, extraction of photosynthetic phenology and monitoring of transpiration.In this review, we first introduce the fundamental theory, the observation systems and technologies and the retrieval method of SIF.Then, we review the applications of SIF in terrestrial ecosystem monitoring.Finally, we propose a roadmap of activities to facilitate future directions and discuss critical emerging applications of SIF in terrestrial ecosystem monitoring that can benefit from cross-disciplinary expertise.

The increasing frequency and amplitude of extreme climatic events may decline ecosystem productivity and disturb the global carbon cycle. Recent and upcoming advances in remote sensing technology, such as hyperspectral reflectance and chlorophyll sun-induced fluorescence (SIF) missions, are boosting research on the monitoring of vegetation responses to heat and drought stress. To understand the impacts of stress on vegetation and the corresponding optical signals that can be sensed from space, it is essential to monitor the continuous dynamics of ecosystem carbon and water fluxes and optical signal responses to environmental changes on the ground. We collected a unique dataset of synergistic observations of remote sensing and carbon-water flux measurements from multiple field sites of different vegetation types. This dataset elucidated variations of physiology, fluxes, and optical signals, including SIF and spectral vegetation indices. For example, in light-sensitive beech forests in Germany, we found that photoprotection is generally active. Gross primary productivity (GPP) and surface conductance (Gs) clearly decreased when heatwaves occurred. On the contrary, chlorophyll content changed only marginally, which was reflected by minimal changes in the chlorophyll index at red edge (CIred). The photochemical reflectance index (PRI), related to non-photochemical quenching (NPQ) via xanthophyll´s cycle, was sensitive to flash heat stress and related to vapor pressure deficit (VPD). But for longer and lower intensity of stress in another event, PRI only changed marginally. SIF was more sensitive to incident radiation (PPFD), but did not decrease with increasing air temperature (Ta) and VPD. However, SIF yield (the ratio of SIF and absorbed photosynthetically active radiation) decreased significantly during the heatwave. In contrast, in the light and heat-tolerant rice paddy in China, we observed that vegetation did not show negative effects at the early growing stage (nutritive growth) during an extreme heatwave (Ta>35 ̊C). Due to the high relative humidity (from evaporated water), VPD remained low despite the high temperatures. GPP increased slightly accompanied by a small decrease of Gs as VPD slightly increased. SIF, SIF yield, and PRI noticeably increased with increasing CIred, indicating that heat might have accelerated the physiology rather than stressed plants in the rice paddy, which could be due to an overall higher temperature optimum compared to the European beach forest. Our results demonstrate that water supply shortage combined with heat waves can cause immediate down-regulation of photosynthesis and that the new remote sensing missions could detect this vegetation response. However, if the water supply is abundant during the heatwave, responses of both physiological and remote sensing parameters may not be sensitive to heat stress. Due to species and ecosystem differences in terms of heat resistance, the global response of vegetation remains hard to predict indicating the need to remotely monitor these responses in order to improve process-based models. The outcomes of this work will possibly provide new insights on the utilization of novel optical remote sensing information for vegetation monitoring during extreme events.

Solar-induced chlorophyll fluorescence (SIF) has been shown to be a novel proxy for terrestrial gross primary production (GPP). A growing number of ground-based automatic SIF observation systems equipped with hemispherical-conical and bi-hemispherical observation configurations have been developed in synergy with EC flux measurements across different ecosystems. However, the difference in the canopy SIF observed by these two types of configurations has not been well studied, which poses challenges in evaluating their performance in tracking GPP. In this study, we investigated SIF from both hemispherical-conical and bi-hemispherical observation configurations for their ability to track GPP in a maize field during the 2020 growth season. We found that bi-hemispherical SIF observations (SIFHemis) showed higher correlations with GPP at both diurnal and seasonal scales, and the superiority of SIFHemis for GPP estimation was also supported by Soil-Canopy-Observation of Photosynthesis and the Energy balance (SCOPE) model simulations. In addition, we found that the SIFHemis-GPP model established at a satellite overpass time (e.g., 09:30) outperformed the corresponding SIFNadir-GPP model in estimating both the half-hourly and daily GPP. The underlying mechanism for the advantage of this SIFHemis-GPP relationship was elucidated by a simplified geometrical optical model, which showed that the diurnal patterns of the observed sunlit and shaded leaves for the SIFHemis were consistent with those of the canopy GPP. Our study recommends a bi-hemispherical configuration setup for its superiority in monitoring GPP dynamics.

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Remotely sensed solar‐induced fluorescence (SIF) has emerged as a novel and powerful approach for terrestrial vegetation monitoring. Continuous measurements of SIF in synergy with concurrent eddy covariance (EC) flux measurements can provide a new opportunity to advance terrestrial ecosystem science. Here, we introduce a network of ground‐based continuous SIF observations at flux tower sites across the mainland China referred to as ChinaSpec. The network consists of 16 tower sites until 2019 including six cropland sites, four grassland sites, four forest sites, and two wetland sites. An automated SIF system was deployed at each of these sites to collect continuous high‐resolution spectra for high‐frequency SIF retrievals in synergy with EC flux measurements. The goal of ChinaSpec is to provide long‐term ground‐based SIF measurements and promote the collaborations between optical remote sensing and EC flux observation communities in China. We present here the details of instrument specifications, data collection and processing procedures, data sharing and utilization protocols, and future plans. Furthermore, we show the examples how ground‐based SIF observations can be used to track vegetation photosynthesis from diurnal to seasonal scales, and to assist in the validation of fluorescence models and satellite SIF products (e.g., from OCO‐2 and TROPOMI) with the measurements from these sites since 2016. This network of SIF observations could improve our understanding of the controls on the biosphere‐atmosphere carbon exchange and enable the improvement of carbon flux predictions. It will also help integrate ground‐based SIF measurements with EC flux networks which will advance ecosystem and carbon cycle researches globally.

Solar‐induced chlorophyll fluorescence (SIF) provides remotely sensible signals for monitoring gross primary production (GPP). Ground‐based multiangle observations of both red and far‐red SIF above wheat and maize canopies were conducted to examine angular effects on SIF. With these new measurements, we were able for the first time to refine and apply an algorithm developed for angular normalization of both red and far‐red SIF measurements. The angular normalization improved the correlation of SIF with GPP derived from eddy covariance measurements at the instantaneous scale (1 min), with increases of the diurnal coefficients of determination (of sunlit SIF with GPP) up to 0.21 for far‐red SIF and 0.3 for red SIF based on analysis on 6 sunny days. The improvement was slightly smaller for far‐red SIF than for red SIF, attributing to that the observed angular variation of SIF in the red band was greater than that in the far‐red band due to weaker multiple scattering in the red band in the canopy. In addition, at the hourly time scale, far‐red sunlit SIF shows its advantage to track GPP for heterogonous canopies, while angular normalization of red SIF is effective for homogeneous canopies. In comparison with another angular normalization method based on the escape ratio using datasets over both wheat and maize canopies, the two kinds of method show similar ability to improve the correlation between SIF and GPP, while the results suggest a limitation of SIF in estimating GPP for dense canopies where the fraction of shaded leaves are large.

Remote sensing image data are often used as input in digital soil mapping (DSM). However, it is difficult to distinguish and identify soil types with less difference in reflectance spectral characteristics, because a small amount of input is not enough to provide enough common features. We consider that the hyper-temporal remote sensing data can be used to extract more common features of soil. The accuracy of DSM is improved by using the common features of soil or effective terrain attributes. We took Mingshui County of the Songnen Plain in northeast China as study area, which is known as a Black soil region. STRM DEM, legacy soil data, and 20 scenes Landsat images of bare soil period from 1984 to 2018 (April and May are considered a period of cultivated soil exposure in the study area), were used, with a maximum likelihood method classifier. A digital soil mapping model was constructed based on hyper-temporal data. Results from the study show that the accuracy of mapping with hyper-temporal classification characteristics, with an overall accuracy of 85.18% and a Kappa coefficient of 0.772, is higher than that of mono-temporal classification characteristics, with an average overall accuracy of 64.35% and an average Kappa coefficient of 0.467. After the introduction of relief degree of land surface (RDLS), the overall accuracy and Kappa coefficient of hyper-temporal mapping were 88.22% and 0.818, higher than the accuracy of other terrain factors. The research results signal the advantages of hyper-temporal remote sensing data in DSM, and the common features were able to improve the accuracy of DSM extracted from hyper-temporal data. This paper provided new insight to explain the impact of diverse terrain on DSM of Black soil region, and the mapping of soil type level could be accomplished more easily by the combination of the two characteristics.

The accurate retrieval of forest functional and structural parameters is of great significance in the scientific research of ecosystem, global change, and carbon and nitrogen cycles. Recently, an unmanned aerial vehicle (UAV) hyperspectral imaging system provides a cost-effective way to capture the hyperspectral imageries from any points of the hemisphere above a forest canopy. However, compared with single-angle hyperspectral images, the multiangle hyperspectral images provide more information about forest functional and structural characteristics. We developed a semiautomatic multiangle observation method using a UAV hyperspectral imaging system and successfully collected the multiangle hyperspectral imageries with clear hotspots of broadleaf and coniferous forest canopies. Our results indicated that the hotspot of a forest canopy had a great effect on the reflectance, normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI) of forests. The maximum values of canopy reflectance and EVI were found at the hotspot position, while the minimum NDVI was at the hotspot. Moreover, the hotspot effect was similar in both broadleaf and coniferous forests. Although the hotspot had no obvious effects on the photochemical reflectance index (PRI), different view zenith angles had a great effect on PRI. Our findings provide a solid foundation for retrieving forest structural parameters using fully automatic multiangle hyperspectral imaging system at both aerial and satellite platforms. (C) 2020 Society of Photo Optical Instrumentation Engineers (SPIE)

While most land use and land cover (LULC) studies have focused on modeling, change detection and driving forces at the class or categorical level, few have focused on the subclass level, especially regarding the quality change within a class such as farmland. The concept of nondominant farmland area (NAF) is proposed in this study to assess within class variability and quantify farmland areas where poor environmental conditions, unsuitable natural factors, natural disasters or unsustainable management practices lead to poor crop growth and thus low yield. A 17-year (2000–2016) time series of the Normalized Difference Vegetation Index (NDVI) was used to develop a NAF extraction model with abnormal features in the NDVI curves and subsequently applied to Heilongjiang province in China. The NAF model was analyzed and assessed from three aspects: agricultural disasters, soil types and medium- and low-yield fields, to determine dominant factors of the NAF patterns. The results suggested that: (1) the NAF model was able to extract a variety of NAF types with an overall accuracy of ~80%. The NAF area accumulated more than 8 years in 17 years is 6.20 thousand km2 in Heilongjiang Province, accounting for 3.75% of the total cultivated land area; (2) the NAF had significant spatial clustering characteristics and temporal variability. 53.24% of the NAF accumulated more than 8 years in 17 years is mainly concentrated in the west of Heilongjiang Province. The inter-annual NAF variability was related with meteorological variations, topography and soil properties; and (3) the spatial and temporal NAF patterns seem to reflect a cumulative impact of meteorological disasters, poor farmland quality, and soil degradation on crop growth. The determinant factors of the observed NAF patterns differed across regions, and must be interpreted in the local context of topography, soil properties and meteorological environment. Spatial and temporal NAF variability could provide useful, diagnostic information for precision farmland management.