Search

• We developed an RRCNet that fuses the deep and shallow spectral features to map RRC. • RRCNet was trained based on an RTM-based "soil-crop-crop residue" spectra dataset. • Use transfer learning (TL) to recalibrate the fully connected layer of RRCNet. • RRCNet + TL can provide more accurate RRC estimates than SI + statistical regression. Accurate determination of rice residue cover (RRC) can improve the monitoring of tillage information. Currently, the accurate determination of RRC using optical remote sensing is hindered by variations in cropland moisture and cover of following crops. The fractional cover (FC) of the soil (f S), crop (f C), and crop residue (f CR) changes (f S + f C + f CR = 1) after the following crop is planted, which increases the difficulty of remote-sensing RRC estimation. Cropland soil moisture and crop residue moisture affect the values of cropland and crop residue spectral indices (CRSIs), thereby reducing the accuracy of remote-sensing RRC estimation. Deep learning techniques (e.g., convolutional neural networks [CNN] and transfer learning [TL]) have been proven to extract the deep features of input images with distortion invariance, such as displacement and scaling, which are similar to moisture and the following crop effects on remote-sensing CRSIs. This study aimed to evaluate the combined use of deep features of cropland spectra extracted by deep learning techniques to estimate the cropland RRC under the effects of variations in cropland moisture and cover of the following crops. This study proposes an RRCNet CNN that fuses deep and shallow features to improve RRC estimation. A PROSAIL radiative transfer model was employed to simulate a cropland "soil–crop–crop residue" mixed spectra dataset (n = 103,068), considering the variations in cropland moisture and the cover of the following crop. The RRCNet was first pre-trained using the simulated dataset, and then the knowledge from the pre-trained RRCNet was updated based on field experimental FCs, RRCs, and Sentinel-2 image spectra using the TL technique. Our study indicates that RRCNet can incorporate shallow and deep spectral features of cropland "soil–crop–crop residue" mixed spectra, providing high-performance FCs and RRC estimation. The FCs and RRC estimates from RRCNet + TL (FCs: R 2 = 0.939, root mean squared error (RMSE) = 0.071; RRC: R 2 = 0.891, RMSE = 0.083) were more accurate than those from CRSI + multiple linear regression, CRSI + random forest, and CRSI + support vector machine (FCs: R 2 = 0.877–907, RMSE = 0.086–0.101; RRC: R 2 = 0.378–0.714, RMSE = 0.133–0.229). We mapped the multistage RRC based on Sentinel-2 multispectral instrument (MSI) images and RRCNet. Tillage information can be inferred from RRC and RRC difference maps changes. [ABSTRACT FROM AUTHOR]

Highlights • A dynamic soil endmember spectrum selection approach for rice residue cover estimation. • Use soil moisture and soil spectral reflectance model to modify soil endmember spectra. • RRC estimation accuracy improved for a soil moisture content lower than 20%. • Distribution of SM in farmland was crucial for RRC estimation. Abstract Crop residue deposited on the soil surface helps protect against water and wind erosion, improve soil quality and increase soil organic matter and soil carbon storage. Linear spectral mixture analysis (LSMA) is an important technique in field crop residue estimation. Traditionally, only one single or fixed standard crop residue and soil endmember spectrum is performed for each of the presented endmember classes or field. Because the variation in soil endmember spectrum signatures significantly changes with soil moisture (SM), spectral unmixing with fixed endmember spectra can lead to poor accuracy of the abundance of the spectral constituents of pure crop residue. Herein, this paper presents a dynamic soil endmember spectrum selection approach for improving the performance of soil and rice residue spectral unmixing analysis in rice residue cover (RRC) estimation. This new approach uses SM and soil spectral reflectance model to modify soil endmember spectra in each spectral unmixing analysis. Two validation datasets computed results have verified the feasibility and correctness of the dynamic soil endmember method. Results indicated that the distribution of SM in farmland was crucial for RRC estimation. Compared with traditional fixed min and fixed mean soil endmember spectrum methods, the results of the method developed herein showed this method significantly improved RRC estimation accuracy for an SM content lower than 20% (volumetric water content) over the traditional methods tested. Therefore, our proposed approach can be used to improve RRC estimation accuracy in harvest field. [ABSTRACT FROM AUTHOR]

Land-cover classification provides the crucial data component for related Earth-science research. Currently, although multiscale remote-sensing images are the main source of data for classifying land-cover, the response of multi-resolution images to land-cover classification remains highly uncertain. In addition, because of the scarcity of time-synchronous multiscale satellite images of certain regions, up-scaling algorithms are generally used to generate and apply multiscale images. However, when using resolution-resampling images, it remains uncertain to what extent spectral loss or information distortion is responsible for the underlying differences in the accuracy of land-cover classification of various landscapes. To clarify this situation, we study the Hetian basin of Changting County in Fujian Province, south-east China by using quasi-synchronous multiple-resolution satellite observations (seven spatial resolution levels: 1 m, 2 m, 4 m, 8 m, 16 m, 30 m, and 50 m) to investigate possible correlations between spatial resolution and the land-cover classification. The classification is obtained by applying a support vector machine spectral classifier to random recordings made in 1875 sample plots. We also explore the effect of using lower-resolution images by comparing the classification results obtained by using several common up-scaling algorithms, such as nearest neighbour (NN), bilinear (BI), cubic convolution (CC), and pixel aggregation (PA). The results indicate that classification accuracy is significantly influenced by the spatial resolution of images (p < 0.05), with the accuracy increasing as the spatial resolution goes from 1 m to 4 m, then decreasing as the spatial resolution decreases beyond 4 m. In addition, for a resolution of 1 m to 30 m, almost all the up-scaled images provide a classification accuracy that differs from that obtained by using the native remote-sensing images of each resolution (p < 0.05), and the difference increases as the spatial-resolution ratio or up-scaling amplification factor increases. According to an analysis of the spatial scale of images using, e.g., multiband spectral reflectance and vegetation index, the up-scaling algorithms are less sensitive to spatial resolution and represent poorly the actual image characteristics. This result is attributed to the strong dependence of the spectral information in up-scaled images on the original images, which leads to discrepancies with respect to actual observations at the given scale. These results indicate that the effects of resolution cannot be ignored and that resampling data may not be adequate for multi-spatial-scale classification compared with the native satellite images. It is thus urgent to obtain an effective up-scaling algorithm that sharply reduces the problems caused by spatial heterogeneity. [ABSTRACT FROM AUTHOR]

The sahelian rainfall regime is characterized by a strong spatial as well as intra- and inter-annual variability. The satellite based African Rainfall Climatology Version 2 (ARC2) daily gridded rainfall estimates with a 0.1° × 0.1° spatial resolution provides the possibility for in-depth studies of seasonal changes over a 33-year period (1983–2015). Here we analyze rainfall regime variables that require daily observations: onset, cessation, and length of the wet season; seasonal rainfall amount; number of rainy days; intensity and frequency of rainfall events; number, length, and cumulative duration of dry spells. Rain gauge stations and MSWEP (Multi-Source Weighted-Ensemble Precipitation) data were used to evaluate the agreement of rainfall variables in both space and time, and trends were analyzed. Overall, ARC2 rainfall variables reliably show the spatio-temporal dynamics of seasonal rainfall over 33 years when compared to gauge and MSWEP data. However, a higher frequency of low rainfall events (<10 mm day −1 ) is found for satellite estimates as compared to gauge data, which also causes disagreements between satellite and gauge based variables due to sensitivity to the number of days with observations (frequency, intensity, and dry spell characteristics). Most rainfall variables (both ARC2 and gauge data) show negative anomalies (except for onset of rainy season) from 1983 until the end of the 1990s, from which anomalies become mostly positive and inter-annual variability is higher. ARC2 data show a strong increase in seasonal rainfall, wet season length (caused by both earlier onset and a late end), number of rainy days, and high rainfall events (>20 mm day −1 ) for the western/central Sahel over the period of analysis, whereas the opposite trend characterizes the eastern part of the Sahel. [ABSTRACT FROM AUTHOR]

The forest fire burned area is one of the most basic factors used to describe forest fires and plays a vital role in damage assessment. The development of the NSSI-NDVI vegetation index triangular space method enables simultaneous calculation of the flammable non-photosynthetic vegetation (NPV), combustible photosynthetic vegetation (PV), and incombustible bare soil (BS) fractional cover in forest areas. This can be used to compensate for the calculation method that was based on NDVI vegetation index only by comparing vegetation cover before and after forest fires, with the omission of the NPV burned area. To this end, the NSSI-NDVI triangular space shape consistency before and after forest fires was elucidated through combustion and ash wetting experiments. In addition, the feasibility of the NSSI-NDVI triangular space method for the accurate calculation of the post-fire vegetation damage area was verified. Finally, the applicability and accuracy of this research method were verified based on 10 m spatial resolution satellite hyperspectral images from before and after the forest fire in Lushan, Sichuan Province, China. The NSSI-NDVI triangular space method was used to calculate the PV, NPV, and BS coverage simultaneously, and component transformation was used to calculate the burned area and burned site separately. [ABSTRACT FROM AUTHOR]

Forest data acquisition, which is of crucial importance for modeling global biogeochemical cycles and climate, makes a contribution to building the ecological Digital Earth (DE). Due to the complex calculations and large volumes of data associated with high-resolution images of large areas, accurate and effective extraction of individual tree crowns remains challenging. In this study, two GeoEye-1 panchromatic images of Beihai and Ningbo in China with areas of 5 and 25 km2, respectively, were used as experimental data to establish a novel method for the automatic extraction of individual tree crowns based on a self-adaptive mutual information (SMI) algorithm and tile computing technology (SMI-TCT). To evaluate the performance of the algorithm, four commonly used algorithms were also applied to extract the individual tree crowns. The overall accuracy of the proposed method for the two experimental areas was superior to that of the four other algorithms, with maximum extraction accuracies of 85.7% and 63.8%. Moreover, the results also indicated that the novel method was suitable for individual tree crowns extraction in sizeable areas because of the multithread parallel computing technology. [ABSTRACT FROM AUTHOR]

As global climate change become more complex, accurately monitoring the impact of drought on main grain producing areas in North China Plain has important guiding implication for regional agricultural production planning. The conventional remote sensing methods only monitor single drought response factors such as soil,vegetation. This method does not reflect the comprehensive information of drought. Based on the soil water stress, vegetation growth status and precipitation deficit in drought developing process, a synthesized drought monitoring model was developed using spatial data mining techniques and multi-source remote sensing data including MODIS and TRMM. For assessing the accuracy of this drought monitoring model, a validation experiment was conducted in Shandong province. The results showed that the main drought events monitored by this model in recent years were consistent with observed droughts in Shandong province. The Synthesized Drought Index (SDI), a drought indicator produced by the model, not only includes agricultural drought informaion but also includes meteorological drought informaion. In the wheat growing period (March-May), the correlation coefficient of accumulated monthly SDI with crop yield as a standardized variable all were exceeding 0.7 (P<0.05) in Heze, Liaocheng and Dezhou, three main wheat producing cities of Shandong province. SDI was negatively correlated with drought affected crop area. The correlation coefficient of monthly SDI with drought affected crop area in wheat (March-May) and maize (July-September) growing period are between -0.67 - -0.85 and all passed significance test (P<0.01) except March (P<0.05). The SDI was also significantly correlated with meteorological drought index. In wheat and maize growing period, the correlation coefficients between monthly SDI and Standardized Precipitation Index (SPI) are between 0.44-0.67 and all correlation coefficients passed P<0.01 significance test except March. This work provides a new approach to comprehensive assessing regional drought. [ABSTRACT FROM AUTHOR]

Drought is a complex hazard caused by the breaking of water balance and it has always an impact on agricultural, ecological and socio-economic spheres. Although the drought indices deriving from remote sensing data have been used to monitor meteorological or agricultural drought, there are no indices that can suitably reflect the comprehensive information of drought from meteorological to agricultural aspects. In this paper, the synthesized drought index (SDI) is defined as a principal component of vegetation condition index (VCI), temperature condition index (TCI) and precipitation condition index (PCI). SDI integrates multi-source remote sensing data from moderate resolution imaging spectroradiometer (MODIS) and tropical rainfall measuring mission (TRMM) and it synthesizes precipitation deficits, soil thermal stress and vegetation growth status in drought process. Therefore, this method is favorable to monitor the comprehensive drought. In our research, a heavy drought process was accurately explored using SDI in Shandong province, China from 2010 to 2011. Finally, a validation was implemented and its results show that SDI is not only strongly correlated with 3-month scales standardized precipitation index (SPI3), but also with variation of crop yield and drought-affected crop areas. It was proved that this index is a comprehensive drought monitoring indicator and it can contain not only the meteorological drought information but also it can reflect the drought influence on agriculture. [ABSTRACT FROM AUTHOR]

Determining oil slick thickness plays an important role in assessing oil spill volume and its environmental impacts on the ocean. In this study, we used a Hyperion image of an oil spill accident area and seawater and fresh crude oil samples collected in the Bohai Sea of China. A well-controlled laboratory experiment was designed to simulate spectral responses to different oil slick thicknesses. Spectral resampling and normalization methods were used to reduce the differences in spectral reflectances between the experimental background seawater sample and real background seawater. Fitting the analysis with laboratory experimental data results showed a linear relationship between normalized oil slick reflectance and normalized oil slick thickness [20th band (R2=0.92938,n=49,p<0.01), 26th band (R2=0.93806,n=49,p<0.01), 29th band (R2=0.93288,n=49,p<0.01)]. By using these statistical models, we successfully determined the normalized oil slick thickness with the Hyperion image. Our results indicate that hyperspectral remote sensing technology is an effective method to monitor oil spills on water. The spectral ranges of visible green and red light were the optimal bands for estimating oil slick thickness in case 2 water. The high, stabilized spectral reflectance of background seawater will be helpful in oil slick thickness inversion. [ABSTRACT FROM AUTHOR]

Offshore oil slicks are significant for both the monitoring of marine spill accidents and the detection of marine oil resources. The use of remote sensing technology to detect the thickness of oil slicks is a major area of research. The reflected light from oil slicks changes with the thickness of the oil. This is the theoretical basis of research on optical remote sensing of offshore oil slicks. A two-beam interference model that considers the offshore oil slick as a flat plate has been developed in this study. A quantitative remote sensing model which describes a series of processes that use oil slick thickness and reflectance as variables is established. The use of the Fresnel equation to analyze parameters in the model indicated that the key property of the quantitative relationship between the oil slick thickness and reflectance was ultimately the disappearance or extinction of the oil slick. This model has been tested and verified by data from offshore oil slick spectral response experiments. Results showed that the oil slick thickness remote sensing model can be theoretically analyzed and is efficient. The research indicated that the major cause of variations in the spectral response as a function of oil slick thickness was the different light-scattering characteristics. These characteristics can be used in remote sensing applications to identify the different types of offshore oil slicks. The theoretical interpretation of each parameter in this model led to the development of a look-up table of the model parameters which will improve the efficiency of future offshore oil slick remote sensing. [ABSTRACT FROM AUTHOR]

Using the seawater taken from Liaodong Bay and crude oil taken from Liaohe OiIfield, a laboratory experiment was designed to study the change of reflectance spectrum of artificial offshore oil slick with its thickness and to identify the spectrum ranges suitable for quantifying the thickness of the oil slick. During the experiment, crude oil was continuously dropped into the seawater to generate artificial oil slick with different thicknesses. After every drop of crude oil was added, reflectance spectrum of the oil slick was measured with a high resolution spectroradiometer (ASD FieldSpec Pro FR). The influence of oil slick thickness on its reflectance spectrum was explored through statistical analysis. The results show that the reflectance of oil slick changes marginally with oil slick thickness and higher than that of seawater in infrared band from 1150 to 2500 nm. This spectrum range can be practically used to distinguish oil slick from seawater. In the spectrum range from 400 to 1150 nm, the reflectance of oil slick decreases with its thickness. The negative power function is the best-fit function expressing the relationship between the reflectance and thickness. The spectral characteristics of oil slick are very distinct at 550 and 645 nm. They are the best wavelengths for monitoring the existence of offshore oil slick and estimating its thickness. [ABSTRACT FROM AUTHOR]

Presents a study on crop spectral reflectance for leaf water stress. Spectral reflectance measurements; Spectral normalization of absorption feature; Relationship between the relative water content and absorption features.

The non-photosynthetic vegetation – soil separation index (NSSI) and normalized difference vegetation index (NDVI) can be used to simultaneously estimate the fractional cover of non-photosynthetic vegetation (fNPV), photosynthetic vegetation (fPV), and bare soil (fBS) in vegetation ecosystems. However, these estimates suffer from problems due to shading by dense vegetation or topography. Based on field-measured data and on hyperspectral data acquired by unmanned aerial vehicles, we analyse how shading affects the morphology of NDVI-NSSI and enhanced vegetation index – NSSI (EVI-NSSI). We also investigate the simultaneous estimation of fNPV, fPV, and fBS. The results show that the NDVI-NSSI normalized difference feature space mitigates the impact of shade. Shade causes the NDVI-NSSI and EVI-NSSI to shift rightward parallel to the BS axis and leftward parallel to the PV axis, respectively. Although such shifts may hinder the determination of NPV, PV, and BS endmembers, they do not affect the spectral separation of NPV. Overall, with the NDVI-NSSI and EVI-NSSI methods, the estimation error for fNPV under shaded conditions is approximately 5% greater than that under illuminated conditions. Shaded green vegetation more strongly affects the estimation error of fPV and fBS in EVI-NSSI than in NDVI-NSSI, whereas fBS and fPV depend more strongly on shade than fNPV. [ABSTRACT FROM AUTHOR]

Forests are the most important component of terrestrial ecosystem; the accurate mapping of tree species is helpful for the management of forestry resources. Moderate- and high-resolution multispectral images have been commonly utilized to identify regional tree species in forest ecosystem, but the accuracy of recognition is still unsatisfactory. To enhance the forest mapping accuracy, this study integrated the land surface phenological metrics and text features of forest canopy on tree species identification based on Gaofen-1 (GF-1) wide field of view (WFV) and time-series images (36 10-day NDVI data), conducted at a forested landscape in Harqin Banner, Northeast China in 2017. The dominant tree species include Pinus tabulaeformis, Larix gmelinii, Populus davidiana, Betula platyphylla, and Quercus mongolica in the study region. The result of forest mapping derived from a 10-day dataset was also compared with the outcome based upon a commonly utilized 30-day dataset in tree species identification. The results indicate that tree species identification accuracy is significantly (p < 0.05) improved with higher temporal resolution (10-day, 79.4%) of images than commonly used monthly data (30-day, 76.14%), and the accuracy can be further increased to 85.13% with a combination of the information derived from principal component analysis (PCA) transformation, phenological metrics (standing for the information of growing season) and texture features. The integration of higher dimensional NDVI data, vegetation growth dynamics and feature of canopy simultaneously will be beneficial to map tree species at the landscape scale. [ABSTRACT FROM AUTHOR]

Accurate estimation of fractional vegetation cover (FVC), crop residue cover (CRC), and bare soil (BS) in agricultural and vegetation-soil ecosystems is critically important. The traditional triangular space method uses a CRC spectral index (SI) and vegetation SI to create a two-dimensional scatter map in which the three vertices represent pure vegetation (FVC = 1), crop residue (CRC = 1), and bare soil (BS = 1). With this method, the CRC, FVC, and BS of each pixel are calculated based on their spatial locations in the triangular space. In practice, soil moisture and crop residue moisture (SM-CRM) affects the values of CRC spectral indices (SIs) for pure crop residue and soil, thereby reducing the accuracy of broadband remote sensing estimates of CRC, FVC, and BS. In the current work, we propose a new method for estimating fractional cover that uses a broadband spectral angle index (BAI) to estimate CRC. The proposed BAI is the included angle between (i) the line from the reflectance at band a to the reflectance at band b and (ii) the line from the reflectance at band b to the reflectance at band c, where bands a and b represent the VIS or NIR bands, respectively, and band c represents the SWIR1 or SWIR2 of the broadband remote sensing band. The proposed BAI method can mitigate the effects of soil and crop residue moisture content on spectral reflectance. This study evaluates proposed BAIs to estimate CRC and BAI-NDVI triangular space method to estimate CRC, FVC, and BS in cropland where water content varies greatly. Several different BAIs were validated using both laboratory-based measurements and field-based experiments using Sentinel-2 multispectral instrument imaging. We used two laboratory-based treatments (mixed spectral reflectance of dry, saturated soil and crop residue and the mixed spectral reflectance of winter wheat leaf and dry, saturated soil and crop residue) to analyze the response of BAIs to CRC, SM-CRM, FVC, and vegetation water content. Next, we evaluated the performance of different BAIs in determining CRC based on the mixed spectral reflectance of crop residue and soil, as well as the performance of the BAI-NDVI triangular space method and linear spectral unmixing analysis to estimate CRC, FVC, and BS from mixed spectral reflectance measurements. Our results indicate that the proposed methods reduce the influence of moisture on broadband CRC SIs, provide accurate estimates of cropland CRC and fractional estimates of CRC, FVC, and BS, and may be applied in croplands where soil and crop residue moisture content varies greatly. Unlabelled Image • Performance of the broadband crop residues indices declines with the increasing moisture. • New broadband crop residue angle index (BAI) is resistant to soil and crop residue moisture effect. • Cropland FVC, CRC and BS estimation accuracy improved using BAI. [ABSTRACT FROM AUTHOR]

Crop residue left in the field after harvest helps to protect against water and wind erosion, increase soil organic matter, and improve soil quality, so a proper estimate of the quantity of crop residue is crucial to optimize tillage and for research into environmental effects. Although remote-sensing-based techniques to estimate crop residue cover (CRC) have proven to be good tools for determining CRC, their application is limited by variations in the moisture of crop residue and soil. In this study, we propose a crop residue angle index (CRAI) to estimate the CRC for four distinct soils with varying soil moisture (SM) content and crop residue moisture (CRM). The current study uses laboratory-based tests ((i) a dry dataset (air-dried soils and crop residues, n = 392); (ii) a wet dataset (wet soils and crop residues, n = 822); (iii) a saturated dataset (saturated soils and crop residues, n = 402); and (iv) all datasets (n = 1616)), which allows us to analysis the soil and crop residue hyperspectral response to varying SM/CRM. The CRAI combines two features that reflect the moisture content in soil and crop residue. The first is the different reflectance of soil and crop residue as a function of moisture in the near-infrared band (833 nm) and short-wave near-infrared band (1670 nm), and the second is different reflectance of soils and crop residues to lignin, cellulose, and moisture in the bands at 2101, 2031, and 2201 nm. The effects of moisture and soil type on the proposed CRAI and selected traditional spectral indices ((i) hyperspectral cellulose absorption index; (ii) hyperspectral shortwave infrared normalized difference residue index; and (iii) selected broad-band spectral indices) were compared by using a laboratory-based dataset. The results show that the SM/CRM significantly affects the broad-band spectral indices and all other spectral indices investigated are less correlated with CRC when using all datasets than when using only the dry, wet, or saturated dataset. Laboratory study suggests that the CRAI is promising for estimating CRC with the four soils and with varying SM/CRM. However, because the CRAI was only validated by a laboratory-based dataset, additional field testing is thus required to verify the use of satellite hyperspectral remote-sensing images for different crops and ecological areas. [ABSTRACT FROM AUTHOR]

Background: Timely and accurate estimates of canopy chlorophyll (Chl) a and b content are crucial for crop growth monitoring and agricultural management. Crop canopy reflectance depends on many factors, which can be divided into the following categories: (i) leaf effects (e.g., leaf pigments), (ii) canopy effects (e.g., Leaf Area Index [LAI]), and (iii) soil background reflectance (e.g., soil reflectance). The estimation of leaf variables, such as Chl contents, from reflectance at the canopy scale is usually less accurate than that at the leaf scale. In this study, we propose a Visible and Near-infrared (NIR) Angle Index (VNAI) to estimate the Chl content of soybean canopy, and soybean canopy Chl maps are produced using visible and NIR unmanned aerial vehicle (UAV) remote sensing images. The VNAI is insensitive to LAI and can be used for the multi-stage estimation of crop canopy Chl content. Results: Eleven previously used vegetation indices (VIs) (e.g., Pigment-specific Normalized Difference Index) were selected for performance comparison. The results showed that (i) most previously used Chl VIs were significantly correlated with LAI, and the proposed VNAI was more sensitive to Chl content than LAI; (ii) the VNAI-based estimates of Chl content were more accurate than those based on the other investigated VIs using (1) simulated, (2) real (field), and (3) real (UAV) datasets. Conclusions: Most previously used Chl VIs were significantly correlated with LAI whereas the proposed VNAI was more sensitive to Chl content than to LAI, indicating that the VNAI may be more strongly correlated with Chl content than these previously used VIs. Multi-stage estimations of the Chl content of cropland obtained using the VNAI and broadband remote sensing images may help to obtain Chl maps with high temporal and spatial resolution. [ABSTRACT FROM AUTHOR]

Spectral variables such as spectral characteristic parameters (SCPs) commonly change with intraday phenology. Empirical retrieval methods, which are generally used in leaf area index (LAI) retrieval due to their simplicity and computational efficiency, typically relate the biophysical parameter of interest to the spectral variable during the whole observation period. Whilst information regarding diurnal changes in spectral variables is necessary and useful in applied contexts. We analysed the diurnal change characteristics of canopy spectral reflectance and SCPs of winter wheat in the jointing stage based on field data collected at fixed sampling points with different vegetation canopies, and validated the effectiveness of data splitting strategy with field data collected in random sample pattern. The key results are as follows: (i) Canopy spectral reflectance of winter wheat in the jointing stage exhibited clear intraday variability, typically presenting a double-peak characteristic occurring from 11:35 to 12:34, where the reflectance changed substantively during this period. (ii) The SCPs of winter wheat in the jointing stage exhibited different diurnal patterns. Specifically, the blue edge position presented 'blue shifts', the yellow edge position generally exhibited steady fluctuations, and the red edge position followed divergent trends between the two sampling points due to differences in the vegetation canopy. Amplitude and area parameters exhibited a double-peak characteristic but there were slight differences between them. (iii) By dividing the whole observation period into sub-periods, the coefficient of variation (CV) of each spectral characteristic parameter can be greatly reduced, whilst the coefficient of determination (R2) of LAI retrieval can be greatly increased. Optimal spectral parameters and sub-periods for LAI retrieval were confirmed based on the diurnal variation of SCPs. To optimize LAI retrieval the suggested spectral parameters are blue edge amplitude, red edge amplitude, and red edge area, and the sub-periods are 09:50–11:35, 11:35–12:34, 12:34–13:50, and 13:50–15:00, respectively. The 11:35–12:34 sub-period should be carefully considered due to possible midday depression of photosynthesis. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) controls the exchange of water and heat energy between the land surface and the atmosphere through evaporation and plant transpiration, and timely and accurate estimates of soil moisture are crucial for many studies. Although remote sensing provides many algorithms to obtain bare-soil moisture on large scales (e.g., Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active and Passive (SMAP) products), these algorithms are restricted to studies with low ground resolution. In the present study, we propose and evaluate three normalized shortwave-infrared (SWIR) difference bare soil moisture indices [NSDSI1 = (B SWIR1 -B SWIR2)/B SWIR1 , NSDSI2 = (B SWIR1 − B SWIR2)/B SWIR2 , NSDSI3 = (B SWIR1 − B SWIR2)/(B SWIR1 + B SWIR2), where, SWIR1 = 1550–1750 nm, SWIR2 = 2100–2300 nm] to estimate the bare-soil moisture content and, based on the water absorption difference between shortwave-infrared bands, use them to map bare-soil moisture with high ground resolution Sentinel-2 MSI images. The three proposed bare-soil moisture indices are obtained by using the different water absorption in shortwave-infrared bands. Four traditional hyperspectral-based bare-soil moisture indices (such as the water index SOIL, or WISOIL, the normalized soil moisture index, or NSMI, etc.) were used as benchmark. The results show that (i) the differences in water absorption between shortwave-infrared bands is linear in soil moisture content; (ii) the combined use of two shortwave-infrared bands from four soils provides more accurate bare-soil moisture estimates than do single shortwave-infrared (SWIR) bands, and (iii) our proposed bare-soil moisture indices can be applied on broadband remote-sensing images (such as Landsat, Sentinel-2 MSI). The bare-soil moisture estimates obtained by using the proposed bare-soil moisture indices may help to obtain bare-soil moisture maps with high ground resolution. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is a critical parameter for the dynamic simulation of land surface processes and for analyzing variations on regional or global scales. Obtaining LST with high spatiotemporal resolution is a subject of intensive and ongoing research. This study proposes a pixel-wise temporal alignment iterative linear regression model for downscaling based on MODIS LST products. This approach allows us to address the problem of high temporal resolution but low spatial resolution of the ERA5 reanalysis LST product while remaining immune to the pixel loss caused by clouds. The hourly ERA5 LST of the study area for 2012–2021 was downscaled to a 1000 m resolution, and its accuracy was verified by comparison with measured data from meteorological stations. The downscaled LST offers intricate details and is faithful to the LST characteristics of distinct land-cover categories. In comparison with other downscaling techniques, the proposed technique is more stable and preserves the spatial distribution of the ERA5 LST with minimal missing pixels. The pixel-wise average R2 and mean absolute error for the MODIS view times are 0.87 and 2.7 K, respectively, for cloud-free conditions on a 1000 m scale. The accuracy verification using data from meteorological stations indicates that the overall error is lower during cloudless periods rather than during overcast periods, during the night rather than during the day, and at MODIS view times rather than at non-view times. The maximum and minimum mean errors are 0.13 K for cloud-free periods and −0.98 K for cloudy periods, indicating a slight underestimation and overestimation, respectively. Conversely, the maximum and minimum mean absolute errors are 2.01 K for the daytime and 0.85 K for the nighttime. Therefore, the model ensures higher accuracy during cloudy periods with only the clear-sky LST used as input data, making it suitable for long-term, all-weather ERA5 LST downscaling. [ABSTRACT FROM AUTHOR]

Adaptive managements on susceptible forested regions not only benefit forest recovery, but also help to improve the stability of soil-degraded area and carbon (C) sequestration. The gain of afforestation on C sink has not been addressed clearly especially in tropical and subtropical regions that will be critical in an era of global warming. Based on the Landsat satellite images, in situ forest survey and archives, we analyzed the spatiotemporal patterns of C density and storage in a typical soil–water conservation area in Changting county in southeastern China, dominated by the pioneer tree Pinus massoniana forest. The results showed that the C density (storage) increased significantly from 20.14 Mg C ha−1 (0.38 Tg C) in 1981 to 43.57 Mg C ha−1 (1.98 Tg C) in 2015 (p < 0.05) and proved the success of ecological management adopted considering the interactions between local residential livelihoods and the features of local forest ecosystem. In addition, the differences of C density and storage across elevational and slope gradients also narrowed over the past 35 years. The landscape also gradually shifted from lower C density to higher C density conditions based on the landscape metrics. The ecological-dominated approaches should be put in a high priority for the issue of C sequestration especially in a changing climate. [ABSTRACT FROM AUTHOR]

Abstract When dealing with multiple growth stages, estimates of above-ground biomass (AGB) based on optical vegetation indices (VIs) are difficult for two reasons: (i) optical VIs saturate at medium-to-high canopy cover, and (ii) organs that grow vertically (e.g., biomass of reproductive organs and stems) are difficult to detect by canopy spectral VIs. Although several significant improvements have been made for estimating AGB by using narrow-band hyperspectral VIs, synthetic aperture radar, laser intensity direction and ranging, the crop surface model technique, and combinations thereof, applications of these new techniques have been limited by cost, availability, data-processing difficulties, and high dimensionality. The present study thus evaluates the use of ultrahigh-ground-resolution image textures, VIs, and combinations thereof to make multiple temporal estimates and maps of AGB covering three winter-wheat growth stages. The selected gray-tone spatial-dependence matrix-based image textures (e.g., variance, entropy, data range, homogeneity, second moment, dissimilarity, contrast, correlation) are calculated from 1-, 2-, 5-, 10-, 15-, 20-, 25-, and 30-cm-ground-resolution images acquired by using an inexpensive RGB sensor mounted on an unmanned aerial vehicle (UAV). Optical-VI data were obtained by using a ground spectrometer to analyze UAV-acquired RGB images. The accuracy of AGB estimates based on optical VIs varies, with validation R 2: 0.59–0.78, root mean square error (RMSE): 1.22–1.59 t/ha, and mean absolute error (MAE): 1.03–1.27 t/ha. The most accurate AGB estimate was obtained by combining image textures and VIs, which gave R 2: 0.89, MAE: 0.67 t/ha, and RMSE: 0.82 t/ha. The results show that (i) the eight selected textures from ultrahigh-ground-resolution images were significantly related to AGB, (ii) the combined use of image textures from 1- to 30-cm-ground-resolution images and VIs can improve the accuracy of AGB estimates as compared with using only optical VIs or image textures alone; and (iii) high AGB values from winter-wheat reproductive growth stages can be accurately estimated by using this method; (iv) high estimates of winter-wheat AGB (8–14 t/ha) using the proposed combined method (DIS1, SE30, B460, B560, B670, EVI2 using MSR) show a 22.63% (nRMSE) improvement compared with using only spectral VIs (LCI, NDVI using MSR), and a 21.24% (nRMSE) improvement compared with using only image textures (COR1, DIS1, SE30, EN30 using MSR). Thus, the combined use of image textures and VIs can help improve estimates of AGB under conditions of high canopy coverage. [ABSTRACT FROM AUTHOR]

Remote sensing image classification is a common application of remote sensing images. In order to improve the performance of Remote sensing image classification, multiple classifier combinations are used to classify the Landsat-8 Operational Land Imager (Landsat-8 OLI) images. Some techniques and classifier combination algorithms are investigated. The classifier ensemble consisting of five member classifiers is constructed. The results of every member classifier are evaluated. The voting strategy is experimented to combine the classification results of the member classifier. The results show that all the classifiers have different performances and the multiple classifier combination provides better performance than a single classifier, and achieves higher overall accuracy of classification. The experiment shows that the multiple classifier combination using producer’s accuracy as voting-weight (MCCmod2 and MCCmod3) present higher classification accuracy than the algorithm using overall accuracy as voting-weight (MCCmod1).And the multiple classifier combinations using different voting-weights affected the classification result in different land-cover types. The multiple classifier combination algorithm presented in this article using voting-weight based on the accuracy of multiple classifier may have stability problems, which need to be addressed in future studies. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) is a bridge between the atmosphere, vegetation and soil, and its dynamics reflect the energy exchange and transformation between the three. Among SM at different soil profiles, root zone soil moisture (RZSM) plays a significant role in vegetation growth. Therefore, reliable estimation of RZSM at the regional scale is of great importance for drought warning, agricultural yield estimation, forest fire monitoring, etc. Many satellite products provide surface soil moisture (SSM) at the thin top layer of the soil, approximately 2 cm from the surface. However, the acquisition of RZSM at the regional scale is still a tough issue to solve, especially in the semi-arid areas with a lack of in situ observations. Linking the dynamics of SSM and RZSM is promising to solve this issue. The soil moisture analytical relationship (SMAR) model can relate RZSM to SSM based on a simplified soil water balance equation, which is suitable for the simulation of soil moisture mechanisms in semi-arid areas. In this study, the Xiliaohe River Basin is the study area. The SMAR model at the pixels where in situ sites were located is established, and parameters (a, b, sw2, sc1) at these pixels are calibrated by a genetic algorithm (GA). Then the spatial parameters are estimated by the random forest (RF) regression method with the soil, meteorological and vegetation characteristics of the study area as explanatory variables. In addition, the importance of soil, climatic and vegetation characteristics for predicting SMAR parameters is analyzed. Finally, the spatial RZSM in the Xiliaohe River Basin is estimated by the SMAR model at the regional scale with the predicted spatial parameters, and the variation of the regional SMAR model performance is discussed. A comparison of estimated RZSM and in-situ RZSM showed that the SMAR model at the point and regional scales can both meet the RMSE benchmark from NASA of 0.06 cm3·cm−3, indicating that the method this study proposed could effectively estimate RZSM in semi-arid areas based on remotely sensed SSM data. [ABSTRACT FROM AUTHOR]

[Display omitted] • We propose a novel decoupled Image and Frequency-domain Spatial-Spectral Framework. • The proposed module extracts multi-scale features from dual domains. • A module can transfer the advantages of convolution to the transformer. • The proposed neural network can achieve better performance. Hyperspectral image classification (HSIC) is a rapidly developing field that utilizes deep learning methods. However, the reliance on convolutional neural networks (CNNs) for spectral–spatial feature extraction presents certain limitations. Specifically, the use of the fixed-position convolutional kernels in CNNs hinders their ability to capture fully the spectral information around the spatial central pixel, thereby overlooking critical differences between features. To address this issue, a decoupled image- and frequency-domain spectral–spatial framework for HSIC was developed in this study. This method incorporates image- and frequency-domain-based multiscale learnable convolutional attention to refine the differentiating features of the different feature distributions. Additionally, a novel frequency-domain information enhancement module was designed to extract the structural shape and texture details under the semantic constraints of the frequency phase, complementing the image domain to improve the extracted feature maps. Furthermore, a simple and efficient hierarchical feature representation module was introduced to extract both local and global information effectively from the fused features. The experimental results obtained using three open datasets and a practical hyperspectral image of the Gaofen-5 satellite demonstrate that the proposed method outperforms other state-of-the-art HSIC methods. [ABSTRACT FROM AUTHOR]

As the invasion of alien species intensifies, the native salt marsh vegetation, especially the ecosystem of Suaeda salsa (S. salsa), in Chinese coastal wetlands has been severely disrupted, significantly impeding its functionality within coastal wetland ecosystems. Chlorophyll content (C ab) is an important parameter for monitoring the growth and health status of vegetation. However, the remote sensing mechanism of C ab for different phenotypes of green and red vegetation influenced by betacyanin under different water and salt conditions is not clear. Therefore, to further clarify the remote sensing mechanism of S. salsa and improve the generality of C ab prediction for different phenotypes under different water and salt conditions, we designed a growth experiment under different groundwater levels and salt concentrations. This experiment was used to simulate the growth of S. salsa in different habitats in nature and investigate the relationship between their physicochemical parameters, spectra, and environmental stress. We propose using the specific absorption coefficient of betacyanin (Beta) to adjust PROSAIL-D to better fit the spectral characteristics of S. salsa. The relationship between Beta and C ab was utilized to further optimize the simulation accuracy of the PROSAIL-D model in different environments. Feature extraction algorithms, including differential enhancement of two-dimensional correlation spectroscopy (FD-2DCOS) and three-band models, were introduced to construct multiple spectral features for establishing a C ab prediction model for S. salsa. The research results showed that: (1) S. salsa had the best response of C ab and spectra to salinity under a higher groundwater level and showed a gradual reddening trend. (2) The spectral curves simulated by the adjusted PROSAIL-D model were more consistent with the spectral characteristics of S. salsa under different water-salt environments, and the simulation effect could be further optimized by using the relationship between Beta and C ab. (3) The FD-2DCOS analysis method we proposed could highlight the useful spectral information related to C ab better than the original 2DCOS algorithm. The final eight spectral features of (1/R 701 –1/R 540)R 540 , Mean(D 590 – 600), Mean(D 660 – 670), Mean(D 677 – 685), FDVI_NRG, FDVI_NIR, Skewness (D 677 – 750) and Position(D 677 – 750) had the best response to C ab of S. salsa , and had the least influence on LAI and soil background. (4) The particle swarm optimization random forest regression (PSO-RFR) model based on eight spectral features and adding 70% measured spectral data performed the best in predicting the actual C ab of S. salsa , with an RMSE of 2.326 μg·cm−2. Although further optimization and calibration of the adjusted PROSAIL-D model and C ab prediction model of S. salsa are needed in the future, this study extended its application to S. salsa , a special local salt marsh vegetation, and promoted the development of C ab monitoring of salt marsh vegetation in coastal wetlands. • C ab of Suaeda salsa responds best to salinity stress under high groundwater table. • The adjusted PROSAIL-D model is more suitable for the simulation of Suaeda salsa. • Pay attention to the ratio of betacyanin and C ab when using PROSAIL-D simulation. • The proposed FD-2DCOS can better highlight the information of C ab in the spectrum. • The constructed PSO-RFR model can better predict the C ab of Suaeda salsa. [ABSTRACT FROM AUTHOR]

The accurate mapping of urban impervious surfaces from remote sensing images is crucial for understanding urban land-cover change and addressing impervious-surface-change-related environment issues. To date, the authors of most studies have built indices to map impervious surfaces based on shortwave infrared (SWIR) or thermal infrared (TIR) bands from middle–low-spatial-resolution remote sensing images. However, this limits the use of high-spatial-resolution remote sensing data (e.g., GaoFen-2, Quickbird, and IKONOS). In addition, the separation of bare soil and impervious surfaces has not been effectively solved. In this article, on the basis of the spectra analysis of impervious surface and non-impervious surface (vegetation, water, soil and non-photosynthetic vegetation (NPV)) data acquired from world-recognized spectral libraries and Sentinel-2 MSI images in different regions and seasons, a novel spectral index named the Normalized Impervious Surface Index (NISI) was proposed for extracting impervious area information by using blue, green, red and near-infrared (NIR) bands. We performed comprehensive assessments for the NISI, and the results demonstrated that the NISI provided the best studied performance in separating the soil and impervious surfaces from Sentinel-2 MSI images. Furthermore, regarding impervious surfaces mapping accuracy, the NISI had an overall accuracy (OA) of 89.28% (±0.258), a producer's accuracy (PA) of 89.76% (±1.754), and a user's accuracy (UA) of 90.68% (±1.309), which were higher than those of machine learning algorithms, thus supporting the NISI as an effective measurement for urban impervious surfaces mapping and analysis. The results indicate the NISI has a high robustness and a good applicability. [ABSTRACT FROM AUTHOR]

Using remote-sensing technology to accurately map the composition and distribution of tree species is vital for sustainable forest resource management. Sentinel-2 data with the dense time-series observations enable to identify tree species. However, few studies clarify the differences in classification using Sentinel-2 images in natural forest and planted forest. Two study areas with different forest environments (planted forest and natural forest) were selected to evaluate the potential of Sentinel-2 imagery. Our results show that red-edge band, short-wave infrared (SWIR) band and vegetation indices (VIs), such as Red-Edge Normalized Difference Vegetation Index (ReNDVI), Soil-Adjusted Vegetation Index (SAVI), and Enhanced Vegetation Index (EVI), have important effects on tree species classification, especially in the growing season. By using two machine learning algorithms (support vector machine [SVM] and random forest [RF]), the results show that the classification accuracy for planted forests (91.27% in SVM and 88.35% in RF) significantly exceeds that of natural forests (84.34% in SVM and 81.03% in RF). This accuracy difference may be related to the spatial heterogeneity inside the forest and the surrounding environmental implications. Although the multi-temporal Sentinel-2 images produce satisfactory accuracy for classifying tree species, further research is needed to improve the classification accuracy. [ABSTRACT FROM AUTHOR]

Different oil spill pollution types could be produced in oil transport and weathering processes. Investigation of these pollution types is beneficial for oil spill recovery and processing. Optical remote sensing techniques play an important role in marine oil spill monitoring and have the ability to identify different oil spill pollution types. Recently, research on oil spill optical remote sensing has made much progress in detecting targets, identifying spectral response characteristics, and formulating theories. Floating black oil, oil slicks, and oil-water mixture in marine oil spill accidents are the main targets to be investigated by optical remote sensors. The visible spectral response differences of these targets are the base of oil spill optical remote sensing research. Bi-directional reflectance distribution function, light interference, absorption, and scattering of targets produce different spectra. Therefore, oil spill optical remote sensing could be used to identify the main oil spill pollution types and estimate oil spill volume. [ABSTRACT FROM AUTHOR]

Most natural forests are mixed forests, a mixed broadleaf-conifer forest is essentially a heterogeneously mixed pixel in remote sensing images. Satellite missions rely on modeling to acquire regional or global vegetation parameter products. However, these retrieval models often assume homogeneous conditions at the pixel level, resulting in a decrease in the inversion accuracy, which is an issue for heterogeneous forests. Therefore, information on the canopy composition of a mixed forest is the basis for accurately retrieving vegetation parameters using remote sensing. Medium and high spatial resolution multispectral time-series data are important sources for canopy conifer-broadleaf ratio estimation because these data have a high frequency and wide coverage. This paper highlights a successful method for estimating the conifer-broadleaf ratio in a mixed forest with diverse tree species and complex canopy structures. Experiments were conducted in the Purple Mountain, Nanjing, Jiangsu Province of China, where we collected leaf area index (LAI) time-series and forest sample plot inventory data. Based on the Invertible Forest Reflectance Model (INFORM), we simulated the normalized difference vegetation index (NDVI) time-series of different conifer-broadleaf ratios. A time-series similarity analysis was performed to determine the typical separable conifer-broadleaf ratios. Fifteen Gaofen-1 (GF-1) satellite images of 2015 were acquired. The conifer-broadleaf ratio estimation was based on the GF-1 NDVI time-series and semi-supervised k-means cluster method, which yielded a high overall accuracy of 83.75%. This study demonstrates the feasibility of accurately estimating separable conifer-broadleaf ratios using field measurement data and GF-1 time series in mixed broadleaf-conifer forests. [ABSTRACT FROM AUTHOR]

Although the differential interferometric synthetic aperture radar (DInSAR) technique has the potential for monitoring ground deformation with millimetric accuracy, it still suffers from temporal and geometric decorrelation. In this paper, the corner reflectors differential interferometry synthetic aperture radar (CRDInSAR) technique was used to overcome the limitations of conventional DInSAR. We studied the basic principles of CRDInSAR, discussed the calculation of the flat earth and topographic phases based on the geometry of satellite and corner reflectors, presented the phase unwrapping approach for the sparse grid of corner reflectors, then investigated the construction of, and solution to, the unwrapped phase system equation. Subsequently, we applied CRDInSAR to monitor the displacement of the Shuping landslide in Hubei Province, China. In this study, we processed five SAR images on a descending pass acquired by the Environmental Satellite (ENVISAT) Advanced Synthetic Aperture Radar (ASAR) sensor from September 2005 to March 2006, and compared the achieved results of CRDInSAR with Global Positioning System (GPS) measurements at the same time of the SAR data acquisition assumed as reference. The result indicates a good agreement between the measurements provided by the two different techniques, which shows that CRDInSAR allows monitoring of slow landslide deformation in low coherence areas and provides accurate results. [ABSTRACT FROM AUTHOR]

• Detected the major crop phenological stages using an improved EVI-curve-based approach over North America during 2000–2016. • Offered the first attempt to characterize the spatial and temporal patterns in crop phenology at a continental scale. • Crop planting and harvesting dates are highly correlated with the min/maximum temperatures. Crop phenology represents an integrative indicator of climate change and plays a vital role in terrestrial carbon dynamics and sustainable agricultural development. However, spatiotemporal variations of crop phenology remain unclear at large scales. This knowledge gap has hindered our ability to realistically quantify the biogeochemical dynamics in agroecosystems, predict future climate, and make informed decisions for climate change mitigation and adaptation. In this study, we improved an EVI-curve-based approach and used it to detect spatiotemporal patterns in cropping intensity and five major phenological stages over North America during 2000–2016 using vegetation index in combination with agricultural survey data and other ancillary maps. Our predicted crop phenological stages showed strong linear relationships with the survey-based datasets, with R2, RMSEs, and MAEs in the ranges of 0.35 –0.99, three to ten days, and two to eight days, respectively. During the study period, the planting dates were advanced by 0.60 days/year (p < 0.01), and harvesting dates were delayed by 0.78 days/year (p < 0.01) over North America. A minimum temperature increase by 1 °C caused a 4.26-day planting advance (r = −0.50, p < 0. 01) or a 0.66-day harvest delay (r = 0.10, p < 0.01). While, a higher maximum temperature resulted in a planting advance by 4.48 days/°C (r = −0.62, p < 0.01) or a harvest advance by 2.22 days/°C (r = −0.40, p < 0.01). Our analysis illustrated evident spatiotemporal variations in crop phenology in response to climate change and management practices. The derived crop phenological datasets and cropping intensity maps can be used in regional climate assessments and in developing adaptation strategies. [ABSTRACT FROM AUTHOR]

Green fractional vegetation cover (f c) is an important phenotypic factor in the fields of agriculture, forestry, and ecology. Spatially explicit monitoring of f c via relative vegetation abundance (RA) algorithms, especially those based on scaled maximum/minimum vegetation index (VI) values, has been widely investigated in remote sensing research. Although many studies have explored the effectiveness of RA algorithms over the past 30 years, a literature review summarizing the corresponding theoretical background, issues, current state-of-the-art techniques, challenges, and prospects has not yet been published. The overall objective of the present study was to accomplish a comprehensive and systematic review of RA algorithms considering these factors based on the scientific papers published from January 1990 to November 2019. This review revealed that the key issues related to RA algorithms is the determination of the appropriate normalized difference vegetation index (NDVI) values of the full vegetation cover and bare soil (denoted hereafter by NDVI ∞ and NDVI s , respectively). The existing methods used to correct for these issues were investigated, and their advantages and disadvantages are discussed in depth. In literature trends, we found that the number of reported studies in which RA algorithms were used has increased consistently over time, and that most authors tend to utilize the linear NDVI model, rather than other models in the RA algorithm family. We also found that RA algorithms have been utilized to analyze the images with spatial resolutions ranging from the sub-meter to kilometer, most commonly, using images of 30-m spatial resolution. Finally, current challenges and forward-looking insights in remote estimation of f c using RA algorithms are discussed to guide future research and directions. [ABSTRACT FROM AUTHOR]

Shadows exist universally in sunlight-source remotely sensed images, and can interfere with the spectral morphological features of green vegetations, resulting in imprecise mathematical algorithms for vegetation monitoring and physiological diagnoses; therefore, research on shadows resulting from forest canopy internal composition is very important. Red edge is an ideal indicator for green vegetation's photosynthesis and biomass because of its strong connection with physicochemical parameters. In this study, red edge parameters (curve slope and reflectance) and the normalized difference vegetation index (NDVI) of two species of coniferous trees in Inner Mongolia, China, were studied using an unmanned aerial vehicle's hyperspectral visible-to-near-infrared images. Positive correlations between vegetation red edge slope and reflectance with different illuminated/shaded canopy proportions were obtained, with all R2s beyond 0.850 (p < 0.01). NDVI values performed steadily under changes of canopy shadow proportions. Therefore, we devised a new vegetation index named normalized difference canopy shadow index (NDCSI) using red edge's reflectance and the NDVI. Positive correlations (R2 = 0.886, p < 0.01) between measured brightness values and NDCSI of validation samples indicated that NDCSI could differentiate illumination/shadow circumstances of a vegetation canopy quantitatively. Combined with the bare soil index (BSI), NDCSI was applied for linear spectral mixture analysis (LSMA) using Sentinel-2 multispectral imaging. Positive correlations (R2 = 0.827, p < 0.01) between measured brightness values and fractional illuminated vegetation cover (FIVC) demonstrate the capacity of NDCSI to accurately calculate the fractional cover of illuminated/shaded vegetation, which can be utilized to calculate and extract the illuminated vegetation canopy from satellite images. [ABSTRACT FROM AUTHOR]

Abstract Colonies of mammals and sea birds are known hotspots for biogeochemical cycles with potentially large element stocks and fast turnover in the soil. Although penguin and seal colonies are sites with potentially extremely fast biogeochemical turnover, these sites as potential sources of GHG have largely been neglected. This study aims to quantify magnitudes and environmental drivers of GHG fluxes effected by the activity of Antarctic fur seal (Arctocephalus gazella) and King penguin (Aptenodytes patagonicus) in South Georgia, sub-Antarctica. In-situ CO 2 and CH 4 gas flux measurements were combined with laboratory incubations of intact soil cores collected from sites along two transects away from a seal and a penguin colony. Variations in laboratory incubations were consistent with the in-situ fluxes measured in February 2017, and showed high ecosystem respiration rates at colonies (mean 44.3 μg CO 2 cm−3 h−1 for seal colony, and 52.9–159 μg CO 2 cm−3 h−1 for penguin colony) and a marked decrease away from these hotspots. Moderate methane production rates were found within the colonies (mean 0.1 ng CH 4 cm−3 h−1 for seal colony, 44–145.5 ng CH 4 cm−3 h−1 for penguin colony), while relatively high consumption rates (mean −1.8 ng CH 4 cm−3 h−1) occurred outside the colonies. Incubations also included N 2 O production rates, which were highly variable within the colonies (1.1–293 ng N 2 O cm−3 h−1 for seal colony, 0.8–594.7 ng N 2 O cm−3 h−1 for penguin colony) and decreased markedly to near zero away from the colonies. Bacterial and pmoA communities and drivers of GHG turnover and microbe community along both transects varied according to the sea animal activity intensity gradient. This is the first study at these latitudes to quantify the overall capacity of in-situ soil methane uptake at hot spots linked to sea animal colonies versus ambient conditions. All colony sites in this study were net sources of N 2 O, while sites beyond colonies, which dominate the ice-free areas in South Georgia, were sinks of methane. Highlights • High CH 4 uptake rates are observed in bare soil areas but decreased towarsds seal and penguin colonies. • Colonies were hotspots for CO 2 , CH 4 and N 2 O emissions due to large excreta deposits. • Spatial trends in gas rates were correlated to microbial community distribution. • Dominate methanotrophs differed between sites. [ABSTRACT FROM AUTHOR]

In vegetation-abundant ecosystems, accurate fractional cover estimation of multiple targets, including non-photosynthetic vegetation (NPV), photosynthetic vegetation (PV) and bare soil (BS) is an important indicator of carbon storage and climate change, and a contributing factor to soil and water conservation, wildfire, and drought. Satellite remote sensing began with sensors limited to the visible and near infrared (VNIR, 380 nm to 1000 nm), and advancing to sensors spanning the whole solar domain (350 nm to 2500 nm) over the last five decades. However, NPV is still difficult to distinguish from BS in satellite images covering large spatial areas, since their spectral reflectances are always mixed and have subtly different spectral features. Many spectral indices have been proposed to identify NPV, however, most include a short-wave infrared band which is lacking on many current and historical spaceborne sensors. To fully utilize current and historical airborne/satellite images with only VNIR bands, a novel spectral index – the VNIR spectral shape index (SSI) – is proposed in this paper. Using spectra of PV, NPV, and BS acquired from world-recognized spectral libraries, the SSI was devised using only blue, green, red, and NIR bands. When applied to Sentinel-2 MSI images, the SSI was effective in estimating/mapping the fractional cover of PV, NPV, and BS using the triangular space method. The resulting fractional cover was evaluated in three 3 km × 2 km study areas including one grassland, one woodland, and one cropland. The spectral reflectance collected by spectroradiometer in field work and digital image collect by digital camera mounted on unmanned aerial vehicle (UAV) support that the triangular space method is able to estimate the fractional cover of photosynthetic and non-photosynthetic vegetation simultaneously, and that endmember selection relying solely on satellite images is reasonable. Comparing the abundance of each target in UAV images to corresponding MSI pixels, the SSI index and triangular space method proved effective for fractional cover estimation in varied vegetated ecosystems. For fractional cover evaluation in grassland, woodland, and cropland, the R2 reaching to 0.59, 0.62, and 0.70, respectively. These results demonstrate that the SSI is feasible for multiple sensors, enabling potential large temporal and/or spatial scale applications. • Spectral index, SSI, is devised to separate soil and non-photosynthetic vegetation. • SSI employs blue, green, red, and NIR bands to catch spectral shape difference. • Four bands in SSI are equipped on most satellite/airborne sensors since 1980s. • SSI-NDVI combination can estimate fraction of land cover using triangle method. [ABSTRACT FROM AUTHOR]

Geometric-optical (GO) model suitable for forest plantation (GOFP) is a GO model for forest plantations at the stand level developed in this study based on a four-scale GO model a Geometric-Optical Model for Sloping Terrains-II (GOST2), which simulates the bidirectional reflectance distribution function (BRDF) for natural forest canopies. In most previous GO models, tree distributions are often assumed to meet the Poisson or Neyman model in a forest; therefore, these models are suitable for simulating BRDF for natural forest canopies. However, in forest plantations, tree distributions are proven to meet the hypergeometric model rather than the Poisson or Neyman model at the stand level. GOFP, in which the tree distributions are described using the hypergeometric model, is proposed to simulate the bidirectional reflectance factor (BRF) of forest plantations at the stand level. The area ratios of the four scene components (sunlit foliage, sunlit ground, shaded foliage, and shaded ground) of GOFP compare well with those simulated by a 3-D canopy visualization technique. A comparison is also made against discrete anisotropic radiative transfer, showing that GOFP has the ability to simulate BRF of forest plantations. Another comparison is made against operational land imager and Moderate Resolution Imaging Spectroradiometer surface reflectance, showing that GOFP with the hypergeometric model outperforms GOST2 with the Poisson and Neyman models. The results further show that the differences in BRFs between GOFP and GOST2 pronouncedly increase with the difference between the reflectance of sunlit foliage ( RT ) and the reflectance of sunlit ground ( RG ), as well as the distances among trees and the number of crowns in a forest plantation, suggesting that GOFP significantly outperforms GOST2 for simulating BRF of forest plantations. [ABSTRACT FROM PUBLISHER]

• Our method focuses on fractional cover (fc) and chlorophyll content (CC). • Confounding effect of fc and CC is well analyzed in VNAI-NDVI-space. • Polar coordinate method (PCM) is based on VNAI-NDVI-space under linear assumption. • PCM surpasses NDVI threshold in differentiating crop maturity. Crop leaf chlorophyll content (CC) and fractional cover (fc) are critical parameters for assessing crop growth and dynamic vegetation changes at regional and global scales. As CC and fc are the key factors dominating crop canopy reflectance, the confounding effects of CC and fc on canopy vegetation spectra and spectral indices (SIs) limit their remote sensing estimation. We combined the Normalized Difference Vegetation Index (NDVI) and Visible and near-infrared (NIR) Angle Index (VNAI) to understand the vegetation canopy SI differences caused by CC and fc (abbreviated as VNAI-NDVI-space). The VNAI-NDVI-space is approximately fan-shaped with high-CC and low-CC edges; the pixels inside the fan-shaped space represent crops with various CC and fc. We proposed a Polar Coordinate method (PCM) for assessing CC and fc. The performance of the VNAI-NDVI-space in assessing CC and fc was tested using a field-based spectrometer and an unmanned aerial vehicle (UAV)-based imaging spectrometer measurement for three soybean fields and six growth stages. Analysis of this imaging led to three conclusions: (i) The VNAI-NDVI-space can be used to analyze the confounding effects of CC and fc on crop canopy SIs; (ii) The VNAI-NDVI-space can track critical crop growth features; the VNAI-NDVI-space is not saturated at medium-to-high vegetation cover; (iii) the (a) confounding effect of CC on fc estimation; and (b) the confounding effect of fc on CC estimation was mitigated by the proposed VNAI-NDVI-space and PCM. The proposed PCM was successfully used to estimate CC and fc from the simulated dataset (CC: r = 0.97, RMSE = 4.10 μg/cm2; fc : r = 0.99, RMSE = 0.10), the field measurements (CC: r = 0.82, RMSE = 5.61 Dualex-units; fc : r = 0.78, RMSE = 0.08), and the UAV measurements (CC: field-PJ, r = 0.87, RMSE = 5.99 Dualex-units, field-PB, r = 0.66, RMSE = 7.29 Dualex-units; fc : r = 0.46, RMSE = 0.11). Thus, VNAI-NDVI-space and PCM are promising for assessing crop CC and fc. [ABSTRACT FROM AUTHOR]

[Display omitted] • A new normalized differential soil organic carbon spectral index was proposed. • Some measured environmental auxiliary data can be replaced by spectral indices. • NDVI, LST, SI, and CI contribute the most to the prediction of SOC. • The PSO-RFR model can better predict the SOC of coastal wetlands. To study the dynamic changes of soil organic carbon (SOC) content in coastal wetlands over the past 23 years, we use the third core area of the Dafeng Elk Nature Reserve in Jiangsu Province, China as the study area. A normalized difference soil spectral index (NDSOC), which is directly related to SOC, was constructed by using the spectral reflectance of Landsat images, and six auxiliary environmental variables related to SOC were extracted: surface temperature, surface moisture, soil salinity, the normalized difference vegetation index (NDVI), surface brightness, and soil texture information. The coastal wetland soil surface SOC inversion model was constructed by multiple linear regression, support vector machine, and particle swarm optimization-based random forest regression (PSO-RFR). By using the best inversion model, we obtain the dynamic distribution of soil surface SOC in the third core area of the Dafeng Elk Reserve for the past 23 years. The results indicate (1) that the NDSOC index constructed based on the short-wave infrared (SWIR) bands 1 and 2 of the Landsat images has a strong indication of SOC. Among the extracted auxiliary environmental variables, the NDVI, soil clay content, surface temperature, and soil salinity contribute more strongly to the SOC content of coastal wetlands, which averaged 27.9 %, 24.4 %, 22.1 %, and 18 %, respectively. (2) The PSO-RFR algorithm provided the best simulation of the surface SOC content of coastal wetlands: the model determination coefficient R 2 is 0.731, the average fitting root mean square error RMSE is 0.917 g/kg, and the fitting uncertainty standard deviation was 0.362 g/kg. (3) From 2000 to 2022, the organic carbon storage and organic carbon density in the third core area of the Dafeng Elk Reserve gradually increased, the highest value was reached in 2016, they were 4.017 (104 t) and 1.718 kg/cm2, respectively. [ABSTRACT FROM AUTHOR]

A laboratory experiment was designed to collect the spectral reflectance of water containing suspended particulate matter (SPM) in terms of its concentration. By performing surface-fitting analysis, an empirical relationship among the wavelength, reflectance, and concentration of SPM in water was obtained. The results showed that a linear effect of the spectral wavelength on spectral reflectance with increasing concentration of SPM in water, and the relationship between the reflectance and concentration of SPM in water is quadratic. Hyperspectral remote sensing is expected to play an important role in estimating the concentration of SPM in water. [ABSTRACT FROM AUTHOR]

A new target detection algorithm named the spectra sort encoding (SSE) algorithm is presented in this paper. It has two steps. Firstly, relative error (cErr) between the reference and image spectra is calculated. When cErr is greater than the relative error defined by the user (rErr), the current pixel is regarded as none-target and encoded as zero. Otherwise, the second step is executed to confirm whether or not the current pixel is target. In this second step, the similarity between reference and image spectra is calculated by sorting them respectively. When the similarity is greater than the identification error (the least error limits between reference and image spectra) defined by the user, the current pixel is encoded as one; otherwise, it is encoded as zero. A detailed description is provided of the backgrounds and principles of SSE, and its accuracy is evaluated using multiple categories. Experiments indicated that when the identification error is 15%, the mean accuracy of SSE is 95%, which is 41.9% higher than that of constrained energy minimization (CEM) and 46.9% higher than that of spectrally second-order derivative (SSD). Results of target detection experiments using Enhanced Thematic Mapper Plus (ETM+) and Hyperion images revealed that it could be used in both multispectral and hyperspectral remote sensing. [ABSTRACT FROM AUTHOR]

Soil moisture content (SMC) has been widely focused and studied in various fields of research, including atmosphere-land water/energy exchange, drought, and vegetation-related topics. Many indices and models have been proposed to illustrate the reflectance features changing with SMC, and to estimate SMC from the reflectance of narrow bands and broadbands, using lab, field, airborne and satellite data. These methods/indices were mostly developed for all levels of SMCs, from saturated to air-dry; however, we found that a single relationship mapping saturated to air-dry can lead to a false impression of accurate estimation. The inconsistency of a regression relationship between spectral reflectance features and SMCs over the whole drying process results in a low estimation accuracy for relatively low SMCs. In this study, a segmentation method is proposed with the goal of a more accurate SMC estimate. The whole drying process of three soil samples, representing varied soil characteristics, is monitored, and the transition points are determined by evaporation rate change (as stage-1 drying with high SMC and stage-2 drying with low SMC). An index, the shortwave normalized index (SNI), is built for SMC estimation, and the SNI index trend during drying is supported by a radiative transfer-based model. As validated, the SNI index is capable of SMC estimates, and the segmentation method improves estimation accuracy astonishingly compared with current methods. • Whole drying process of soils can be segmented into two stages. • Segmentation can markedly improve soil moisture content estimate accuracy. • Reflective remote sensing has the potential to find the regression relationship. • Index is built using shortwave infrared wavelengths for moisture content estimate. • Regression result is supported by a radiative transfer-based model. [ABSTRACT FROM AUTHOR]

Abstract: Due to rapid economic development, high levels of potentially harmful elements are continuously being released into the suburban soils of the Nanjing area, China. Conventional methods for investigating soil potentially harmful element contamination based on raster sampling and chemical analysis are time consuming and relatively expensive. Reflectance spectroscopy within the visible-near-infrared region has been widely used to predict soil constituents due to its rapidity, convenience and accuracy. The objective of this study was to examine the possibility of using soil reflectance spectra as a rapid method to simultaneously assess contaminant metals (Ni, Cr, Cu, Hg, Pb, Zn) and As in the suburban soils of the Nanjing area. One hundred and twenty soil samples were collected for chemical analyses and spectral measurements. Prediction of contaminant elements was achieved by a partial least-square regression (PLSR) approach. According to their relationships with Fe, the seven contaminant elements could be categorized into two groups. The results showed that the prediction accuracy for Group II (Ni, Cr, Cu and Hg) was higher than that for Group I (Pb, Zn and As). This finding was consistent with the fact that the correlation coefficients between Group II and Fe were higher than that between Group I and Fe. It was concluded that element-sorption by spectrally active Fe oxides was the major mechanism by which to predict spectrally featureless contaminant elements. This conclusion was strengthened by the fact that the PLSR regression coefficients, which revealed the most important wavelengths for prediction, were attributed to absorption features of Fe oxides. Future study with real remote sensing data and field measurements are strongly recommended. [Copyright &y& Elsevier]

As a widely used ground-based optical instrument, the LAI-2000 or LAI-2200 plant canopy analyzer (PCA) (Li-Cor, Inc., Lincoln, NE) is designed to measure the plant effective leaf area index (Le) by measuring the canopy gap fraction at several limited or discrete view zenith angles (VZAs) (usually five VZAs: 7, 23, 38, 53, and 68°) based on Miller's equation. Miller's equation requires the probability of radiative transmission through the canopy to be measured over the hemisphere, i.e., VZAs in the range from 0 to 90°. However, the PCA view angle ranges are confined to several limited ranges or discrete sectors. The magnitude of the error produced by the discretization of VZAs in the leaf area index measurements remains difficult to determine. In this study, a theoretical deduction was first presented to definitely prove why the limited or discrete VZAs or ranges can affect the Le measured with the PCA, and the specific error caused by the limited or discrete VZAs was described quantitatively. The results show that: (1) the weight coefficient of the last PCA ring is the main cause of the error; (2) the error is closely related to the leaf inclination angles (IAs)—the Le measured with the PCA can be significantly overestimated for canopies with planophile IAs, whereas it can be underestimated for erectophile IAs; and (3) the error can be enhanced with the increment of the discrete degree of PCA rings or VZAs, such as using four or three PCA rings. Two corrections for the error are presented and validated in three crop canopies. Interestingly, although the leaf IA type cannot influence the Le calculated by Miller's equation in the hemispheric space, it affects the Le measured with the PCA using the discrete form of Miller's equation for several discrete VZAs. [ABSTRACT FROM AUTHOR]

Aerosol is an essential parameter for assessing the atmospheric environmental quality, and accurate monitoring of the aerosol optical depth (AOD) is of great significance in climate research and environmental protection. Based on Landsat 8 Operational Land Imager (OLI) images and MODIS09A1 surface reflectance products under clear skies with limited cloud cover, we retrieved the AODs in Nanjing City from 2017 to 2018 using the combined Dark Target (DT) and Deep Blue (DB) methods. The retrieval accuracy was validated by in-situ CE-318 measurements and MOD04_3K aerosol products. Furthermore, we analyzed the spatiotemporal distribution of the AODs and discussed a case of high AOD distribution. The results showed that: (1) Validated by CE-318 and MOD04_3K data, the correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE) of the retrieved AODs were 0.874 and 0.802, 0.134 and 0.188, and 0.099 and 0.138, respectively. Hence, the combined DT and DB algorithms used in this study exhibited a higher performance than the MOD04_3K-obtained aerosol products. (2) Under static and stable meteorological conditions, the average annual AOD in Nanjing was 0.47. At the spatial scale, the AODs showed relatively high values in the north and west, low in the south, and the lowest in the center. At the seasonal scale, the AODs were highest in the summer, followed by spring, winter, and autumn. Moreover, changes were significantly higher in the summer than in the other three seasons, with little differences among spring, autumn, and winter. (3) Based on the spatial and seasonal characteristics of the AOD distribution in Nanjing, a case of high AOD distribution caused by a large area of external pollution and local meteorological conditions was discussed, indicating that it could provide extra details of the AOD distribution to analyze air pollution sources using fine spatial resolution like in the Landsat 8 OLI. [ABSTRACT FROM AUTHOR]

To investigate aerosol radiative effects, the Sun–Sky Radiometer Observation Network (SONET) has performed long-term observations of columnar atmospheric aerosol properties at 20 distributed stations around China. The aerosol direct radiative forcing (RF) and efficiency (RFE, the rate at which the atmosphere is forced per unit of aerosol optical depth) were estimated using radiative transfer model simulations based on the ground-based observations dating back to 2009. Results of multi-year monthly average RF illustrated that: the dust-dominant aerosol population at arid and semi-arid sites exerted moderate cooling effects (−8.0~−31.2 W/m2) at the top and bottom of atmosphere (TOA and BOA); RF at continental background site was very weak (−0.8~−2.9 W/m2); fine-mode dominant aerosols at urban and suburban sites exerted moderate cooling effects (−9.3~−24.1 W/m2) at TOA but more significant cooling effects (−19.4~−50.6 W/m2) at BOA; RF at coastal sites was comparable with values of urban sites (−5.5~−19.5 W/m2 at TOA, and −15.6~−44.6 W/m2 at BOA), owing to combined influences by marine and urban–industrial aerosols. Differences between RFE at TOA and BOA indicated that coarse-mode dominant aerosols at arid, semi-arid, and continental background sites were less efficient to warm the atmosphere; but fine-mode dominant aerosols at urban, suburban, and coastal sites were shown to be more efficient to heat the atmosphere. [ABSTRACT FROM AUTHOR]

• Exposure time since glacier retreat enhanced soil development, CO 2 production and CH 4 consumption. • Soil labile C and moisture were key limiting factors for CO 2 production. • Fresh penguin guano deposition stimulated CO 2 and N 2 O production and decreased CH 4 consumption. • N 2 O production was not influenced by exposure time but increased strongly due to penguin activity. The effects of soil succession after glacial retreat and fertilisation by marine animals are known to have major impacts on soil greenhouse gas (GHG) fluxes in polar terrestrial ecosystems. While in many polar coastal areas retreating glaciers open up new ground for marine animals to colonise, little is known about the combination of both factors on the local GHG budget. We studied the magnitude of GHG fluxes (CO 2 , CH 4 and N 2 O) on the combined effect of glacial retreat and penguin-induced fertilisation along a transect protruding into the world's largest King Penguin (Aptenodytes patagonicus) colony at Saint Andrews Bay on sub-Antarctic South Georgia. GHG production and consumption rates were assessed based on laboratory incubations of intact soil cores and nutrients and water additional experimental incubations. The oldest soils along the transect show significant higher contents of soil carbon, nutrients and moisture and were strongly influenced by penguin activity. We found a net CH 4 consumption along the entire transect with a marked decrease within the penguin colony. CO 2 production strongly increased along the transect, while N 2 O production rates were low near the glacier front and increased markedly within the penguin colony. Controlled applications of guano resulted in a significant increase in CO 2 and N 2 O production, and decrease in CH 4 consumption, except for sites already strongly influenced by penguin activity. The results show that soil microbial activity promptly catalyses a turnover of soil C and atmospheric methane oxidation in de-glaciated forelands. The methane oxidizers, however, may increase relatively slowly in their capacity to oxidise atmospheric CH 4. Results show also that the increase of nutrients by penguins reduces CH 4 oxidation whereas N 2 O production is greatly increased. A future expansion of penguins into newly available ice-free polar coastal areas may therefore markedly increase the local GHG budget. [ABSTRACT FROM AUTHOR]

Forest three-dimensional (3-D) structure, in the vertical dimension, consists of at least two components, including overstory and a forest background matrix (i.e., shrubs, grass, and bare earth). Quantitatively characterizing the proportions of forest sunlit (i.e., sunlit overstory and forest background) and shaded (i.e., shaded overstory and forest background) components is a crucial step in simulating the spatial variations of bidirectional reflectance distribution function (BRDF) of a forest canopy. By developing a Voxel-based sorest sunlit and shaded (VFSS) approach driven by aerial laser scanning data (ALS), we investigated the spatial variations of the forest sunlit and shaded components in a heterogeneous urban forest park (Washington Park Arboretum) with abundant tree species and a homogeneous natural forest area (Panther Creek). Meanwhile, we validated the forest canopy directional reflectance at both solar principal and perpendicular planes at the plot level. Moreover, we explored the effects of ALS data characteristics and forest stand conditions on the estimation accuracy of forest sunlit and shaded components. Our results show that (1) ALS data effectively stratify overstory and forest background with the accuracy decreasing from 87% to 65% as forest densities increase; (2) the root mean square errors (RMSEs) between the modeled- and ALS-based proportions of forest sunlit and shaded components range from 5.8% to 11.1% affected by forest densities; and (3) the scan angles and flight directions have apparent effects on the estimation accuracy of forest sunlit and shaded components. This work provides a solid foundation to investigate the spatial variations of directional forest canopy reflectance with a high spatial resolution of 1 m. [ABSTRACT FROM AUTHOR]

The extraction of information about individual trees is essential to supporting the growing of fruit in orchard management. Data acquired from spectral sensors mounted on unmanned aerial vehicles (UAVs) have very high spatial and temporal resolution. However, an efficient and reliable method for extracting information about individual trees with irregular tree-crown shapes and a complicated background is lacking. In this study, we developed and tested the performance of an approach, based on UAV imagery, to extracting information about individual trees in an orchard with a complicated background that includes apple trees (Plot 1) and pear trees (Plot 2). The workflow involves the construction of a digital orthophoto map (DOM), digital surface models (DSMs), and digital terrain models (DTMs) using the Structure from Motion (SfM) and Multi-View Stereo (MVS) approaches, as well as the calculation of the Excess Green minus Excess Red Index (ExGR) and the selection of various thresholds. Furthermore, a local-maxima filter method and marker-controlled watershed segmentation were used for the detection and delineation, respectively, of individual trees. The accuracy of the proposed method was evaluated by comparing its results with manual estimates of the numbers of trees and the areas and diameters of tree-crowns, all three of which parameters were obtained from the DOM. The results of the proposed method are in good agreement with these manual estimates: The F-scores for the estimated numbers of individual trees were 99.0% and 99.3% in Plot 1 and Plot 2, respectively, while the Producer's Accuracy (PA) and User's Accuracy (UA) for the delineation of individual tree-crowns were above 95% for both of the plots. For the area of individual tree-crowns, root-mean-square error (RMSE) values of 0.72 m2 and 0.48 m2 were obtained for Plot 1 and Plot 2, respectively, while for the diameter of individual tree-crowns, RMSE values of 0.39 m and 0.26 m were obtained for Plot 1 (339 trees correctly identified) and Plot 2 (203 trees correctly identified), respectively. Both the areas and diameters of individual tree-crowns were overestimated to varying degrees. [ABSTRACT FROM AUTHOR]

Winter wheat is one of the major cereal crops in the world. Monitoring and mapping its spatial distribution has significant implications for agriculture management, water resources utilization, and food security. Generally, winter wheat has distinguished phenological stages during the growing season, which form a unique EVI (Enhanced Vegetation Index) time series curve and differ considerably from other crop types and natural vegetation. Since early 2000, the MODIS EVI product has become the primary dataset for satellite-based crop monitoring at large scales due to its high temporal resolution, huge observation scope, and timely availability. However, the intraclass variability of winter wheat caused by field conditions and agricultural practices might lower the mapping accuracy, which has received little attention in previous studies. Here, we present a winter wheat mapping approach that integrates the variables derived from the MODIS EVI time series taking into account intraclass variability. We applied this approach to two winter wheat concentration areas, the state of Kansas in the U.S. and the North China Plain region (NCP). The results were evaluated against crop-specific maps or statistical data at the state/regional level, county level, and site level. Compared with statistical data, the accuracies in Kansas and the NCP were 95.1% and 92.9% at the state/regional level with R2 (Coefficient of Determination) values of 0.96 and 0.71 at the county level, respectively. Overall accuracies in confusion matrix were evaluated by validation samples in both Kansas (90.3%) and the NCP (85.0%) at the site level. Comparisons with methods without considering intraclass variability demonstrated that winter wheat mapping accuracies were improved by 17% in Kansas and 15% in the NCP using the improved approach. Further analysis indicated that our approach performed better in areas with lower landscape fragmentation, which may partly explain the relatively higher accuracy of winter wheat mapping in Kansas. This study provides a new perspective for generating multiple subclasses as training inputs to decrease the intraclass differences for crop type detection based on the MODIS EVI time series. This approach provides a flexible framework with few variables and fewer training samples that could facilitate its application to multiple-crop-type mapping at large scales. [ABSTRACT FROM AUTHOR]