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This study introduces a first assessment of the capabilities of PRISMA (PRecursore IperSpettrale della Missione Applicativa)—the new hyperspectral satellite sensor of the Italian Space Agency (ASI)—for Non-Photosynthetic Vegetation (NPV) monitoring, a topic which is becoming very relevant in the field of sustainable agriculture, being an indicator of crop residue (CR) presence in the field. Data-sets collected during the mission validation phase in croplands are used for mapping the NPV presence and for modelling the diagnostic absorption band of cellulose around 2.1 μm with an Exponential Gaussian Optimization approach, in the perspective of the prediction of the abundance of crop residues. Results proved that PRISMA data are suitable for these tasks, and call for further investigation to achieve quantitative estimates of specific biophysical variables, also in the framework of other hyperspectral missions.

Crop residues serve many important functions in agricultural conservation including preserving soil moisture, building soil organic carbon, and preventing erosion. Percent crop residue cover on a field surface reflects the outcome of tillage intensity and crop management practices. Previous studies using proximal hyperspectral remote sensing have demonstrated accurate measurement of percent residue cover using residue indices that characterize cellulose and lignin absorption features found between 2100 nm and 2300 nm in the shortwave infrared (SWIR) region of the electromagnetic spectrum. The 2014 launch of the WorldView-3 (WV3) satellite has now provided a space-borne platform for the collection of narrow band SWIR reflectance imagery capable of measuring these cellulose and lignin absorption features. In this study, WorldView-3 SWIR imagery (14 May 2015) was acquired over farmland on the Eastern Shore of Chesapeake Bay (Maryland, USA), was converted to surface reflectance, and eight different SWIR reflectance indices were calculated. On-farm photographic sampling was used to measure percent residue cover at a total of 174 locations in 10 agricultural fields, ranging from plow-till to continuous no-till management, and these in situ measurements were used to develop percent residue cover prediction models from the SWIR indices using both polynomial and linear least squares regressions. Analysis was limited to agricultural fields with minimal green vegetation (Normalized Difference Vegetation Index < 0.3) due to expected interference of vegetation with the SWIR indices. In the resulting residue prediction models, spectrally narrow residue indices including the Shortwave Infrared Normalized Difference Residue Index (SINDRI) and the Lignin Cellulose Absorption Index (LCA) were determined to be more accurate than spectrally broad Landsat-compatible indices such as the Normalized Difference Tillage Index (NDTI), as determined by respective R2 values of 0.94, 0.92, and 0.84 and respective residual mean squared errors (RMSE) of 7.15, 8.40, and 12.00. Additionally, SINDRI and LCA were more resistant to interference from low levels of green vegetation. The model with the highest correlation (2nd order polynomial SINDRI, R2 = 0.94) was used to convert the SWIR imagery into a map of crop residue cover for non-vegetated agricultural fields throughout the imagery extent, describing the distribution of tillage intensity within the farm landscape. WorldView-3 satellite imagery provides spectrally narrow SWIR reflectance measurements that show utility for a robust mapping of crop residue cover.

This research reports the findings of a Landsat Next expert review panel that evaluated the use of narrow shortwave infrared (SWIR) reflectance bands to measure ligno-cellulose absorption features centered near 2100 and 2300 nm, with the objective of measuring and mapping non-photosynthetic vegetation (NPV), crop residue cover, and the adoption of conservation tillage practices within agricultural landscapes. Results could also apply to detection of NPV in pasture, grazing lands, and non-agricultural settings. Currently, there are no satellite data sources that provide narrowband or hyperspectral SWIR imagery at sufficient volume to map NPV at a regional scale. The Landsat Next mission, currently under design and expected to launch in the late 2020’s, provides the opportunity for achieving increased SWIR sampling and spectral resolution with the adoption of new sensor technology. This study employed hyperspectral data collected from 916 agricultural field locations with varying fractional NPV, fractional green vegetation, and surface moisture contents. These spectra were processed to generate narrow bands with centers at 2040, 2100, 2210, 2260, and 2230 nm, at various bandwidths, that were subsequently used to derive 13 NPV spectral indices from each spectrum. For crop residues with minimal green vegetation cover, two-band indices derived from 2210 and 2260 nm bands were top performers for measuring NPV (R^(2) = 0.81, RMSE = 0.13) using bandwidths of 30 to 50 nm, and the addition of a third band at 2100 nm increased resistance to atmospheric correction residuals and improved mission continuity with Landsat 8 Operational Land Imager Band 7. For prediction of NPV over a full range of green vegetation cover, the Cellulose Absorption Index, derived from 2040, 2100, and 2210 nm bands, was top performer (R^(2) = 0.77, RMSE = 0.17), but required a narrow (≤20 nm) bandwidth at 2040 nm to avoid interference from atmospheric carbon dioxide absorption. In comparison, broadband NPV indices utilizing Landsat 8 bands centered at 1610 and 2200 nm performed poorly in measuring fractional NPV (R^(2) = 0.44), with significantly increased interference from green vegetation

It is very difficult and complex to acquire photosynthetic vegetation (PV) and non-PV (NPV) fractions (fPV and fNPV) using multispectral satellite sensors because estimations of fPV and fNPV are influenced by many factors, such as background-noise interference of pixel-, spatial-, and spectral-scale effects. In this study, comparisons between Sentinel-2A Multispectral Instrument (S2 MSI), Landsat-8 Operational Land Imager (L8 OLI), and GF1 Wide Field View (GF1 WFV) sensors for retrieving sparse photosynthetic and non-photosynthetic vegetation coverage are presented. The analysis employed a linear spectral-mixture model (LSMM) and nonlinear spectral-mixture model (NSMM) to unmix pixels with different spectral and spatial resolution images based on field endmembers; the estimated endmember fractions were later validated with reference to fraction measurements. The results demonstrated that: (1) with higher spatial and spectral resolution, the S2 MSI sensor had a clear advantage for retrieving PV and NPV fractions compared to L8 OLI and GF1 WFV sensors; (2) through incorporating more red edge (RE) and near-infrared (NIR) bands, the accuracy of NPV fraction estimation could be greatly improved; (3) nonlinear spectral mixing effects were not obvious on the 10–30 m spatial scale for desert vegetation; (4) in arid regions, a shadow endmember is a significant factor for sparse vegetation coverage estimated with remote-sensing data. The estimated NPV fractions were especially affected by the shadow effects and could increase root mean square by 50%. The utilized approaches in the study could effectively assess the performance of major multispectral sensors to extract fPV and fNPV through the novel method of spectral-mixture analysis.

Non-photosynthetic vegetation fractional cover (fNPV) is a key indicator of vegetation decline and ecological health. Traditional inversion models assume identical spectral signatures for the same vegetation cover class across entire study areas. Spectral variations occur among regions due to divergent soil properties and vegetation types. To address this limitation, extensive ground sampling was conducted; ground observation data from multiple regions were utilized to establish localized spectral libraries, thereby enhancing spectral variability representation within the study area while concurrently optimizing vegetation indices across different sensor systems. The results reveal that, within the optimized spectral mixture analysis model, the coefficient of determination (R2) for fNPV using the NPV soil separation index (NSSI) for Sentinel sensor is 0.6258, and that of fPV using the modified soil adjusted vegetation index (MSAVI) is 0.8055. The MSAVI-NSSI achieved an R2 of 0.7825 for fNPV and 0.8725 for photosynthetic vegetation fractional cover (fPV). Optimized vegetation indices also yielded favorable validation results. Landsat’s theoretical predictions improved by 0.1725, with validated results up by 0.1635. MODIS showed improvements of 0.1365 and 0.1923, respectively. This enhancement significantly improves the accuracy of NPV fractional cover identification, providing critical insights for vegetation ecological health assessment in arid and semi-arid regions under global warming. Furthermore, by optimizing the spectral constraint weights in remote sensing images, a solution is provided for the long-term monitoring of vegetation health status.

This study focused on optimizing the placement of shortwave infrared (SWIR) bands for pixel-level estimation of fractional crop residue cover (fR) for the upcoming Landsat Next mission. We applied an iterative wavelength shift approach to a database of crop residue field spectra collected in Beltsville, Maryland, USA (n = 916) and computed generalized two- and three-band spectral indices for all wavelength combinations between 2000 and 2350 nm, then used these indices to model field-measured fR. A subset of the full dataset with a Normalized Difference Vegetation Index (NDVI) < 0.3 threshold (n = 643) was generated to evaluate green vegetation impacts on fR estimation. For the two-band wavelength shift analyses applied to the NDVI < 0.3 dataset, a generalized normalized difference using 2226 nm and 2263 nm bands produced the top fR estimation performance (R2 = 0.8222; RMSE = 0.1296). These findings were similar to the established two-band Shortwave Infrared Normalized Difference Residue Index (SINDRI) (R2 = 0.8145; RMSE = 0.1324). Performance of the two-band generalized normalized difference and SINDRI decreased for the full-NDVI dataset (R2 = 0.5865 and 0.4144, respectively). For the three-band wavelength shift analyses applied to the NDVI < 0.3 dataset, a generalized ratio-based index with a 2031–2085–2216 nm band combination, closely matching established Cellulose Absorption Index (CAI) bands, was top performing (R2 = 0.8397; RMSE = 0.1231). Three-band indices with CAI-type wavelengths maintained top fR estimation performance for the full-NDVI dataset with a 2036–2111–2217 nm band combination (R2 = 0.7581; RMSE = 0.1548). The 2036–2111–2217 nm band combination was also top performing in fR estimation (R2 = 0.8690; RMSE = 0.0970) for an additional analysis assessing combined green vegetation cover and surface moisture effects. Our results indicate that a three-band configuration with band centers and wavelength tolerances of 2036 nm (±5 nm), 2097 nm (±14 nm), and 2214 (±11 nm) would optimize Landsat Next SWIR bands for fR estimation.

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.

Non-photosynthetic vegetation (NPV) plays a key role in soil conservation, which in turn is important in sustainable agriculture and carbon farming. For mapping NPV image spectroscopy proved to outperform multispectral sensors. PRISMA (PRecursore IperSpettrale della Missione Applicativa) is the forerunner of a new era of hyperspectral satellite missions, providing the proper spectral resolution for NPV mapping. This study takes advantage from both spectroscopy and machine-learning techniques. Exponential Gaussian Optimization was used for modelling known absorption bands (cellulose-lignin, pigments, water content and clays), resulting in a reduced feature space, which is split by a decision tree (DT) for mapping different field conditions (emerging, green and standing dead vegetation, crop residue and bare soil). DT training and validation exploited reference data, collected during PRISMA overpasses on a large farmland. Mapping results are accurate both at pixel and parcel level (O.A. > 90%; K > 0.9). Field status and crop rotation trajectories through time are derived by processing 12 images over 2020 and 2021. Results proved that PRISMA data are suitable for mapping field conditions at parcel scale with high confidence level. This is important in the perspective of other hyperspectral missions and is a premise toward quantitative estimates of NPV biophysical variable.

Determining the responses of non-photosynthetic vegetation (NPV) and photosynthetic vegetation (PV) communities to climate change is crucial in illustrating the sensitivity and sustainability of these ecosystems. In this study, we evaluated the accuracy of inverting NPV and PV using Landsat imagery with random forest (RF), backpropagation neural network (BPNN), and fully connected neural network (FCNN) models. Additionally, we inverted MODIS NPV and PV time-series data using spectral unmixing. Based on this, we analyzed the responses of NPV and PV to precipitation and drought across different ecological regions. The main conclusions are as follows: (1) In NPV remote sensing inversion, the softmax activation function demonstrates greater advantages over the ReLU activation function. Specifically, the use of the softmax function results in an approximate increase of 0.35 in the R2 value. (2) Compared with a five-layer FCNN with 128 neurons and a three-layer BPNN with 12 neurons, a random forest model with over 50 trees and 5 leaf nodes provides better inversion results for NPV and PV (R2_RF-NPV = 0.843, R2_RF-PV = 0.861). (3) Long-term drought or heavy rainfall events can affect the utilization of precipitation by NPV and PV. There is a high correlation between extreme precipitation events following prolonged drought and an increase in PV coverage. (4) Under long-term drought conditions, the vegetation in the study area responded to precipitation during the last winter and growing season. This study provides an illustration of the response of semi-arid ecosystems to drought and wetting events, thereby offering a data basis for the effect evaluation of afforestation projects.

Estimating the fractional coverage of the photosynthetic vegetation (fPV) and non-photosynthetic vegetation (fNPV) is essential for assessing the growth conditions of vegetation growth in arid areas and for monitoring environmental changes and desertification. The aim of this study was to estimate the fPV, fNPV and the fractional coverage of the bare soil (fBS) in the lower reaches of Tarim River quantitatively. The study acquired field data during September 2020 for obtaining the fPV, fNPV and fBS. Firstly, six photosynthetic vegetation indices (PVIs) and six non-photosynthetic vegetation indices (NPVIs) were calculated from Sentinel-2A image data. The PVIs include normalized difference vegetation index (NDVI), ratio vegetation index (RVI), soil adjusted vegetation index (SAVI), modified soil adjusted vegetation index (MSAVI), reduced simple ratio index (RSR) and global environment monitoring index (GEMI). Meanwhile, normalized difference index (NDI), normalized difference tillage index (NDTI), normalized difference senescent vegetation index (NDSVI), soil tillage index (STI), shortwave infrared ratio (SWIR32) and dead fuel index (DFI) constitutes the NPVIs. We then established linear regression model of different PVIs and fPV, and NPVIs and fNPV, respectively. Finally, we applied the GEMI-DFI model to analyze the spatial and seasonal variation of fPV and fNPV in the study area in 2020. The results showed that the GEMI and fPV revealed the best correlation coefficient (R2) of 0.59, while DFI and fNPV had the best correlation of R2 = 0.45. The accuracy of fPV, fNPV and fBS based on the determined PVIs and NPVIs as calculated by GEMI-DFI model are 0.69, 0.58 and 0.43, respectively. The fPV and fNPV are consistent with the vegetation phonological development characteristics in the study area. The study concluded that the application of the GEMI-DFI model in the fPV and fNPV estimation was sufficiently significant for monitoring the spatial and seasonal variation of vegetation and its ecological functions in arid areas.