Global land cover dynamics alter energy, water, and greenhouse gas exchange between land and atmosphere, affecting local to global weather and climate change. Although reforestation can provide localized cooling, ongoing land use land cover (LULC) shifts are expected to exacerbate urban heat island impacts. In this study, we monitored spatiotemporal changes in green cover in response to land use transformation associated with the Khyber Pakhtunkhwa (KPK) provincial government’s Billion Tree Tsunami Project (BTTP) and the Ravi Urban Development Plan (RUDP) initiated by the provincial government of Punjab, both in Pakistan. The land change modeler (LCM) was used to assess the land cover changes and transformations between 2000 and 2020 across Punjab and KPK. Furthermore, a curve fit linear regression model (CFLRM) and sensitivity analysis were employed to analyze the impacts of land cover dynamics on land surface temperature (LST) and carbon emissions (CE). Results indicated a significant increase in green fraction of +5.35% under the BTTP, achieved by utilizing the bare land with an effective transition of 4375.87 km². However, across the Punjab province, an alarming reduction in green fraction cover by −1.77% and increase in artificial surfaces by +1.26% was noted. A significant decrease in mean monthly LST by −4.3 °C was noted in response to the BTTP policy, while an increase of 5.3 °C was observed associated with the RUDP. A substantial increase in LST by 0.17 °C was observed associated with transformation of vegetation to artificial surfaces. An effective decrease in LST by −0.21 °C was observed over the opposite transition. Furthermore, sensitivity analysis suggested that LST fluctuations are affecting the % of CO₂ emission. The current findings can assist policymakers in revisiting their policies to promote ecological conservation and sustainability in urban planning.

High-quality Normalized Difference Vegetation Index (NDVI) time series are essential in studying vegetation phenology, dynamic monitoring, and global change. Gap filling is the most important issue in reconstructing NDVI time series from satellites with high spatial resolution, e.g., the Landsat series and Chinese GaoFen-1/6 series. Due to the sparse revisit frequencies of high-resolution satellites, traditional reconstruction approaches face the challenge of dealing with large gaps in raw NDVI time series data. In this paper, a climate incorporated gap-filling (CGF) method is proposed for the reconstruction of Landsat historical NDVI time series data. The CGF model considers the relationship of the NDVI time series and climate conditions between two adjacent years. Climate variables, including downward solar shortwave radiation, precipitation, and temperature, are used to characterize the constrain factors of vegetation growth. Radial basis function networks (RBFNs) are used to link the NDVI time series between two adjacent years with variabilities in climatic conditions. An RBFN predicted a background NDVI time series in the target year, and the observed NDVI values in this year were used to adjust the predicted NDVI time series. Finally, the NDVI time series were recursively reconstructed from 2018 to 1986. The experiments were performed in a heterogeneous region in the Qilian Mountains. The results demonstrate that the proposed method can accurately reconstruct and generate continuous 30 m 8-day NDVI time series using Landsat observations. The CGF method outperforms traditional time series reconstruction methods (e.g., the harmonic analysis of time series (HANTS) and Savitzky-Golay (SG) filter methods) when the raw time series is contaminated with large gaps, which widely exist in Landsat images.

Methods that link different models for investigating the retrieval of canopy biophysical/structural variables have been substantially adopted in the remote sensing community. To retrieve global biophysical parameters from multiangle data, the kernel-driven bidirectional reflectance distribution function (BRDF) model has been widely applied to satellite multiangle observations to model (interpolate/extrapolate) the bidirectional reflectance factor (BRF) in an arbitrary direction of viewing and solar geometries. Such modeled BRFs, as an essential information source, are then input into an inversion procedure that is devised through a large number of simulation analyses from some widely used physical models that can generalize such an inversion relationship between the BRFs (or their simple algebraic composite) and the biophysical/structural parameter. Therefore, evaluation of such a link between physical models and kernel-driven models contributes to the development of such inversion procedures to accurately retrieve vegetation properties, particularly based on the operational global BRDF parameters derived from satellite multiangle observations (e.g., MODIS). In this study, the main objective is to investigate the potential for linking a popular physical model (PROSAIL) with the widely used kernel-driven Ross-Li models. To do this, the BRFs and albedo are generated by the physical PROSAIL in a forward model, and then the simulated BRFs are input into the kernel-driven BRDF model for retrieval of the BRFs and albedo in the same viewing and solar geometries. To further strengthen such an investigation, a variety of field-measured multiangle reflectances have also been used to investigate the potential for linking these two models. For simulated BRFs generated by the PROSAIL model at 659 and 865 nm, the two models are generally comparable to each other, and the resultant root mean square errors (RMSEs) are 0.0092 and 0.0355, respectively, although some discrepancy in the simulated BRFs can be found at large average leaf angle (ALA) values. Unsurprisingly, albedos generated by the method are quite consistent, and 99.98% and 97.99% of the simulated white sky albedo (WSA) has a divergence less than 0.02. For the field measurements, the kernel-driven model presents somewhat better model-observation congruence than the PROSAIL model. The results show that these models have an overall good consistency for both field-measured and model-simulated BRFs. Therefore, there is potential for linking these two models for looking into the retrieval of canopy biophysical/structural variables through a simulation method, particularly from the current archive of the global routine MODIS BRDF parameters that were produced by the kernel-driven BRDF model; however, erectophile vegetation must be further examined.

Forest-canopy height is an important parameter for the estimation of forest biomass and terrestrial carbon flux and climate-change research at regional and global scales. Currently, various methods combining Light Detection and Ranging (LiDAR) data with various auxiliary data, particularly satellite remotely sensed reflectances, have been widely used to produce spatially continuous canopy-height products. However, current methods in use for remote sensing reflectances mainly focus on the nadir view direction, while anisotropic reflectances, which are theoretically more sensitive to the forest canopy height in the multiangle remote sensing field, have rarely been explored. Here, we attempted to examine the potential of using modeled multiangle reflectances at three typical viewing angles (i.e., from the hotspot, darkspot, and nadir directions) to estimate forest-canopy height as auxiliary data sources. First, the sensitivities of the typical angular reflectances as a function of forest canopy height were fully examined using the Extended Fourier Amplitude Sensitivity Test (EFAST) method based on the 4-scale Bidirectional Reflectance Distribution Function (BRDF) model simulations. This indicated that reflectances in the off-nadir viewing directions are generally sensitive to canopy-height variations. Then, the canopy heights were extracted from airborne Laser Vegetation Imaging Sensor (LVIS) data, which were further divided into training and validation data. Moderate Resolution Imaging Spectroradiometer (MODIS) multiangle reflectances at typical viewing angles were calculated from the MODIS BRDF parameter product (MCD43A1, version 6) as partial training-input data, based on a hotspot-adjusted, kernel-driven linear BRDF model. Subsequently, the Random Forest (RF) machine learning model was trained to acquire the relationship between the extracted canopy heights and the corresponding MODIS typical viewing reflectances. The trained model was further applied to estimate the canopy height metrics in the study areas of Howland Forest, Harvard Forest, and Bartlett Forest. Finally, the estimated canopy heights were independently validated by canopy heights extracted from the LVIS data. The results indicate that the canopy heights modeled through this method exhibit generally high accordance with the LVIS-derived canopy heights (R = 0.65−0.67; RMSE = 3.63−5.78). The results suggest that the MODIS multiangle reflectance data at typical observation angles contain important information regarding forest canopy height and can, therefore, be used to estimate forest canopy height for various ecological applications.

Bidirectional reflectance distribution function (BRDF) archetypes extracted from the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo product over the global Earth Observing System Land Validation Core Sites can be used to simplify BRDF models. The present study attempts to evaluate the representativeness of BRDF archetypes for surface reflectance anisotropy. Five-year forward-modeled MODIS multi-angular reflectance (MCD-ref) and aditional actual MODIS multi-angular observations (MCD-obs) in four growing periods in 2008 over three tiles were taken as validation data. First, BRDF archetypes in the principal plane were qualitatively compared with the time-series MODIS BRDF product of randomly sampled pixels. Secondly, BRDF archetypes were used to fit MCD-ref, and the average root-mean-squared errors (RMSEs) over each tile were examined for these five years. Finally, both BRDF archetypes and the MODIS BRDF were used to fit MCD-obs, and the histograms of the fit-RMSEs were compared. The consistency of the directional reflectance between the BRDF archetypes and MODIS BRDFs in nadir-view, hotspot and entire viewing hemisphere at 30° and 50° solar geometries were also examined. The results confirm that BRDF archetypes are representative of surface reflectance anisotropy for available snow-free MODIS data.

Vegetation index (VI) derived from remotely sensed images is a proxy of terrestrial vegetation information and widely used in land monitoring and global change studies. Recently, the prediction of vegetation properties has been an interest in related communities. With the accumulation of satellite records over the past few decades, the spatial–temporal prediction of VI becomes feasible. In this letter, we developed deep recurrent neural networks (RNNs) with long short-term memory (LSTM) and gated recurrent units (GRUs) to predict the short-term VI based on historical observations. The pixel-based fully connected networks GRU and LSTM (FCGRU and FCLSTM) and patch-based convolutional networks (ConvGRU and ConvLSTM) are established and compared with the traditional multilayer perceptron (MLP) model. Moderate Resolution Imaging Spectroradiometer (MODIS) and Sentinel-2 normalized difference VI (NDVI) data sets were used in the experiments. The prediction performance is evaluated globally in different regions, different vegetation types, and different growing seasons. Results demonstrate that the RNN models can predict VI with high accuracy (average root mean square error (RMSE) around 0.03), which is superior to the MLP model. In general, the pixel-based RNN models performed better than the patch-based models especially in regions with a larger proportion of outliers. And the prediction accuracy is stable over different vegetation types and growing seasons.