The leaf chlorophyll content (Chl_leaf) is a crucial vegetation parameter in carbon cycle modelling and agricultural monitoring at local, regional and global scales. The red‐edge spectral region is sensitive to variations in Chl_leaf. An increasing number of sensors are capable of sampling red‐edge bands, providing opportunities to estimate Chl_leaf. However, the contributions of canopy/foliar/soil factors are always combined in the reflectance signal, which limits the generalizability of vegetation index (VI)‐based Chl_leaf inversions. This study aims to propose a new red‐edge chlorophyll index to decouple the effects of the canopy and soil background from the Chl_leaf estimation. The chlorophyll sensitive index (CSI) was proposed, and the regression equations between the CSI and Chl_leaf were acquired using PROSAIL (PROSPECT + SAIL) and the 4‐Scale‐PROSPECT model. Sensitivity analyses showed that the CSI is resistant to variations in the canopy structure and soil background. Validation results obtained using 308 ground‐measured samples over nine sites world‐wide revealed that CSI improves the Chl_leaf retrieval accuracy (root mean square error (RMSE = 9.39 μg cm⁻²) compared with the existing Medium Resolution Imaging Spectrometer (MERIS) terrestrial chlorophyll index (MTCI; RMSE = 13.00 μg cm⁻²). Moreover, the CSI method steadily achieves a highly accurate inversion under different LAI and Chl_leaf conditions. Based on the CSI regression method, a Chl_leaf product with a 30‐m/10‐day resolution across China was generated. The CSI is sensitive to Chl_leaf but resistant to canopy structure and soil moisture parameters, and it has the potential to explicitly retrieve leaf‐scale biochemistry in ecosystem modelling and ecological applications.

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.

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.

Five countries in the Lancang–Mekong region, including Myanmar, Laos, Thailand, Cambodia, and Vietnam, are facing the threat of deforestation, despite having a high level of forest coverage. Quantitatively assessing the forest ecosystem status and its variations based on remote sensing products for vegetation parameters is a crucial prerequisite for the ongoing phase of our future project. In this study, we analyzed forest health in the year 2020 using four vegetation indicators: forest coverage index (FCI), leaf area index (LAI), fraction of green vegetation cover (FVC), and gross primary productivity (GPP). Additionally, we introduced an ecosystem quality index (EQI) to assess the quality of forest health. To understand the long-term trends in the vegetation indicators and EQI, we also performed a linear regression analysis from 2010 to 2020. The results revealed that Laos ranked as the top-performing country for forest ecosystem status in the Lancang–Mekong region in 2020. However, the long-term trend analysis results showed that Cambodia experienced the most significant decline across all indicators, while Vietnam and Thailand demonstrated varying degrees of improvement. This study provides a quality assessment of forest health and its variations in the Lancang–Mekong region, which is crucial for implementing effective conservation strategies.

Leaf area index (LAI) plays an important role in models of climate, hydrology, and ecosystem productivity. The physical model-based inversion method is a practical approach for large-scale LAI inversion. However, the ill-posed inversion problem, due to the limited constraint of inaccurate input parameters, is the dominant source of inversion errors. For instance, variables related to leaf optical properties are always set as constants or have large ranges, instead of the actual leaf reflectance of pixel vegetation in the current model-based inversions. This paper proposes to estimate LAI with the actual leaf optical property of pixels, calculated from the leaf chlorophyll content (Chl_leaf) product, using a three-dimensional stochastic radiative transfer model (3D-RTM)-based, look-up table method. The parameter characterizing leaf optical properties in the 3D-RTM-based LAI inversion algorithm, single scattering albedo (SSA), is calculated with the Chl_leaf product, instead of setting fixed values across a growing season. An algorithm to invert LAI with the dynamic SSA of the red band (SSA_red) is proposed. The retrieval index (RI) increases from less than 42% to 100%, and the RMSE decreases to less than 0.28 in the simulations. The validation results show that the RMSE of the dynamic SSA decreases from 1.338 to 0.511, compared with the existing 3D-RTM-based LUT algorithm. The overestimation problem under high LAI conditions is reduced.