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Tropical and subtropical evergreen broadleaved forests (TEFs) contribute more than one‐third of terrestrial gross primary productivity (GPP). However, the continental‐scale leaf phenology‐photosynthesis nexus over TEFs is still poorly understood to date. This knowledge gap hinders most light use efficiency (LUE) models from accurately simulating the GPP seasonality in TEFs. Leaf age is the crucial plant trait to link the dynamics of leaf phenology with GPP seasonality. Thus, here we incorporated the seasonal leaf area index of different leaf age cohorts into a widely used LUE model (i.e., EC‐LUE) and proposed a novel leaf age‐dependent LUE model (denoted as LA‐LUE model). At the site level, the LA‐LUE model (average R2 =.59, average root‐mean‐square error [RMSE] = 1.23 gC m−2 day−1) performs better than the EC‐LUE model in simulating the GPP seasonality across the nine TEFs sites (average R2 =.18; average RMSE = 1.87 gC m−2 day−1). At the continental scale, the monthly GPP estimates from the LA‐LUE model are consistent with FLUXCOM GPP data (R2 =.80; average RMSE = 1.74 gC m−2 day−1), and satellite‐based GPP data retrieved from the global Orbiting Carbon Observatory‐2 (OCO‐2) based solar‐induced chlorophyll fluorescence (SIF) product (GOSIF) (R2 =.64; average RMSE = 1.90 gC m−2 day−1) and the reconstructed TROPOspheric Monitoring Instrument SIF dataset using machine learning algorithms (RTSIF) (R2 =.78; average RMSE = 1.88 gC m−2 day−1). Typically, the estimated monthly GPP not only successfully represents the unimodal GPP seasonality near the Tropics of Cancer and Capricorn, but also captures well the bimodal GPP seasonality near the Equator. Overall, this study for the first time integrates the leaf age information into the satellite‐based LUE model and provides a feasible implementation for mapping the continental‐scale GPP seasonality over the entire TEFs. [ABSTRACT FROM AUTHOR]

With the rapid development of remote sensing technologies, the frequency of observations of the same location is increasing, and many satellites and sensors produced a large amount of time series images. These images make long-term change detection and dynamic characteristic estimation of ground features possible. However, conventional remote sensing image change detection methods mostly rely on manual visual interpretation and supervised or unsupervised computer-aided classification. Traditional methods always face many bottlenecks when processing big and fast-growing datasets, such as low computational efficiency, low level of automation, and different identification standards and accuracies caused by different operators. With the rapid accumulation of remote sensing data, it has become an important but more challenging task to conduct change detection in a more precise, automated and standardized way. The development of geointelligent computing technologies provides a means of solving these problems and improve the accuracy and efficiency of remote sensing image change detection. In this paper, we presented a novel deep learning model called nest network(NestNet) based on a convolutional neural network to improve the accuracy of the automatic change detection task by using remotely sensed time series images. NestNet extracts the respective features of bi-temporal images using an encoding parallel module and subsequently employs absolute different operations to process the features of two images. Compared with change detection method based on U-Shaped network plus plus (UNet++), the parallel module improves the efficiency of NestNet. Finally, a decoding module is used to generate a predicted change image. This paper compares NestNet to traditional methods and state-of-the-art deep learning models on two datasets. The experimental results demonstrate that the accuracy of NestNet is better than that of state-of-the-art methods. It can be concluded that the NestNet model is a potential approach for change detection using high resolution remote sensing images. [ABSTRACT FROM AUTHOR]

The objective of this study was to evaluate the performance of moderate resolution imaging spectroradiometer (MODIS) 250 m based TSS (Total Suspended Solids) retrieval model for developing the model of wide-range TSS concentrations. Using field spectral and TSS data (5.8–577.2 mg/l) collected from estuary and coast of China, we calibrated ( N = 40) and validated ( N = 20) the 6 TSS retrieval models in forms of single band (B1 or B2), difference of the two bands, sediment index, band ratio and log-ratio by the least-squares technique. Results showed that the quadratic model of log-ratio is of the best accuracy (Calibration: R 2 = 0.752, N = 40. Validation: TSS > 31 mg/l, RMSE = 37.9 mg/l, N = 12; TSS ⩽ 31 mg / l , RMSE = 3.25 mg/l, N = 8). We also found that the spectral log-ratio values increased with the increasing of TSS when TSS < 31 mg/l, but decreased with increasing TSS when TSS > 31 mg/l. The findings of quadratic curve vertex (log-ratio: ∼1.58) in the proposed TSS model indicated that the spectral log-ratio may be sensitive to TSS of both low and high concentrations. We further applied the preferred model to retrieve TSS concentration from the MODIS 250-m images in turbid estuarial and clear coastal waters, and obtained a good mapping accuracy from the result (TSS > 31 mg/l: RMSE = 38.6 mg/l, MRE = 24.7%, N = 17; TSS ⩽ 31 mg / l : RMSE = 2.1 mg/l, MRE = 19.5%, N = 11). The vertex-based methodology of TSS model developed in the study is applicable in mapping TSS concentrations that are of a wide range variation in estuarine and coastal water bodies based on MODIS 250-m image, and as well to maximize its application potential due to high imaging frequency (two times during daytime) and appropriate space resolution of MODIS images. Therefore, the method can be used to detect the high temporal variability of TSS during tidal cycle. As such, the application of MODIS-water quality detection technology can be extended to coastal sediment movement. [ABSTRACT FROM AUTHOR]