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Nutrient surplus in intense production system such as plastic greenhouse vegetable production (PGVP) may affects soil degradation and further influences sustainable utilization. In this study, we analyzed nutrient balance of nitrogen (N), phosphorous (P) and potassium (K) in PGVP and measured soil fertility properties, pH and electrical conductivity (EC) in the North, the Middle and the South of Shaanxi Province, northwestern China. Considerable nutrient surplus was found in PGVP systems, and the average nutrient balance of N, P and K were 1407.0, 682.9 and 1169.1 kg ha −1  season −1 , respectively. Soil organic matter, total N, total P, total K, available P and available K in PGVP soil were 27%, 49%, 42%, 3%, 200% and 54% greater than those levels in open fields, respectively. Average pH value in greenhouse soil was 0.53 lower than that in open field, and EC value in greenhouse soil (547.11 μS cm −1 ) was more than triple that in open fields (157.14 μS cm −1 ). Soil acidification and secondary salinization has occurred in PGVP. Soil P/K ratio in PGVP systems reflected that nutrient surplus in the South (0.080) and the Middle (0.077) were greater than those in the North (0.061). The differences in nutrient accumulation rate and soil pH changes among different regions were the comprehensive results affecting by both fertilization and soil types. We deduced that fertilization in PGVP in Shaanxi Province was excessive, which may further accelerate soil nutrient accumulation, acidification and secondary salinization.

The proliferation of plastic-covered greenhouse (PCG) farming has resulted in high horticulture crop yields worldwide during the last few decades. A proper and cost-effective PCG monitoring method is necessary for maintaining sustainable horticulture and high-quality agricultural production with less plastic pollution. Remote sensing applications for mapping PCG have received great attention from the scientific community in recent years. In this paper, a comparative study was carried out in two plastic-covered greenhouse areas in Loukkos perimeter in Morocco and Dalat City in Vietnam to test PCG mapping accuracy of high spatial resolution RapidEye and PlanetScope satellite data and to understand the differences in PCG mapping quality due to topographic effects. Medium-resolution Landsat-8 OLI and Sentinel-2 MSI imagery were also applied. Moreover, two classification algorithms—retrogressive plastic greenhouse index (RPGI) and a supervised classification algorithm using random forest (RF)—were used for mapping PCG. The findings reveal that RF outperforms RPGI. Overall, the mapping accuracy achieved exceeded 90% in both study areas, except for the RPGI method using Landsat-8 data (PCG mapping accuracy using Landsat data varied between 87.4 and 89%). Furthermore, PCGs were better detected by PlanetScope data than by RapidEye imagery due to the differences in the spectral range. Better performance in Loukkos perimeter can be explained by the study area’s topography; Dalat City and surrounding areas are situated in mountainous terrain. The results obtained from this study indicate that spectral indices can be used as a cost-effective tool for mapping PCG under cloud-free conditions. PCG mapping using RF classifiers resulted in accurate PCG mapping without topographic factors’ influence.

Plastic-covered greenhouse (PCG) farming has been practiced worldwide, particularly in highland areas, for planting fruits and garden plants. Highland areas in south-central Vietnam have been witnessing an expansion of PCG farming in recent decades. For sustainable management of plastic-covered greenhouse farming areas, a proper and continuous monitoring of their spatial patterns is necessary. In this study, we used spaceborne multispectral imagery (Landsat-8 OLI and Sentinel-2 MSI) for automatic detection and mapping of plastic covered greenhouse farming areas in and surrounding areas of Dalat City in Lam Dong province, south-central Vietnam. Unsupervised (K-Means algorithm) and automatic delineation using spectral indices were applied to the Sentinel-2 and Landsat-8 data for mapping plastic-covered greenhouses in Dalat City and surrounding areas. The results were evaluated using field data; automatic spectral index-based mapping of PCGs using Sentinel-2 and Landsat-8 data was found as robust in terms of the overall accuracy. The overall accuracies of PCG maps derived from Sentinel-2 and Landsat-8 data by applying spectral indices were 90.8% and 88.7%, respectively, and those of unsupervised K-Means classification were estimated as 73.2% and 71.7%, respectively. This study estimated that about 2425 ha of plastic-covered greenhouses existed within in Dalat City in 2020.

The rapid and precise acquisition of the agricultural plastic greenhouse (PG) spatial distribution is essential in understanding PG usage and degradation, ensuring agricultural production, and protecting the ecological environment and human health. It is of great practical significance to realize the effective utilization of remote sensing images in the agricultural field and improve the extraction accuracy of PG remote sensing data. In this study, Landsat operational land imager (OLI) remote sensing images were used as data sources, and Shandong Province, which has the largest PG distribution in China, was selected as the study area. PGs in the study area were identified by means of contour recognition, feature set construction of the spatial structure, and machine learning. The results were as follows. (1) Through an optimal segmentation parameter approach, it was determined that the optimal segmentation scale for size, shape, and compactness should be set at 20, 0.8, and 0.5, respectively, which significantly improved PG contour recognition. (2) Among the 72 feature variables for PG spatial recognition, the number of features and classification accuracy showed a trend of first gradually increasing and then decreasing. Among them, fifteen feature variables, including the mean of bands 2 and 5; six index features (NDWI, GNDVI, SWIR1_NIR, NDVI, and PMLI); two shape features, the density and shape index; and two texture features, the contrast and standard deviation, played an important role. (3) According to the recall rate, accuracy rate, and F-value of three machine learning methods, random forest (RDF), CART decision tree (CART), and support vector machine (SVM), SVM had the best classification effect. The classification method described in this paper can accurately extract continuous plastic greenhouses through remote sensing images and provide a reference for the application of facility agriculture and non-point-source pollution control.

Insulated plastic greenhouses (IPG) were a new type of facility emerging in production in China. The mechanism of construct parameters on the indoor solar radiation deserves further study. A mathematical model that can well describe the spatial and temporal distribution of solar radiation in the greenhouse was established. Based on this model, the effects of multiple construct parameters, such as insulation blanket shading, height–span ratio, greenhouse azimuth, and geographical latitude, on the indoor solar radiation of IPG were quantitatively specified. The results showed that the spatial and temporal distribution of inside solar radiation was highly variable along the span direction of IPG. And by analyzing the simulation data, it was found that for different heights of greenhouses, the corresponding optimal span is different. Therefore, in the construction of greenhouses, the appropriate height–span ratio should be selected according to the planting demand. And by simulating the inside solar radiation under different greenhouse azimuth angles, it was found that accumulated daily solar radiation in the greenhouse reaches the optimum value when the greenhouse azimuth angle is in the range of 0–20°. This paper can provide theoretical guidance for the design and optimization of structure parameters of IPG in China.

The fast and accurate extraction of plastic greenhouses over large areas is important for environmental and agricultural management. Traditional spectral index methods and object-based methods can suffer from poor transferability or high computational costs. Current deep learning-based algorithms are seldom specifically aimed at extracting plastic greenhouses at large scales. To extract plastic greenhouses at large scales with high accuracy, this study proposed a new deep learning-based network, U-FDS Net, specifically for plastic greenhouse extraction over large areas. U-FDS Net combines full-scale dense connections and adaptive deep supervision and has strong future fusion capabilities, allowing more accurate extraction results. To test the extraction accuracy, this study compiled new greenhouse datasets covering Beijing and Shandong with a total number of more than 12,000 image samples. The results showed that the proposed U-FDS net is particularly suitable for complex backgrounds and reducing false positive conditions for nongreenhouse ground objects, with the highest mIoU (mean intersection over union) an increase of ~2%. This study provides a high-performance method for plastic greenhouse extraction to enable environmental management, pollution control and agricultural plans.

To improve the multi-resolution segmentation (MRS) quality of plastic greenhouses (PGs) in GaoFen-2 (GF-2) images, the effects of atmospheric correction and image enhancement on effective PG segments (EPGSs) were evaluated. A new semi-automatic method was also proposed to extract EPGSs in an accurate and efficient way. Firstly, GF-2 images were preprocessed via atmospheric correction, orthographical correction, registration, fusion, linear compression, or spatial filtering, and, then, boundary-removed point samples with adjustable density were made based on reference polygons by taking advantage of the characteristics of chessboard segmentation. Subsequently, the point samples were used to quickly and accurately extract segments containing 70% or greater of PG pixels in each MRS result. Finally, the extracted EPGSs were compared and analyzed via intersection over union (IoU), over-segmentation index (OSI), under-segmentation index (USI), error index of total area (ETA), and composite error index (CEI). The experimental results show that, along with the change in control variables, the optimal scale parameter, time of segmentation, IoU, OSI, USI, and CEI all showed strong changing trends, with the values of ETA all close to 0. Furthermore, compared with the control group, all the CEIs of the EPGSs extracted from those corrected and enhanced images resulted in lower values, and an optimal CEI involved linearly compressing the DN value of the atmospheric-corrected fusion image to 0–255, and then using Fast Fourier Transform and a circular low-pass filter with a radius of 800 pixels to filter from the spatial frequency domain; in this case, the CEI had a minimum value of 0.159. The results of this study indicate that the 70% design in the experiment is a reasonable pixel ratio to determine the EPGSs, and the OSI-USI-ETA-CEI pattern can be more effective than IoU when it is needed to evaluate the quality of EPGSs. Moreover, taking into consideration heterogeneity and target characteristics, atmospheric correction and image enhancement prior to MRS can improve the quality of EPGSs.

Soil environment and water quality face large pressure due to the rapid expansion of greenhouse cultivation in China. However, studies rarely provide the linkage between farmers’ practices and soil degradation in greenhouse cultivation field. In this study, a field survey and sampling of greenhouse cultivation soil were conducted in five regions of China to investigate the accumulation and variation characteristics of soil ion compositions in the field. First, the pH, ion compositions, and electrical conductivity (EC) of 132 composite soil samples were analyzed. Second, farmers’ practices with regard to fertilizer, crop yield, and soil degradation processes were surveyed. Lastly, soil nutrient status was evaluated by different grades, and the principal component analysis method was used to analyze the main sources of soil ion compositions. Results of the study reveal the following: (1) Enrichment of greenhouse soil nutrient was mainly caused by excessive fertilization, which introduced the secondary salinization phenomenon for 3–5 years in plastic greenhouse and 1–3 years in multispan greenhouse. (2) Significant changes between the EC and salt ion composition of open soil and greenhouse cultivated soil were observed. The contents of nitrate nitrogen and ammonium nitrogen in the greenhouse soil were high. (3) After a certain period of cultivation in the greenhouse, salt accumulation, pH decline, and varying degrees of acidification were observed in the soil profile. The relationship between soil pH and EC values indicated that the balance of soil compositions was broken. The recommended methods for sustaining greenhouse cultivation include balanced fertilization, rotation practices, and reasonable water utilization in the field.

Medium resolution satellite data, such as Sentinel-2 of the Copernicus programme, offer great new opportunities for the agricultural sector, and provide insights on soil surface characteristics and their management. Soil monitoring requires a high-quality dataset of uncovered and plastic covered agricultural soil. We developed a methodology to identify uncovered soil pixels in agricultural parcels during seedbed preparation and considered the impacts of clouds and shadows, vegetation cover, and artificial covers, such as those of greenhouses and plastic mulch films. We preserved the spatial and temporal integrity of parcels in the process and analysed spectral anomalies and their sources. The approach is based on freely available tools, namely Google Earth Engine and R Programming packages. We tested the methodology on the northern region of Belgium, which is characterised by small, fragmented parcels. We selected a period between mid-April to end-May, when active agricultural management practices leave the soil bare in preparation for the main cropping season. The spectral angle mapper was used to identify soil covered by non-plastic greenhouses or temporary soil covers, such as plastic mulch films. The effect of underlying soil on temporary covers was considered. The retrogressive plastic greenhouse index was used for detecting plastic greenhouses. The result was a high quality dataset of potential bare uncovered agricultural soil that allows further soil surface characterisation. This offered an improved understanding of the use of artificial covers, their spatial distribution, and their corresponding crops during the considered period. Artificial covers occurred most frequently in maize parcels. The approach resulted in precision values exceeding 0.9 for the detection of temporary covers and non-plastic greenhouses and a sensitivity value exceeding 0.95 for non-plastic and plastic greenhouses.

In recent years, plastic greenhouse vegetable cultivation (PGVC) has expanded worldwide, particularly in China, where it accounts for more than 90% of all global PGVC operations. As compared with conventional agricultural methods, PGVC has doubled crop yields by extending growing seasons and intensifying agriculture. PGVC also offers more ecosystem services relative to conventional approaches, including greater soil carbon sequestration, lower water consumption, and improved soil protection at regional scales. The economic benefits of this easily implemented agricultural method are attractive to small-holder farmers. However, greater environmental impacts (eg greenhouse-gas emissions, generation of large amounts of plastic waste) are associated with PGVC than with conventional approaches. Here, we review what is currently known about PGVC and identify future research priorities that will comprehensively assess the ecosystem services offered by this method of cultivation, as well as its environmental impacts and socioeconomic benefits.